Continental generalization of an AI system for clinical seizure recognition BackgroundElectroencephalogram (EEG) monitoring and objective seizure identification is an essential clinical investigation for some patients with epilepsy. Accurate annotation is done through a time-consuming process by EEG specialists. Computer-assisted systems for seizure detection currently lack extensive clinical utility due to retrospective, patient-specific, and/or irreproducible studies that result in low sensitivity or high false positives in clinical tests. We aim to significantly reduce the time and resources on data annotation by demonstrating a continental generalization of seizure detection that balances sensitivity and specificity.

MethodsThis is a prospective inference test of artificial intelligence on nearly 14,590 hours of adult EEG data from patients with epilepsy between 2011 and 2019 in a hospital in Sydney, Australia. The inference set includes patients with different types and frequencies of seizures across a wide range of ages and EEG recording hours. The artificial intelligence (AI) is a convolutional long short-term memory network that is trained on a USA-based dataset. The Australian set is about 16 times larger than the US training dataset with very long interictal periods (between seizures), which is way more realistic than the training set and makes our false positives highly reliable. We validated our inference model in an AI-assisted mode with a human expert arbiter and a result review panel of expert neurologists and EEG specialists on 66 sessions to demonstrate achievement of the same performance with over an order-of-magnitude reduction in time.

FindingsOur inference on 1,006 EEG recording sessions on the Australian dataset achieved 76.68% with nearly 56 [0, 115] false alarms per 24 hours on average, against legacy ground-truth annotations by human experts, conducted independently over nine years. Our pilot test of 66 sessions with a human arbiter, and reviewed ground truth by a panel of experts, confirmed an identical human performance of 92.19% with an AI-assisted system, while the time requirements reduce significantly from 90 to 7.62 minutes on average.

InterpretationAccurate and objective seizure counting is an important factor in epilepsy. An AI-assisted system can help improve efficiency and accuracy alongside human experts, particularly in low and middle-income countries with limited expert human resources.

FundingsSOAR Fellowship from The University of Sydney, a Microsoft AI for Accessibility grant, and a Research Training Program (RTP) support provided by the Australian Government.

Research in contextO_ST_ABSEvidence before this studyC_ST_ABSDuring the development of our artificial intelligence (AI) system, we did a systematic review of the scientific literature with search via PubMed for research articles published on seizure detection with the following inclusion criteria: (1) Tests or inference evaluation is conducted on large-scale clinical EEG data; (2) Generalization is attempted or potentials for generalization is considered, e.g., in commercialized tools; (3) Seizure detection delay and real-time (aka. online) operation were not considered critical in this context as long as the test was conducted on raw EEG data. Note that ICU seizure detection or portable seizure alert systems are relying on detection delay and real-time needs. Our keywords include "prospective seizure detection", "automated seizure detection", "non-patient specific seizure detection", "seizure detection on continuous EEG", and "deep learning-based seizure detection" and "machine learning-based seizure detection". We found that the only two categories of works meet our criteria: two research papers published in 2020 and works published by commercial tools developers. We cited a recent review of 89 deep learning-based seizure detection, all of which are retrospective. One work from Stanford reported seizure detection on all ages (pediatric to adult ages) using post-acquisition EEG recordings and provided an avenue for independent evaluation by providing a test on a publicly available Temple University Hospital (TUH) EEG dataset. The other work pivoted on algorithmic-assisted real-time seizure risk monitoring in continuous EEG in neonatal intensive care unit (NICU) with 128 neonates (32 with seizures) showing about 20% improvement in seizure identification over 130 neonates (38 with seizures) with no algorithmic assistance.

Commercial tools we studied are Encevis (EpiScan), Besa, and Persyst. There is a recent comparative study on these tools on 81 patients. Encevis is reported as the best performing tool, and hence we provided a comparative study with Encevis ver. 1.9.2. Encevis is also the only tool that provided an avenue for comparative study on publicly available EEG data. The Stanford work, published in 2020, confirms many false positives with Persyst 13. We excluded our tests on Persyst 14 as it highly under-performed relative to Encevis. Only Stanfords work provides code availability. We compared our results with Stanfords work outcome and provided pilot test results with the Encevis (EpiScan) tool on the Australian dataset, which shows a considerably lower sensitivity.

Added value of this studyTo the best of our knowledge, the current study is the first continental generalization that demonstrates the potential to achieve an expert human-level seizure recognition rate in a clinical setting and in just a fraction of time. The two datasets used in this study are recorded with different infrastructure, which adds to the independence of inference from hardware types and improves clinical utility. This is particularly important as 80% of patients with epilepsy live in low and middle-income countries with limited resources, particularly EEG specialists and neurologists.

Implications of all the available evidenceOur results support the potential benefits of deep learning AI in clinical settings for seizure recognition and its contribution to significant sensitivity over available solutions. Our AI-assisted system achieves more than a ten-fold increase in time efficiency and reports identical performance to human experts for EEG interpretation with access to great neurophysiology support and auxiliary data. Our findings, particularly our tests on an available commercial tool, recommend that the evaluation, test, or inference in AI systems be performed on different datasets, with diverse infrastructures, and on large-scale and realistic sets with long interictal periods.
Laminar Mechanisms of Saccadic Suppression in Primate Visual Cortex Saccades are a ubiquitous and crucial component of our visual system, allowing for the efficient deployment of the fovea and its accompanying neural resources. Initiation of a saccade is known to cause saccadic suppression, a temporary reduction in visual sensitivity1, 2 and visual cortical firing rates3-6. While saccadic suppression has been well characterized at the level of perception and single neurons, relatively little is known about the visual cortical networks governing this phenomenon. Here we examine the effects of saccadic suppression on distinct neural subpopulations within visual area V4. We find cortical layer- and cell type-specific differences in the magnitude and timing of peri-saccadic modulation. Neurons in the input layer show changes in firing rate and inter-neuronal correlations prior to saccade onset, indicating that this layer receives information about impending saccades. Putative inhibitory interneurons in the input layer elevate their firing rate during saccades, suggesting they play a role in suppressing the activity of other cortical subpopulations. A computational model of this circuit recapitulates our empirical observations and demonstrates that an input layer-targeting pathway can initiate saccadic suppression by enhancing local inhibitory activity. Collectively, our results provide a mechanistic understanding of how eye movement signaling interacts with cortical circuitry to enforce visual stability.
Decoding regulatory specificity of human ribosomal proteins Human ribosomes, made of around 80 ribosomal proteins (RPs) and four ribosomal RNAs, have long been thought as uniform passive protein-making factories with little regulatory function. Recently, accumulating evidence showed heterogeneity of RP composition in ribosomes responsible for regulating gene expression in development and tumorigenesis. However, a comprehensive understanding of regulatory spectrum of RPs is still lacking. In this study, we conducted a systematic survey of regulatory specificity of human RPs on global gene expression. We quantified translational and transcriptional changes of gene expression upon deficiency of 75 RPs, including 44 from the large subunit (60S) and 31 from the small subunit (40S), by ribosomal profiling and RNA sequencing analysis. We showed that RP deficiency induced diverse expression changes, particularly at the translational level. RPs were subjected to co-translational regulation under ribosomal stress where deficiency of the 60S and the 40S RPs had opposite effects on the two subunits. RP deficiency perturbed expression of genes related to cell cycle, cellular metabolism, signal transduction and development. Deficiency of RPs from the 60S led to a greater repression effect on cell growth than that from the 40S by perturbing P53 signaling and cell cycle pathways. To demonstrate functional specificity of RPs, we showed that RPS8 deficiency stimulated cellular apoptosis and RPL13 or RPL18 deficiency promoted cellular senescence. We also showed that RPL11 and RPL15 played important roles in retina development and angiogenesis, respectively. Overall, our study demonstrated a widespread regulatory role of RPs in controlling cellular activity, providing an important resource which can offer novel insights into ribosome regulation in human diseases and cancer. Production of Offspring from Azoospermic Mice with Meiotic Failure: Precise Biparental Meiosis within Halved Oocytes While the large volume of mammalian oocytes is necessary for embryo development, it can lead to error-prone chromosomal segregation during meiosis. Conversely, we hypothesized that smaller oocytes would have a great unidentified potential to stabilize unstable meiosis and improve the development of the resultant embryos. Here, we show that reducing ooplasmic volume can rescue highly error-prone fertilization using primary spermatocytes by preventing segregation errors of chromosomes during biparental meiosis. High-resolution live-imaging analysis revealed that erroneous chromosome segregation occurred in most (90%) spermatocyte-injected oocytes of normal size, but could be ameliorated to 40% in halved oocytes. The birth rate improved remarkably from 1% to 19% (P < 0.0001). Importantly, this technique enabled the production of offspring from azoospermic mice with spermatocyte arrest caused by STX2 deficiency, an azoospermia factor also found in humans. Thus, contrary to popular opinion, oocytes inherently have a strong potential for precise meiotic divisions, which can be evoked by reduction of the ooplasmic volume. Their potential might help rescue cases of untreatable human azoospermia with spermatocyte arrest. SERCA function is impaired in skeletal and cardiac muscles from young DBA/2J mdx mice The DBA/2J (D2) mdx mouse has emerged as a more severe model of Duchenne muscular dystrophy when compared to the traditional C57BL/10 (C57) mdx mouse. Here, we questioned whether sarco(endo)plasmic reticulum Ca2+-ATPase (SERCA) function would differ in muscles from young D2 and C57 mdx mice. In gastrocnemius muscles, both D2- and C57 mdx mice exhibited signs of impaired Ca2+ uptake, however, this was more severe in D2 mdx mice. Maximal SERCA activity was lowered only in D2 mdx gastrocnemius muscles and not C57 mdx muscles. Furthermore, in the left ventricle and diaphragm, Ca2+ uptake was impaired in C57 mdx muscles with lowered rates of Ca2+ uptake compared with C57 WT mice, whereas in muscles from D2 mdx mice, rates of Ca2+ uptake were unattainable due to the severe impairments in their ability to transport Ca2+. Overall, our study demonstrates that SERCA function is drastically impaired in young D2 mdx mice. A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Multiple groups have produced recommendations for functions that biomedical repositories should support, with many using requirements of the FAIR data principles as guidelines. However, FAIR is but one set of principles that has arisen out of the open science community. They are joined by principles governing open science, data citation and trustworthiness, all of which are important aspects for biomedical data repositories to support. Together, these define a framework for data repositories that we call OFCT: Open, FAIR, Citable and Trustworthy. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and piloted the instrument by evaluating eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). Repositories included both specialist repositories that focused on a particular data type or domain, in this case diabetes and metabolomics, and generalist repositories that accept all data types and domains. The goal of this work was both to obtain a sense of how much the design of current biomedical data repositories align with these principles and to augment the dkNET listing with additional information that may be important to investigators trying to choose a repository, e.g., does the repository fully support data citation? The evaluation was performed from March to November 2020 through inspection of documentation and interaction with the sites by the authors. Overall, although there was little explicit acknowledgement of any of the OFCT principles in our sample, the majority of repositories provided at least some support for their tenets. Inhibition of BCMA signaling alters protein translation and Multiple Myeloma growth Recent T Cell therapies have been effective in the treatment of hematological cancers. However, immunotoxicity and treatment relapse pose significant clinical challenges. Here, we revealed distinctive requirement for neutralizing TNF receptor ligands APRIL and BAFF in MM and DLBCL. Furthermore, we investigated the use of BCMA decoy receptor (sBCMA-Fc) as a therapeutic inhibitor of ARPIL and BAFF. While wild-type sBCMA-Fc successfully blocked APRIL signaling with picomolar binding affinity, inhibiting tumor growth in MM models, it lacked efficacy in inhibiting DLBCL progression due to its weak binding for BAFF. To expand the therapeutic utility of sBCMA-Fc, using a protein engineering approach, we generated an affinity enhanced mutant sBCMA-Fc fusion molecule (sBCMA-Fc V3) with 4-folds and 500-folds enhancement in binding to APRIL and BAFF respectively. The sBCMA-Fc V3 clone significantly enhanced antitumor activity against both MM and DLBCL. Importantly, sBCMA-Fc V3 was proven to be a viable clinical candidate by showing adequate toxicity profile and on-target mechanism of action in nonhuman primates.

SUMMARYThis study demonstrates the dichotomous function of APRIL and BAFF in MM and DLBCL, that can be safely targeted by an engineered fusion protein designed to trap APRIL and BAFF with ultra-high binding affinity. Immune complex solubility and size govern individual Fc-gamma receptor responses: Novel biomarkers for autoimmune diseases Fc-gamma receptor (Fc{gamma}R) activation by soluble IgG immune complexes (sICs) represents a major mechanism of inflammation in certain autoimmune diseases such as systemic lupus erythematosus (SLE). A robust and scalable test system allowing for the detection and quantification of sIC bioactivity is missing. Previously described Fc{gamma}R interaction assays are limited to certain Fc{gamma}Rs, lack scalability and flexibility, are not indicative of receptor activation or lack sensitivity towards sIC size. We developed a comprehensive reporter cell panel detecting individual activation of Fc{gamma}Rs from humans and the mouse. The reporter cell lines were integrated into an assay format that provides flexible read-outs enabling the quantification of sIC reactivity via ELISA or a fast detection using flow cytometry. This identified Fc{gamma}RIIA(H) and Fc{gamma}RIIIA as the most sIC-sensitive Fc{gamma}Rs in our test system. Applying the assay we demonstrate that sICs versus immobilized ICs are fundamentally different Fc{gamma}R-ligands with regard to Fc{gamma}R preference and signal strength. Reaching a detection limit in the very low nanomolar range, the assay proved also to be sensitive to sIC stoichiometry and size enabling for the first time a complete reproduction of the Heidelberger-Kendall precipitation curve in terms of immune receptor activation. Analyzing sera from SLE patients and mouse models of lupus and arthritis proved that sIC-dependent Fc{gamma}R activation has predictive capabilities regarding severity of SLE disease. The new methodology provides a sensitive, scalable and comprehensive tool to evaluate the size, amount and bioactivity of sICs in all settings.

One Sentence SummaryIn this study we established a comprehensive Fc{gamma}R reporter cell assay enabling the detection and quantification of soluble immune complexes generated in experimental and clinical settings. Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits. Elucidation of SARS-CoV-2 budding mechanisms through molecular dynamics simulations of M and E protein complexes SARS-CoV-2 and other coronaviruses pose major threats to global health, yet computational efforts to understand them have largely overlooked the process of budding, a key part of the coronavirus life cycle. When expressed together, coronavirus M and E proteins are sufficient to facilitate budding into the ER-Golgi intermediate compartment (ERGIC). To help elucidate budding, we ran atomistic molecular dynamics (MD) simulations using the Feig laboratorys refined structural models of the SARS-CoV-2 M protein dimer and E protein pentamer. Our MD simulations consisted of M protein dimers and E protein pentamers in patches of membrane. By examining where these proteins induced membrane curvature in silico, we obtained insights around how the budding process may occur. Multiple M protein dimers acted together to induce global membrane curvature through protein-lipid interactions while E protein pentamers kept the membrane planar. These results could eventually help guide development of antiviral therapeutics which inhibit coronavirus budding. Computation of the mitochondrial age distribution along the axon length We describe a compartmental model of mitochondrial transport in axons, which we apply to compute mitochondrial age at different distances from the soma. The model predicts that the average mitochondrial age at the tip of an axon with a length of 1 cm is approximately 22 hours. The mitochondria are youngest closest to the soma and their age scales approximately linearly with distance from the soma. To the best of the authors knowledge, this is the first attempt to predict the spatial distribution of mitochondrial age within an axon. A sensitivity study of the mean age of mitochondria to various model parameters is also presented. Agrobacterium mediated transformation and deciphering SNPs in TaLr67 gene homeologs for gene editing in wheat. Diseases adversely affect grain yield of crop plants. Leaf rust is a major disease of wheat. As race-specific resistance breaks down, introduction of newer sources of resistance often from older accessions is necessary. Linkage drag from the donor accessions adversely impacts grain yield. As a result, CIMMYT breeding effort has shifted to using durable resistance, also referred to as race non-specific host resistance, which allows slow rusting but maintains grain yield. One of the key genes for durable resistance is Lr67 and the resistant form of this gene is absent in CIMMYT elite lines. Hence, we have initiated efforts to convert the susceptible copy of Lr67 into its resistant form directly in these elite lines using gene editing. This would eliminate backcrossing and thus save time as well as eliminate linkage drag that would accompany the resistant copy of the gene if it were to be introgressed from an older accession. As first steps, we have isolated and sequenced the genomic copies from each of the A, B, and D genome of the Lr67 gene from three elite lines and an experimental line. Identification of more than 50 single nucleotide polymorphisms (SNPs) in the open reading frames (ORF) among these lines would be useful in designing the guide RNA molecules with precision. Further, we have streamlined genetic transformation of these elite lines, a prerequisite step for gene editing. Entorhinal cortex epigenome-wide association study highlights four novel loci showing differential methylation in Alzheimer's disease BackgroundStudies on DNA methylation (DNAm) in Alzheimers disease (AD) have recently highlighted several genomic loci showing association with disease onset and progression.

MethodsHere, we conducted an epigenome-wide association study (EWAS) using DNAm profiles in entorhinal cortex (EC) from 149 AD patients and control brains and combined these with two previously published EC datasets by meta-analysis (total n=337).

ResultsWe identified 12 cytosine-phosphate-guanine (CpG) sites showing epigenome-wide significant association with either case-control status or Braaks tau-staging. Four of these CpGs, located in proximity to CNFN/LIPE, TENT5A, PALD1/PRF1, and DIRAS1, represent novel findings. Integrating DNAm levels with RNA sequencing-based mRNA expression data generated in the same individuals showed significant DNAm-mRNA correlations for 6 of the 12 significant CpGs. Lastly, by calculating rates of epigenetic age acceleration using two recently proposed "epigenetic clock" estimators we found a significant association with accelerated epigenetic aging in AD patients vs. controls.

ConclusionIn summary, our study represents the hitherto most comprehensive EWAS in AD using EC and highlights several novel differentially methylated loci with potential effects on gene expression. Hidden dynamics of human proteasome autoregulation during protein degradation The cellular functions are executed by macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important complexes at atomic level remains a key challenge in the methodology of computational biophysics. Here we introduce a deep-learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of nonequilibrium conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. Applications of this methodology to six experimental datasets demonstrated its generality in breaking resolution barrier of visualizing dynamic components of functional complexes, in choreographing continuous inter-subunit motions and in exploring their  hidden conformational space. Importantly, AlphaCryo4D identified 64 conformers of the substrate-bound human 26S proteasome and reconstituted single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which together illuminates a grand hierarchical allostery for proteasome autoregulation. Unevolved proteins from modern and prebiotic amino acids manifest distinct structural profiles Natural proteins represent numerous but tiny structure/function islands in a vast ocean of possible protein sequences, most of which has not been explored by either biological evolution or research. Recent studies have suggested this uncharted sequence space possesses surprisingly high structural propensity, but development of an understanding of this phenomenon has been awaiting a systematic high-throughput approach.

Here, we designed, prepared, and characterized two combinatorial protein libraries consisting of randomized proteins, each 105 residues in length. The first library constructed proteins from the entire canonical alphabet of 20 amino acids. The second library used a subset of only 10 residues (A,S,D,G,L,I,P,T,E,V) that represent a consensus view of plausibly available amino acids through prebiotic chemistry. Our study shows that compact conformations resistant to proteolysis are (i) abundant (up to 40%) in random sequence space, (ii) independent of general Hsp70 chaperone system activity, and (iii) not granted solely by "late" and complex amino acid additions. The Hsp70 chaperone system effectively increases solubility and refoldability of the canonical alphabet but has only a minor impact on the "early" library. The early alphabet proteins are inherently more soluble and refoldable, possibly assisted by the cell-like environment in which these assays were performed.

Our work indicates that both early and modern amino acids are predisposed to supporting protein structure (either in forms of oligomers or globular/molten globule structures) and that protein structure may not be a unique outcome of evolution. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. A novel nongenetic murine model of hyperglycemia and hyperlipidemia-associated aggravated atherosclerosis ObjectiveAtherosclerosis, the main pathology underlying cardiovascular diseases is accelerated in diabetic patients. Genetic mouse models require breeding efforts which are time-consuming and costly. Our aim was to establish a new nongenetic model of inducible metabolic risk factors that mimics hyperlipidemia, hyperglycemia, or both and allows the detection of phenotypic differences dependent on the metabolic stressor(s).

Methods and ResultsWild-type mice were injected with gain-of-function PCSK9D377Y (proprotein convertase subtilisin/kexin type 9) mutant adeno-associated viral particles (AAV) and streptozotocin and fed either a high-fat diet (HFD) for 12 or 20 weeks or a high-cholesterol/high-fat diet (Paigen diet, PD) for 8 weeks. To evaluate atherosclerosis, two different vascular sites (aortic sinus and the truncus of the brachiocephalic artery) were examined in the mice. Combined hyperlipidemic and hyperglycemic (HGHCi) mice fed a HFD or PD displayed characteristic features of aggravated atherosclerosis when compared to hyperlipidemia (HCi HFD or PD) mice alone. Atherosclerotic plaques of HGHCi HFD animals were larger, showed a less stable phenotype (measured by the increased necrotic core area, reduced fibrous cap thickness, and less -SMA-positive area) and had more inflammation (increased plasma IL-1{beta} level, aortic pro-inflammatory gene expression, and MOMA-2-positive cells in the BCA) after 20 weeks of HFD. Differences between the HGHCi and HCi HFD models were confirmed using RNA-seq analysis of aortic tissue, revealing that significantly more genes were dysregulated in mice with combined hyperlipidemia and hyperglycemia than in the hyperlipidemia-only group. The HGHCi-associated genes were related to pathways regulating inflammation (increased Cd68, iNos, and Tnfa expression) and extracellular matrix degradation (Adamts4 and Mmp14). When comparing HFD with PD, the PD aggravated atherosclerosis to a greater extent in mice and showed plaque formation after 8 weeks. Hyperlipidemic and hyperglycemic mice fed a PD (HGHCi PD) showed less collagen (Sirius red) and increased inflammation (CD68-positive cells) within aortic plaques than hyperlipidemic mice (HCi PD). HGHCi-PD mice represent a directly inducible hyperglycemic atherosclerosis model compared with HFD-fed mice, in which atherosclerosis is severe by 8 weeks.

ConclusionWe established a nongenetically inducible mouse model allowing comparative analyses of atherosclerosis in HCi and HGHCi conditions and its modification by diet, allowing analyses of multiple metabolic hits in mice. Consumer risk perception towards pesticide stained tomatoes in Uganda. BackgroundTomatoes are consumeddaily. Unfortunately, abuse of pesticides application by vegetable growers in Uganda increases risks of exposing consumers through the pesticide residues, as it may be above European Union Maximum Residue Limits (used as a standard in Uganda). This study is aimed to determine consumer attitudes and risk perception towards pesticide stained tomatoes available on the Ugandan local markets.

MethodsA cross-sectional study sampled 468 household consumers in four districts one from each region of Uganda. In each district, 60 household members from three randomly selected Sub Counties were interviewed. In addition, in each district 9 tomato handlers (3 tomato farmers, 3 tomato retailers and 3 tomato wholesalers) participated in Focus Group Discussion (FGDs) perdistrict. Collected data was entered into MS-Excel 13 and exported into STATA SE 14.0 for cleaning and analysis at below a 5% level of significance and 95% Confidence Intervals (CI). Proportion of risk perceptions and attitudes were computed and presented as percentages while factors associated with risk perception were determined using Fisher exact test. Qualitative data was analyzed using deductive and inductive approaches under thematic content analysis.

ResultsMore than half, 54.2% (253/468), of the respondents were females, mean age was 37 years (SD=13.13, ranging from 18 to 88 years). Half of the respondents, 50.9% (238/467), were farmers by occupation and 40.3% (188/468) had completed upper primary education. Only 5.0% (20/396) of consumers reported a high risk perception towards tomatoes stained with pesticide residues, the rest, 95.0% (376/396), were buying pesticide stained tomatoes despite their awareness about the possible health effects. The main reason for buying the pesticide stained tomatoes was that a majority, 59.0% (230/390), lacked an alternative to stained tomatoes. However, consumers generally had a negative attitude towards pesticide stained tomatoes, with 67.0% (313/468) of the consumers disagreeing to a statement that tomatoes sold on the market are safe. Consumer risk perception was significantly associated with consumer awareness about residues in the tomatoes; where the proportino of consumers who were aware of the risk of pesticide stained tomatoes were 42.8 times more likely not to buy stained tomatoes compared to the proportion of those who were not aware. OR, 42.8 (95% CI: 10.76-170.28). However, level of education P(0.975), gender P(0.581) and age-group P(0.680) were not associated with consumer risk perception after Fisher-Exact tests analysis. (95% CI and 5% level of significance).

ConclusionConsumer risk perception on pesticide stained tomatoes among Ugandan consumers ranked low with a majority of consumers buying tomatoes stained with pesticide residuesdue to lack of an alternative, except for a few who had a high risk perception about the pesticide health effects. Genetic coupling of life-history and aerobic performance in Atlantic salmon indicates potential constrains on life-history evolution A better understanding of the genetic and phenotypic architecture underlying life-history variation is a longstanding aim in biology. Theories suggest energy metabolism determines life-history variation by modulating resource acquisition and allocation trade-offs, but the genetic underpinnings of the relationship and its dependence on ecological conditions have rarely been demonstrated. The strong genetic determination of age-at-maturity by two unlinked genomic regions (vgll3 and six6) makes Atlantic salmon (Salmo salar) an ideal model to address these questions. Using more than 250 juveniles in common garden conditions, we quantified the covariation between metabolic phenotypes -standard and maximum metabolic rates (SMR and MMR), and aerobic scope (AS) - and the life-history genomic regions and tested if food availability modulates the relationships. We found that the early maturation genotype in vgll3 was associated with higher MMR and consequently AS. Additionally, MMR exhibited physiological epistasis; it was decreased when late maturation genotypes co-occurred in both genomic regions. Contrary to our expectation, the life-history genotypes had no effects on SMR. Further, food availability had no effect on the genetic covariation, suggesting a lack of genotype-by-environment interactions. Our results provide insights on the key organismal processes that link energy use at the juvenile-stage to age-at-maturity, indicating potential mechanisms by which metabolism and life-history can coevolve. A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion Differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has been previously explored using synthetically engineered heterogenous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to more systematically explore the key parameters that drive the emergence of different patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. The model was used to predict how varying initial cell seedings, cadherin induction probabilities, or homotypic adhesion strengths between cells impact spheroid patterning, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was then deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern. Artificial seawater based long-term culture of colonial ascidians Tunicates are highly diverse marine invertebrate filter-feeders that are vertebrates closest relatives. These organisms, despite a drastically different body plan during their adulthood, have a tissue complexity related to that of vertebrates. Ascidians, which compose most of the Tunicata, are benthic sessile hermaphrodites that reproduce sexually through a motile tadpole larval stage. Over half of the known ascidians species are able to reproduce asexually by budding, typically leading to the formation of colonies where animals, called zooids, are interconnected through an external vascular system. In addition, colonial ascidians are established models for important biological processes including allorecognition, immunobiology, aging, angiogenesis and whole-body regeneration. However, the current paucity in breeding infrastructures limits the study of these animals to coastal regions.

To promote a wider scientific spreading and popularity of colonial ascidians, we have developed a flexible recirculating husbandry setup for their long-term in-lab culture. Our system is inspired both by the flow-through aquariums used by coastal ascidian labs, as well as by the recirculating in-lab systems used for zebrafish research. Our hybrid system thus combines colony breeding, water filtering and food culturing in a semi-automated system where specimens develop on hanging microscopy glass slides. Temperature, light/dark cycles, flow speed and feeding rates can be controlled independently in four different breeding environments to provide room for species-specific optimization as well as for running experiments. This setup is complemented with a quarantine for the acclimatization of wild isolates.

Herein we present our success in breeding Botrylloides diegensis, a species of colonial ascidians, for more than 3 years in recirculating artificial seawater over 600 km away from their natural habitat. We show that colonies adapt well to in-lab culturing provided that a suitable marine microbiome is present, and that a specific strain can be isolated, propagated and efficiently used for research over prolonged periods of time. The flexible and modular structure of our system can be scaled and adapted to the needs of specific species, such as Botryllus schlosseri, as well as of particular laboratory spaces. Overall, we show that Botrylloides diegensis can be proficiently bred in-land and suggest that our results can be extended to other species of colonial ascidians to promote research on these fascinating animals.

HighlightsO_LIFirst artificial seawater based recirculating aquaculture for colonial ascidians
C_LIO_LIOver 3 years of continuous breeding
C_LIO_LISemi-automated setup with minimized maintenance
C_LIO_LIGood biomass production for strain propagation
C_LIO_LI4 different culture conditions for optimized breeding for species of interest
C_LI Evaluation of US state pollinator plans using 3 evidence-based policymaking frameworks After the US federal government created a national pollinator protection plan in 2015, many states followed with their own. Since their goal is to promote pollinating insect conservation, we wanted to know whether the state plans are using best practices for evidence-based science policy. In early 2019 we found and downloaded every existing, publicly available US state pollinator protection plan. We then used content analysis to assess the goals, scope, and implementation of state-level pollinator protection plans across the US. This analysis was conducted using three distinct frameworks for evidence-based policymaking: US Department of Interior Adaptive Resources Management (ARM), US Environmental Protection Agency management pollinator protection plan (MP3) guidance, and Pew Trusts Pew-MacAthur Results First Project elements of evidence-based state policymaking (PEW) framework. Then we scored them using the framework criteria, to assess whether the plans were using known best practices for evidence based policymaking. Of the 31 states with a state pollinator plan, Connecticut was the state with the lowest total score across the three evaluation frameworks. The state with the highest overall scores, across the three frameworks, was Missouri. Most states did not score highly on the majority of the frameworks. Overall, many state plans were lacking policy elements that address monitoring, evaluation, and adjustment. These missing elements impact states ability to achieve their conservation goals. Our results indicate that states can improve their pollinator conservation policies to better match evidence-based science policy guidance, regardless of which framework is used. Experimental evolution reveals post-transcriptional regulation as a novel driver of Leishmania fitness gain The protozoan parasite Leishmania donovani causes fatal human visceral leishmaniasis in absence of treatment. Genome instability has been recognized as a driver in Leishmania fitness gain in response to environmental change or chemotherapy. How genome instability generates beneficial phenotypes despite potential deleterious gene dosage effects is unknown. Here we address this important open question applying experimental evolution and integrative systems approaches on parasites adapting to in vitro culture. Phenotypic analyses of parasites from early and late stages of culture adaptation revealed an important fitness tradeoff, with selection for accelerated growth in promastigote culture (fitness gain) impairing infectivity (fitness costs). Comparative genomics, transcriptomics and proteomics analyses revealed a complex regulatory network driving parasite fitness, with genome instability causing highly reproducible, gene dosage-dependent changes in protein abundance linked to post-transcriptional regulation. These in turn were associated with a gene dosage-independent reduction in abundance of flagellar transcripts and a coordinated increase in abundance of coding and non-coding RNAs implicated in ribosomal biogenesis and protein translation. We correlated differential expression of small nucleolar RNAs (snoRNAs) with changes in rRNA modification, providing first evidence that Leishmania fitness gain in culture may be controlled by post-transcriptional and epitranscriptomic regulation. Our findings propose a novel model for Leishmania fitness gain in culture, where differential regulation of mRNA stability and the generation of fitness-adapted ribosomes may potentially filter deleterious from beneficial gene dosage effects and provide proteomic robustness to genetically heterogenous, adapting parasite populations. This model challenges the current, genome-centric approach to Leishmania epidemiology and identifies the Leishmania transcriptome and non-coding small RNome as potential novel sources for the discovery of biomarkers that may be associated with parasite phenotypic adaptation in clinical settings. Lower limb in sprinters with larger relative mass but not larger normalized moment of inertia Inhomogeneous muscle hypertrophy in response to daily motor executions is a morphological adaptation in humans. Inhomogeneous morphological adaptation would change the mass distribution, which in turn may affect the mechanical difficulty in moving from an inertia perspective; however, the morphological adaptation from the inertia perspective has not been examined. Here we show no corresponding differences in the normalized mass and normalized moment of inertia between the sprinters and the untrained non-sprinters. We analyzed the fat- and water-separated magnetic resonance images from the lower limbs of 11 male sprinters (100 m best time of 10.44-10.83 s) and 12 untrained non-sprinters. The lower-limb relative mass was significantly larger in sprinters (18.7{+/-}0.7% body mass) than in non-sprinters (17.6{+/-}0.6% body mass), while the normalized moment of inertia of the lower limb around the hip in the anatomical position was similar between them (0.044{+/-}0.002 vs. 0.042{+/-}0.002 [a. u.]). The thigh relative mass in sprinters (12.9{+/-}0.4% body mass) was significantly larger than that in non-sprinters (11.9{+/-}0.4% body mass), whereas the shank and foot relative masses were similar. We revealed that the adaptation to sprinting in humans does not relatively increase the mechanical difficulty to swing the lower limb in terms of inertia, even though the lower-limb mass increases, reflecting muscular hypertrophy. This is suggested to be one of the significant advantages of human morphology for adaptability to explosive motor tasks.

NEW & NOTEWORTHYWe compared the lower-limb inertia properties between male sprinters and untrained non-sprinters by using the fat- and water-separated magnetic resonance images. Sprinters had lower limbs with a larger relative mass but not a larger normalized moment of inertia than non-sprinters. We suggest one of the practical advantages of human morphological characteristics for their adaptation to explosive motor tasks, which provides a novel perspective to the literature for understanding human morphological adaptability. Intramolecular 13C signals inform about carbon allocation in plants Stable isotopes at natural abundance are key tools to study physiological processes occurring outside the temporal scope of manipulation and monitoring experiments. Whole-molecule carbon isotope ratios (13C/12C) enable assessments of plant carbon uptake yet conceal information about carbon allocation. Here, we identify an intramolecular 13C/12C signal at treering glucose C-5 and C-6 and develop experimentally testable theories on its origin. More specifically, we assess the potential of processes within C3 metabolism for signal introduction based (inter alia) on constraints on signal propagation posed by metabolic networks. We propose that the intramolecular signal reports carbon allocation into major metabolic pathways in actively photosynthesising leaf cells including the anaplerotic, shikimate, and non-mevalonate pathway. We support our theoretical framework by linking it to previously reported whole-molecule 13C/12C increases in cellulose of ozone-treated Betula pendula and a highly significant relationship between the intramolecular signal and tropospheric ozone concentration. Our theory postulates a pronounced preference of leaf-cytosolic triose-phosphate isomerase to catalyse the forward reaction in vivo (dihydroxyacetone phosphate to glyceraldehyde 3-phosphate). In conclusion, intramolecular 13C/12C analysis resolves information about carbon uptake and allocation enabling more comprehensive assessments of carbon metabolism than whole-molecule 13C/12C analysis.

HighlightIntramolecular 13C/12C analysis resolves information about carbon uptake and allocation (and associated environmental controls) enabling more comprehensive assessments of carbon metabolism, plant-environment interactions, and environmental variability than whole-molecule 13C/12C analysis. How to Approach Machine Learning-based Prediction of Drug/Compound-Target Interactions The identification of drug/compound-target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been applied. As a relatively new data-driven paradigm, proteochemometric (PCM) modeling utilizes both protein and compound properties as a pair at the input level and processes them via statistical/machine learning. The representation of input samples (i.e., proteins and their ligands) in the form of quantitative feature vectors is crucial for the extraction of interaction-related properties during the artificial learning and subsequent prediction of DTIs. Lately, the representation learning approach, in which input samples are automatically featurized via training and applying a machine/deep learning model, has been utilized in biomedical sciences. In this study, we performed a comprehensive investigation of different computational approaches/techniques for protein featurization (including both conventional approaches and the novel learned embeddings), data preparation and exploration, machine learning-based modeling, and performance evaluation with the aim of achieving better data representations and more successful learning in DTI prediction. For this, we first constructed realistic and challenging benchmark datasets on small, medium, and large scales to be used as reliable gold standards for specific DTI modeling tasks. We developed and applied a network analysis-based splitting strategy to divide datasets into structurally different training and test folds. Using these datasets together with various featurization methods, we trained and tested DTI prediction models and evaluated their performance from different angles. Our main findings can be summarized under 3 items: (i) random splitting of datasets into train and test folds lead to near-complete data memorization and produce highly over-optimistic results, as a result, it should be avoided, (ii) learned protein sequence embeddings work well in DTI prediction and offer high potential, even though no information related to protein structures, interactions or biochemical properties is utilized during their generation, and (iii) PCM models tend to learn from compound features and leave out protein features, mostly due to the natural bias in DTI data, indicating the requirement for new and unbiased datasets. We hope this study will aid researchers in designing robust and high-performing data-driven DTI prediction systems that have real-world translational value in drug discovery.