Dissolved organic matter (DOM) plays a key role in global biogeochemical cycles and experiences changes in molecular composition as it undergoes processing. In the semi-closed basins of the oligotrophic Mediterranean Sea, these gradual molecular modifications can be observed in close proximity. In order to extend the spatial resolution of information on DOM molecular composition available from ultrahigh resolution mass spectrometry in this area, we relate this data to optical (fluorescence and absorption spectroscopy) measurements. Covariance between molecular formulae signal intensities and carbon-specific fluorescence intensities was examined by means of Spearman’s rank correlations. Fifty two per cent of the assigned molecular formulae were associated with at least one optical parameter, accounting for 70% of the total mass spectrum signal intensity. Furthermore, we obtained significant multiple linear regressions between optical and intensity-weighted molecular indices. The resulting regression equations were used to estimate molecular parameters such as the double bond equivalent, degradation state and occurrence of unsaturated aliphatic compounds from optical measurements. The statistical linkages between DOM molecular and optical properties illustrate that the simple, rapid and cost-efficient optical spectroscopic measurements provide valuable proxy information on the molecular composition of open ocean marine DOM. It is well established that DOM plays a key role in marine biogeochemical cycles: it constitutes the primary source of nutrients and energy for heterotrophic growth3, regulates the UV and visible light absorption4, undergoes photochemical processing5, acts as a trace metal ligand6 and presents antioxidant activity for minimizing the negative effects of free radicals in aquatic organisms7 among other services to the marine ecosystems. DOM is mainly produced in the ocean epipelagic layer (0–150 m depth) as a result of phytoplankton photosynthesis and subsequent food web interactions8. Most of this recently produced DOM is biologically labile and thus has a short lifetime10. However, a small fraction of that DOM escapes rapid mineralization because it is originally resistant or it is transformed into resistant materials through abiotic processes or during the microbial processing of bioavailable DOM11. This DOM which escapes rapid remineralization accumulates in the surface layer for eventual export to the dark ocean (>150 m depth) by convective overturning and vertical mixing13.Marine dissolved organic matter (DOM) comprises a complex mixture of molecules essentially uncharacterized and present at very low concentrations4. Furthermore, a fraction of CDOM emits fluorescence; it is called fluorescent DOM (FDOM) and it is widely used to trace the reactivity, composition17, sources and chemical structure of DOM22. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) is a widely used technique nowadays to distinguish thousands of molecular formulae constituting the DOM pool. Although this technique does not unambiguously reveal the structure of DOM compounds, it offers new possibilities for the characterization of individual formulae or classes of molecules. The linkage between optical and chemical properties of DOM in natural waters is a subject of rising interest among aquatic biogeochemists23. Recent studies have combined FT-ICR-MS and optical measurements to better understand the composition of DOM in natural waters25 but few of them have directly related optical and molecular signatures31. These studies, mainly focused on freshwater and coastal systems, have reported significant relationships between optical indices and molecular formulae. However, DOM of terrestrial and marine origin is characterized by contrasting molecular size and composition, as well as absorption and fluorescence spectral properties32. Therefore, the results obtained in previous studies conducted in freshwater and coastal systems should not be directly generalized to open ocean waters.A small fraction of DOM absorbs light in the UV and visible range of the spectrum; it is called colored DOM (CDOM) and it is present in the ocean ubiquitouslyIn this study, we link the fluorescence and absorption of DOM with the molecular signatures obtained from the FT-ICR-MS data of 29 samples from the open epi–, meso– and bathypelagic waters of the Mediterranean Sea. Our main objective is to determine the covariance between optical and molecular signatures in this low-CDOM ocean system and examine similarities and differences with the relationships found in high-CDOM freshwater and coastal systems. Furthermore, we aim to go a step ahead by establishing semi-quantitative relationships between an assortment of calculated optical and molecular indices. Our final goal is the use of fluorescence and absorption spectroscopy measurements as a proxy for the indices obtained from molecular data, which are more time demanding and costly to obtain. Note that these relationships do not imply a direct causation but the existence of a common cause or causes of variation between optical and molecular indices.a are shown in Figure During the trans-Mediterranean cruise HOTMIX  to pH < 2 and the ampoules were flame sealed and stored in the dark at 4 °C until analysis in the base laboratory. DOC concentration was determined with a Shimadzu TOC-V organic carbon analyzer by high temperature catalytic oxidation (HTCO). Potassium hydrogen phthalate  was used to calibrate the system daily. The precision of the equipment was ±1 μmol L−1. The accuracy was checked daily with the DOC reference materials provided by D. A. Hansell .Aliquots of 10 mL of the filtrate were collected for DOC quantification in precombusted  glass ampoules. These samples were acidified with H−1)33. The carbon-specific absorption coefficient at 254 nm  was calculated.Absorbance of CDOM was measured on board using a double beam Perkin Elmer lambda 850 spectrophotometer equipped with 10 cm path length quartz cuvettes for sample and reference . Spectral scans were collected from 250 nm to 750 nm at 1 nm intervals and constant room temperature. The absorbance was converted into Naeperian absorption coefficient . A four-component model with the most common fluorescent peaks of aquatic environments was obtained  while p-terphenyl and tetraphenylbutadiene methacrylate blocks (Starna) were used to check the instrument signal intensity at the excitation-emission wavelengths characteristic of the aromatic amino acids and humic-like substances, respectively. Excitation (Ex)-emission (Em) matrices (EEMs) were generated by combining 22 fluorescence emission spectra from 300 to 560 nm at excitation wavelengths ranging from 240 to 450 nm at 10 nm intervals. Rayleigh scatter bands were removed mathematically from the EEMs. Parallel factor analysis (PARAFAC) was used to decompose the fluorescence signal of the EEMs into the underlying individual fluorescent componentsd Figure , two of 37, we decided to use the latter to allow direct comparison with previous studies. The selected Ex/Em wavelengths were 250/435 nm (peak A) due to general humic substances; 340/440 nm (peak C) due to humic substances of terrestrial origin; 320/410 nm (peak M) due to humic substances of marine origin; and 280/350 nm (peak T) and 270/304 nm (peak B) due to protein-like substances. The fluorescence of each peak was determined by subtracting the Milli-Q blank peak height from the sample average peak height. All the samples were normalized using a solution of quinine sulphate dihydrate  and tryptophan  standards in H2SO4 0.05 M  allowing to express fluorescence in Normalized Fluorescence Intensity Units (NFIU)38. The fluorescence of each peak was then calculated as the mean of 4 individual measurements, which presented a coefficient of variation of <6 ± 2% (n = 30) for all the peaks. Carbon-specific fluorescence of humic-like  and protein-like (peak T*) substances were calculated dividing the fluorescence intensity by the DOC concentration (in NFIU L mg−1 C). Humic-/protein-like and terrestrial/marine fluorescence ratios  were also calculated. In addition, we calculated fluorescence-based indices such as the fluorescence (FI)39, freshness (FrI)29, biological (BIX)40 and humification (HIX)41 indices . SPE-DOM methanol extracts were diluted with ultrapure water and methanol (MS grade) to yield a DOC concentration of 15 mg C L−1 and a methanol-to-water ratio of 1:1 (v/v) for analysis by ultrahigh-resolution mass spectrometry using a Solarix FT-ICR-MS  connected to a 15 Tesla superconducting magnet (Bruker Biospin). Molecular formulae were assigned to the detected masses43. The signal intensity of each identified molecular formula was normalized to the sum of all molecular formula intensities with signal-to-noise ratio (S/N) higher than 5 in each sample, so we can interpret the FT-ICR-MS data semi-quantitatively44. The degree of unsaturation of a compound was assessed based on its molecular formula and was expressed as the double bond equivalent (DBE)46 and the degradation index (Ideg) was calculated47. The identified molecular formulae were assigned to compound groups based on established molar ratios, modified aromaticity index, DBE and heteroatom contents as described elsewhere43. This assignment into molecular compound groups is not an unambiguous method for molecular structure determination, but it provides a useful overview of likely structures behind a given set of molecular formulae. The averages per sample, weighted by relative intensity of the detected masses, of molecular weight (MW), DBE and abundance of compound groups were also calculated. The reproducibility and the detection limit of the FT-ICR-MS method was assessed in previous works49 by performing multiple measurements of the same reference sample. This sample, collected at a depth of 670 m at the Natural Energy Laboratory of Hawaii Authority (NELHA) in Kona, Hawaii50, was also used as an internal reference sample to assess and correct for instrument variability over time. In our work, the detection limit was established at a fixed signal-to-noise ratio (S/N) higher than 4. In addition, we tested the reproducibility of the peak detection using NELHA sample replicates (n = 13) and found a coefficient of variation of 13 ± 6%.For SPE-DOM isolation, filtered seawater samples (2 L) were acidified to pH 2 and the DOM was extracted on board using PPL cartridges (Agilent)a254*, FI, FrI, HIX and BIX were correlated with all assigned molecular formulae applying Spearman’s rank correlations. Each individual optical parameter was correlated with each individual molecule. Spearman’s rank correlation coefficients (r) greater than 0.43 were considered significant at the 99% confidence limit (Student’s t-test for a set of 29 samples). Therefore, all molecular formulae correlating with r > 0.43 with a given optical index were assigned to that optical index. A similar approach was applied in previous studies29. In addition, multiple linear regression models between molecular parameters and optical indices were calculated using Pearson’s regressions. To determine if our sample size allows to detect significant differences we performed a power analysis for n = 29. We found that the power was higher than 0.72 for all variables. We therefore assume that the sample size n = 29 is appropriate to detect significant differences. To validate our models we performed a cross validation exercise. We used 80% of the samples to obtain the models and then validated them with the remaining 20% of the samples. The subsets to obtain and validate the models were chosen randomly and repeated 1000 times. Then, we calculated the error of the estimation for the 1000 runs and obtained mean errors. All the statistical analyses were performed in R51 . Figure 52, Fig. Carbon-specific fluorescence peak intensities, fluorescence ratios, −1 were observed at the deep chlorophyll maximum (DCM) and minimum concentrations of about 42 μmol C L−1 were found in the deep waters  increased with depth although leveling off from about 750 m to the bottom  of this material58. The peak C* minimum at the DCM can also be attributed to the vulnerability of humic-like substances to photobleaching in the upper mixed layer59. Conversely, the vertical profile of the carbon-specific fluorescence of protein-like substances (peak T*) was opposite to peak C* , used as an aromaticity index18, showed a decreasing profile with depth, although it remained constant from about 750 m to the bottom , followed by unsaturated aliphatics (13.7 ± 0.7%), polyphenols (11.8 ± 0.2%), peptides (2.5 ± 0.1%), saturated fatty acids with heteroatoms (0.78 ± 0.05%) and sugars with heteroatoms (0.6 ± 0.1%). Carboxyl-rich alicyclic molecules (CRAM)67 accounted for 21.9 ± 0.6% of the total number of molecular formulae. Note that 94% of the molecular formulae assigned to CRAM were also classified as highly unsaturated compounds. All assigned molecular formulae were represented in a van Krevelen diagram , 46% of the molecular formulae  correlated with one or more optical parameter  with any optical parameter was different for the positive and negative correlations  and colloidal aggregates69. ESI is a soft ionization technique that preferentially ionizes polar functional groups that renders DOM molecules their water solubility70.We explored the covariability of each one of the 3689 molecular formulae identified from the FT-ICR-MS analysis with the C-specific fluorescence intensities, fluorescence indices and absorption coefficients described in the Material and Methods section by means of Spearman’s rank correlations (99% confidence limit). Our statistical analysis indicated that 52% of the 3689 assigned molecular formulae were correlated, either positively or negatively, with one or more optical parameter. These molecular formulae accounted for 70% of the mass spectrum peak intensities. Considering only the positive correlations  between the molecular formulae and humic-like compounds and fluorescence ratios  were fewer compared to the negative correlations (r < −0.43), however they accounted for higher relative spectrum intensities  which are proxies for recent biologic activity.About 70% of the mass spectrum intensity of highly unsaturated compounds, the most abundant group in our samples Figure , correlaa254*, FI, FrI and BIX. Similarly, peptides correlated negatively with peak A*, C*, M*, the ratios A/T, C/T, M/T and HIX, but only positively with peak T* and a254*. These results are in agreement with the fact that saturated fatty acids are presumably bioavailable molecules72.The saturated fatty acids correlated only negatively with peak A*, C* and M*, the ratios A/T, C/T and M/T and HIX and positively with peak T*, The distribution of all the molecules positively correlated with peak C* and peak T* are graphically shown in Figure Once we have verified the existence of significant correlations between DOM molecular formulae and optical properties, we propose using multiple linear regressions to estimate molecular indices from optical indices. First, a correlation matrix including salinity (S), potential temperature (θ), peak C*, peak T* and a254* was obtained (data not shown) to check for covariance between these variables. On basis of this analysis, Peak C* and peak T* along with θ were chosen for performing the multiple regressions. These regression models were fitted using the 29 samples for which we have peak C* and peak T* measurements together with FT-ICR-MS molecular parameters. The cross validation exercise of the multiple regression of each molecular index with peak C*, peak T* and θ  yielded estimated mean errors of 1, 5, 7 and 1% for DBE, Ideg, UA and CRAM, respectively. Therefore, the models were able to estimate molecular parameters from optical indices with a relatively low error. Then, we calculated the mean and standard deviation of the regression coefficients obtained after the 1000 times that each regression model was run to perform the cross validation exercise. These mean regression coefficients . Using θ, peak C* and peak T* we were then able to estimate molecular indices  and build up detailed maps of their distribution for the whole Mediterranean Sea. The model goodness is confirmed by the low mean error of the estimates from the cross validation exercise, by Fig. 2 > 0.65, p < 0.0001) and reliable detailed spatial molecular information from FDOM measurements can be gained. Regarding these distributions (Fig. The mean regression coefficients in Table ons Fig. , the uppons Fig.  the mismons Fig.  the poinons Fig. , the newOverall, in this work we have shown that optical and molecular properties correlated significantly in the Mediterranean Sea. In detail, we have observed relationships between different SPE-DOM groups of molecules and specific DOM fluorescence or absorption parameters. Furthermore, we have established, for the first time, empirical multiple regression models to estimate molecular parameters from optical measurements in open ocean waters.Supplementaty Information


In the 2nd stage, various exposure levels and distinct molecules were applied to evaluate the estimated molecular concentration discrepancy with the quantified ones. High correlation (r2 = 0.9684) between Ktrans and transcranial mechanical index (MI) implies Ktrans can serve as an in vivo imaging index to mirror FUS-BBB opening scale. When testing various molecules with the size ranging 1–149 kDa, an overall correlation of r2 = 0.9915 between quantified and predicted concentrations was reached, suggesting the established model can provide reasonably accurate estimation. Our work demonstrates the feasibility of estimating molecular penetration through FUS-BBB opening via DCE-MRI and may facilitate development of FUS-induced BBB opening in brain drug delivery.Focused ultrasound (FUS) exposure with microbubbles can transiently open the blood-brain barrier (BBB) to deliver therapeutic molecules into CNS tissues. However, delivered molecular distribution/concentration at the target need to be controlled. Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI) is a well-established protocol for monitoring the pharmacokinetic/pharmacodynamic behavior of FUS-BBB opening. This study investigates the feasibility of using DCE-MRI to estimate molecular CNS penetration under various exposure conditions and molecule sizes. In the 1 But this barrier also prevents the delivery of many potentially effective diagnostic or therapeutic agents, limiting the effectiveness of potential treatments for CNS diseases. Burst-type focused ultrasound (FUS) combined with circulating microbubbles has been verified to increase the permeability of the BBB in a non-invasive, localized, transient and reversible manner6. In the past decade, the feasibility of FUS-induced BBB opening has been well documented in multiple in vivo animal models11 for increasing local concentrations of therapeutic agents for delivery into the CNS. This technology has recently been adopted to be applied clinically to enhance chemotherapeutic agent  delivery for human malignant brain tumor treatment, and preliminarily demonstrate its feasibility14.The blood–brain barrier (BBB) is a highly specialized structure of central nervous system (CNS) blood vessels and capillaries that comprises arachnoid membranes, cerebral capillary endothelial cells, and the choroid plexus epithelium. The BBB protects the normal brain parenchyma from foreign toxic substances because it blocks 98% of molecules weighing in excess of 400 Da17. Previous studies have found a high degree of correlation between the scale of FUS-induced BBB opening and MI using signal intensity (SI) change of contrast-enhanced magnetic resonance imaging (CE-MRI)18. In addition, MI can serve as an index to identify thresholds (0.46 MI) of FUS-induced BBB opening occurrence17, and to indicate adverse effects such as extensive erythrocyte extravasations or brain damage (MI >0.6) after FUS exposure at various central frequencies 19. Furthermore, MI is also a useful index to describe microbubble-present acoustic cavitation. In the low MI level (0.41~0.6), the inertial cavitation effect was not detected and the FUS-induced BBB opening was found to rely purely on a stable cavitation effect. Once exposure levels exceeded this range , both inertial and stable cavitation are involved in the BBB-opening process19. Many studies have discussed the relation between MI and BBB opening, but so far none have addressed the feasibility of using MI as a gauge to measure concentrations of molecular substances with various molecular weights delivered into the brain following FUS-induced BBB opening.To gauge the level of FUS-induced BBB opening, the mechanical index (MI) which is defined as the peak negative acoustic pressure over the square root of the frequency  reflects the scale of inertial cavitation and mechanical bio-effectsin vivo evaluation of the degree and distribution of BBB opening. In particular, dynamic contrast enhanced MRI (DCE-MRI) has been reported to provide a comprehensive description of dynamic change in FUS-induced BBB opening by calculating the pharmacodynamic (PD) and pharmacokinetic (PK) parameters when administering an MR contrast agent (Gd-DTPA). The MR PD parameter, Gd-based area-under-curve (Gd-AUC) is obtained by accumulating a series of time-dependent Gd-DTPA concentrations and can characterize PD changes of the BBB-opening region20. Preclinical studies have shown that Gd-AUC is highly correlated with EB-albumin complex accumulation in the brain and has the potential to predict the PD behavior and biodistribution of therapeutic agents20. The MR PK parameters, Ktrans (which describes the influx transfer constant between extracellular extravascular space (EES) and blood plasma) and Ve  can describe dynamic change from BBB-opening to BBB-closure. Many studies have verified that the PK parameters can represent the scale of BBB opening and are highly dependent on FUS acoustic pressure22. Furthermore, a high correlation (r2 > = 0.7) was found between the PK value and the concentration of dye surrogates or therapeutic agents21. All evidence points to MR PK/PD parameters from DCE-MRI acquired immediately after FUS exposure as providing accurate predictions of the amount of molecular substances that will be delivered.Magnetic resonance imaging (MRI) is one of the most reliable tools for post-operational 26. For example, FUS-enhanced delivery of liposomal doxorubicin (DOX) was evaluated for glioma treatment27. Liposomal-Dox delivery with FUS significantly inhibited tumor growth compared with chemotherapy alone and improved animal survival by nearly 100% in three weekly treatment sessions29. Other chemotherapeutic agents, such as 1,3-bis (2-chloroethyl)-1-nitrosourea (BCNU), Temozolomide (TMZ), Bevacizumab have also been evaluated. Concentrations of all agents clearly increased in the FUS exposure region and tumor progression was also controlled to improve median survival 32. However, the impact of tumor treatment is determined by the amount of drug been delivered, which is determined by FUS exposure level and the molecular size of the drug. To date, the relationship of exposure conditions to therapeutic agent size has been largely overlooked and in-vivo MRI based predictions of the penetration rates of various molecular substances into the CNS cannot be made without sacrificing the subject animal.While FUS-induced BBB opening has a wide range of applications, preclinical studies have focused on the feasibility testing for various therapeutic agents. Several previous studies have confirmed that FUS exposure allows various drugs to permeate the BBB in increased concentrations and other clinically relevant effectsin vivo molecular penetration at various sizes of therapeutic agents based on DCE-MRI. Relationships between DCE-MRI index (Ktrans), ultrasound exposure level , and delivered molecular concentration (molecules including Gd-DTPA (1 kDa) and Trypan blue-albumin complex (~70 kDa)) was calibrated in the 1st stage. In the 2nd stage, we tested whether 1st stage calibration and established model could be applied to estimate molecular penetration form other distinct molecular substances (Dextran (40 kDa), Evans blue (EB)-albumin complex (~68 kDa), and Bevacizumab (149 kDa)) into rat brain under various FUS exposure level. The estimated molecular concentration was compared with quantification results to verify the accuracy and feasibility of the proposed estimation model. The scheme was summarized and shown in Fig. This study investigates the feasibility to establish a CNS drug delivery approach to estimate  Gd-DTPA  kDa and st Stage).Relationship of FUS-BBB opening degree with various exposure levels  and original pressure (0.5–1.25 MI) to produce exposure level in the range of 0.41–1.12 MI . Details of the animal experiments are summarized in Supplementary Table trans in Fig. e in supplementary Fig. trans mapping. Compared to the contralateral brain , the value clearly increased from a low to high FUS level (Ktrans level increased from 0.00632 to 0.0137 min−1 with MI exposure level from 0.41- to 1.12-MI) and presented a high correlation with MI . Such a high correlation between transcranial MI and Ktrans/Ve implies the BBB-opened degree is highly dependent on the actual FUS exposure level. Thus, a relatively higher correlation of Ktrans was adopted for following analysis, with linear relationship between and transcranial MI can be established as follows:trans (in min−1). Since the correlation of this linear equation was then used to estimate the transcranial MI for molecular penetration prediction in the 2nd stage.BBB kinetic change induced by FUS exposure was observed by DCE-MRI image indexs, Ktrans value and transcranial MI in the 2nd stage.Two substances, Gd-DTPA and Trypan blue  with molecular sizes of 1 and 70 kDa respectively were delivered to assess the relation of molecular size and penetration under the different FUS exposure conditions , EB-albumin complex (68 kDa), and Bevacizumab (149 kDa) under different of FUS exposure levels were examined  was then able to be obtained via Eq.  with a κ of nearly 1.After knowing the estimated transcranial MI of 9–12 groups and molecular size of delivered substances, the prediction model  when gauging the BBB opening via transcranial MI19. It therefore implies and supports that Ktrans can serve as a reliable image index to inversely estimate a transcranial MI level reliably, and we have validated this concept via demonstrating a high accuracy in predicting CNS molecular penetration.During FUS exposure, the transcranial ultrasound level will be affect by many uncertain factors such as difference skull thicknessIn this work, we used wide range of BBB-opened exposure range (ranging from 0.41~1.12 MI) and substances with two different sizes  to establish Eq.  with a BBB permeability ranged from 0.01 to 0.02 min−1 at 0.84 MI in Park et al. was also fell short of our predicted outcomes21. It might be due to: (1) The model establishment and validation falls within the range of 1–149 kDa and hence reliable prediction of extrapolated molecular penetration estimation may not be guaranteed, and (2) Chemotherapeutic agents such as BNCU already preserve good blood-to-brain permeability or doxorubicin exists active efflux transporter pumping function, violating a key assumption made in the proposed model that the molecular influx behavior between the plasma and EES during FUS-BBB opening should be dominant and efflux behavior can be neglected10.Gd-DTPA (1 kDa) has the smallest molecular size used in the proposed concentration predicted model, and clinical drugs with molecular size smaller than 1 kDa may result in less accurate penetration estimation. We have previously delivered 1,3-bis(2-chloroethyl)-1-nitrosourea  to into rat brain tissue under 0.36–1.1-MI and the drug concentrations in brain tissue was only about half value when compare with our estimation by our predicted modeltrans value and quantified concentrations of the delivered substances for each FUS exposure in the 2nd stage. Concentration values substantially increased with Ktrans level from 0.001 to 0.016 min−1. Gd-DTPA had the highest concentrations due to its small molecular size, and also had highest correlation (r2 = 0.8753) with Ktrans value. The Dextran and EB-albumin complexes were grouped together as the molecular sizes of both fall in the middle range of all tested substances, and the Dextran/EB-albumin complexes were found to have an intermediate molecular penetration with a lower correlation (r2 = 0.8174) than Gd-DTPA. Bevacizumab had the largest molecular size, and also the lowest molecular penetration and correlation (r2 = 0.6064). The bevacizumab concentration began to increase when Ktrnas exceeded 0.008 ~0.009 min−1 (estimated MI is 0.61~0.69 MI), but was otherwise close zero, indicating a 0.61~0.69 MI threshold for bevacizumab to cross through BBB. Below this range, the FUS-induced BBB opening size in the exposure area was too small to allow significant bevacizumab penetration. Chen et al. delivered dextran with 4 different molecular sizes  to assess the BBB opening size by FUS with 3 different MI values by FUS35. They reported a BBB opening of up to 70 kDa at 0.4 MI, and larger than 500 kDa when MI increased to 0.69 MI. This result implied a threshold MI for substances with larger molecular sizes, and also explains our finding of the decreased correlation of bevacizumab penetration with Ktrans when MI is lower than 0.61~0.69 MI .All testing molecules groups were subjected to DCE-MRI and Fig. 36. It implies that microbubble concentration should serve as a dominant factor but not microbubble type, and the proposed prediction approach may be able to apply among microbubble types. Furthermore, the study does not consider the pharmacological properties of the delivered substances such as molecular structure, lipophilicity, solubility, acidity, and absorption. Prior to clinical applications, more experimentation and evaluation is needed to generalize the prediction model, including wider exposure protocols and greater variety of delivery substances.The proposed prediction model for estimating molecular penetration is subject to other limitations. The model only considers FUS exposure energy, and neglects other FUS exposure parameters such as burst length, burst repetition rate or microbubble concentration. We also do not evaluate the model’s applicability for variable generalized exposure. However, previous studies have compared three different microbubbles  in terms of their effect on BBB opening, and was reported to have similar BBB-opening effects and persistence among various types of microbubbles This study proposes a CNS drug delivery approach to estimate concentrations of various therapeutic substances delivered via FUS-induced BBB opening. The small error range  between quantified and predicted concentrations raise the possibility to applications in medical imaging to monitor and estimate molecular penetration into the CNS during ultrasound-assisted brain drug delivery.In Vivo Experiments) guidelines for the care and use of laboratory animals. A total of 97 animals  were randomly assigned into 19 experimental groups. This study assessed the delivery outcomes of molecular substances with five different sizes –albumin complex, ~70 kDa; Evans blue (EB)-albumin complex, ~68 kDa; bevacizumab, 149 kDa). Various combinations of exposure frequency and pressure  were used to characterize the scale of BBB-opening. The study was performed by two stages: In the 1st stage, groups 1~8 were used to quantitate the two molecular delivery  and establish its relation with DCE-MRI; in the 2nd stage, four molecular delivery  was performed on groups 9~19 to evaluate the discrepancy between the estimated molecular concentration and actual measured concentrations. Details of the animal experiments are summarized in Supplementary Tables Animal experiments were carried out in accordance with the approved guidelines for the Care and Use of Laboratory Animals. All experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Chang Gung University and performed according to ARRIVE  group.The FUS instrument consists of a function generator , a power amplifier  and a 0.4-MHz FUS transducer , a 0.5-MHz FUS transducer , and a 1-MHz FUS transducer . Transducers were measured in a free field filled with deionized/degassed water by a needle type hydrophone. The diameter and length of the half-maximum acoustic pressure of the FUS field were respectively 2.3 and 12 mm for 0.4-MHz FUS, 3 and 8 mm for 0.5-MHz FUS, and 1.2 and 9.8 mm for 1-MHz FUS. The transcranial pressure loss was also measured with an 2 and continually maintained with 2% isoflurane mixed air during FUS-induced BBB opening. The fur overlying the FUS area was removed for FUS penetration. The animals were placed in a prone position directly under an acrylic water tank with a 4 × 4 cm2 window sealed with a thin polyethylene membrane to allow the ultrasound to penetrate through its base. The space between the skull and the window was filled with ultrasound gel and animals received burst-mode FUS at anterior-posterior (AP) 0 mm and midline (ML) −3.5 mm from bregma. Lipid-shell Sulfur hexafluoride (SF6) ultrasound microbubbles  and heparin  were administered intravenously after dilution with normal saline solution to a total volume of 0.3 ml. Immediately following microbubble injection, burst-mode FUS was delivered with a burst length of 10 ms, pulse-repetition frequency of 1 Hz and duration of 90–120 s. The biological effects induced by this microbubble dosage and FUS pressure have been previously documented30. After exposure, substances with different molecular sizes  were administrated intravenously and then DCE-MRI was conducted with the intravenous administration of MRI contrast agent Gd-DTPA  to obtain pharmacokinetic parameters. Two hours after FUS exposure, all animals were sacrificed and molecule quantification  was conducted. The time line for experimental procedures is presented in supplementary Fig. All animals were initially anesthetized with 3% isoflurane in 100% Oin-vitro measurements in our previous study, the correlation between spin-lattice relaxivity (R1 = 1/T1) mapping and the Gd-DTPA concentration were all determined using a 7-Tesla MR scanner 20. In some experimental animal groups, the FUS-induced BBB opening was monitored using an MR scanner and a 4-channel surface coil . Each rat was placed in an acrylic holder, positioned in the center of the magnet, and anesthetized with isoflurane gas (1–2%) at 50–70 breaths/min during the entire MRI procedure.The 20. Following the 20th acquisition, a diluted bolus of Gd-DTPA was IV administrated through a catheter at an infusion rate of 6 mL/min. A series of T1-weighted images were sequentially acquired over a period of 10 min and MRI data were collected for pharmacokinetic analysis by a custom Matlab  program.Following FUS-induced BBB opening, the distribution and dynamics of Gd-DTPA leakage were investigated. Animals were immediately relocated to the MR scanning room to acquire T1-weighted images of DCE-MRI with multiple flip angles. R1 maps and Gd-DTPA concentrations were calculated by transferring these multiple flip angle group images trans was obtained to characterize the kinetic behavior of the FUS-induced BBB opening by analyzing a series of Gd-DTPA enhanced T1-weighted images taken over 10 min. Gd-DTPA concentrations were calculated from SI changes of the T1-weighted image, using conversion equations similar to those used in previous study10. To calculate the kinetic parameters, the Gd-DTPA concentration curve was fit to the extend Kety model39 which accounts for the presence of separate extracellular and intravascular compartments. The time-dependent concentration of the contrast agent in tissue can then be described as:p(t) is the contrast agent concentration in the blood plasma ), Ct(t) is the contrast concentration in the tissue, Ktrans is the transfer rate constant from the intravascular system to the EES, and Vp and Ve are respectively the capillary plasma volume and distribution volume of the contrast agent in the EES (per unit volume of tissue). The SIs of rat brains were converted to Ct(t) values on the Gd-DTPA concentration time curve, and Cp(t) was chosen from a region of interest (ROI) in the vein sinus. Ktrans/Ve were fitted pixel-by-pixel, using the least squares function in the Matlab optimization toolbox  to generate PK parameter maps. A circular ROI was assigned at the BBB opening region to calculate average Ktrans/Ve values for the kinetic analysis of BBB opening.The DCE-MRI PK parameter K10 was generated by fitting the signal intensity of pre-contrast T1 images acquired with multiple flip angles. S(t) is the signal intensity of the T1 image over time and S(0) is the signal intensity before the contrast injection. Second, the R1 value and Gd-DTPA concentration were calibrated in vitro from our previous study20 and the linear relationship is well presented in supplementary Fig. The T1-weighted images from the DCE-MRI at 10 min following Gd-DTPA IV administration were selected for Gd-DTPA quantification of the BBB opened region. First, spin-lattice relaxivity R1 (=1/T1) maps were calculated by following equation:The animals in the TB and EB injection group were sacrificed after DCE-MRI for TB- and EB-albumin complex quantification. All animals were first deeply anesthetized with 10% chloral hydrate and infused with heparinized saline through the cardiac ventricle until a colorless infusion fluid was obtained from the atrium. After the rats had been sacrificed by decapitation, the hemispheres of the brain were separated along the transverse suture. Both hemispheres were then weighed and placed in formamide (1 mL/100 mg) at 60 °C for 24 h. The sample was centrifuged for 20 min at 14,000 rpm. The concentration of TB and EB extracted from each brain was determined spectrophotometrically at 595 nm and 620 nm respectively and compared with a standard graph created by recording optical densities from serial dilutions of TB or EB in formamide solutions with blank brain tissues, which were cleared by centrifugation  with a UV detector , a pump , and a 4.6 × 250-mm C-18 column (SUPELCOSIL). The mobile phase was an acetonitrile–water  mixture delivered at a flow rate of 1 mL/min at 25 °C and with a measuring wavelength of 335 nm. Dextran standard solutions (0.1–0.8 mg/mL) were prepared and 10 µL of the standard solutions were analyzed using HPLC to establish a standard curve . Brain homogenates were transferred to a 15 mL conical centrifuge tube and the Dextran was extracted with acetonitrile by means of 30 min sonication at 10 °C. After centrifugation, the supernatant was reserved and the pellet was re-extracted twice with 1 mL of acetonitrile. The combined supernatants were filtered (0.22 µm), extracted with 1 mL acetonitrile, and diluted with acetonitrile to a final volume of 4 mL. 10 µL of each brain extract were analyzed by using HPLC and the dextran concentrations calculated from the standard curve. The animals in the bevacizumab injection group were sacrificed after FUS exposure and DCE-MRI. The extracted bevacizumab was analyzed with HPLC as previously describedtrans) and molecular substance quantification data are presented as mean ± standard deviation of the mean and analyzed by one-way ANOVA. Differences were considered to be statistically significant when p < 0.05. The coefficients of Eqs  by two researchers blind to animal assignment. The DCE-MRI parameter data (Ks of Eqs  and (2) Supplementary information


Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints. To address these issues, we propose Mordred, a developed descriptor-calculation software application that can calculate more than 1800 two- and three-dimensional descriptors. It is freely available via GitHub. Mordred can be easily installed and used in the command line interface, as a web application, or as a high-flexibility Python package on all major platforms . Performance benchmark results show that Mordred is at least twice as fast as the well-known PaDEL-Descriptor and it can calculate descriptors for large molecules, which cannot be accomplished by other software. Owing to its good performance, convenience, number of descriptors, and a lax licensing constraint, Mordred is a promising choice of molecular descriptor calculation software that can be utilized for cheminformatics studies, such as those on quantitative structure–property relationships.The online version of this article (10.1186/s13321-018-0258-y) contains supplementary material, which is available to authorized users. A molecular descriptor is defined as the “final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment” . Many tySplit the dataset into training and test datasets for evaluating the predicted performance of the model.Calculate numerous molecular descriptors of each compound in the datasets.Construct a reliable model of the training dataset to predict the target activity or property from these calculated descriptors using classification or regression methods , and random forest).Evaluate the performance of the constructed model by predicting the target activities of the compounds in the test dataset that are not used for model construction.The steps in a general procedure of QSPR model construction using molecular descriptors are outlined below.Although both open-source and proprietary software have been developed for calculating molecular descriptors—such as PaDEL-Descriptor , BlueDesMeanwhile, BlueDesc is a simple software program for calculating descriptors. Because BlueDesc can output the results in libSVM input file format, users can easily construct SVM models. However, BlueDesc can calculate only 174 descriptors, and it has no configurable options. ChemoPy, a free software environment that calculates both 2D and 3D descriptors, can calculate 1135 descriptors. ChemoPy is available as a Python package and is convenient for constructing models using Python machine-learning packages. However, it can be difficult to employ it by non-Python users who are not familiar with the construction of the Python interface.  To date, ChemoPy only supports Python 2.Furthermore, PyDPI can calculate small molecule descriptors as well as protein descriptors. However, it has advantages and limitations that are similar to those of ChemoPy. In addition, Rcpi  is the In this paper, we propose Mordred, a newly developed molecular-descriptor calculation software program. The software can calculate more than 1800 descriptors at high speed, and it can be used from the CLI and web UI, as well as with Python 2 and 3 libraries. Mordred was released under the three-clause Berkeley Software Distribution (BSD) license, which allows both commercial and non-commercial use.Mordred was designed to be a software program that is easy to install and use, supports abundant molecular descriptors, has a high calculation speed, and includes automated tests. Molecular-descriptor calculation programs usually have many dependent software programs that must be manually installed. All direct dependent libraries in Mordred, except for RDKit and NumPy , are codPreprocessing of molecules affects the descriptor values in most molecular-descriptor calculators. However, for each descriptor, Mordred automatically preprocesses molecules . For example, a topological index descriptor was reported by Balaban et al. . along wMordred calculates more than 1800 default molecular descriptors, including all those implemented by RDKit (seven modules) and original implementations 42 modules) Table . To note modules ® Core™ i7-5930 K CPU, DDR4-2133 (quad channel) 64 GB memory machine.). On the contrary, most other software packages have difficulty in calculating complicated descriptors for large molecules; e.g., PaDEL-Descriptor falls into a missing value from a “time out” in such cases. For further information on these improvements, the Mordred documentation can be referenced.To implement molecular descriptors that can efficiently perform very large calculations, certain calculation algorithms were improved. Accordingly, Mordred can calculate all descriptors of molecules as large as a maitotoxin  in an acceptable calculation time , Descriptor class (preset of Descriptor), module , or a list of these in a Calculator instance. To calculate descriptors of a single molecule, an instance of Calculator can be used as a function. To calculate descriptors of multiple molecules, the map method in Calculator can be used. The map method can calculate descriptors in the CPU parallel using the multiprocessing module in the standard Python library (Code 5).These kinds of applications have missing values, even if there are no bugs in them. For example, a part of the autocorrelation descriptor cannot be calculated for small molecules. However, unintended missing values might cause errors or result in incorrect values. Therefore, it is important to reduce the missing values caused by software bugs or calculation timeouts. In Mordred, when the result is a missing value, an instance of the subclass MissingValueBase is returned. It contains an Exception that notifies users about the issue that triggered the exception (Code 6).As an option, if the expression of “MissingValue” is not necessary, these missing values can be filled with “NaN” or deleted by the fill_missing or drop_missing method of the Result class. Moreover, users can obtain results as a dictionary using the asdict method of the Result class (Code 7).The product term is often used to consider the interaction between features in a linear model. Mordred can easily calculate the product term using the “descriptor arithmetic” feature. Instances of descriptors can be calculated by employing a unary/binary operator with other descriptors (Code 8).The full executable code example of Code 2–8 can be found in Additional file Several descriptors are algorithmically improved in Mordred. A summary of the improvements is shown in Table ® Core™ i7-5930 K CPU, DDR4-2133 (quad channel) 64 GB memory machine.For example, to calculate the longest path from atom 1 to atom 5 shown in Fig. The Chi descriptor is calculated by matching all Smiles Arbitrary Target Specification (SMARTS) patterns in CDK. This approach is time consuming and cannot be used to calculate the higher-order Chi descriptor. The depth-first search is used in Mordred to overcome these issues.The Framework descriptor is calculated using the function to match the molecular framework in CDK. However, during this process, this function also performs extra calculations to solve more general cases, such as enumerating the atoms in a molecular framework. Those calculations are not necessary for the derivation of the Framework descriptor and they are removed in Mordred.The MolecularId descriptor calls the internal pure function with the same arguments many times in CDK. The returned values of these function calls are cached to avoid same calculations in Mordred.The CLI is additionally implemented for non-Python users (Code 9). Accordingly, the entire set or subset of molecular descriptors can be calculated in CPU parallel computing. The results are provided in CSV format.Mordred is also available as a web application Fig. . The webhttp://mordred.phs.osaka-u.ac.jp. This demonstration page works well on most widely used web browsers with current versions, such as Safari version 10+, Google Chrome version 61+, Internet Explorer version 11+, Microsoft Edge version 40+, and Firefox version 55+. However, certain browsers on some devices with legacy graphics drivers may not support this page, in which case another browser should be considered.The web interface can work with a multi-core processor. Submitted tasks are fairly processed. A demonstration server is available at The usage of this demonstration page is quite simple. Briefly, a SMILE or SDF file containing single or multiple structures can be uploaded from the front page. Hydrogens can be automatically added when needed. “Generate 3D conformer” and “Desalt” buttons provide two preprocessing options. When the submission succeeds, the structures are displayed at the center of the screen. When multiple structures are uploaded, the structure can be shifted without refreshing the page by clicking the arrow buttons. If 3D conformers are provided or created, conformers can be scaled by using the middle button of the mouse, and they can be rotated by the left button of the mouse. The uploaded molecules can be downloaded in both SMILES and SDF formats by clicking the download button, which is on the right of the displayed file name. Atom names can be confirmed by hovering over the mouse.A configuration button on the top right provides the selection options for the following descriptor calculations. By clicking the “calculate” button, users can obtain the values and summaries of descriptors, as well as the calculation log. To maintain server safety, the maximum molecule number that can be uploaded at one time is limited to 200 in this demonstration server. However, this limitation is not set by default when the user constructs his/her own server.® Core™ i7-5930 K CPU, DDR4-2133 (quad channel) 64 GB memory machine. We used entries from the KEGG-drug database [To determine the speed of the calculation of molecular descriptors, we performed a benchmark test. All tests were performed on an Inteldatabase  as the bWe used libPaDEL-Descriptor, which is included in the source code of PaDEL-Descriptor, to evaluate the performance of the PaDEL-Descriptor because it excludes the extra descriptor calculation time, such as the file IO and molecular preprocessing. In addition, it evaluates the exact calculation time. First, each molecular descriptor was calculated three times to perform a just-in-time compilation. Then, each molecular descriptor was calculated ten times to evaluate the performance. Garbage collection was performed before each molecular-descriptor calculation. In the Mordred benchmark test, calculations were carried out ten times after disabling garbage collection.The CLI benchmark test was performed to estimate real-world and multi-processor performance. We used the same machine. Up to 100 atoms compounds were used from the same dataset to avoid timeouts in PaDEL-Descriptor. We performed a benchmark test using one to six  threads (in PaDEL-Descriptor) or processes (in Mordred). Each benchmark was repeated ten times.The total calculation time per molecule is shown in Fig. The results of the CLI benchmark test are shown in Fig. Mordred is superior to other software programs in several aspects. First, it is open-source software. Moreover, Mordred and all software used by it are licensed under the non-copyleft open-source license. This implies that it can be used in proprietary software. In addition, users can easily report a bug, request new features, and send patches on GitHub.Second, it is easy to use. Mordred can be installed using only one command, whereas other Python molecular-descriptor calculation libraries  have more dependencies that require manual installation. The CLI and web interface can also be launched using a single command. Third, it has high flexibility. Mordred can calculate uncommon-range descriptors without modifying the source code. Users can create new molecular descriptors, such as the product term, by easily using the descriptor arithmetic feature. Additionally, the Mordred library can be simply accessed by using the presets in the Descriptor class. Finally, it is fast. Mordred can calculate an entire molecular descriptor twice as fast as PaDEL-Descriptor. Furthermore, the time complexity for some molecular descriptors is reduced in Mordred. As a result, it can be used for calculating the descriptors for large molecules that cannot be calculated using other software.There are some potential defects in Mordred and/or its dependent libraries. We are receptive to feedback, questions, and bug reports via both email and GitHub.https://github.com/mordred-descriptor.In this paper, we proposed one molecular descriptor calculation software named Mordred. This software is easy to install and use, generates abundant molecular descriptors in a high calculation speed. It can be installed using a single command and has a web interface. Mordred can calculate more than 1800 descriptors and calculates all descriptors in acceptable time. Our proposed software was released under a 3-clause BSD license on Additional file 1. The variations among descriptor values calculated by Mordred and other software programs.Additional file 2. A confirmation of the variation among descriptor values calculated by Mordred and other software programs .The results of some descriptors  are not available in all software because the descriptor of the molecule cannot be algorithmically calculated.Additional file 3. All descriptor list.Additional file 4. Code examples.


The discovery of tunneling and giant magnetoresistance in inorganic spin valves has led to a revolution in the field of magnetic memory and the significant increase in the storage capacity of modern hard drives. Simultaneously, given their inexpensive production, flexibility and diverse applications, molecular-based organic materials have become extremely important in electronic devices and circuitry. A combination of spintronics and organic electronics is expected to lead to a new generation of spin-based devices. These devices are expected to bring a wide range of exciting, new fields of application and products for organic/molecular spintronics.The work of the Research Unit “Towards Molecular Spintronics” funded by the German Science Foundation  (DFG FOR 1154) was focused on the ultimate down-scaled functional unit which integrates the spintronic functionality into one single molecule. In our ambitious approach towards molecular spintronics we combined two interdisciplinary research fields based in otherwise disjunctive research communities: organic electronics and molecular magnetism. This required close collaboration of experts in physics , chemistry, materials science, and electrical engineering. The partners involved in this consortium are located at four Saxonian universities  as well as the Leibniz-Institut für Festkörper- und Werkstoff-Forschung (IFW) in Dresden, Germany.The activities in this research unit were tailored to systematically address the whole research and development chain from molecule synthesis and molecular film deposition via fundamental characterization and theoretical understanding to device demonstration and integration.Tailoring and fundamental characterization of magnetic molecules: Magnetic molecules for implementation into devices were synthesized and fundamentally investigated by theoretical density functional methods and experimental scanning probe techniques as well as optical and magnetic measurements on bulk materials. Already, at this stage, basic compatibility aspects concerning device processing had to be taken into account.Fabrication, characterization, and optimization of molecular thin films and interfaces: Various deposition techniques to create suitable molecular films were tested for a variety of molecules. The structure, morphology, and molecular orientation of the layers were fundamentally characterized and optimized, taking into account that for device integration, the molecular layers need to obey certain boundary conditions, such as long-term stability, process compatibility, and the ability to integrate with electrode materials. Progress in this respect was only made possible by a continuous feedback from basic characterization and technology projects that allowed targeted and efficient synthesis of appropriate molecules and molecular films.Device demonstration and on-chip integration: Rolled-up nanotechnology was used to create vertically stacked electronic devices with an organic tunnel transport layer for sensing applications. Horizontally stacked two-terminal and four-terminal devices photo sensors and magnetic field sensors were developed for large-scale integration purposes.This complex research and development chain required various synthesis methods and theoretical approaches for prediction of molecular and devices properties. This included high-end and complementary characterization methods , as well as (magneto-)optical and electron spectroscopy, electrical measurements, new technological concepts, and sophisticated processing facilities. Such a complex chain can only become and remain successfully operational thanks to the enthusiastic cooperation between the groups, and in particular, between the young researchers. We would therefore like to express our deepest gratitude to all principal investigators, young researchers, associated researchers, and the coordinator (Jane Eisentraut) of the Research Unit for the great working atmosphere throughout the past six and a half years.60 interfaces [In this Thematic Series we summarize selected examples of the collaborative work in which at least two groups were involved. The range of considered molecules spans from heterotrinuclear bis(oxamato)-type and bis(oxamidato)-type complexes –3, to exterfaces , magnetoterfaces , spin-crterfaces , ferromaterfaces , and theterfaces . We are terfaces , which dBeilstein Journal of Nanotechnology for their highly professional support and always very fast feedback. We would also like to thank all referees for their effort and constructive criticism.We truly appreciate the open access policy of the Beilstein-Institut, which made this Thematic Series possible. We are greatly indebted to the team at the Georgeta Salvan and Dietrich R. T. ZahnChemnitz, October 2017


The precursor LbL film was first constructed with poly-L-lysine and alginic acid via electrostatic interaction. Then the HVJ-E particles were immobilized on the cationic PLL outermost surface. The HVJ-E adsorption was confirmed by surface wettability test, scanning laser microscopy, scanning electron microscopy, and confocal laser microscopy. The immobilized HVJ-E particles were released from the nanofibers under physiological condition. In vitro cytotoxic assay demonstrated that the released HVJ-E from nanofibers induced cancer cell deaths. This surface immobilization technique is possible to perform on anti-cancer drug incorporated nanofibers that enables the fibers to show chemotherapy and immunotherapy simultaneously for an effective eradication of tumor cells in vivo.Inactivated Hemagglutinating Virus of Japan Envelope (HVJ-E) was immobilized on electrospun nanofibers of poly( N-isopropylacrylamide (NIPAAm) [in vitro compared with individual treatment. According to the world healthcare organization’s (WHO) report in 2012, 8.2 million deaths worldwide could be attributed to cancer ,4. We ha(NIPAAm) ,7 based (NIPAAm) ,8. The cet al. have recently shown a new type of immune treatment using inactivated Hemagglutinating Virus of Japan Envelope (HVJ-E). HVJ-E is prepared by irradiating UV light to destroy the internal RNA of the HVJ (or Sendai) virus [in vitro and in vivo. Kurooka et al. reported an injected HVJ-E particle induces interleukin-6 (IL-6) production that enhances a strong anti-tumor immunity in vivo [in vitro which was reported by Kawaguchi et al. [ε-caprolactone) (PCL). The surface of PCL nanofiber is coated with polyelectrolytes via layer-by-layer (LbL) assembly to construct the precursor film for HVJ-E immobilization. Release of the HVJ-E particles in physiological conditions and inductions of tumor cell deaths are studied through in vitro tests.In recent years, cancer immunotherapy has emerged as the fourth treatment modality, in addition to surgery, chemotherapy, and radiotherapy ,10. The i) virus . Althougi) virus ,13,14. I in vivo ,16. The i et al. . They seε-caprolactone) (PCL) in this study because PCL is biocompatible, biodegradable, and widely used in the biomaterial/biomedical fields such as tissue engineering and drug delivery [ε-caprolactone monomer was polymerized by a method described previously [et al., for example, reported that the bone morphogenetic protein 2 (BMP-2) immobilized PLL/hyaluronic acid based LbL films on a bone implant enhanced the bone regeneration in an embedded site [N-hydroxysuccinimide (NHS) were used to crosslink the carbonic acid groups of ALG and amino groups of PLL in LbL multilayers [−1 and at 1515–1600 cm−1 were assigned to the N-H stretching and amide II in crosslinked PLL-ALG structure in LbL multilayer, respectively. This observation suggested the successful immobilization of LbL multilayer on PCL nanofibers. After the crosslinking reaction, PLL outermost crosslinked nanofibers were immersed into HEPES/NaCl solutions containing 3000 Hemagglutinating unit/mL (HAU/mL) of HVJ-E to obtain HVJ-E adsorbed LbL coated PCL nanofibers. HVJ-E has an anionic zeta potential in physiological pH and could adsorb on the final PLL cationic positively charged layer [We choose to make nanofiber meshes with poly and scanning electron microscopy (SEM). SLM images of PCL, LbL coated PCL nanofibers, and HVJ-E immobilized LbL coated PCL nanofibers are shown in One of the great advantages of nanofiber structure compared to two dimensional substrates is the large surface area and porosity of the nanofiber . TherefoThe release of HVJ-E particles from the nanofibers was examined by measuring the absorbance at 280 nm by UV-vis . The desin vitro tests to observe the viability and proliferation ability of tumor cells. Metastatic prostate cancer PC-3 cells were cultured in 24-well plates. PC-3 cells were chosen because previous study suggested that the PC-3 cells have a HVJ-E receptor GD1a that enables them to capture HVJ-E particles [in vitro HVJ-E release study and cytotoxic assay as shown in In this part, we attempted to confirm whether the released HVJ-E from nanofibers induce tumor cell death. We conducted articles . A 1 cm et al. reported that the inside RNA fragments of HVJ-E were recognized by retinoic acid-inducible gene I (RIG-I) [Because Matsushima-Miyagi  (RIG-I) . Althougɛ-Captolactone monomer was purchased from Tokyo Kasei . Poly-L-lysine hydrobromide , sodium alginate (low viscosity), and pKH-26 staining kit were purchased from Sigma Aldrich . Freeze dried HVJ-E (GE-016) was purchased from Ishihara Sangyo Kaisha, Ltd. . RPMI-1640 and Dulbecco’s phosphate buffer saline  were purchased from Nacalai tesque . HEPES and cell counting kit-8 were purchased by Dojindo . Prostate cancer PC-3 cell line was provided by RIKEN Cell Bank . PCL was synthesized by ring-opening polymerization with tin octanoate . The obtin vacuo at room temperature before ATR-FTIR measurement. All of the measurements were performed under identical conditions .Poly-L-lysine (PLL) and alginic acid (ALG) were dissolved in pH 7.4 of HEPES/NaCl buffer (10 mM HEPES and 150 mM NaCl). The concentration of each solution was adjusted to the concentration of 1.0 mg/mL and 5.0 mg/mL respectively. Firstly, PCL nanofibers were dipped into 0.1 mg/mL of poly ethylene imine (PEI) solution for 10 min to fabricate a precursor film on the fiber. The fibers were then dipped into HEPES/NaCl buffer for 2 min. The PEI coated nanofibers were dipped into the ALG solution for 10 min, followed by washing with HEPES/NaCl buffer for 2 min. The ALG outermost fiber was dipped into PLL solution for 10 min to form the LbL multilayer films. This adsorption/washing process was continued alternatively to assemble the LbL multilayer on PCL nanofibers. The EDC and NHS were dissolved in HEPES/NaCl buffer and the concentration was adjusted to 800 mM and 200 mM. These solutions were mixed together before the crosslinking reaction. The PCL nanofiber with PLL on the outermost fibers was dipped into an EDC-NHS mixed solution for 4 h at 4 °C. After the reaction, the fibers were dipped into HEPES/NaCl buffer overnight to remove unreacted EDC. Attenuated total reflection Fourier transform infrared (ATRFTIR) spectrum was measured on a FTIR spectrophotometer  equipped with universal ATR sampling accessory for surface analysis. The samples had been dried The HVJ-E fibers were prepared first by crosslinking PLL outermost LbL coated fibers, followed by sterilization with UV light for 15 min. The crosslinked LbL nanofibers were then dipped into 3000 HAU/mL of HVJ-E solution (HEPES/NaCl buffer) for 2 h. The fibers were then washed two times with HEPES/NaCl buffer.Scanning Electron Microscope (SEM) image was taken by SU-8000  for the examination of nanofiber shapes after platinum coating. Confocal microscope analysis was performed by SP-5 . FITC labelled PLL and ALG were assembled on PCL nanofibers as the above described method. Then the sample was observed by Ar laser and He/Ne laser. The 1 cm × 1 cm of HVJ-E immobilized LbL coated nanofibers were immersed in 5 mL of PBS. The solution was incubated at 37 °C in the stirring machine (55 times/min). 1 mL of the solution was collected at 1, 2, 4, 8, 24, 48, and 72 h and 1 mL of PBS was added each time, respectively. The absorbance at 280 nm of each sample was measured to examine the concentration of HVJ-E solution and the value was converted to concentration by the prepared standard curve.The 10,000 cells of PC-3 cells were cultured in a 24-well plate for 24 h. 1cm × 1 cm of PCL fibers, LbL coated nanofibers and HVJ-E fibers were put in each well and again cultured for 24, 48, and 72 h independently. After each time period, the fibers were removed and 50 µL of cell counting kit-8 reagent was then added to each well and incubated for 2 h at 37 °C. The absorbance was measured at 450 nm by an UV plate reader.In vitro cytotoxic assays demonstrated that the released HVJ-E from nanofibers induced cancer cell deaths. We believe that the HVJ-E nanofiber offers significant promise for the cancer immunotherapy because the proposed method perhaps regarded as a simple, versatile, and an inexpensive bottom-up nanofabrication technique. In addition, this surface immobilization technique is possible to perform on anti-cancer drug incorporated nanofibers that enables the fibers to show chemotherapy and immunotherapy at the same time to effectively eradicate tumor cells in vivo. In this study, we fabricated HVJ-E immobilized electrospun nanofiber meshes by using the LbL technique. As the HVJ-E particles are fragile, first we prepared precursor LbL film on the surface of PCL nanofibers with PLL and ALG via electrostatic interaction. Then the HVJ-E particles were immobilized on the cationic PLL outermost surface. These immobilized HVJ-E particles were released from the nanofibers under physiological condition.


We adopt the Noether Symmetry Approach to study the related dynamical systems and to find cosmological solutions.We consider the cosmology derived from  Nowadays, one of the most important problems in Physics is to understand the late-time accelerated expansion of the universe. Besides, large scale structure, ranging from galaxies to superclusters, presents the problem of missing matter, i.e. the luminous matter is not sufficient in order to guarantee the stability and the evolution of self-gravitating astrophysical systems. There are several candidates to explain these phenomena and the most popular ones are the dark energy and the dark matter, i.e. cosmic fluids gravitationally interacting and leading the evolution of the Hubble flow but without any electromagnetic counterpart.In particular, the physics underlying the dark energy is still not understood since it behaves as a repulsive gravitational force in contrast to the usual gravitational field. In general, there exist two ways to study the dark energy problem: 1) one can retain the General Relativity (GR) and introduce a new kind of fluid which possess a negative pressure , or (2) one can think that GR needs to be modified at high energy levels and hence the dark energy comes out from these modifications. One can change the left hand side of the Einstein field equations related with the theory of gravity  in it (see the review paper of Ref. [f(T) gravity remains a second order theory whereas the straightforward GR extension, f(R) gravity is a fourth-order one in metric formalism. However, the price to pay in this approach is that f(T) is not invariant under local Lorentz transformations, and hence different vierbeins could arise by different field equations [From the geometrical point of view, these spacetimes are different from the ones considered in GR. From the point of view of the field equations, the teleparallel equivalent of General Relativity (TEGR) is equivalent to GR  gravity was proposed in [f(T) is extended to f, where B is a boundary term related with the Ricci scalar via f(T) and f(R) under suitable limits. Some cosmological features as reconstruction techniques and thermodynamics have been studied in [f gravity presents several interesting features by which it is possible to unify, under the same standard, issues coming from f(T) and f(R) gravity.Recently, a new generalization of the standard posed in . In thisudied in  under thudied in , a non-mudied in . In summf by the so-called Noether symmetry approach. This technique proved to be very useful for several reasons: (1) it allows one to fix physically interesting cosmological models related to the conserved quantities  [Here, we will explore cosmological solutions coming from entials) ; (2) theentials) ; (3) symentials) .f(T) gravity and f gravity. f cosmology and particular cases that can be derived from it are introduced in Sect. f gravity. In particular, we derive the Noether vector field and derive the Noether conditions for the function f. In the related subsections, we study particular cases of f function discussing, in particular, how it reduces to f(T), f(R) and f gravities. The main point of this section is to demonstrate how several classes of modified gravity theories can be reduced to the f paradigm. Discussion and conclusions are drawn in Sect. The plan of this paper is as follows: In Sect. f(T) gravity. In this theory, the vierbeins or tetrad fields T and it is usually called the torsion scalar. This quantity is a topological object and the TEGR is constructed by defining the actionR and the torsion scalar T differs by a boundary term viaWe briefly present the basis of the teleparallel equivalent of general relativity (TEGR) and its generalization, the so-called f(T) is a smooth function of the torsion scalar. It is easy to see that, by setting f(R) gravity since now the boundary term in  gravity. The above action (f(T) depends also on the boundary term B. The action reads as follows [f is a smooth function of two scalar fields, i.e. both the scalar torsion T and the boundary term B. The motivation of this action comes out from the fact that from f(T) gravity, it is not possible to find an equivalent theory of its metric counterpart, the f(R) gravity. From the above action, we can easily see that the f(R) and f(T) can be recovered by assuming Now, we can straightforwardly generalize  by consi term in  contribu follows :9\documef(T), f(R), and f gravities.By varying the above action with respect to the tetrad field, we get the field equationsf gravity. In particular to find exact cosmological solutions by the Noether symmetry approach. Let us consider f cosmology in a flat Friedmann–Lemaître–Robertson–Walker (FLRW) universe. The spatially flat FLRW metric in Cartesian coordinates reads as follows:a(t) is the scale factor of the universe. This metric can be constructed by the following tetrad field:f is not invariant under Lorentz transformations, one needs to be very careful with the choice of the tetrad. For instance, the unwanted condition In this paper, we are interested in cosmological consequences of  of Ref.  since itp(t) are the energy density and pressure of the cosmological fluid respectively. It is clear that by setting f(R) gravity with the standard notation (see for example [f(T) gravity given byf can be viewed as a special case of f gravity since we can choose f should be a more natural theory to consider than f as we will discuss below.By considering a standard perfect fluid as a content of the universe and using the above tetrad, we find that the modified Friedmann equations are given by example ). Immedisee also . Note tht. Here, a(t), T(t), and B(t) are, respectively, the scale factor, the torsion scalar and the boundary term defined in the FLRW metric. The Euler–Lagrange equations are given byIn the specific case we are dealing with, the cosmological equations can be derived both from the field Eq.  or deduca, T, B in order to derive the f action as follows:T and B, we finda, T, and B. If we choose f(T) cosmology with the Lagrangian [f(R) gravity [T and B are given byf point-like Lagrangian is given byLet us consider the canonical variables grangian 29\docume gravity 30\documeThe Noether symmetry approach has been widely used in the literature to find cosmological solutions in modified gravity . There are two different strategies to solve it and to find symmetries: (1) one can directly solve the system  and search for the related symmetries [f cosmology.In general, a Noether symmetry for a given Lagrangian exists if the conditionmmetries . From a mmetries . We willk and m are constants, we find that the unique solution of (f(T) function. This comes from the fact that B is a boundary term so that a linear form of the function in B does not introduce any change in the field equations. Hence, this kind of function gives the same results as reported in [For a power-law like function given by ution of –49) is  is \docuorted in .k and m are constants. From (a(t) gives, for T and B given by (Let us now study the case where the function takes the formts. From –48), it, it50\dofunction –49), we, we50\donerality . Let us given by  and 22)50\documeg(a) is an arbitrary function of the scale factor. Therefore, from (u(t) isE being the Hamiltonian (the energy) of the system and The case grangian  reads as also in  where a f gravity are discussed. We will see that all symmetries found in earlier studies for f(T), f(R), and f can be achieved starting from the Noether symmetry equations of Eqs.  cosmology.In the next subsections, remarkable theories that can be recovered from  of Eqs. –49) der derf gravity. The cases studied in [a and the r.h.s. only on T, so that C is a constant. Thus, it is easy to solve the above equation yieldingf(T) gravity admits power-law cosmological solutions of the form of f(T) cosmology is in [The first remarkable example is udied in , 29 are shown in , 29, usigy is in .f(R) gravity by assuming f(R) gravity becomesf(R) gravity. In doing this, Eqs.  is an arbitrary function depending on a. Note that the latter solution is very similar to the one found in (f(R) gravity admits power-law solution of the formWe can recover quations –49) rel relf(R) ns as in , we requns as in , that isfound in  for the  that in  where sof, we require to change the function f result the same as those studied in [Let us now discuss the case where use Eqs. –35) and and\docuudied in  with theT and its related boundary term T and B, can be taken into account. If not assumed in a trivial way, that is linear in B, interesting features come out from the combinations of T and B, in particular, the possibility to relate f(T) and f(R) gravity under the same standard. This means that not only GR and TEGR  result in the “same” effective theory but also their extensions, also if conceptually very different, can show analogies and similitudes.In this paper, we discussed an extension of modified teleparallel gravity including functions of the torsion scalar f gravity are a general standard to find solutions both in the starting theory and in the particular cases like f(T), f(R), and f. In this last case, the Noether technique allows one to deal with curvature R and torsion T scalars as two scalar fields.Here we consider the Noether symmetry approach in order to investigate the related cosmologies. The main result is that the Noether vector fields emerging from The related cosmological solutions are of physical interest and, essentially, all the main cosmological behaviors can be recovered. However, this is only a preliminary study where no effective comparison with observations has been made and only toy models have been analyzed in order to test the technique.f gravity as in [T and B as mother theory by which all extensions and modifications of GR can be generated.In forthcoming studies, we will adopt an approach for ty as in , where tudied in . Under t


A central goal of cognitive neuroscience is to decode human brain activity—that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive—that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model—Generalized Correspondence Latent Dirichlet Allocation—that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text—enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity. A central goal of cognitive neuroscience is to decode human brain activity—i.e., to be able to infer mental processes from observed patterns of whole-brain activity. However, existing approaches to brain decoding suffer from a number of important limitations—for example, they often work only in one narrow domain of cognition, and cannot be easily generalized to novel contexts. Here we address such limitations by introducing a simple probabilistic framework based on a novel topic modeling approach. We use our approach to extract a set of highly interpretable latent “topics” from a large meta-analytic database of over 11,000 published fMRI studies. Each topic is associated with a single brain region and a set of semantically coherent cognitive functions. We demonstrate how these topics can be used to automatically “decode” brain activity in an open-ended way, enabling researchers to draw tentative conclusions about mental function on the basis of virtually any pattern of whole-brain activity. We highlight several important features of our framework, notably including the ability to take into account knowledge of the experimental context and/or prior experimenter belief. Although researchers have dedicated increasing effort to the challenges of brain decoding . The database contains activation coordinates that were automatically extracted from 11,409 published fMRI studies , as well as associated semantic terms extracted from the corresponding article abstracts. Further details have been reported in previous studies [All data used to train the GC-LDA topic model came from the Neurosynth database [ studies ,31,70.The 500-subject release of the Human Connectome Project. We focused on single-subject whole-brain beta maps from several functional tasks. In all cases, we used experimental contrasts predefined by the HCP research team and included in the “preprocessed” data release . Studied contrasts included the comparison between faces and shapes in the Emotion task; between language and math conditions in the Language-Math condition; between social and non-social motion in the Social Cognition task. (2) NeuroVault.org maps. We downloaded two sets of maps from the NeuroVault whole-brain image repository: (i) a completely random set of 100 images , and (ii) a random set of 300 NeuroVault images that had been previously manually annotated using the Cognitive Atlas ontology for a completely different purpose . (3) BrainMap ICA and Yeo et al. author-topic “cognitive component” maps. We obtained these two sets of maps—reported in Smith et al. [For decoding analyses, we used whole-brain maps obtained from several sources, including: (1) h et al.  and Yeo h et al. , respectresample_img function in the nilearn package; nilearn.github.io). Such transformations are imperfect and subject to considerable error, but we nevertheless opted for the simplicity of such an approach seeing as our goal was to illustrate the application of the GC-LDA model rather than to draw concrete inferences about any of the tested images. Once in the MNI152 space, we used the standard gray matter mask from the FSL package [All analyses were conducted in the standard MNI152 2mm space. Images that were not nominally in this space  were transformed to the target using an affine transformation with continuous interpolation , and a multinomial distribution ϕt over all of the unique types of linguistic features (consisting of unigrams and bigrams) in the corpus. This model is a generative model, meaning that it learns the joint probability distribution of all variables, and thus describes a process that can produce new approximations of the observed data (the linguistic features and activation coordinates) via a set of latent (unobserved) topics.A high-level schematic of the model we employ is presented in d is generated by first sampling a multinomial probability distribution θd over topics from a Dirichlet prior distribution. Then, to generate each activation peak x in the document, the document first samples a topic y from its distribution over topics θd and then samples a peak activation at location x from the spatial distribution associated with topic y. To generate each word in the document, a topic z is sampled proportional to the number of times that the document sampled activations peaks from each topic, and then a word token w is sampled from topic z’s probability distribution over word types ϕz. To illustrate this process, consider the example Document 1 shown in The model assumes that each document Because the model enforces a correspondence between the frequency with which documents sample their words and activations from each topic, the model ensures that over the document corpus, the linguistic features associated with each topic will be closely related to the topic’s spatial distribution over activations. More specifically, the model will identify a topic-specific distribution over neural activations that tends to co-occur with the topic’s linguistic features across the corpus.The general framework of the GC-LDA model allows the experimenter to choose any valid probability distribution for the spatial component of each topic. The results displayed in Figs T topics, where each topic has an associated spatial probability distribution over the coordinates in the brain, as well as a multinomial distribution over linguistic features. The model additionally learns the topic mixture weights for each document.Given a formalized generative process for any of these models, we can use Bayesian inference methods to learn all of the latent (unobserved) parameters of this model from the observed data ), where T is the number of topics, R is the number of subregions, and NW and NX are the number of word tokens and peak activation tokens, respectively.Although Bayesian statistics can have poor scaling behavior with respect to the number of observed variables p , inferenribed in , the comT) × Word-type (W) matrix of conditional probabilities PT×W, where cell Pij is the probability p(t = i|w = j) that the model assigns word type w from the text input to the ith topic. This matrix is computed, using Bayes’ rule, from the topic’s probability distributions over word types ΦW×T in the trained GC-LDA model, as follows:p(t). We then obtain a vector of topic weights τ for the entire input by summing over the all word tokens w in the input; i.e.,AT×V, where cell Aij reflects the smoothed conditional probability p(v = j|t = i) that the model samples an activation at brain voxel j  from topic i. The rows of this matrix are (smoothed versions of) the images displayed in τ ∙ A.For text-to-image decoding , we firsNote that the resulting values cannot be interpreted as probabilities, because we deliberately sum over words in the input rather than computing the joint probability. The reason for this is that, while the latter approach is technically feasible, it typically produces very similar results for short inputs, and produces unstable results when the input sentence exceeds a few words in length .PT×V, where cell Pij reflects the conditional probability p(t = i|v = j) that the model assigns the activation at voxel j in the input to the ith topic. This matrix is computed from the trained GC-LDA model by first computing the empirically observed probability of sampling of each voxel from each topic , and then renormalizing these probabilities using Bayes’ rule, under the same uniform prior assumption used above for text-to-image decoding  as if the prior, p(t), was uniform over all topics). We then sum over all of the input activations to obtain a vector of topic weights τ for the given input. Lastly, we project the topic weights into the word space by multiplying the vector of topic weights τ by the Topic × Word matrix ΦW×T, where cell Φij reflects the conditional probability p(w = i|t = j) that the model samples the ith word type from topic j: τ ∙ Φ.For discrete coordinate-to-text decoding , we repePT×V by the similarity of the input image to each of the GC-LDA topic maps. We compute a vector of topic weights τ as:PT×V is the Topic × Voxel matrix of conditional probabilities of assigning an activation at voxel v to topic t, and IV is the vectorized whole-brain input image. We then project the topic weights into word space in the same way as for the discrete coordinates: τ ∙ Φ. The scale of the resulting values is arbitrary, and depends on the input image, but the rank-ordering of terms is instructive and typically converges closely with human-annotated labels.To decode text from continuous whole-brain images , a slighp(t) when computing the matrices PT×V and PT×W. The decoder results will then directly reflect the adjustment in both the text-to-image and image-to-text directions. However, researchers are unlikely to have strong intuitions about the relative base rates of the latent topics themselves. More commonly, they will instead wish to update the priors indirectly, based on a more intuitive expression of the experimental context or prior belief. This can be accomplished by “seeding” the priors with image and/or text inputs. In this case, the procedure can be thought of as a two-step application of the decoding methods described above. On the first pass, the input image or text is used to estimate values of τ (no further output is generated). On the second pass, the τ computed during the first pass is used as an informative prior p(t) in computing matrix PT×V or PT×W as described previously, and this updated matrix is applied to the actual image or text to be decoded. This procedure can repeat an indefinite number of times, as in a typical Bayesian context .Specifying priors on the GC-LDA topics can in principle be accomplished directly, by simply setting the desired prior probabilities on topics http://nipy.org/nibabel) and the nilearn library (http://nilearn.github.io) to plot brain slices. Word clouds were generated using the word_cloud package (https://github.com/amueller/word_cloud). An open-source implementation of our GC-LDA model, including documentation and examples, is publicly available on GitHub (https://github.com/timothyrubin/python_gclda). The topic maps reported here are available as a collection of interactive, downloadable whole-brain maps from the NeuroVault website (http://neurovault.org).All of the methods and analyses were implemented in the Python programming language. We used the standard scientific Python stack for analysis: Numpy  as the bS1 Supporting Results(PDF)Click here for additional data file.S1 FigEach row represents a single topic. For each topic, the word cloud displays the top semantic associates .(JPG)Click here for additional data file.S2 Fig(JPG)Click here for additional data file.S3 Fig(JPG)Click here for additional data file.S4 Fig2) for the fitted reconstruction model, and a scatter plot of all voxels (right).Topic-based reconstruction of whole-brain activity maps. Representative examples from (A) the set of 20 BrainMap ICA components reported in Smith et al. . (B) the(JPG)Click here for additional data file.S5 Fig(JPG)Click here for additional data file.S6 Fig(JPG)Click here for additional data file.S7 FigLabels in white indicate human-annotated cognitive atlas paradigm, when available.(JPG)Click here for additional data file.