bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy
Authors: Guerler Aysam , Baker Dannon , van den Beek Marius , Gruening Bjoern , Bouvier Dave , Coraor Nate , Shank Stephen D. , Zehr Jordan D. , Schatz Michael C. , Nekrutenko Anton 
Published: March 2021

AbstractProtein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3 (Nsp3). We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor (MOG) and dipeptidyl peptidase-4 (DPP4). The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation.The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
Authors: Alcantara Ryan S. , Edwards W. Brent , Millet Guillaume Y. , Grabowski Alena M. 
Published: March 2021

AbstractGround reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory. Previous studies used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data.19 subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects.The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accurately of quantifying and monitoring external loads experienced by the body when running.
Portable framework to deploy deep learning segmentation models for medical images
Authors: Iyer Aditi , Locastro Eve , Apte Aditya P. , Veeraraghavan Harini , Deasy Joseph O. 
Published: March 2021

PurposeThis work presents a framework for deployment of deep learning image segmentation models for medical images across different operating systems and programming languages.MethodsComputational Environment for Radiological Research (CERR) platform was extended for deploying deep learning-based segmentation models to leverage CERR’s existing functionality for radiological data import, transformation, management, and visualization. The framework is compatible with MATLAB as well as GNU Octave and Python for license-free use. Pre and post processing configurations including parameters for pre-processing images, population of channels, and post-processing segmentations was standardized using JSON format. CPU and GPU implementations of pre-trained deep learning segmentation models were packaged using Singularity containers for use in Linux and Conda environment archives for Windows, macOS and Linux operating systems. The framework accepts images in various formats including DICOM and CERR’s planC and outputs segmentation in various formats including DICOM RTSTRUCT and planC objects. The ability to access the results readily in planC format enables visualization as well as radiomics and dosimetric analysis. The framework can be readily deployed in clinical software such as MIM via their extensions.ResultsThe open-source, GPL copyrighted framework developed in this work has been successfully used to deploy Deep Learning based segmentation models for five in-house developed and published models. These models span various treatment sites (H&N, Lung and Prostate) and modalities (CT, MR). Documentation for their usage and demo workflow is provided athttps://github.com/cerr/CERR/wiki/Auto-Segmentation-models. The framework has also been used in clinical workflow for segmenting images for treatment planning and for segmenting publicly available large datasets for outcomes studies.ConclusionsThis work presented a comprehensive, open-source framework for deploying deep learning-based medical image segmentation models. The framework was used to translate the developed models to clinic as well as reproducible and consistent image segmentation across institutions, facilitating multi-institutional outcomes modeling studies.
Accounting for errors in data improves timing in single-cell cancer evolution
Authors: Chen Kylie , Moravec Jiří C. , Gavryushkin Alex , Welch David , Drummond Alexei J. 
Published: March 2021

AbstractSingle-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data is more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.
Combined docking and machine learning identifies key molecular determinants of ligand pharmacological activity on β2 adrenoceptor
Authors: Jiménez-Rosés Mireia , Morgan Bradley Angus , Sigstad Maria Jimenez , Zoe Tran T.D. , Srivastava Rohini , Bunsuz Asuman , Borrega-Román Leire , Hompluem Pattarin , Cullum Sean A. , Harwood Clare R. , Koers Eline J. , Sykes David A. , Styles Iain B. , Veprintsev Dmitry B. 
Published: March 2021

AbstractG protein coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands which bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesised that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75,000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure-activity relationship that identifies small changes to the ligands that invert their activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.
Accessibility to sequential working memory fluctuates unconsciously in a theta phase-dependent manner
Authors: Ideriha Takuya , Ushiyama Junichi 
Published: March 2021

ABSTRACTWorking memory is active short-term memory storage that is easily accessible for later utilization. There is emerging evidence that memorized items are represented rhythmically on the specific phase of the theta-band (4–7 Hz) neural oscillation. However, it is still unknown how this process impacts the accessibility to the active memory storage. Here we show that simply memorizing sequential information causes theta-band fluctuation in our behaviour (i.e., reaction time, RT). We measured RTs to a visual probe that appeared at sequentially memorized locations after a random interval. Consequently, RTs to the probes fluctuated in the theta range as a function of the random interval, and the behavioural rhythmicity supported the hypothesis of the phase-dependent coding of sequential working memory. The current results demonstrate that our behaviour fluctuates unconsciously in the theta-range when recalling sequential memory, suggesting that accessibility to sequential working memory is rhythmic rather than stable, possibly reflecting theta-phase dependent coding.
Conformational specificity of opioid receptors is determined by subcellular location irrespective of agonist
Authors: Crilly Stephanie E. , Ko Wooree , Weinberg Zara Y. , Puthenveedu Manojkumar A. 
Published: March 2021

AbstractThe prevailing model for the variety in drug responses is that they stabilize distinct active states of their G protein-coupled receptor (GPCR) targets, allowing coupling to different effectors. However, whether the same ligand can produce different GPCR active states based on the environment of receptors in cells is a fundamental unanswered question. Here we address this question using live cell imaging of conformational biosensors that read out distinct active conformations of the δ-opioid receptor (DOR), a physiologically relevant GPCR localized to Golgi and the surface in neurons. We show that, although Golgi and surface pools of DOR regulated cAMP, the two pools engaged distinct conformational biosensors in response to the same ligand. Further, DOR recruited arrestin on the plasma membrane but not the Golgi. Our results suggest that the same agonist drives different conformations of a GPCR at different locations, allowing receptor coupling to distinct effectors at different locations.
Identification of a Major Locus for Flowering Pattern Sheds Light on Plant Architecture Diversification in Cultivated Peanut
Authors: Kunta Srinivas , Chu Ye , Levy Yael , Harel Arye , Abbo Shahal , Ozias-Akins Peggy , Hovav Ran 
Published: March 2021

AbstractFlowering pattern is a major taxonomic characteristic differentiating the two main subspecies of cultivated peanut (Arachis hypogaeaL.). subsp.fastigiatapossessing flowers on the mainstem (MSF) and a sequential flowering pattern, whereas subsp.hypogaealacks flowers on the mainstem and exhibits an alternate flowering pattern. This character is considered the main contributor to plant architecture and the adaptability of each subgroup to specific growing conditions. Evidence indicates that flowering pattern differentiation occurred during the several thousand years of domestication and diversification in South America. However, the exact genetic mechanism that controls flowering pattern and the molecular changes that led to its historical diversification in peanut are unknown. We investigated the genetics of the flowering pattern in a recombinant inbred population of 259 lines (RILs), derivatives of anA. hypogaeaandA. fastigiatacross. RILs segregated 1:1 in both the sequential/alternative and the MSF-plus/MSF-minus traits, indicating a single gene effect. Using the Axiom_Arachis2 SNP-array, MSF was mapped to a 1.7 Mbp segment on chromosome B02 of the cultivatedA. hypogaea. Significant haplotype conservation was found for this locus in the USA peanut mini core collection, suggesting a possible selection uponhypogaea/fastigiataspeciation. Furthermore, a candidateTerminal Flowering 1-like(AhTFL1) gene was identified within the MSF region, in which a 1492 bp deletion occurred in thefastigiataline that leads to a truncated protein product. Remapping MSF in the RIL population with theAhTFL1deletion as a marker increased the LOD score from 53.3 to 158.8 with no recombination. The same deletion was also found to co-segregate with the phenotype in two EMS-mutagenized M2 families, suggesting a hotspot for large mutational deletion or gene conversion that may play a role in evolution. BLASTX analysis showed that the most similar homologous gene forTFL1-likein soybean isDet1, which previously was shown to control shoot determination. Sequence analysis of theTFL-1in a series of domesticated lines showed thatTFL1was subjected to gain/loss events of the deletion, partly explaining the evolution of MSF in Arachis. Altogether, these results support the role ofAhTFL-1in peanut speciation during domestication and modern cultivation.
Natal and adult dispersal among four elephant seal colonies
Authors: Zeno Ramona , Condit Richard , Allen Sarah G. , Duncan Garrett 
Published: March 2021

AbstractDispersal plays a key role fostering recovery of endangered species because reoccupying a former range can only happen via dispersal. The northern elephant seal (Mirounga angustirostris) is a large, marine predator that was nearly exterminated in the 19th century by over-hunting. Once they were afforded protection from harvest, the species spread from a single remnant colony to reoccupy its former range. As colonies in central California were reestablished during the 1960s-1990s, tagged seals documented northward dispersal from southern California. The central California colonies are now large and well-established, and tagging programs at the four northernmost colonies allowed us to quantify the extent and direction of dispersal. Natal dispersal by females was highest from the southernmost colony at Piedras Blancas, where 61% of surviving females emigrated to breed. Dispersal from the other three colonies was much lower, 5.6% from SE Farallon Island, 10.3% from Año Nuevo, and 16.6% from Point Reyes. Adult dispersal of females, after breeding, was rare, with an annual rate < 2%. Juvenile dispersal is thus frequent in elephant seals, highest northward but also occurring southward, suggesting that continued expansion to new colonies throughout the west coast is probable.
A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images
Authors: Su Andrew , Lee HoJoon , Tan Xiao , Suarez Carlos J. , Andor Noemi , Nguyen Quan , Ji Hanlee P. 
Published: March 2021

ABSTRACTDeep learning cancer classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches depends on prior annotation through a pathologist. This initial step relying on a manual, low-resolution, time-consuming process is highly variable and subject to observer variance. To address this issue, we developed a novel method, H&E Molecular neural network (HEMnet). This two-step process utilises immunohistochemistry as an initial molecular label for cancer cells on a H&E image and then we train a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, we show that HEMnet accurately distinguishes colorectal cancer from normal tissue at high resolution without the need for an initial manual histopathologic evaluation. Our validation study using histopathology images from TCGA samples accurately estimates tumour purity. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at:https://github.com/BiomedicalMachineLearning/HEMnet
Transcriptomic analysis of temporal shifts in berry development between two grapevine cultivars of the Pinot family reveals potential genes controlling ripening time
Authors: Theine Jens , Holtgräwe Daniela , Herzog Katja , Schwander Florian , Kicherer Anna , Hausmann Ludger , Viehöver Prisca , Töpfer Reinhard , Weisshaar Bernd 
Published: March 2021

AbstractBackgroundGrapevine cultivars of the Pinot family represent clonally propagated mutants with major phenotypic and physiological differences, such as different colour or shifted ripening time, as well as changes in important viticultural traits. Specifically, the cultivars ‘Pinot Noir’ (PN) and ‘Pinot Noir Precoce’ (PNP, early ripening) flower at the same time, but vary in the beginning of berry ripening (veraison) and, consequently, harvest time. In addition to genotype, seasonal climatic conditions (i.e. high temperatures) also affect ripening times. To reveal possible regulatory genes that affect the timing of veraison onset, we investigated differences in gene expression profiles between PN and PNP throughout berry development with a closely meshed time series and over two separate years.ResultsThe difference in the duration of berry formation between PN and PNP was quantified to be approximately two weeks under the growth conditions applied, using plant material with a proven PN and PNP clonal relationship. Clusters of co-expressed genes and differentially expressed genes (DEGs) were detected which reflect the shift in the timing of veraison onset. Functional annotation of these DEGs fit to observed phenotypic and physiological changes during berry development. In total, we observed 3,342 DEGs in 2014 and 2,745 DEGs in 2017 between PN and PNP, with 1,923 DEGs across both years. Among these, 388 DEGs were identified as veraison-specific and 12 were considered as berry ripening time regulatory candidates. The expression profiles revealed two candidate genes for ripening time control which we designatedVviRTIC1andVviRTIC2(VIT_210s0071g01145 and VIT_200s0366g00020, respectively). These genes likely contribute the phenotypic differences observed between PN and PNP.ConclusionsMany of the 1,923 DEGs show highly similar expression profiles in both cultivars if the patterns are aligned according to developmental stage. In our work, putative genes differentially expressed between PNP and PN which could control ripening time as well as veraison-specific genes were identified. We point out connections of these genes to molecular events during berry development and discuss potential candidate genes which may control ripening time. Two of these candidates were observed to be differentially expressed in the early berry development phase. Several down-regulated genes during berry ripening are annotated as auxin response factors / ARFs. Conceivably, general changes in auxin signaling may cause the earlier ripening phenotype of PNP.
Facilitating the design of combination therapy in cancer using multipartite network models: Emphasis on acute myeloid leukemia
Authors: Jafari Mohieddin , Mirzaie Mehdi , Bao Jie , Barneh Farnaz , Zheng Shuyu , Eriksson Johanna , Tang Jing 
Published: March 2021

AbstractFrom the drug discovery perspective, combination therapy is recommended in cancer due to efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time- and cost-consuming. Here, we offer a novel strategy of predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug response data on patient samples. In the present study, we used Beat AML and GDSC, two comprehensive datasets based on patient-derived and cell line-based samples, to show the potential of multipartite network modeling in cancer combinatorial therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network, which models the interaction of drugs and biological samples. Then, clusters of network communities were identified in two projected networks based on the topological structure of networks. Chemical structures, drug-target networks, protein-protein interactions, and signaling networks were used to corroborate the intra-cluster homogeneity. We further leveraged the community structures within the drug-based multipartite networks to discover effective multi-targeted drug combinations, and the synergy levels which were supported with more evidence using the DrugComb and the ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapyin vitroexperiment using three acute myeloid leukemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve treatment of AML.
Structure-function analysis of purified proanthocyanidins reveals a role for polymer size in suppressing inflammatory responses
Authors: Andersen-Civil Audrey Inge Schytz , Leppä Milla Marleena , Thamsborg Stig M. , Salminen Juha-Pekka , R.Williams Andrew 
Published: March 2021

AbstractProanthocyanidins (PAC) are dietary compounds that have been extensively studied for beneficial health effects due to their anti-inflammatory properties. However, the structure-function relationships of PAC and their mode-of-action remain obscure. Here, we isolated a wide range of diverse PAC polymer mixtures of unprecedented purity from plant material. Polymer size was a key factor in determining the ability of PAC to regulate inflammatory cytokine responses in murine macrophages. PAC polymers with a medium (9.1) mean degree of polymerization (mDP) induced substantial transcriptomic changes, whereas PAC with either low (2.6) or high (12.3) mDP were significantly less active. Short-term oral treatment of mice with PAC modulated gene pathways connected to nutrient metabolism and inflammation in ileal tissue in a polymerization-dependent manner. Mechanistically, the bioactive PAC polymers modulated autophagic flux and directly inhibited lipopolysaccharide-induced autophagy. Collectively, our results highlight the importance of defined structural features in the health-promoting effects of PAC-rich foods.
RNase III-mediated processing of atrans-acting bacterial sRNA and itscis-encoded antagonist
Authors: Svensson Sarah L. , Sharma Cynthia M. 
Published: March 2021

ABSTRACTBacterial small RNAs (sRNAs) are important post-transcriptional regulators in stress responses and virulence. They can be derived from an expanding list of genomic contexts, such as processing from parental transcripts by RNase E. The role of RNase III in sRNA biogenesis is less well understood despite its well-known roles in rRNA processing, RNA decay, and cleavage of sRNA-mRNA duplexes. Here, we show that RNase III processes a pair ofcis-encoded sRNAs (CJnc190 and CJnc180) of the foodborne pathogenCampylobacter jejuni. While CJnc180 processing by RNase III requires CJnc190, In contrast, RNase III processes CJnc190 independent of CJnc180 via cleavage of an intramolecular duplex. We also show that CJnc190 directly represses translation of the colonization factor PtmG by targeting a G-rich ribosome binding site, and uncover that CJnc180 is acis-acting antagonist of CJnc190, indirectly affectingptmGregulation. Our study highlights a role for RNase III in sRNA biogenesis and addscis-encoded RNAs to the expanding diversity of transcripts that antagonize bacterial sRNAs.