Prostate cancer (PCa) is the second leading cause of cancer death in men. PCa progression can be associated with obesity. Signal transducer and activator of transcription-3 (STAT3) plays a crucial role in PCa growth. However, whether STAT3 plays a role in high-fat diet (HFD)-associated PCa growth is unknown. Our data show that HFD feeding increases tumor size, STAT3 phosphorylation, and palmitic acid (PA) level in the xenograft tissues of the PCa-bearing xenograft mouse model. In vitro studies show that PA increases STAT3 expression and phosphorylation (STAT3-Y705) in PCa. Computational modeling suggests strong and stable binding between PA and unphosphorylated STAT3 at R593 and N538. The binding changes STAT3 structure and activity. Functional studies show that both STAT3 mutants (R583A and N538A) and STAT3 dominant negative significantly reduce PA-enhanced STAT3 phosphorylation, PA-increased PCa cell proliferation, migration, and invasion. In the xenograft mouse models, the HFD-increased tumor growth and STAT3 phosphorylation in tumors are reversed by STAT3 inhibition. Our study not only demonstrates the regulatory role of PA/STAT3 axis in HFD-associated PCa growth but also suggests a novel mechanism of how STAT3 is activated by PA. Our data suggest STAT3 as a therapeutic target for the treatment of HFD-associated PCa. The National Cancer Institute reported that ~11.2% of men will be diagnosed with PCa at some point during their lifetime2. Androgen deprivation treatment reduces symptoms in about 70–80% of patients with advanced PCa, but most tumors relapse within 2 years to an incurable androgen-independent state. Furthermore, PCa progression has been reported to be associated with obesity. A meta-analysis study encompassing 3,569,926 PCa patients showed that obesity is significantly correlated with the aggressive PCa and increased risk of the PCa-associated mortality3.Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer death among men4. Fatty acids in the HFD, especially palmitic acid (PA), affect the LNCaP xenograft tumor growth by elevating the expression of macrophage inhibitory cytokine5.High-fat diet (HFD) affects PCa growth by changing the tumor microenvironment. HFD increases the myeloid-derived suppressor cells fraction and the M1/M2 macrophage ratio in the tumor, and the tumor growth is associated with interleukin-6 (IL-6) secreted by the prostatic macrophages6. Interestingly, PCa cells have a dominant uptake of fatty acids over glucose for energy production7. This re-programmed metabolism is unique to PCa, and further implicates the role of HFD or dietary fat per se on PCa growth.PCa cells can uptake the fatty acids from extracellular space as demonstrated by an experiment with Fourier transform infrared micro-spectroscopy in which the PCa cells uptake isotopically labeled fatty acids from adipocytes8. STAT3 can be activated by cytokines and growth factors. For example, in the patients, the elevated high circulating levels of IL-6 activate STAT39. STAT3 plays a crucial role in PCa cell proliferation, survival, and differentiation11. STAT3 also facilitates immune evasion of PCa by negatively regulating cellular and innate immune response15. Nevertheless, whether STAT3 plays in a role in HFD-associated PCa growth has never been explored.Signal transducer and activator of transcription-3 (STAT3) is a transcription factor; its aberrant activation is observed in ~50% of PCa patientsWe first established a HFD-associated PCa xenograft mouse model to study the role of STAT3 in HFD-associated PCa growth. We subcutaneously inoculated DU145 cells in the nude mice, which were then randomly divided into HFD-feeding group or a matched control diet (CD)-feeding group. After 2 weeks of dietary intervention, the body weight Fig.  and the Next, we examined how HFD feeding increased STAT3 phosphorylation. Since the fatty acid contents are different between HFD and CD . We found that the lipid metabolites of the xenograft tissues Fig.  showed d16. We found that the PCa cells cultured with HFD-feeding mouse serum had significantly higher lipid contents than those cultured with CD-feeding mouse serum or those cultured without serum  levels of STAT3 and peroxisome proliferator-activated receptor-γ (PPAR-γ) in both DU145  expression145 Fig.  and PC3 145 Fig. . The upr145 Fig. . However145 Fig. . These dOur data suggest that PA increases STAT3 protein level, which subsequently increases the amount of phosphorylated STAT3 in PC3, DU145, and TRAMPC-1 cells. However, in LNCaP cells, PA increases the STAT3 phosphorylation without increasing STAT3 total protein level. These interesting results imply that there are parallel mechanisms of action underlying the enhanced STAT3 phosphorylation in PCa cells upon PA challenge.10 or other upstream signaling molecules such as epidermal growth factor receptor (EGFR) and extracellular signal-regulated kinase (ERK)18, we first investigated whether JAK, EGRF, and ERK activities mediated the PA-increased STAT3 phosphorylation in PCa. We found that PA treatment did not affect JAK2 phosphorylation at Y1007/1008 residues To explore other possible mechanisms of action underlying how PA increased STAT3 phosphorylation in PCa cells, we examined whether there was a direct interaction between PA and STAT3, which may facilitate STAT3 phosphorylation and activation. To investigate the interaction between PA and unphosphorylated STAT3 (USTAT3), we performed molecular docking and molecular dynamics (MD) by using ICM-Pro and YASARA. The molecular docking between USTAT3 and STA, a STAT3 inhibitor, was also performed as control. The binding energy of USTAT3-PA and USTAT3-STA complex were −24.98 and −18.57 kcal/mol, respectively. The three-dimensional ribbon model of the USTAT3-PA and USTAT3-STA complex were presented in Fig. To investigate whether the changes of USTAT3 structure might facilitate the activation of STAT3, we generated a computational homology model of human STAT3 dimer. The surface presentation of the STAT3 homology model was presented in Fig. 21 in PCa cells. We found that PA could not increase the phosphorylation of these STAT3 mutants and STAT3 dominant negative , and PA levels in the xenograft tissues of the PCa-bearing mouse model. Subsequent in vitro studies showed that PA enhanced STAT3 phosphorylation at Y705, and to a less extent at Y727. The increase in STAT3 phosphorylation may be underlined by two mechanisms of action. First, PA increased STAT3 mRNA and protein levels, which then increased the amount of the p-STAT3-Y705. Second, computational modeling suggests strong and stable binding between PA and USTAT3 at R593 and N538, which changes the STAT3 structure and affects its activity. Indeed, functional studies showed that STAT3 mutants R583A and N538A significantly reduced PA-enhanced STAT3 phosphorylation, and the PA-increased PCa cell proliferation, migration, and invasion. The role of STAT3 in PA-enhanced PCa growth is also suggested in animal studies in which HFD or PAD increased PCa growth and was reversed by STAT3 inhibitor.The progression from non-castration-resistant PCa (non-CRPC) to CRPC usually occurs within 2–3 years26 or express endogenous STAT327. Indeed, our data showed that PC3 has a relatively low basal level of STAT3 compared to other PCa cell lines. Nevertheless, upon PA challenge, p-STAT3-Y705 is significantly increased in the PC3 cells. Similarly, in Mora et al.8 study, the very low expression of STAT3 in PC3 and LNca cells can be significantly increased upon IL-6 stimulation. Our data suggest that the role of STAT3 in mediating HFD-associated PCa growth is not affected by the basal level of STAT3 in the PCa.Here, we have identified p-STAT3-Y705, which drives the HFD-associated PCa growth even the PCa may have different endogenous STAT3 levels. PC3 cells have been reported to have no endogenous STAT332, activation of STAT3 by phosphorylating at Y705 is known to be mediated by EGFR, JAK2, or ERK activities, or by IL-6 or leptin stimulation. Interestingly, our data suggest that PA may directly regulate STAT3 activity. Indeed, a previous study showed that the signal-transducing adaptor protein-2 (STAP-2), which contains a YXXQ motif in the C-terminal region, serves as a potent STAT3-binding site and that STAP-2 binds to STAT3 and activates STAT333. Our data suggest that there are strong and stable bindings between PA and the USTAT3, which lead to a change of the relative positions of subunits A and B. PA induces rotation of the two subunits and tend to have the same horizontal plane, while the STA, a STAT3 inhibitor, stabilizes the relative positions of subunit A and B. Thus, we speculate that the changes of USTAT3 structure is involved in the activation of STAT3 upon PA challenge. Our speculation is further supported by our finding that the computational homology model of human STAT3 dimer has the subunits A and B of STAT3 kept in the same horizontal plane, which resembles the activated STAT3. Furthermore, we found that the TYR-640, GLU-637, and SER-636 in SH2 domain and the THR-708, LEU-706, and TYR-705 in transactivaion domain are involved in the interaction of the two subunits. Of note, TYR-705 has been demonstrated as the key residue for STAT3 dimer formation. These results strongly suggest that the structural change of USTAT3 induced by PA underlies the activation of STAT3 upon the PA challenge. Furthermore, STAT3 mutants with the potential PA-binding sites removed could not be phosphorylated by PA, and the PA-enhanced cell proliferation, migration, and invasion are reduced. These results strongly suggest that these potential binding sites on STAT3 are important in mediating the PA effects.In other cancer types7. Our study also suggests that the uptake of fatty acids varies between different PCa cell lines; DU145 cells uptake both PA and stearic acids from the HFD-fed mouse serum, while PC3 and TRAMP-C1 cells do not uptake stearic acid but only PA. A possible reason is that DU145 cells takes up fatty acid-binding protein 4 that increases fatty acid binding34, which may facilitate the uptake of 18C stearic acid.Unlike many other cancer cells, PCa cells mainly rely on the uptake and metabolism of fatty acids11. Our data suggest that a regulatory role of the PA/STAT3 axis in HFD-associated PCa growth, which will have great implication because PA is the dominant saturated fatty acid in HFD. Our data not only provide insight into the pathogenesis of HFD-associated PCa but also open new avenues for the study of other HFD-associated cancer growth in relation to STAT3 activity.In PCa, both clinical and experimental studies have revealed the elevated expressions of STAT3 and its target genes in clinical samples, which are positively correlated with the aggressiveness and frequency of the metastasis in patients5 cells in 100 µl PBS and inoculated subcutaneously into each mouse. When the tumor grows to ~80 mm3 in size, mice were randomly selected to have either HFD  or the corresponding matched CD . Alternatively, they had a diet rich in PA (D16042106) or the corresponding matched CD (D17041705). All these diets are formulated by Research Diets, Inc. The fatty acid compositions of these diets are shown in 22 was given at e dose of 2.5 mg/kg when the dietary intervention started. Both diet and water were supplied ad libitum. Body weight and tumor size of the xenograft mouse models were measured every day.All animal experimentation was approved and conducted in accordance with the guidelines from Hong Kong Baptist University and was endorsed by the University Human and Animal Subject Committee and the Department of Health, the Government of Hong Kong Special Administration Region. Male nude mice of 4 weeks old were purchased from the Chinese University of Hong Kong. DU145 cells (ATCC) were washed in phosphate-buffered saline (PBS) containing 5 mM EDTA, the cell number and viability were examined using trypan blue. Single-cell suspension of >90% viability was suspended at 1 × 102PO4, 1.5 ml of chloroform, and 0.5 ml of methanol. After vortex for 2 min and centrifugation at 2000 × g, the lower phase was collected and evaporated under a nitrogen stream. The residue was reconstituted in 10 μl of isopropanol, diluted with 90 μl of methanol, and subjected to free fatty acid and phospholipid analyses36. Xenograft samples (100 mg) were homogenized using Polyton PT 6600 in 750 μl of prechilled 10% trichloroacetic acid supplemented with 500 ng/ml of mixed internal standard hexadecanoic-15,15,16,16,16-d5 acid . The tissue homogenates were centrifuged at 14,500 × g for 10 min at 4 °C. The resulting supernatants were then extracted twice with 5 ml of chloroform/methanol solution  as described36. An Agilent 6540 UHD Accurate-Mass Q-TOF LC/MS (quadrupole time-of-flight liquid chromatography-mass spectrometry) mass spectrometer (Agilent Technologies) was connected to an Agilent 1290 Infinity UHPLC via an ESI (electrospray ionization) ion source for the analysis of total lipids. An Agilent 6450 Triple Quadrupole LC/MS system accompanied with MassHunter Workstation software  was connected to an Agilent 1290 Infinity UHPLC for specific quantification of the lipids36.For the sample preparation, blood samples were withdrawn by heart puncture and xenografts were dissected after mice were scarified. For lipid extraction, to each 0.2 ml serum sample were added 0.3 ml of 0.5 M KHm/z values, respectively. The data were also normalized by internal standard. We employed one-way analysis of variance (ANOVA) to do the statistical analysis. The p value of ANOVA was set to 0.05 (corresponding with the significance level of 95%). We also performed fold change (FC) analysis to further filter the entities, and only those entities with FC >2 were selected.With the MassHunter Workstation software , ions were extracted by molecular features characterized by retention time (RT), intensity in apex of chromatographic peak, and accurate mass. These results were then analyzed by Mass Profiler Professional (MPP) software . We first set up a filtration platform to further filter the initial entities before doing PCA. Only entities with abundance above 3000 cps were selected. These entities were then passed a tolerance window of 0.15 min and 2 mDa chosen for alignment of RT and 2 atmosphere at 37 °C. In each experimental setup, equal number of cells were incubated for 24 h with equal volume of the mixed medium that contained 1:1 (v/v) in ratio of the full medium to mouse serum16. For PA treatment, cells were incubated with PA at the indicated concentration in the presence of 1% fatty acid-free bovine serum albumin (BSA) (Sigma-Aldrich), and 1% fatty acid-free BSA alone served as vehicle control. DU145 and PC3 cells were transfected with either Stat3-Y705F (Addgene), Stat3-R6938A, Stat3-N538A, or Stat3-R6938A/N538A plasmid using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. Stat3-R6938A, Stat3-N538A, and Stat3-R6938A/N538A were constructed with QuikChange Site-Directed Mutagenesis Kit following the manufacturer’s instructions.Human PCa cells  and murine PCa cells (TRAMP-C1 cell line) were purchased from the American Type Culture Collection  and maintained in full medium, which was high glucose Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin  in a humidified 5% CO4 cells) were placed in each well of a 96-well plate. Proliferation assay was carried out 24 or 48 h after treatment. Fluorescent signals were detected by fluorescence excitation/emission maxima 480/535 nm.We measured the cell proliferation with CyQUANT® Direct Cell Proliferation Assay Kit  following the manufacturer’s instruction. Briefly, PCa cells  following the manufacturer’s instruction. Samples of 10,000 stained cells were analyzed by using a flow cytometer as previously described.37. Briefly, to quantify staining, Oil Red-O was extracted from the cells with isopropanol containing 4% Nonidet P-40, and optical density was then measured at a wavelength of 520 nm.Lipids are stained by Oil Red-O staining as previously describedTotal RNA was isolated using TRIzol reagent . Reverse transcription was performed with oligo-dT using MMLV reverse transcriptase  according to the manufacturer’s protocol. Quantitative real-time PCR was carried out by monitoring the increase in fluorescence of SYBR green with the ViiA 7 Real-Time PCR System . The primer sets used were as follows: CD36, forward: AGATGCAGCCTCATTTCCAC, reverse: GCCTTGGATGGAAGAACAAA. TP53, forward: ACCTATGGAAACTACTTCCTGAAA, reverse: CTGGCATTCTGGGAGCTTCA. Cyclin D1, forward: AGCTGTGCATCTACACCGAC, reverse: GAAATCGTGCGGGGTCATTG. NF-κB, forward: GCAGATGGCCCATACCTTCA, reverse: CACCATGTCCTTGGGTCCAG. GATA2, forward: ATCAAGCCCAAGCGAAGACT, reverse: CATGGTCAGTGGCCTGTTAAC. β-Catenin, forward: CTGCTGTTTTGTTCCGAATGT, reverse: CCATTGGCTCTGTTCTGAAG. SERBP1, forward: CAAGGCCATCGACTACATT, reverse: TTGCTTTTGTGGACAGCAGT. PPAR-γ, forward: GCCCTTTGGTGACTTTATGGA, reverse: GCAGCAGGTTGTCTTGGATG. STAT3, forward: CCTGAAGCTGACCCAGGTAGC, reverse: CACCTTCACCATTATTTCCAAAC. NRF-1, forward: GCTGGACACCATCCTGAATC, reverse: CCTTCTGCTTCATCTGTCGC. N-myc, forward: TCCACCAGCAGCACAACTATG, reverse: GTCTAGCAAGTCCGAGCGTGT. c-Myc, forward: AAAGGCCCCCAAGGTAGTTA, reverse: GCACAAGAGTTCCGTAGCTG. β-Actin, forward: CATGTACGTTGCTATCCAGGC, reverse: CTCCTTAATGTCACGCACGAT. Each sample was amplified in triplicate for quantification. Data were analyzed by relative quantitation using the ∆∆Ct method, where ΔCt is the difference in threshold cycle values between the targets and β-actin, and ∆∆Ct is the difference between the treatment and vehicle control groups. All samples were analyzed in triplicate and normalized to actin.38. Briefly, PCa cells were seeded in 24-well plates and transiently co-transfected with STAT3 reporter plasmid 4xM67 pTATA TK-Luc or Twist-1 promoter reporter construct Twist-1-Luc  with Renilla luciferase-expressing plasmid PRLCMV  by use of Lipofectamine 2000 . After 48 h, cells were treated with PA at the indicated concentration with 1% fatty acid-free BSA for 24 h, and then lysed in 100 ml of passive lysis buffer  in each well. A 25-ml aliquot of cell lysate was subjected to a luciferase assay by using the Dual Luciferase Assay Kit . STAT3-luciferase activity was measured by EnVision Mutilabel Reader . Relative luciferase activity was calculated after the activity of STAT3-dependent firefly luciferase had been normalized to that of Renilla luciferase. All values are expressed as fold induction relative to basal activity.Luciferase assay was done as described previouslyThe nitrocellulose membrane  carrying transferred proteins was incubated at 4 °C overnight with corresponding antibody at 1:1000 ratio. Antibodies against STAT3, phospho-STAT3 (TYR-705), phospho-STAT3 (Y727), JAK2, phospho-JAK2 (Y1007/1008), JAK2, phospho-EGFR (Y845), phospho-EGFR (Y992), phospho-EGFR (Y1068), phospho-EGFR (Y1148), phospho-ERK (42/44 kDa), IL-6, phosphorylated leptin receptor, β-actin, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were obtained from Cell Signaling Biotechnology or Abcam, USA. Immunodetection was accomplished using horseradish peroxidase-conjugated secondary antibody, followed by detection by ECL .Molecular docking was performed to clarify the binding mechanism between USTAT3 (PDB ID: 3CWG) with PA (ZINC ID: 6072466) or STA (ZINC ID: 162014). The protein for molecular docking simulation was prepared by removing water molecules and bound ligands. The binding pocket between USTAT3 subunits was defined in this study as the protein–ligand binding site of STAT3. ICM-Pro was utilized for molecular docking. PyMol was used to visualize the USTAT3-PA and USTAT3-STA complexes. MD simulation was performed by YASARA, and the AMBER 03 forcefield was used to run all simulations. Specifically, 0.9% NaCl served as solvation of the receptor–ligand complex in a dodecahedron box, with a distance of 5 Å between the box and the solute. The initiation of simulated annealing minimizations was set at 298 K, with velocities scaling down with 0.9 every ten steps lasting for 5 ps. Following energy minimization, temperature of the system was adjusted utilizing a Berendsen thermostat to minimize the influence of temperature control. In addition, velocities were rescaled every 100 simulation steps, whenever the mean of last 100 detected temperatures converged. Finally, 50 ns MD simulations were conducted at a rate of 2 fs, and the coordinates of the complexes were saved every 10 ps.The SWISS-MODEL, a homology-modeling tool, was used to generate a homology model of human STAT3. The human amino acid sequence of STAT3 (NP_644805.1) was obtained from NCBI. The three-dimensional crystal structure of the mouse STAT3 monomer (PDB ID: 1BG1) was employed as template for homology modeling. MD simulation was performed according to above methods to verify the stability of the STAT3 homology model.Wounds were created by scratching the confluent cell monolayer using a plastic pipette tip, and any loose cellular debris or detached cells were removed by PBS wash. The cells were pre-treated with mitomycin at 5 µg/ml for 24 h. Then, these cells were treated with PA at 12.5 µM with 1% fatty acid-free BSA or 1% fatty acid-free BSA alone as vehicle control for 16 h. The gaps of the wounds were observed with phase contrast microscopy and digitally photographed under 100 × magnifications.5 cells/ml in Dulbecco's modified Eagle’s medium containing PA at 12.5 or 25 µM with 1% fatty acid-free BSA or 1% fatty acid-free BSA were seeded on upper wells and allowed to invade for 24 h. The non-invading cells were removed by scrubbing with cotton swab and the cells on the lower surface of the membrane were stained with crystal violet. The invasive capacity was quantified by counting the cells that migrated to the lower side of the filters in different fields under a microscope at ×200. Data were expressed as the percentage of invasion in control.Cell invasion was determined by using BD BioCoat™ Matrigel™ invasion chamber (BD Biosciences) according to the manufacturer’s instruction. In brief, 0.75 ml of serum-starved culture medium was added to the lower chamber along with chemoattractants, and aliquots of 0.5 ml of cell suspensions consisting of 5 × 10The xenograft tissues were fixed in 4% paraformaldehyde, embedded in paraffin, and then sliced up (4 μm). The slices were stained with the corresponding antibody, or with hematoxylin and eosin before being examined and quantified.t test, using the statistical software GraphPad Prism 5.0. P < 0.05 was considered statistically significant. *P value < 0.05; **p value < 0.01.All data are expressed as mean ± standard error of the mean. Statistical analysis was performed by the Student’s Supplementary figure legendsSupplementary FiguresSupplementary Tables


We test the ability of Very High Resolution satellite (VHR) imagery to detect stranded whales using both manual and automated methods. We use the 2015 mass mortality event in the Gulf of Penas locality, central Patagonia, Chile, as an initial case study. This event was the largest known mass mortality of baleen whales, with at least 343 whales, mainly sei whales , documented as stranding. However, even with such a large number of whales, due to the remote location of the gulf the strandings went unrecorded for several weeks. Aerial and boat surveys of the area were conducted two to four months after the mortality event. In this study we use 50cm resolution WorldView2 imagery to identify and count strandings from two archival images acquired just after the stranding event and two months before the aerial and ground surveys, and to test manual and automated methods of detecting stranded whales. Our findings show that whales are easily detected manually in the images but due to the heterogeneous colouration of decomposing whales, spectral indices are unsuitable for automatic detection. Our satellite counts suggest that, at the time the satellite images were taken, more whales were stranded than recorded in the aerial survey, possibly due to the non-comprehensive coverage of the aerial survey or movement of the carcases between survey acquisition. With even higher resolution imagery now available, satellite imagery may be a cost effective alternative to aerial surveys for future assessment of the extent of mass whale stranding events, especially in remote and inaccessible areas. Temporal and spatial patterns of whale strandings  and Mass Mortality Events (MME) can be an important marker for temporal variability in oceanographic conditions . The cauEfficient detection of strandings is a major challenge in many areas, as resources tend to be concentrated on activities associated with carcass sampling and analysis, rather than on the initial detection. The scale of strandings can also be hard to identify, particularly in remote and inaccessible regions. In remote locations, Very High Resolution (VHR) remote sensing technology can offer a lower cost, large-scale means of monitoring for whale stranding events, permitting more rapid response times in order to identify and collect data on MMEs and improved understanding of the temporal and spatial extent of these events. Satellite technology has immense potential for the large-scale study of animal populations, even including the detection of live whales at sea –22.Balaenoptera borealis) stranding event in the Chilean Central Patagonia region in early 2015 was the largest baleen whale mass stranding ever recorded, with 343 primarily sei whales synchronously stranding along the coast between 46 and 48°S .The long, complex and indented coastline of southern Chile would be time consuming and difficult to examine manually at the resolution required to identify whale strandings in satellite imagery. We therefore explored automated and semi-automated procedures, to see if they could help with the process of finding stranded whales in the satellite images. Our initial analysis was based on a simple spectral target-finding algorithm, relying purely on the differences in the spectral profile of the beached whales. While the pixel-based analysis could identify whales, there were many errors of commission and omission. SAM analysis identified 61% of the manually identified whales in Image 1 but only 22% of those manually identified in Image 2. These results reflect the substantial variation in pixels seen within the spectral profiles of whales in both images. This is probably due to the heterogeneous spectral reflectivity of each whale carcass due to different states of decay; carcass images shown in Häussermann et al.  show a lIt may be possible to improve the chances of detection and reduce the number of false positives by using more complex Rule-Based [e.g. ] or ObjeHere we demonstrate how the strategic collection of satellite imagery has potential to provide a quick and relatively reliable view of a whale stranding event. Limitations in using satellite imagery in comparison to traditional surveys are:Discrimination of individuals can be difficult when whales are tightly grouped Automation of the approach is not yet reliable and manual searching of images can be time-consuming; whales cannot be directly identified to species.Cloudy weather conditions when the image is collected can reduce resolution.Satellite imagery cannot be tasked immediately (e.g. a time window of at least a week has to be given with the request) which would limit urgent response to a stranding notification, nevertheless in comparison to the current time of response of several weeks-months at the studied area, for example, it represents a great improvement. In this sense, it could be a powerful tool for monitoring events in remote areas, such as certain parts of Chilean Patagonia, where no regular visits are regularly implemented due to extreme weather conditions and elevated cost. may also reduce this problem as it has greater discrimination power. With respect to image search times, manual searching has proven to be the most reliable means of identifying whales at present, but with careful planning a combination of manual and semi-automated searching is likely to reduce image searching times and make large-scale satellite imagery interrogation more feasible. Identification of whales to species would require investigation following indication of a stranding event using satellite imagery. In some situations, it may be possible to provide tentative diagnosis based on size and general morphology, but where carcasses are involved and whales are often ventral-side up, direct inspection is usually necessary .The minimum size at which animals can be discerned is not yet understood, and is likely to vary depending on the resolution of the image collected, the size of the animal, contrast with its surrounding environment and possible confounding features. The highest current resolution 31 cm imagery was not available at the time of the 2015 Gulf of Penas mass stranding event, but collection of a small amount of this imagery in 2017 from the same area confirms that whales are much easier to identify with this higher resolution data see .Further work, using high-resolution images to establish the capacity of the technique to discern cetaceans of different sizes is important. This will help us to understand if, for example, calf strandings are missed, and the capacity of the approach to monitor strandings of smaller cetaceans. A systematic search is needed to determine which archival imagery may exist for other historical events and may be informative on these questions. Further work is also required to establish the sizes of animals that can be discerned, by obtaining satellite images of known-size stranded cetaceans of differing sizes. This is also important for analyzing strandings of odontocetes, which occur much more regularly than mass strandings of baleen whales. One potentially very useful application of this technique is to provide regular snapshots of remote areas where whale strandings are relatively likely, in order to monitor stranding patterns and ‘catch’ events close to the time that they happen. South American Patagonia, Tasmania, New Zealand and the Falkland Islands  have large areas of remote coastline and regularly experience cetacean group strandings; these may be ideal candidates for such monitoring.This study confirms the 2015 MME in the Gulf of Penas, and, by a quantifiable comparison with aerial and boat survey data, shows that large whales can be reliably detected and counted in VHR satellite imagery. The ease of access to satellite imagery, low potential cost, large spatial coverage and relatively simple logistics means that VHR satellite imagery could become a valuable tool for monitoring MME and other strandings events in remote areas.Our paper also suggests that the 343 whales counted in the Gulf of Penas mass mortality event is an underestimate. Analysis of other VHR imagery from the satellite archive could help further quantify the magnitude of the underestimate and the full extent of the stranding area. More work is needed to construct an automated or semi-automated procedure to count stranded whales. Future work should focus on the investigation of machine learning algorithms, including rule based and object based automated analysis with the use of higher resolution (31cm) satellite imagery to improve capacity to search the imagery. Overall, satellite imagery may be a cost effective and safer alternative to aerial survey for future assessment of the extent of mass whale stranding events.S1 TableNear distance relates to how close, in metres, each whale was to the nearest corresponding whale in Hausserman et al. 2018. See this publication for a full list of the whales found on the aerial and boat survey. For confidence see main text: 1 = high, 2 = moderate, 3 = low.(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


Watermelon is an economically important vegetable fruit worldwide. The objective of this study was to conduct a genetic diversity of 68 watermelon accessions using single nucleotide polymorphisms (SNPs). Genotyping by sequencing (GBS) was used to discover SNPs and assess genetic diversity and population structure using STRUCTURE and discriminant analysis of principal components (DAPC) in watermelon accessions. Two groups of watermelons were used: 1) highly utilized 41 watermelon accessions at the National Agrobiodiversity Center (NAC) at the Rural Development Administration in South Korea; and 2) 27 Korean commercial watermelons. Results revealed the presence of four clusters within the populations differentiated principally based on seed companies. In addition, there was higher genetic differentiation among commercial watermelons of each company. It is hypothesized that the results obtained from this study would contribute towards the expansion of this crop as well as providing data about genetic diversity, which would be useful for the preservation of genetic resources or for future breeding programs. Watermelon is an important vegetable fruit crop for human consumption. Watermelon, ranking among the top five most-frequently purchased fruits, is cultivated globally, with a per capita annual consumption of ~7 kg . AccordiAs watermelon contains various functional factors such as lycopene and citrulline, many seed companies have been leading the development of various watermelon cultivars to cater to customers . In geneCitrullus species. These studies revealed low levels of DNA polymorphism among cultivated watermelons but high genetic diversity among the Citrullus subspecies [Several molecular markers have been effectively used to assess the genetic diversity of watermelon. Isozymes , RAPD 99, AFLP , and SSRbspecies ,13.Plant genetic resources have been an intriguing research topic as one of the most essential natural resources, resulting in major advance in the field . Gene baIn this study, 68 watermelon accessions were used. Among them, highly utilized 41 watermelon accessions were obtained from the National Agrobiodiversity Center (NAC) at the Rural Development Administration in South Korea and 27 Korean commercial watermelons were obtained from each seed company .w/v) agarose gels and spectrophotometry. DNA was extracted from 30 mg of freeze-dried leaf tissue of each genotype. The QIAGEN plant mini kit  was used for the extraction. DNA quality and quantity of each sample were determined with electrophoresis in 1%  with Synergy HTX Multi-Mode Reader  and normalized to 12.5 ng/μL. DNA was digested with ApeKI (New England Biolabs) at 75 °C for 3 h. The libraries from restriction enzyme digestions for genotyping-by-sequencing (GBS) were constructed according to the protocols as described previously with minor modifications ,18. The Adapter ligated samples were pooled as one sample, and purified using NucleoSpin® Gel and PCR Clean-up Kit (MACHEREY-NAGEL GmbH & Co. KG). The pooled ligations were amplified in 50 uL reaction by multiplexing PCR using AccuPower Pfu PCR Premix (Bioneer) and 25 pmol of each primer as mentioned below: 5′- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′ and 5′- CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT-3′. The PCR products were evaluated for the distribution of fragment sizes with BioAnalyzer 2100 (Agilent Technologies). The GBS libraries were sequenced on the Illumina NextSeq500  with the length of 150 bp single-end reads. https://basespace.illumina.com). The demultiplexing was firstly done by bcl2fastq software with one mismatch per index from provided index sequences in sample sheet. Preprocessed sequence reads were subjected to Stacks v2.0, ‘process_radtags’ module to confirm the demultiplexed reads and check restriction enzyme site. Then, a quality control for per-base quality of reads and removal of potential adaptor sequences was performed using FastQC and Cutadapt (Citrullus lanatus subsp. vulgaris cv. 97103 reference genome (watermelon_v1) using Bowtie2 [https://broadinstitute.github.io/picard/) were used to add read groups to the reads, making available to utilize reads for Genome analysis toolkit 3.7 (GATK) pipeline. GATK was used to perform local realignments of reads to correct misalignments due to the presence of indels  [The raw data file was generated in binary base call (BCL) format and directly forwarded to bcl2fastq in BaseSpace  . The “Haguments) .adegenet package [find.clusters function was used to detect the number of clusters in the population. It uses K-means clustering which decomposes the total variance of a variable into between-group and within-group components. The best number of subpopulations has the lowest associated Bayesian Information Criterion (BIC). A cross-validation function  was used to confirm the correct number of PC to be retained. In this analysis, the data is divided into two sets: a training set (90% of the data) and a validation set (10% of the data). The member of each group is selected by stratified random sampling, which ensures that at least one member of each group or population in the original data is represented in both training and validation sets. DAPC is carried out on the training set with variable numbers of retained PCs. The degree to which the analysis is able to predict accurately the group membership of excluded individuals  is used to identify the optimal number of PCs to be retained. At each level of PC retention, the sampling and DAPC procedures are repeated many times [The population structure was analyzed by a DAPC  using th package  for R sony times . The besBayesian-based clustering was performed using STRUCTURE v.2.3.4  by testiThe phylogenetic network was calculated using neighbor-net in SplitsTree4 . The anaCitrullus lanatus subsp. vulgaris cv. 97103 v1′. In the 68 watermelon accessions, an average of 2,024,970 (80.4%) reads was aligned to the reference genome. Among them, WM48 had the highest mapping rate (83.1%) and WM51 had the lowest (74.7%). For the 68 watermelon accessions, sequencing of the GBS library yielded 171 million reads of good quality . The ranConsidering only the successfully mapped reads from 68 watermelon accessions, SNPs were discovered and genotypes were called by analyzing the single master alignment file with GATK . A totalK = 4) based on the delta K  were performed. The STRUCTURE results suggested the best grouping number  using the neighbor-net method . The cluFst = 0.068). Observed heterozygote of four clusters ranged from 0.099 (cluster 1) to 0.193 (cluster 4), with an average of 0.134 (Results in of 0.134 . The ranFst) among the four clusters was higher (0.190) in clusters 1 and 4 compared to other clusters  was observed between clusters 2 and 3.The coefficient of genetic differentiation (clusters . ClusterK = 4) based on the delta K  were detected in 27 Korean commercial watermelons and these clusters were used to analyze the DAPC B. Four fThe genetic diversity among 27 Korean commercial watermelon accessions was also assessed with the phylogenetic network (PN) using the neighbor-net method . Groups Fst = 0.445). Observed heterozygote of three clusters ranged from 0.049 (KOR_C3) to 0.170 (KOR_C2) with an average of 0.094  among the four clusters was higher (0.424) in KOR_C1 and C2 compared to others  . Genotyping by sequencing (GBS) is a high-throughput and cost-effective technology to detect and genotype a large number of polymorphisms at the genome-scale . VariousPrunus avium L. compared to STRUCTURE using SNP chips and SSR markers [In this study, 68 watermelon accessions were divided into four clusters and two populations by STRUCTURE, respectively . In addi markers ,37.Fst = 0.068)  of four clusters in 68 watermelon accessions was low (= 0.068) . AccordiAccording to the result of the DAPC analysis, 27 Korean commercial watermelons were separated by each company. Among them, 14 watermelon accessions of KOR_C1 and KOR_C3 developed in Farm-hannong and 13 watermelon accessions belonging to KOR_C2 were mixed with the three main companies, Nongwoo, Sinnong and PPS. PPS is the subsidiary company of Nongwoo and the owner of Sinnong is a former watermelon breeder of Nongwoo. It can be attributed to the same gene pool for watermelons developed or sold by the three companies. Unlike the Nongwoo (KOR_C2), the watermelon accessions by Farm-hannong were divided into two groups (KOR_C1 and C3) probably due to mergers and acquisitions between companies. The watermelons belonging to KOR_C1 were developed at Heungnongjongmyo. Heungnongjongmyo was acquired and annexed by Seminnis in 1998 and the Seminnis were annexed by Monsanto in 2005. With the acquisition of the Monsanto Korean back in 2012, the Heungnongjongmyo and the Farm-hannong became one company. Therefore, it is possible to assume that the watermelon accessions present currently in the same company are genetically different. In this study, highly utilized 41 watermelon accessions collected and conserved at the National Agrobiodiversity Center (NAC) at the Rural Development Administration in Korea and 27 commercial watermelons in Korea were analyzed using GBS to analyze the genetic diversity and population structure. The results of genetic diversity and population structure in 68 watermelon accessions showed the high level of heterozygosity in each watermelon accession and the low level of genetic differentiation between the clusters. In addition, 27 Korean commercial watermelons were divided into three clusters based on their seed companies and showed lower level of heterozygosity than 68 watermelon accessions. In general, commercial watermelons have been produced and sold by seed companies in the form of F1 ,4. To de


Cocos nucifera L., and its widespread consumption owes to its unique composition of sugars, minerals, vitamins, enzymes, and hormones. Probiotic fermentation of CW may facilitate the development of an improved functional beverage with probiotic benefits; therefore, we aimed to produce a fermented CW beverage using the potential probiotic Lactobacillus casei L4. CW was fermented with L. casei L4 for 48 h at 35°C, and the pH, organic acid-production rate, antioxidant activity, antibacterial activity, sugar, mineral, vitamin B12 levels, and total viable bacteria counts were investigated at 24 and 48 h. We demonstrated that the fermentation of CW with probiotic lactobacilli increased the cell viability count. Vitamin B12 production was highest in the extracellular environment at 48 h (11.47 μg/mL), while the total phenolic content was significantly (p < 0.05) higher in the fermented CW at 48 h  than observed with the other investigated groups or time points. The fermented materials exhibited the highest 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical-scavenging activities at 48 h . The levels of most minerals remained unchanged in the fermented CW, except for calcium, manganese, phosphorus, and sodium. Furthermore, the culture supernatant from fermented CW inhibited the growth of foodborne pathogens such as Bacillus cereus, Listeria monocytogenes, Staphylococcus aureus, and Salmonella typhi, although the degree of inhibition varied between the species. Moreover, adding 15% honey and artificial coconut flavor to the fermented CW resulted in a better-tasting product, as demonstrated by a sensory-evaluation test. The obtained results indicated that the CW product fermented by L. casei L4 may be used as a novel functional beverage containing both electrolytes and probiotics, and can serve as a good vehicle for preparing a wider range of novel products.Coconut water (CW) is a clear, nutritive liquid found as the coconut endosperm of green coconuts such as  Cocos nucifera L. belongs to the Arecaceae  family and is an important member of the monocotyledons, which are grown throughout tropical and sub-tropical regions  regions . DebMand regions .The functional food field has developed in recent years. Functional beverages are defined as non-alcoholic drinks reinforced with ingredients such as herbs, vitamins, minerals, amino acids, or probiotics, and provide specific health benefits beyond those in other common food sources . The regLactobacillus and Bifidobacterium genera are predominantly associated with food fermentation , the cell count was measured, and this suspension was used as a starter culture.The culture was propagated in sterile de Man Rogosa Sharpe (MRS) broth  for 24 h at 35°C. Stocks were prepared routinely in the MRS medium. To prepare the starter culture, the broth culture was centrifuged at 8000 × L. casei L4 in MRS medium  for 48 h at 35°C. Samples were collected aseptically at 0, 24, and 48 h, and immediately stored at −80°C. During a preliminary 96-h fermentation study, the fermented product was collected at 24-h intervals and tested for viable counts, antioxidant activity, and total acidity. Based on the results of the preliminary study, 48 h was selected as the fermentation time.Fresh green coconuts (approximately 5 months old) were obtained from a local market in Thanjavur, Tamil Nadu, India. The CW was obtained by perforating the coconut with a sterile knife after the primary epicarp was brushed and washed with distilled water. The water from 30 green coconuts was collected. The initial total solid content (°Brix) was 6.41% and the pH was 6.49. The CW was pre-filtered through a 0.65-μm polyethersulphone filter (Sigma-Aldrich) before being filtered through 0.45-μm filter paper . The pH of filtered CW was adjusted to 6.5 to achieve the optimum pH for the growth of S medium . Filtereg for 10 min at 4°C. The culture broth and biomass were used to determine several parameters.Supernatants were obtained from the culture samples after centrifugation at 8000 × The pH was determined using a digital pH meter . The total titratable acidity was measured by homogenizing 10 g of each sample with 90 mL distilled water, followed by titration with 0.1 N NaOH using 0.1% (w/v) phenolphthalein in 95% ethanol as an indicator. Samples collected at 0 h were used as a control.Lactic acid bacteria viable counts on MRS agar plates were obtained for the fermented products. One milliliter of each sample was diluted with 0.1% (w/v) bacteriological peptone (Sigma-Aldrich), and the diluted samples (0.1 mL) were spread on triplicate MRS agar plates. Viable LAB counts were determined up through 2 days of storage at room temperature, and then every week for 4 weeks after storage at 4°C. The viability was recorded as the number of colony-forming units (CFU) per mL, and the cell concentration was expressed as log of the CFU/mL.Bacillus cereus MTCC6629, Listeria monocytogenes MTCC1143, Staphylococcus aureus MTCC737, and Salmonella typhi ATCC19430. Briefly, each strain was aerobically grown overnight in brain heart infusion  broth at 37°C and centrifuged , after which the pellets were washed and resuspended in sterile saline solution. A 1-mL aliquot of each bacterial suspension was spread onto BHI soft agar plates , and 50 μL of each cell-free supernatant was dispensed into wells containing BHI agar . The plates were aerobically incubated at 37°C for 48 h, and the diameter (mm) of the growth-inhibition zones around each well was measured. MRS broth was used as the negative control, and un-inoculated CW was used as the positive control.The antibacterial activities of the culture supernatants were determined using the agar-diffusion method . CultureSugars and organic acids were quantified by high-performance liquid chromatography as previously described  and ideng for 10 min to collect the supernatant. The concentration of minerals in the supernatant was then determined by atomic absorption spectrophotometry  and flame photometry methods  × 100, where Acontrol is the absorbance of the blank sample, and Asample is the absorbance of the investigated sample. Samples collected at 0 h were used as controls.Total phenolic contents were determined according to the Folin–Ciocalteu colorimetric method described by Vitamin B12 contents in the culture supernatants and biomass were determined to measure the intracellular contents and extracellular contents, respectively, following the method described by Sensory evaluation of the fermented coconut beverages was performed after 28 days of storage at 4°C by a group of 20 panelists (age group: 21–60 years old) using the Hedonic 5-point scale. The participants evaluated the beverages based on the color, appearance, flavor, texture, and overall acceptance. Three variations of this beverage were provided to panelists: fermented CW, fermented CW supplemented with 15% honey, and fermented CW supplemented with 15% (v/v) honey and 0.5 % (w/v) coconut flavor .p < 0.05 was considered statistically significant, and the results are expressed as mean values ± standard deviations.The obtained data were analyzed by one-way analysis of variance, and Tukey’s test was employed to assess differences between treatments. All statistical analyses were performed using OriginPro software . Lactobacillus casei L4 showed a good growth rate during the fermentation period (Table 1). The cell count significantly increased at 24 h, compared with that in the 0 h  and thereafter further increased slightly to 9.47 log CFU/mL at 48 h. This cell growth was accompanied by a significant increase (p < 0.05) in total acidity, which was 0.77% after 48 h when the pH dropped to 3.32. High viability counts are necessary to obtain the desired levels of acid production and pH reduction, which affects the organoleptic properties of the product. Further, the bacterial cells survived at a count of 7.4 log CFU/mL after a 1-week incubation at room temperature (25–33°C), while, after a 28-day storage at 4°C, the cell count was 7.84 log CFUs/mL. Similarly, CW fermented with Lactobacillus acidophilus and L. casei had considerably high viability, with cell counts of 5.04 × 107 and 1.80 × 108CFUs/mL, respectively, after 28 days of storage at 4°C  against foodborne pathogens  ranged between >2 mm and >5 mm , showing that the L. casei L4 culture had antioxidant activity. These findings are in agreement with the results of L. plantarum DW12 at 48 h was 73.4% for ABTS and 54.87% for DPPH. Recently, Saccharomyces cerevisiae.Antioxidant foods and beverages have become increasingly popular because of their potential health benefits, which are attributed mostly to their bioactive compounds . No stanFigure 2). Phenolic compounds, widely distributed in plants, are the most abundant antioxidants in the human diet , and significant changes in the sugar composition were observed after 24 h of fermentation, compared with that found after 0 h. The glucose and fructose levels in fermented CW were 26.7 and 25.1 g/L, respectively, after 48 h of fermentation (Table 3). Similarly, significant reductions in sugar levels in CW fermented with L. plantarum AC-1  as well. The levels of the main organic acids of raw CW  decreased, but that of tartaric acid increased in fermented CW (Table 4). Among the organic acids, the levels of lactic acid increased most prominently to 6.8 and 7.9 g/L after 24 and 48 h, respectively. L. casei is homofermentative and, therefore, produced lactic acid as major organic acid. Similar results were reported by S. cerevisiae , consistent with previous reports (p < 0.05) in the fermented CW compared with those in unfermented fresh CW (Table 5). Sodium is important for maintaining water balance and neural and muscular functions. Additionally, calcium salts provide skeletal rigidity, whereas calcium ions are involved in many metabolic processes . Iron, copper, and zinc were detected at low levels in fermented and unfermented CW (Table 5), and these essential trace elements are required for physiological growth and antioxidative activity . However, no differences between the intracellular and extracellular vitamin B12 concentrations were observed at 24 h. Extracellular B12 levels were significantly higher (p < 0.05) at 48 h, compared with intracellular B12 levels. Recently, L. plantarum DW12. Therefore, we demonstrated here that the L. casei L4 strain can be used for vitamin B12 production as well. In Japan and the United States, the recommended dietary amount of vitamin B12 for adults is 2.5 μg/day . No significant differences in the color or appearance of the samples were observed, but the addition of 15% honey and coconut flavor (recipe C) significantly enhanced the flavor, odor, and overall acceptance of the beverage, indicating that the recipe C was generally acceptable to most of the test subjects. This outcome was most likely due to the decreased acidity and increased sweetness and aroma of this beverage after adding honey and coconut flavor. Our results agree with those reported by L. plantarum DW12. Further, Using 20 survey subjects, we evaluated the acceptability of fermented CW (recipe A), fermented CW supplemented with 15% honey (recipe B), and fermented CW supplemented with 15% honey and coconut flavor (recipe C) (Lactobacillus casei L4, a strain shown to have potential probiotic activities, was demonstrated here to be useful in producing a fermented CW beverage. Following the 48-h fermentation with L. casei L4, we determined that the fermented beverage contains essential minerals, vitamin B12, antioxidants, and has antimicrobial activity. Furthermore, even after 28-day storage at 4°C, fermented broth retained the desired L. casei L4 levels, which are in accordance with the daily recommended probiotic dose. The fermented CW supplemented with 15% honey and artificial coconut flavor was shown to have the highest acceptance rate by the panelists. Further investigations are required for the development of inexpensive L. casei L4-fermented functional beverages with various health benefits.SG designed the work. SG and VS contributed to the experimental analyses. SS helped in the data interpretation, statistical analysis, proofread of the manuscript. VS and SP supervised the work. SG wrote the manuscript. All authors reviewed and contributed to improving the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The ability of maize populations/landraces to tolerate drastically extreme environments over the past four centuries in Algeria leads to characterize these genetic resources for germplasm management as well as the identification of the best landraces useful for genetic improvement. Thus, the aim of the present work was a fingerprinting of an Algerian maize collection (47 landraces) from Saharan oasis by using 24 agro-morphological traits and18 Simple Sequence Repeats to evaluate genetic diversity and population structure.Phenotypic traits showed large significant variation in which earliness, plant size, ear and kernel features and crop yield appeared the most discriminant traits among landraces by using principal component analysis (PCA). One hundred ninety-seven different alleles were detected with a high mean number of allele per locus (10.9). The selected SSR were highly informative with PIC values > 0.65 as well as an overall genetic diversity (0.47) highlighting a broad genetic variability in the analyzed landraces. Genetic structure analysis revealed a high genetic differentiation among the 47 maize landraces with an overall Fst value (0.33). Cluster analysis for morphological traits as well as for SSR markers grouped the 47 Algerian populations regardless their geographic origin.Maize from Algerian desert harbors a wide genetic diversity offering a source of novel/unique alleles useful for maize breeding programs to face the ongoing and future major challenge, the climate changes.The online version of this article (10.1186/s12863-018-0655-2) contains supplementary material, which is available to authorized users. Maize is one of the most diverse crop in the world , charactMastoura, Ktania, Safra, Abidiya, Dora (in Arabic), Tifsi or Engafouli (in Tamacheq) [The introduction of maize in African agro-systems goes back to the sixteenth century, as previously reported . In Algeamacheq) . Over thamacheq) . More inamacheq) . TherefoAlthough phenotypic variations represent an ideal tool to determine, the agronomic crops performance as well as being inexpensive and comfortable , they suAlthough maize germplasm from Algerian desert was recently studied by phenotypic and genetic analysis , 19, to ANOVA showed a significant block effect for 4 out of 24 traits . The seven checks in each replicate significantly differed for many traits except EMG, EV, ASI, ED, EL, HMC and KYP, whereas landraces significantly differed for 13 out of 24 traits including DS, DA, EH, PLH, ERN, ED, CD, RD, KL,KW, EW, 1000 KW and HMC  to7.04 (KMA). However, BTH showed the highest 1000 KW (323.6 g) whereas TBN recorded the lowest value (139.95 g)  followed by PLH and EH (r = 0.92). Interestingly, KYP is highly correlated with PLH and EH, DA and DS, ERN and NKR, while others significant correlations between traits were observed , ERN and KW (r = − 0.39), K% and 1000 KW (r = − 0.62)  to 20 (umc1222) with an average of 10.94 alleles per locus (Table I = 0.84) exhibiting the highest values for umc1335  and the lowest for phi127  (Table umc1335) to 0.31 (phi127) with an whole average value of 0.62  as well as in umc1403 (2 out of 7). Thirteen landraces out of the 47 revealed unique alleles  was the highest in YAK 4.53) and the lowest in EID (2.63), and gene diversity (H) of a single landrace ranged from 0.38 (EID) to 0.60 (YAK).  revealed that genetic diversity within populations contributes to 70% of overall genetic diversity  for the two clustering matrices tasted by Mantel’s test revealed a highly significant but negative correlation between the agro-morphological and SSR information.The correlation coefficient  within our collection was determined K = 2 based on ΔK peaks Fig. . ConsideLandraces represent an odd genetic resource for crops with a high genetic variation useful for modern breeding programs . ApproxiTherefore, the conservation of landraces as genetic resources and the management of such gene pools are important by ensuring suitable agronomic and genetic knowledge . In the Algerian maize is a promising source of favorable alleles for stress tolerance, but it has been neglected until recently , thus thThe principal component analysis (PCA) was performed to cluster landraces based on discriminant morphologic traits. According to Clifford and Stephenson , the firThe first step for crop improvement strategies consist on the assessment of genetic variation and the relationships among landraces/varieties/populations. SSR markers represent an efficient tool in providing direct and reliable information for a helpful management and conservation of germplasm collections. The genetic diversity of 47 Algerian maize landraces was analyzed in bulk fingerprinting method, leading to detect high levels of polymorphism . Higher results were reported by Wasala and Prasanna , which fAlmost 50% of the SSR used in our study was highly informative (PIC > 0.65) and the overall gene diversity (0.46) was higher compared to the previous report on 15 Algerian landraces (0.40)  and compumc1222 and umc1335), showing a high number of alleles . Furthermore, plant populations harboring unique alleles, due to SSR high mutation rate [Regardless the abundance of SSR in maize genome and their high polymorphism , 42, theion rate , but alsion rate , are repion rate . It is nion rate  observedion rate  led to tion rate , 45, 49.The population structure based on the Bayesian admixture-model is in agreement with PCA and cluster analyses as well as the geographic origin of landraces. This result appears in agree with previous studies , 50, whiEven if maize is an allogamous plant species, in agreement with our results, a higher genetic variation within population than among them was yet described in other studies . Da SilvA negative correlation between morphological and genetic distance matrices was revealed by the Mantel test, in agreement with previous report on ryegrass varieties by Roldan-Ruiz et al. . This suThe Algerian maize landraces hold a significant genetic diversity suggesting their potential adaptation to the extreme Sahara environment, according to genetic parameters and the clustering based on both molecular and agro-morphological markers. Four centuries of growing in the Sahara could lead to a widening of gene pool diversity in maize germplasm, due to genetic recombination, mutations, natural and human selection, as stated for plant populations by Hartl and Clark . NaturalThe maize germplasm used in this study consisted of forty-seven populations/landraces (a landrace of out-pollinated plant species can be defined “population”) collected during 2009 and 2010 in different Oasis of South-Western Algeria (from 27°52′ to 31°37’ N latitude and 0°17′ to 2°13’ W longitude). In particular, six and thirty-eight populations/landraces were collected from Bechar and Adrar oasis, respectively; whereas three populations/landraces were collected from Gherdaia (1) and Saida (2)  in the sub humid North of Algeria during the spring-2013 under field conditions. The experimental design was an Augmented design with three blocks  and seve− 1).Each experimental plot consisted of one row, 30 hills per row and one grain per hill. Rows were spaced 0.70 m apart and hills were 0.20 m. The data were recorded on ten random plants for twenty-four agro-morphological traits using the standard descriptors suggested by the International Board for Plant Genetic Resources  (Additiov/v) sodium hypochlorite solution, rinsed with deionized water and transferred for germination in Petri dish (diameter 9 cm), on filter paper with 0.5 mM CaSO4 at 26 °C in dark condition for one week.Maize seeds of each landrace were surface sterilized for 20 min in 20%  , whereas the Polymorphic information content (PIC) for each SSR locus was also estimated using Cervus v. 3.0.7 software . The Analysis of Molecular Variance (AMOVA) was also performed to analyze genetic variation among and within individuals [http://cmpg.unibe.ch/sofware/arlequin35) testing Fst by 9999 random permutations. A dendrogram to define differences among populations was constructed based on Nei and Li [To determine genetic parameters such as number of alleles (A), average of different alleles (Na), effective number of allele (e), Nei’s gene diversity index (H), Shannon diversity index (I), forboth SSR loci and population GenAlex software version 6.3 was utilized . Privateividuals  by Arleqi and Li  pair-wisi and Li  using MEi and Li .In addition, model-based (Bayesian) clustering was performed to evaluate genetic relationship among individuals and population structure by using Software package STRUCTURE . The proAdditional file 1:Table S4. Augmented ANOVA (mean squares) for 24 agro-morphological traits in 47 Algerian maize landraces and 7 checks. (DOCX 17 kb)Additional file 2:Table S5. Correlation matrix of 24 agro-morphological traits used to characterize 47 Algerian maize landraces. (XLSX 16 kb)Additional file 3:Table S6. Eigenvalues, variances and coefficients associated with first three principal components. (DOCX 15 kb)Additional file 4:Figure S1. Principal component analysis of the 47 Algerian maize landraces based on 24 agro-morphological traits. (JPEG 152 kb)Additional file 5:Table S7. Landraces summary statistics based on SSR analysis. (DOCX 14 kb)Additional file 6:Table S8. Analysis of Molecular Variance. (DOCX 12 kb)Additional file 7:Table S9. Partition of the gene diversity among clusters. (DOCX 12 kb)Additional file 8:Table S1. List of the 47 Algerian maize landraces. (DOCX 22 kb)Additional file 9:Table S2. Agro-morphological traits recorded in the 47 Algerian maize landraces. (DOCX 15 kb)Additional file 10:Table S3. Primers used to detect SSR marker. (DOCX 14 kb)


The increases were greatest in cities of mid to high latitude, in humid temperate and dry climate regions, and with large seasonal variation in temperature. Cities are likely to experience large increases in hottest month mean temperatures under high GHG emissions trajectories, which will often present substantial challenges to adaptation and health protection.The so far largely unabated emissions of greenhouse gases (GHGs) are expected to increase global temperatures substantially over this century. We quantify the patterns of increases for 246 globally-representative cities in the Sustainable Healthy Urban Environments (SHUE) database. We used an ensemble of 18 global climate models (GCMs) run under a low (RCP2.6) and high (RCP8.5) emissions scenario to estimate the increase in monthly mean temperatures by 2050 and 2100 based on 30-year averages. Model simulations were from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Annual mean temperature increases were 0.93 degrees Celsius by 2050 and 1.10 degrees Celsius by 2100 under RCP2.6, and 1.27 and 4.15 degrees Celsius under RCP8.5, but with substantial city-to-city variation. By 2100, under RCP2.6 no city exceeds an increase in T Cities are already home to more than half of the world’s population , they geHowever, the populations of cities are also potentially vulnerable to the consequences of climate change, including the direct effects of increased heat . Some deFor assessing climate change impacts and adaptation responses at fine spatial scales, such as those of regions and cities, dynamical downscaling techniques can be applied to regional climate models (RCMs), driven by global climate models (GCMs) . HoweverIn this paper, we use a large ensemble of GCMs to examine the patterns of temperature rise that may be expected in cities across the globe under a high and a low GHG emissions trajectory. The analysis is based on data from the Sustainable Health Urban Environments (SHUE) project, which has developed a database of information on a globally-distributed sample of cities . The bro2The SHUE database includes information on a random sample of 246 global cities with populations over 15,000 obtained from GeoNames , stratif2.1Monthly simulated climate data was estimated for SHUE cities using 18 GCMs under a low GHG emissions scenario, Representative Concentration Pathway (RCP) 2.6, and a high emissions scenario, RCP8.5 –15. RCP2http://pcmdi9.llnl.gov). 18 GCMs were available for this variable (The model simulations were from Coupled Model Intercomparison Project Phase 5 (CMPI5) , which pvariable . Since eA simple bias adjustment was performed at the grid box level for each GCM in order to improve agreement with observations, in this case the CRU-TSv3.22 dataset (which is provided on the same 0.5 degree grid) . In thisThe final step in pre-processing the GCM data was to take the set of latitude and longitude coordinates for the 246 SHUE cities and to extract the data for the grid box in which each city is located. In coastal areas, the nearest land grid box was used.2.2Location: the coordinates (latitude and longitude) of each city obtained from GeoNames .Population size: estimates of city populations obtained from GeoNames.Ecoregion: the Bailey’s ecoregion in which the city is located . The ecoThe climate data were combined with the following information on the characteristics of each city, where available: 2.3mean for a baseline period , a near-future period , and a far-future period . The ensemble mean  was calculated, together with the annual mean for all series . Analyses presented here are based on changes in the annual average of Tmean and for the hottest and coldest months of the year in 2050 and 2100 compared with 2017. The model outputs are presented as changes from the baseline period, rather than as absolute values. The focus on changes in temperature (together with the bias adjustment described above) should help to reduce, though not eliminate, the impact on the analysis of model biases and shortcomings, including some of those related to the relatively coarse spatial scale of the GCMs [For each city, we calculated 30-year averages of monthly TChanges in temperatures for SHUE cities estimated by the GCMs were analysed in relation to markers of their geographical location, including their coordinates (latitude/longitude), Bailey’s ecoregion domain, and WHO region . We also analysed the results in relation to city population size. The analyses were performed using simple tabulation and graphical methods, including analyses of both uni-variate and bi-variate distributions. Where there was no data on city-level characteristics for a given city, the city was excluded from that part of the analysis.33.1Based on the simple average across the 18 GCMs, the mean annual temperature increase in SHUE cities (relative to 2017) was estimated to be 0.93 degrees Celsius by 2050 and 1.10 degrees Celsius by 2100 under RCP2.6, and 1.27 degrees Celsius by 2050 and 4.15 degrees Celsius by 2100 under RCP8.5 . The cormean for the hottast month of the year of greatet than 2 degrees Celsius, all under RCP8.5. However, by 2100, all cities exceeded 2 degrees Celsius increase for the hottest month, with the largest increases close to  7 degrees Celsius. Under RCP8.5, by 2100, the Tmean in the hottest month will on average exceed 40 °C in three cities .By 2050, there was considerable overlap in the temperature increases experienced in SHUE cities under RCP2.6 and RCP8.5 . There wmean for the hottest month compared with the increases for the coldest month.Cities with large increases for the hottest month of the year generally had comparably large increases for the coldest month. However, some more northerly cities with colder winter climates  had relatively larger increases for the coldest month by comparison with the hottest month of the year, while southern European cities, including Madrid and Katerini, had relatively large increases in T3.2The magnitude of the temperature increases for the hottest month in relation to latitude and ecoregion domain are shown in mean for the hottest month was smallest for humid tropical regions, cities in these regions tend to have high relative and absolute humidity, and small diurnal and seasonal variation in ambient temperatures. Cities in the temperate and dry regions, however, with the largest temperature increments for the Tmean of the hottest month according to our estimates, tend to have generally lower relative humidity and appreciably greater diurnal and seasonal variation.The differences in temperature changes in the hottest and coldest months by ecoregion domain  are potemean and the winter–summer differences in temperature as reflected by the difference in Tmean of the hottest and coldest months of the year. The figure demonstrates the considerable challenges for adaptation faced by cities that experience substantial warming during the hottest month of the year yet also extremely cold wintertime conditions.n = 16) are in the range of 3 degrees Celsius to just over 5 degrees Celsius. It is worth noting that There was no clear pattern of association between the temperature increase for the hottest month and city size (population) . The tem4The results of these analysis provide an insight into the temperature increases that may occur in cities over this century under high and low GHG emissions trajectories. The results confirm substantial increases in hottest month temperatures in all cities under RCP8.5 by the end of the century, with many likely to experience increases of 4 to 7 degrees Celsius, especially in cities at higher latitudes with temperate or dry climates. More generally, the work demonstrates the desirability of having consistent data on a large and globally-representative sample of cities. Such information can enable a better understanding of interactions between climate change and other environmental health issues, including the potential to achieve co-benefits across multiple risks through actions to improve urban sustainability [mean for the hottest month would usually represent an appreciable increase above the threshold for heat deaths [Although we do not here attempt to quantify the health and social impacts of these temperature rises, in a recent multi-country analysis of temperature-related mortality, the ‘optimum temperature’ , was found to vary from around the 60th percentile of the daily distribution in tropical areas to around the 80–90th percentile in temperate regions . Hence, t deaths –23, and mean for the hottest month, these cities also tend to have high humidity and small diurnal and seasonal variations in temperature. The combination of heat and humidity (as reflected by high wet bulb globe temperatures and similar indices) presents a particular physiological stress [However, the variation in temperature increases with regard to latitude and ecoregion domain may also indicate different implications for adaptation responses. Although cities in low latitude tropical climates generally had more modest increases in Tl stress ,25 that l stress , or to dl stress .The data we present do not provide information on future changes in extremes of temperature. However, the fact that the temperature increases for the hottest month were generally similar to the temperature increase for the whole year  suggestsAmong the main strengths of our study is the fact that the analysis was of a globally representative sample of cities and so reflects variation of urban populations with regard to region, city size, and economic development. Its results were also based on an ensemble of 18 global climate models, though here we show only mean values rather than presenting the suite of individual model results to show the diversity of their results. On the other hand, the analysis was of change in (dry bulb) temperatures alone, taking no account humidity, or evidence on the precise form of the distribution of daily temperatures. Moreover, the analyses did not attempt to incorporate the urban heat island (UHI) effect—the name given to the occurrence of higher outdoor temperatures in metropolitan areas compared with those of the surrounding countryside caused by the thermal properties  of the surfaces and materials found in urban landscapes, the reduced evapotranspiration from reduced natural vegetation, and the waste heat production from anthropogenic activities –30. The Combined assessment of temperature and humidity, UHI effects, and more detailed assessment of the implications for health are all important steps for further research, therefore. However, the main policy implications are clear. The first is the importance of aggressive reduction in GHG emissions in order to help reduce the more extreme temperature rises for urban populations that could be expected from emissions trajectories similar to that represented by RCP8.5. The second is to ensure adaptation planning takes account of the likelihood of very sizeable temperature increases in urban centres around the globe that will lead many populations to be exposed to temperatures well beyond those of their current experience.www.mdpi.com/2225-1154/5/4/93/s1.The following are available online at Table S1 and S2


Drosophila melanogaster, recently discovered synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest a motion model that is increasingly intricate when compared with the ubiquitous Hassenstein-Reichardt model. By contrast, our knowledge of OFF-pathway (T5) has been incomplete. Here, we present a conclusive and comprehensive connectome that, for the first time, integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. Although the two pathways are probably evolutionarily linked and exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In  Chlorophanus, and later Over half a century ago, The fly’s optic lobe consists of four consecutive neuropils: the lamina, medulla, lobula, and lobula plate. Each of these comprises columnar units that correspond to the array of ommatidia in the retina. The motion pathway in the optic lobe arises from the photoreceptor cells (PRs), which receive light signals in the compound eye and extend their axons to the lamina. R1–R6 cells expressing rhodopsin rh1 provide signals to lamina monopolar cells in the lamina cartridges, which project to the distal medulla. The lamina neurons are presynaptic to various types of medulla neurons in the distal medulla. Among them, medulla columnar neurons including Mi, Tm, and TmY cells further provide inputs to the dendritic arbors of T4 in the M10 layer of the medulla and T5 in the Lo1 layer of the lobula.Drosophila melanogaster  in the ON-edge motion pathway . Using sComplementary to the T4 cells, narrow-field T5 cells constitute the first output stage of the OFF-edge pathway , and somFinally, T4 and T5 cell axons transfer motion information to the fourth neuropil, or lobula plate, where it is integrated and further processed to extract specific motion modalities, before being conducted to the central brain by visual projection neurons (VPNs). VPNs include various types of lobula plate tangential neurons (LPTCs) and lobula plate/lobula columnar cells .The ON and OFF motion pathways are similar in their function, component neurons, and patterns of synaptic connections. Both T4 and T5 cells are direction-selective neurons, and each is further grouped into four subtypes: T4 as T4a, T4b, T4c and T4d; and T5 as T5a, T5b, T5c, and T5d. These T4 and T5 cells specifically signal motion in the four canonical directions. The subtypes a–d detect front-to-back, back-to-front, upward, and downward motion, respectively . Each suGiven the dimensional constraints of the respective ssTEM and FIB-SEM datasets, however, the T4 and T5 pathways, and their respective input neurons, have been reconstructed independently in separate reports using 3D-EM methods. Series of ultrathin sections have been used to identify medulla cell inputs to T4 cells; these included medulla intrinsic (Mi) and transmedulla (Tm) cells but not their terminals in the lobula, which were lacking from the EM dataset . SimilarDrosophila.In this study, we exhaustively identified the synaptic inputs to T5 cells, and described their spatial layouts. We also assessed the anatomical properties of the dendritic terminals of T4 and T5, after identifying all neurons that have synaptic contacts with the motion-sensing output cells in the medulla and lobula. As a result, our report concludes the connectomic analysis of both the ON- and the OFF- motion-sensitive pathways in The FIB-SEM dataset used in this study covers the entire depth of the medulla, lobula, and lobula plate, as well as the proximal depth of the lamina , and is On the basis of reconstructed neurons and their synapses, we comprehensively identified input and output neurons of T4 and T5 in the medulla and lobula . The TmYThe wide-field neurites of CT1 are, in several respects, a novel player among the input neurons to T4 and T5. First, they form repeated synaptic modules in each neuropil column of the medulla and lobula , where tAnother cell type, TmY15 , which hWe also found that two types of Lo1-specific wide-field cells form synaptic inputs to the T5 terminals . One of The T2 cell is postsynaptic at a few contacts from each of the four T5 subtypes in the lobula . T2 has The present dataset also reveals some novel minority connections that have a few synaptic contacts. Two types of neurons, Mi13 and TmY18, receive input from T4 cells but lack direct output back to T4  and 3A. To identify candidate inputs to an EMD, we mapped the synaptic sites distributed over the dendrites of both T4 and T5 cells and compared these maps with plots for T4 made by In T4, synapses from all of the neurons were distributed in the manner previously reported . In the Among the neurons providing inputs to T4 and T5 cell dendrites , TmY15, The spatial distributions of inputs from these neurons to T4 and T5 were calculated by annotating the neurons according to the columns they occupy in the medulla and lobula. The shape of the columns and the array formed by their cross-sections differ in different strata and according to their location in the visual field. The array of columns is considerably compressed in the A–P axis in M10 and Lo1 when compared with the hexagonal array of the compound eye and the corresponding array of underlying lamina cartridges , so the To identify input partners for T4 and T5, in addition to tracing and identifying the neurons themselves, we also needed to annotate their synaptic sites rapidly, accurately and comprehensively. For this task, we initially identified both pre- and postsynaptic sites by recruiting an automated synapse prediction algorithm, which annotated hundreds of thousands of synaptic sites efficiently. To evaluate the quality of the prediction algorithm, and to check if such predicted synapses could be used for connectivity and functional analyses, we then exhaustively checked the predicted input and output synapses of representative T4 and T5 cells manually, and corrected them as required. Neuron-to-neuron synaptic connections that were identified on the basis of manually annotated synapses correlated strongly with the identified connections based on auto-predicted synapses, indicating that neuronal connections of the pathways could initially be predicted without comprehensive labor-intensive manual synapse annotations. Comparing input neurons to T4 with those to T5 then enabled us to identify similarities in the cell types and the numbers of their synapses that underlie common computational strategies. The validity of such comparisons rests, of course, on the assumption that transmission at each anatomical synapse generates a unit postsynaptic signal of fixed size.To assess the quality of the prediction, we counted numbers of synapses (both presynaptic and postsynaptic contacts) before and after manual proofreading of the T4 and T5 dendrites. For both counts, the numbers of output and input synapses before proofreading (predicted synapses) are plotted against the numbers of synapses after proofreading (proofread synapses) for the input/output synapses of T4 cell dendrites , and T5 On the basis of this result, the spatial distributions of input neurons to T4 and T5 cells were mapped using predicted rather than manually proofread synapses  in both the ON-pathway of T4 in the medulla, where CT1 is excited by Mi1 and Tm3 , and theThe functional significance of TmY15 in motion processing must remain speculative. Not only does this cell receive a wide range of inputs from various types of cells in the medulla, lobula, and lobula plate, but it also has much weaker synaptic contacts with T4 and T5 in M10 and Lo1 when compared with other Mi or Tm cell inputs. It specifically innervates the second and third strata (Lop2 and Lop3) in the lobula plate, and preliminary observations show that it receives inputs from cells T4b, T4c, T5b, and T5c, suggesting that TmY15 may also work as a feedback loop to suppress responses in T4 and T5 during regressive and upward motions.In summary, our report comprehensively identifies input and output neurons of the dendritic arbors of T4 and T5 cells, and uses a single dataset to reveal (at synapse level) the detailed similarities between the connections of these two motion-signaling output cells. Together with the functional contribution of individual neurons in the motion-detection circuits shown in several studies , the detDrosophila melanogaster, a cross between homozygous 1118w and CS wild type  , a genet In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Drosophila brain" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Alexander Borst as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by K VijayRaghavan as the Senior Editor.Thank you for submitting your article "Comparative connectivity analysis of the ON- and OFF-edge motion pathways in the The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Drosophila optic lobe. The dataset is superior to previous EM datasets because it is of higher isotropic resolution and encompasses the complete pathway from the lamina to the lobula plate. From this dataset they reconstructed five T4 and T5 neurons – elementary motion detectors in the fly visual system – detected all their synaptic contacts  and traced and identified the connected cell types. While the circuitry in the ON-selective pathway had already been described in detail previously, the authors were able to demonstrate for the first time the spatial distribution of the presynaptic elements to OFF-selective T5 cells, namely inputs segregated into distal, central, and basal dendritic inputs. In addition to confirming already published findings , they describe a new potential key-player in the T5-circuit, CT1.The authors used a focused ion beam scanning electron microscope (FIB-SEM) to acquire high resolution images of a large fraction of a Drosophila visual system with EM data, and, thus, is a timely and interesting addition to this field.The data presented here by Shinomiya and colleagues, especially the new evidence for the spatial distribution of T5 inputs, is convincing and with no doubt of great interest for the scientific community. In particular, the manuscript aims at constraining the possible circuits for direction selectivity in the Essential revisions:General criticism:The quality of the writing as well as the structure of the manuscript are very unsatisfying. Overall poor referencing. The figures are poorly linked to the text. The order of figures and the Results part of the manuscript do not follow a consistent logic.Section-by-section criticism:Drosophila OFF-edge motion pathway.A) Title: I would propose to focus on the new finding, and not on the comparison with the T4 inputs. I would propose the following title: Connectivity analysis of the B) Introduction: There are some abrupt jumps from modeling (first section) to anatomy without introducing the optic lobe structure at all, pointing out the roles of the described elements in the context of the motion vision circuitry. I would propose to restructure the introduction: 1) Models for motion detection. 2) General architecture of the optic lobe. 3) Functional evidence; split ON v. OFF . 4) Anatomical evidence already published (presynaptic elements to T4 and T5). 5) Functional evidence.C) Results: The figure referencing in the Results part does not follow any logic. Every part of every figure should be referenced in the text before the next one. I would suggest to rearrange the figures in the beginning of the Results: 1) Change the order of Figures 2 and 3. 2) First describe the inputs to T4 and T5 and then their anatomy. 3) Move Figure 6 to supplements. 4) The last two sections (about CT1 and TmY15) would better fit to the corresponding section where anatomy and connectivity is described.D) Discussion: The second paragraph of the subsection “Three-way EMD circuit model” is largely confusing and does not contribute to the understanding of the subject. I would suggest removing this section from the manuscript. The Discussion would profit from referencing the corresponding figures more regularly.Another concern is that the interpretation of the connectomics data is exclusively done through the lens of the three-arm model from Haag et al., 2016, which incorporates both Hassenstein-Reichardt style preferred direction enhancement and Barlow-Levick style null direction suppression. This is an important model but there is, however, no consensus at the moment on how direction selective signals emerge, with studies reporting both/either preferred direction enhancement and/or null direction suppression . While it is tempting to focus on the three-arm model, because of their and previous findings that the inputs are separated into three clusters, it also seems overly restrictive. It is important to mention alternative models, and how this connectomics data might inform these.Loosely related to this point, there is no mention of silencing experiments performed by various groups . Even though these are not always conclusive, they are directly relevant to the final model put forward in Figure 7.Drosophila brain" for further consideration at eLife. Your revised article has been favorably evaluated by K VijayRaghavan as the Senior Editor, and a Reviewing Editor.Thank you for resubmitting your work entitled "Comparisons between the ON- and OFF-edge motion pathways in the The revised manuscript has improved substantially, both in its structure as well as in the writing. However, before it can go to press, the authors should fix the following things:1) Writing:Some sentences are still too long and need to be shortened. In general, the manuscript would profit from one of the authors smoothing out the language.The following sentences from the Introduction might serve as an example: “Functionally, both T4 and T5 cells are direction-selective, and each is further grouped into four subtypes: T4 as T4a, T4b, T4c and T4d, and T5 as T5a, T5b, T5c, and T5d. T4 and T5 cells specifically extract motion in the four canonical directions. The subtypes a-d, detect front-to-back, back-to-front, upward, and downward motion, respectively one each .”In a similar way, the last paragraph of the Introduction justifies the present study because previous data sets used different methods and are each lacking specific parts of the optic lobe. But then it diffuses again in details like: “The latter are paired Tm1 and Tm2 cells with multiple Tm4 cells that all receive L2 input, and a fourth cell Tm9 , a medulla target of L3, the third lamina neuron in each cartridge. Thus Tm1 and Tm2 provide input from a single column while Tm4 cells provide input originating from multiple columns. Tm9 in turn provides a candidate substrate for L3's combinatorial action with L2 .” I would simply delete these sentences, focusing on the main point made above.2) Other major point:In the third paragraph of the subsection “Identifying the synaptic partners of T4 and T5 cells2”, it states Tm1, Tm4, and Tm9 as CT1 input. The first and sixth paragraphs of the subsection “EMD circuit models based on actual connections”, state only Tm1 and Tm9, while in the second paragraph of the subsection “Possible roles for CT1 and TmY15”, the authors write that Tm1 and Tm4 are presynaptic to CT1. In Figure 6 only Tm1 and Tm9 are depicted as CT1 inputs. Please also clarify where these data come from and how strong these inputs are (synapse numbers). Essential revisions:General criticism:The quality of the writing as well as the structure of the manuscript are very unsatisfying. Overall poor referencing. The figures are poorly linked to the text. The order of figures and the Results part of the manuscript do not follow a consistent logic.Section-by-section criticism:A) Title: I would propose to focus on the new finding, and not on the comparison with the T4 inputs. I would propose the following title: Connectivity analysis of the Drosophila OFF-edge motion pathway.Drosophila brain.”We agree with the suggestion that the title should focus on the new findings, i.e. on the OFF-edge pathway. A major merit of our work is, however, we suggest, that both pathways, those to T5 (which is new) and to T4 (which is an extended update) have now been made from the same FIB-SEM dataset using identical methods without biases, and discuss motion processing models by referring to previous works on both ON and OFF pathways. These use some of the same pathways. So, while we agree to the need to underemphasize the T4 pathway, we think there is still merit in referring to its comparison. We therefore now use the following title: “Comparisons between the ON- and OFF-edge motion pathways in the B) Introduction: There are some abrupt jumps from modeling (first section) to anatomy without introducing the optic lobe structure at all, pointing out the roles of the described elements in the context of the motion vision circuitry. I would propose to restructure the introduction: 1) Models for motion detection. 2) General architecture of the optic lobe. 3) Functional evidence; split ON v. OFF . 4) Anatomical evidence already published (presynaptic elements to T4 and T5). 5) Functional evidence.We agree and have significantly revised the entire Introduction. We have followed the reviewer’s suggested ordering of topics starting with models for motion detection. The general architecture of the optic lobe is now covered by new text in the second paragraph of the Introduction. We then follow this by reporting the division into ON and OFF pathways, in the fourth paragraph of the Introduction.C) Results: The figure referencing in the Results part does not follow any logic. Every part of every figure should be referenced in the text before the next one. I would suggest to rearrange the figures in the beginning of the Results: 1) Change the order of Figures 2 and 3. 2) First describe the inputs to T4 and T5 and then their anatomy.We have followed these suggestions from the reviewer and now reverse Figures 2 and 3 from their order in the previous text. We are careful to cite the figures and figure panels in order.3) Move Figure 6 to supplements.We have done this. The figure is now Figure 5—figure supplement 1.4) The last two sections (about CT1 and TmY15) would better fit to the corresponding section where anatomy and connectivity is described.We agree with the reviewer’s suggestion and the description for CT1 and TmY15 are now moved to the anatomy section (subsection “Identifying the synaptic partners of T4 and T5 cells”).D) Discussion: The second paragraph of the subsection “Three-way EMD circuit model” is largely confusing and does not contribute to the understanding of the subject. I would suggest removing this section from the manuscript. The Discussion would profit from referencing the corresponding figures more regularly.We now realize that part of our original text was incorrect, and we have dealt with this. We think this helps to remedy the reviewer’s confusion. We think it important to compare the responses of Mi9 and Tm9, moreover, and therefore decide to retain the edited version of this paragraph.Another concern is that the interpretation of the connectomics data is exclusively done through the lens of the three-arm model from Haag et al., 2016, which incorporates both Hassenstein-Reichardt style preferred direction enhancement and Barlow-Levick style null direction suppression. This is an important model but there is, however, no consensus at the moment on how direction selective signals emerge, with studies reporting both/either preferred direction enhancement and/or null direction suppression . While it is tempting to focus on the three-arm model, because of their and previous findings that the inputs are separated into three clusters, it also seems overly restrictive. It is important to mention alternative models, and how this connectomics data might inform these.We absolutely agree, and in the first paragraph of the Discussion, we have therefore cited the additional references  mentioned by the reviewer although we do not think that these models fit our anatomical observations as clearly as the three-arm model of Haag et al., 2016.Loosely related to this point, there is no mention of silencing experiments performed by various groups . Even though these are not always conclusive, they are directly relevant to the final model put forward in Figure 7.The studies mentioned by the reviewer refer to both ON and OFF pathways. They are now cited and provide an additional reason to retain reference to both pathways in the title to our report .The revised manuscript has improved substantially, both in its structure as well as in the writing. However, before it can go to press, the authors should fix the following things: 1) Writing: Some sentences are still too long and need to be shortened. In general, the manuscript would profit from one of the authors smoothening the language. The following sentences from the Introduction might serve as an example: “Functionally, both T4 and T5 cells are direction-selective, and each is further grouped into four subtypes: T4 as T4a, T4b, T4c and T4d, and T5 as T5a, T5b, T5c, and T5d. T4 and T5 cells specifically extract motion in the four canonical directions. The subtypes a-d, detect front-to-back, back-to-front, upward, and downward motion, respectively one each .”In a similar way, the last paragraph of the Introduction justifies the present study because previous data sets used different methods and are each lacking specific parts of the optic lobe. But then it diffuses again in details like: “The latter are paired Tm1 and Tm2 cells with multiple Tm4 cells that all receive L2 input, and a fourth cell Tm9 , a medulla target of L3, the third lamina neuron in each cartridge. Thus Tm1 and Tm2 provide input from a single column while Tm4 cells provide input originating from multiple columns. Tm9 in turn provides a candidate substrate for L3's combinatorial action with L2 .” I would simply delete these sentences, focusing on the main point made above.We carefully rechecked the manuscript and did our best to streamline sentences. We hope the entire article now reads smoothly.We admit that the part (“The latter are paired Tm1 and Tm2 cells …”) was off the topic and also redundant. We therefore now removed this part from the text.2) Other major point:In the third paragraph of the subsection “Identifying the synaptic partners of T4 and T5 cells2”, it states Tm1, Tm4, and Tm9 as CT1 input. The first and sixth paragraphs of the subsection “EMD circuit models based on actual connections”, state only Tm1 and Tm9, while in the second paragraph of the subsection “Possible roles for CT1 and TmY15”, the authors write that Tm1 and Tm4 are presynaptic to CT1. In Figure 6 only Tm1 and Tm9 are depicted as CT1 inputs. Please also clarify where these data come from and how strong these inputs are (synapse numbers).Including Tm4 as a major CT1 input was our mistake. While both CT1 receives inputs from both Tm9 and Tm1 with substantial numbers of synapses ), Tm4 has virtually no synapses with CT1 ). So we have removed Tm4 from these statements and added synapse numbers for Tm9 and Tm1. We apologize for the confusion.