bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Multichannel Artifact Prediction and Removal for Brain Machine Interfaces and Neural Prosthetics
Authors: Sadeghi Najafabadi Mina , Chen Longtu , Dutta Kelsey , Norris Ashley , Feng Bin , Schnupp Jan WH , Rosskothen-Kuhl Nicole , Read Heather , Escabi Monty 
Published: October 2019

ABSTRACTNeural implants that electrically stimulate neural tissue such as deep brain stimulators, cochlear implants (CI), and vagal nerve stimulators are becoming the routine treatment options for various diseases. Optimizing electrical stimulation paradigms requires closed-loop stimulation using simultaneous recordings of evoked neural activity in real time. Stimulus-evoked artifacts at the recording site are generally orders of magnitude larger than the neural signals, which challenge the interpretation of evoked neural activity. We developed a generalized artifact removal algorithm that can be applied in a variety of neural recording modalities. The procedure leverages known electrical stimulation currents to derive optimal filters that are used to predict and remove artifacts. We validated the procedure using paired recordings and electrical stimulation from sciatic nerve axons, high-rate bilateral CI stimulation, and concurrent multichannel stimulation in auditory midbrain and recordings in auditory cortex. We demonstrate a vast enhancement in the quality of recording even for high-throughput multi-site stimulation with typical improvements in the signal-to-noise ratio between 20-40 dB. The algorithm is efficient, can be scaled to arbitrary number of sites, and is applicable in range of recording modalities. It has numerous benefits over existing approaches and thus should be valuable for emerging neural recording and stimulation technologies.
Individual neuronal subtypes control initial myelin sheath growth and stabilization
Authors: Nelson Heather N. , Treichel Anthony J. , Eggum Erin N. , Martell Madeline R. , Kaiser Amanda J. , Trudel Allie G. , Gronseth James R. , Maas Samantha T. , Bergen Silas , Hines Jacob H. 
Published: October 2019

AbstractBackgroundIn the developing central nervous system, pre-myelinating oligodendrocytes sample candidate nerve axons by extending and retracting process extensions. Some contacts stabilize, leading to the initiation of axon wrapping, nascent myelin sheath formation, concentric wrapping and sheath elongation, and sheath stabilization or pruning by oligodendrocytes. Although axonal signals influence the overall process of myelination, the precise oligodendrocyte behaviors that require signaling from axons are not completely understood. In this study, we investigated whether oligodendrocyte behaviors during the early events of myelination are mediated by an oligodendrocyte-intrinsic myelination program or are over-ridden by axonal factors.MethodsTo address this, we utilized in vivo time-lapse imaging in embryonic and larval zebrafish spinal cord during the initial hours and days of axon wrapping and myelination. Transgenic reporter lines marked individual axon subtypes or oligodendrocyte membranes.ResultsIn the larval zebrafish spinal cord, individual axon subtypes supported distinct nascent sheath growth rates and stabilization frequencies. Oligodendrocytes ensheathed individual axon subtypes at different rates during a two-day period after initial axon wrapping. When descending reticulospinal axons were ablated, local spinal axons supported a constant ensheathment rate despite the increased ratio of oligodendrocytes to target axons.ConclusionWe conclude that properties of individual axon subtypes instruct oligodendrocyte behaviors during initial stages of myelination by differentially controlling nascent sheath growth and stabilization.
Presence or absence of Ras-dimerization shows distinct kinetic signature in Ras-Raf interaction
Authors: Sarkar Sumantra , Garcia Angel E. 
Published: October 2019

AbstractIn eukaryotes, MAPK pathways play a central role in making several important cellular decisions, including cell proliferation and development of an organism. Ras, a small GTPase, interacts with the protein Raf to create activated Ras-Raf complex (Raf dimer) that activates the downstream effectors in the ERK pathway, one of the many MAPK pathways. Malfunctioning Ras-Raf “switches” cause almost 30% of all known cancer. Hence, understanding Ras-Raf interaction is of paramount importance. Despite decades of research, the detailed mechanism of Ras-Raf interaction is still unclear. It has been hypothesized that Ras dimerization is necessary to create the activated Raf dimer. Although there are circumstantial evidences supporting the Ras dimerization hypothesis, direct proof of Ras dimerization is still inconclusive. In the absence of conclusive direct experimental proof, this hypothesis can only be examined through indirect evidences of Ras dimerization. In this paper, using a multi-scale simulation technique, we provide multiple criteria that distinguishes an activation mechanism involving Ras dimerization from another mechanism that does not involve Ras dimerization. The provided criteria will be useful in the investigation of not only Ras-Raf interaction but also other two-protein interactions.
Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction
Authors: Cheng Li-Hsin , Lin Che 
Published: October 2019

AbstractMotivationBreast cancer is a heterogeneous disease. In order to guide proper treatment decisions for each individual patient, there is an urgent need for robust prognostic biomarkers that allow reliable prognosis prediction. Gene feature selection on microarray data is an approach to systematically discover potential biomarkers. However, common pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and thus tend to select genes that lack biological insights. In addition, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We therefore combined systems biology feature selection with ensemble learning in this study, aiming to address the above challenges and select genes with biological insights, as well as robust prognostic predictive power. Moreover, in order to capture the complex molecular processes of breast cancer, where multiple disease-contributing genes may exist and interact, we adopted a multi-gene approach to predict the prognosis status using machine learning classifiers.ResultsWe systematically evaluated three different ensemble approaches that all improved the original systems biology feature selector. We found that compared to the most popular data-perturbation approach, function perturbation can produce significant improvement with just a few ensembles. Among all, the hybrid ensemble approach led to the most robust feature selection result, and the identified genes were shown to be highly involved in pathways, such as ubiquitination and cell cycle. Final prognosis prediction models were constructed using the identified genes and clinical information as input features. Among all models, bimodal deep neural network (DNN) achieved the highest AUC (area under receiver operating characteristic curve) in test performance evaluation, where subsequent survival analysis also verified its ability to differentiate patients with different prognosis statuses. In summary, the study demonstrated the potential of ensemble learning to improve gene feature selection robustness, as well as the potential of bimodal DNN in providing reliable prognosis prediction and guiding precision medicine.
Paradigm of Vanadium pentoxide nanoparticle-induced autophagy and apoptosis in triple-negative breast cancer cells
Authors: Pillai Suma Parvathy Radhakrishna , Padmanabhan Renjini A. , Telukutla Srinivasa Reddy , Ravindran Rishith , Velikkakath Anoop Kumar G. , Dekiwadia Chaitali D. , Paul Willi , Shenoy Sachin J. , Laloraya Malini , Srinivasula Srinivasa M. , Bhosale Sheshanath V. , Jayasree Ramapurath S. 
Published: October 2019

AbstractChemo-resistance remains the main hurdle to cancer therapy, challenging the improvement of clinical outcomes in cancer patients. Therefore, exploratory studies to address chemo-resistance through various approaches are highly rewarding. Nanomedicine is a promising recent advancement in this direction. Comprehensive studies to understand the precise molecular interactions of nanomaterials is necessary to validate their specific “nano induced” effects. Here, we illustrate in detail the specific biological interactions of vanadium pentoxide nanoparticles (VnNp) on triple-negative breast cancer cells and provide initial insights towards its potential in breast cancer management at the cellular level. VnNp shows a time-dependent anti-oxidant and pro-oxidant property in vitro. These nanoparticles specifically accumulate in the lysosomes and mitochondria, modulate various cellular processes including impaired lysosomal function, mitochondrial damage, and induce autophagy. At more extended periods, VnNp influences cell cycle arrest and inhibits cell migration potentiating the onset of apoptosis. Preliminary in vivo studies, on exposing healthy Swiss albino mice to VnNp demonstrated normal blood parameters, organ distribution, and tissue redox balance which further indicated the absence of any adverse organ toxicity. Hence, we foresee tumor-targeting VnNp as a potential drug molecule for future cancer management.
Estimates of cortical column orientation improve MEG source inversion
Authors: Bonaiuto James J , Afdideh Fardin , Ferez Maxime , Wagstyl Konrad , Mattout Jérémie , Bonnefond Mathilde , Barnes Gareth R , Bestmann Sven 
Published: October 2019

AbstractDetermining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.
Mechano-modulatory synthetic niches for liver organoid derivation
Authors: Sorrentino Giovanni , Rezakhani Saba , Yildiz Ece , Nuciforo Sandro , Heim Markus H. , Lutolf Matthias P. , Schoonjans Kristina 
Published: October 2019

AbstractThe recent demonstration that primary cells from the liver can be expanded in vitro as organoids holds enormous promise for regenerative medicine and disease modeling1–5. The use of three-dimensional (3D) cultures based on ill-defined and potentially immunogenic matrices, however, hampers the translation of liver organoid technology into real-life applications6. We here used chemically defined hydrogels for the efficient derivation of both mouse and human hepatic organoids. Organoid growth was found to be highly stiffness-sensitive and dependent on yes-associated protein 1 (YAP) activity. However, in contrast to intestinal organoids7, YAP-mediated stiffness sensitivity was independent of acto-myosin contractility, requiring instead activation of the Src family of kinases (SFKs). Aberrant matrix stiffness on the other hand led to a shift in the progenitor phenotype, resulting in compromised proliferative capacity. Finally, we demonstrate the unprecedented establishment of biopsy-derived human liver organoids without the use of animal components at any step of the process. Our approach thus opens up exciting perspectives for the establishment of protocols for liver organoid-based regenerative medicine.
Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain at different anatomic sites
Authors: Tsepilov Yakov A. , Freidin Maxim B. , Shadrina Alexandra S. , Sharapov Sodbo Z. , Elgaeva Elizaveta E. , van Zundert Jan , Karssen Lennart C. , Suri Pradeep , Williams Frances M.K. , Aulchenko Yurii S. 
Published: October 2019

AbstractChronic musculoskeletal pain has a negative impact on all aspects of human life. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. In this research, we aimed to reduce phenotype heterogeneity and reveal genes and pathways shared by chronic musculoskeletal pain at four locations: back, neck/shoulder, hip, and knee. Our study was based on the results of genome-wide association studies performed using UK Biobank data with a total sample size of 456,000 individuals. We applied principal component analysis based on the matrix of genetic covariances between the studied pain traits and constructed four genetically independent phenotypes (GIPs). The leading GIP (GIP1) explains the largest proportion of the genetic variance of and covariance between the analyzed phenotypes (78.4%), and the later GIPs (GIP2-4) explain progressively less. We identified and replicated five loci associated with GIP1 and one locus associated with GIP2. The genes decisively prioritized for the GIP1-associated loci were SLC39A8, ECM1, and FOXP2. For the remaining two GIP1-associated loci, we proposed several candidates (AMIGO3, BSN, RBM6, FAM212A, RNF123, UBA7 and MIR7114, NSMF, NOXA1, GRIN1), but were unable to prioritize any of them convincingly. The most likely causal gene in the locus associated with GIP2 was GDF5. For GIP1, gene set/tissue/cell type enrichment analyses identified multiple terms related to the nervous system. Genetic correlations analysis revealed a genetic overlap between GIP1 and osteoarthritis as well as a set of anthropometric (such as overweight and waist circumference), sociodemographic (such as age of first birth and college completion) and psychiatric/personality (such as depressive symptoms and neuroticism) traits. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and likely reflecting pain perception and processing.
Empirical Analysis of Phylogenetic Quasi-Terraces
Authors: Breitling Paula , Stamatakis Alexandros , Chernomor Olga , Bettisworth Ben , Reszczynski Lukasz 
Published: October 2019

AbstractTerraces in phylogenetic tree space are, among other things, important for the design of tree space search strategies. While the phenomenon of phylogenetic terraces is already known for unlinked partition models on partitioned phylogenomic data sets, it has not yet been studied if an analogous structure is present under linked and scaled partition models. To this end, we analyze aspects such as the log-likelihood distributions, likelihood-based significance tests, and nearest neighborhood interchanges on the trees residing on a terrace and compare their distributions among unlinked, linked, and scaled partition models. Our study shows that there exists a terrace-like structure under linked and scaled partition models as well. We denote this phenomenon as quasi-terrace. Therefore quasi-terraces should be taken into account in the design of tree search algorithms as well as when reporting results on ‘the’ final tree topology in empirical phylogenetic studies.
Tandem paired nicking promotes precise genome editing with scarce interference by p53
Authors: Hyodo Toshinori , Rahman Md Lutfur , Karnan Sivasundaram , Ito Takuji , Toyoda Atsushi , Ota Akinobu , Wahiduzzaman Md , Tsuzuki Shinobu , Okada Yohei , Hosokawa Yoshitaka , Konishi Hiroyuki 
Published: October 2019

SummaryTargeted knock-in mediated by double-stranded DNA cleavage is accompanied by unwanted insertions and deletions (indels) at on-target and off-target sites. A nick-mediated approach scarcely generates indels but exhibits reduced efficiency of targeted knock-in. Here, we demonstrate that tandem paired nicking, a method for targeted knock-in involving two Cas9 nickases that create nicks at the homologous regions of the donor DNA and the genome in the same strand, scarcely creates indels at the edited genomic loci, while permitting the efficiency of targeted knock-in largely equivalent to that of the Cas9 nuclease-based approach. Tandem paired nicking seems to accomplish targeted knock-in via DNA recombination analogous to Holliday’s model, and creates intended genetic changes in the genome without introducing additional nucleotide changes such as silent mutations. Targeted knock-in through tandem paired nicking neither triggers significant p53 activation nor occurs preferentially in p53-suppressed cells. These properties of tandem paired nicking demonstrate its utility in precision genome engineering.
Genotypic Variation in Below-to Aboveground Systemic Induction of Glucosinolates Mediates Plant Fitness Consequences under Herbivore Attack
Authors: Bakhtiari Moe , Rasmann Sergio 
Published: October 2019

AbstractPlants defend themselves against herbivore attack by constitutively producing toxic secondary metabolites, as well as by inducing them during herbivore feeding. Induction of secondary metabolites can cross plant tissue boundaries, such as from root to shoot. However, whether the potential for plants to systemically induce secondary metabolites from roots to shoots shows genetic variability, and thus, potentially, is under selection conferring fitness benefits to the plants is an open question. To address this question, we induced 26 maternal plant families of the wild speciesCardamine hirsutabelowground (BG) using the wound-mimicking phytohormone jasmonic acid (JA). We measured resistance against a generalist (Spodoptera littoralis) and a specialist (Pieris brassicae) herbivore species, as well as the production of glucosinolates (GSLs) in plants. We showed that BG induction increased AG resistance against the generalist but not against the specialist, and found substantial plant family-level variation for resistance and GSL induction. We further found that the systemic induction of several GSLs tempered the negative effects of herbivory on total seed set production. Using a widespread natural system, we thus confirm that BG to AG induction has a strong genetic component, and it can be under positive selection by increasing plant fitness. We suggest that natural variation in systemic induction is in part dictated by allocation trade-offs between constitutive and inducible GSL production, as well as natural variation in AG and BG herbivore attack in nature.
Proteomic and metabolomic profiling underlines the stage- and time-dependent effects of high temperature on grape berry metabolism
Authors: Lecourieux David , Kappel Christian , Claverol Stéphane , Pieri Philippe , Feil Regina , Lunn John E. , Bonneu Marc , Wang Lijun , Gomès Eric , Delrot Serge , Lecourieux Fatma 
Published: October 2019

ABSTRACTClimate change scenarios predict an increase in mean air temperatures and in the frequency, intensity, and length of extreme temperature events in many wine-growing regions worldwide. Because elevated temperature has detrimental effects on the berry growth and composition, it threatens the economic and environmental sustainability of wine production. Using Cabernet Sauvignon fruit-bearing cuttings, we investigated the effects of high temperature (HT) on grapevine berries through a label-free shotgun proteomic analysis coupled to a complementary metabolomic study. Among the 2279 proteins identified, 592 differentially abundant proteins were found in berries exposed to HT. The gene ontology categories “Stress”, “Protein”, “Secondary metabolism” and “Cell wall” were predominantly altered under HT. High temperatures strongly impaired carbohydrate and energy metabolism, and the effects depended on the stage of development and duration of treatment. Transcript amounts correlated poorly with protein expression levels in HT berries, highlighting the value of proteomic studies in the context of heat stress. Furthermore, this work reveals that HT alters key proteins driving berry development and ripening. Finally, we provide a list of differentially abundant proteins that can be considered as potential markers for developing or selecting grape varieties that are better adapted to warmer climates or extreme heat waves.
AMR - An R Package for Working with Antimicrobial Resistance Data
Authors: Berends Matthijs S. , Luz Christian F. , Friedrich Alexander W. , Sinha Bhanu N.M. , Albers Casper J. , Glasner Corinna 
Published: October 2019

AbstractAntimicrobial resistance is an increasing threat to global health. Evidence for this trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and guidelines are in place for this process as well as for reporting data on (inter-)national levels. However, there is a gap in the availability of standardized and reproducible tools for working with laboratory data to produce the required reports. It is known that extensive efforts in data cleaning and validation are required when working with data from laboratory information systems. Furthermore, the global spread and relevance of antimicrobial resistance demands to incorporate international reference data in the analysis process.In this paper, we introduce the AMR package for R that aims at closing this gap by providing tools to simplify antimicrobial resistance data cleaning and analysis, while incorporating international guidelines and scientifically reliable reference data. The AMR package enables standardized and reproducible antimicrobial resistance analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. The AMR package works independently of any laboratory information system and provides several functions to integrate into international workflows (e.g., WHONET software provided by the World Health Organization).