bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of mentee and mentor gender on scientific productivity of applicants for NIH training fellowships
Authors: Yu Hao , Willis Kristine A. , Litovitz Aviva , Harriman Robert M. , Davis Matthew T. , Meyer Payam , Busse Brad , Meseroll Rebecca A. , Wijayatilake Hashanthi D. , Perkins Matthew J. , Anderson James M. , Santangelo George M. 
Published: February 2021

AbstractSeveral studies have suggested that women in science are less productive than men, and that this gap contributes to their under-representation in the ranks of senior researchers. However, few studies have examined the role of mentoring, and in particular mentor gender, on the productivity of female scientists early in their careers. Such efforts are limited by the difficulties of unambiguously linking mentees to their mentors and measuring the research productivity resulting from those relationships. Here we use our novel author disambiguation solution to investigate the role of self-identified gender in mentorship of 12,932 trainees who either successfully or unsuccessfully applied to the National Institutes of Health for research fellowships between fiscal years 2011 and 2017, applying a multi-dimensional framework to assess productivity. We found that, after normalizing for the funding level of mentors, the productivity of female and male mentees is indistinguishable; it is also independent of the gender of the mentor, other than in measures of clinical impact, where women mentored by women outperform other mentee-mentor dyads.
Mitochondrial ATP Synthase Subunit d, a Component of the Peripheral Stalk, is Essential for Growth and Heat Stress Tolerance inArabidopsis thaliana
Authors: Liu Tianxiang , Arsenault Jesse , Vierling Elizabeth , Kim Minsoo 
Published: February 2021

SUMMARYAs rapid changes in climate threaten global crop yields, an understanding of plant heat stress tolerance is increasingly relevant. Heat stress tolerance involves the coordinated action of many cellular processes and is particularly energy demanding. We acquired a knockout mutant and generated knockdown lines inArabidopsis thalianaof the d subunit of mitochondrial ATP synthase (gene name:ATPQ, AT3G52300, referred to hereafter asATPd), a subunit of the peripheral stalk, and used these to investigate the phenotypic significance of this subunit in normal growth and heat stress tolerance. Homozygous knockout mutants forATPdcould not be obtained due to gametophytic defects, while heterozygotes possess no visible phenotype. Therefore, we used RNAi to create knockdown plant lines for further studies. Proteomic analysis and blue native gels revealed thatATPddownregulation impairs only subunits of the mitochondrial ATP synthase (complex V of the electron transport chain). Knockdown plants were more sensitive to heat stress, had abnormal leaf morphology, and were severely slow growing compared to wild type. These results indicate thatATPdplays a crucial role in proper function of the mitochondrial ATP synthase holoenzyme, which, when reduced, leads to wide-ranging defects in energy-demanding cellular processes. In knockdown plants, more hydrogen peroxide accumulated and mitochondrial dysfunction stimulon (MDS) genes were activated. These data establish the essential structural role ofATPdand provide new information about complex V assembly and quality control, as well as support the importance of mitochondrial respiration in normal plant growth and heat stress tolerance.SIGNIFICANCE STATEMENTThe energy converter, mitochondrial ATP synthase, is critical for all organisms, but the functional importance of ATP synthase subunit d remains largely unknown in plants. We demonstrate the contributions of subunit d to plant growth, development, and heat stress tolerance, as well as to ATP synthase stability, ROS signaling and mitochondrial dysfunction regulation.
Autophagy as a mechanism for adaptive prediction-mediated emergence of drug resistance
Authors: Nivedita Nivedita , Aitchison John D. , Baliga Nitin S. 
Published: February 2021

ABSTRACTDrug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-FOA). Here, we demonstrate that mutations selected across multiple laboratory evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. We propose a model to explain how extensive genome-wide genetic interactions of autophagy facilitates emergence of AP over short evolutionary timescales to potentiate selection of resistance-conferring mutations.
Improving deep learning-based protein distance prediction in CASP14
Authors: Guo Zhiye , Wu Tianqi , Liu Jian , Hou Jie , Cheng Jianlin 
Published: February 2021

AbstractAccurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the F1 measure. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps.
Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning
Authors: Chen Qiaomin , Zheng Bangyou , Chen Tong , Chapman Scott 
Published: February 2021

AbstractA major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. This can be addressed by using radiative transfer models (RTMs) to generate training dataset representing ‘real-world’ data in situations with varying crop types and growth status as well as various observation configurations. However, this approach can lead to “ill-posed” problems related to assumptions in the sampling strategy and due to uncertainty in the model, resulting in unsatisfactory inversion results for retrieval of target variables. In order to address this problem, this research investigates a practical way to generate higher quality ‘synthetic’ training data by integrating a crop growth model (CGM, in this case APSIM) with an RTM (in this case PROSAIL). This allows control of uncertainties of the RTM by imposing biological constraints on distribution and co-distribution of related variables. Subsequently, the method was theoretically validated on two types of synthetic dataset generated by PROSAIL or the coupling of APSIM and PROSAIL through comparing estimation precision for leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter (Cm) and leaf water content (Cw). Additionally, the capabilities of current deep learning techniques using high spectral resolution hyperspectral data were investigated. The main findings include: (1) Feedforward neural network (FFNN) provided with appropriate configuration is a promising technique to retrieve crop traits from input features consisting of 1 nm-wide hyperspectral bands across 400-2500 nm range and observation configuration (solar and viewing angles), leading to a precise joint estimation forLAI(RMSE=0.061 m2m-2),Cab(RMSE=1.42 µg cm-2),Cm(RMSE=0.000176 g cm-2) andCw(RMSE=0.000319 g cm-2); (2) For the aim of model simplification, a narrower range in 400-1100 nm without observation configuration in input of FFNN model provided less precise estimation forLAI(RMSE=0.087 m2m-2),Cab(RMSE=1.92 µg cm-2),Cm(RMSE=0.000299 g cm-2) andCw(RMSE=0.001271 g cm-2); (3) The introduction of biological constraints in training datasets improved FFNN model performance in both average precision and stability, resulting in a much accurate estimation forLAI(RMSE=0.006 m2m-2),Cab(RMSE=0.45 µg cm-2),Cm(RMSE=0.000039 g cm-2) andCw(RMSE=0.000072 g cm-2), and this improvement could be further increased by enriching sample diversity in training dataset.
Year-round dynamics of amplicon sequence variant communities differ among eukaryotes,Mimiviridae, and prokaryotes in a coastal ecosystem
Authors: Prodinger Florian , Endo Hisashi , Takano Yoshihito , Li Yanze , Tominaga Kento , Isozaki Tatsuhiro , Blanc-Mathieu Romain , Gotoh Yasuhiro , Tetsuya Hayashi , Taniguchi Etsunori , Nagasaki Keizo , Yoshida Takashi , Ogata Hiroyuki 
Published: February 2021

AbstractCoastal seawater is the habitat of different microbial communities. These communities are affected by seasonal environmental changes and fluctuating nutrient availability, as well as competitive and cooperative interspecific interactions. In this work, we investigated the seasonal dynamics of communities of eukaryotes, a major group of double-stranded DNA viruses infecting eukaryotes (i.e.Mimiviridae),as well as prokaryotes in the Uranouchi Inlet, Kochi, Japan. We performed metabarcoding using ribosomal RNA genes and theMimiviridae polBgene as marker genes in 43 seawater samples collected during 20 months. Communities characterized by the compositions of amplicon sequence variants (ASVs) showed synchronic seasonal cycles for eukaryotes,Mimiviridae,and prokaryotes. However, the community dynamics showed intriguing differences in several aspects such as the recovery rate after a year. We further show that the differences in the community dynamics can be explained by differences in the recurrence/persistence levels of individual ASVs among eukaryotes,Mimiviridae,and prokaryotes.MimiviridaeASVs were less persistent than eukaryotic ASVs, and prokaryotic ASVs were the most persistent. We argue that the differences in the specificity of interactions (i.e. virus-eukaryotevsprokaryote-eukaryote) as well as the survival strategies are at the origin of the distinct community dynamics among eukaryotes,Mimiviridae,and prokaryotes.One sentence summaryA one year observation of coastal microbial communities revealed similar but different community dynamics for eukaryotes, a group of large viruses, and prokaryotes.
Central inhibition of P2Y12R differentially regulates survival and neuronal loss in MPTP-induced Parkinsonism in mice
Authors: Iring András , Tóth Adrián , Baranyi Mária , Otrokocsi Lilla , Módis László V. , Gölöncsér Flóra , Varga Bernadett , Hortobágyi Tibor , Bereczki Dániel , Dénes Ádám , Sperlágh Beáta 
Published: February 2021

AbstractParkinson’s disease (PD) is a chronic, progressive neurodegenerative condition; characterized with the degeneration of the nigrostriatal dopaminergic pathway and neuroinflammation. During PD progression, microglia, the resident immune cells in the central nervous system (CNS) display altered activity, but their role in maintaining PD development has remained unclear to date. The purinergic P2Y12 receptor (P2Y12R), which is exclusively expressed on the microglia in the CNS has been shown to regulate microglial activity and responses; however, the function of the P2Y12R in PD is unknown. Here we show that while pharmacological or genetic targeting of P2Y12R previous to disease onset augments acute mortality, these interventions protect against neurodegenerative cell loss and the development of neuroinflammation in vivo. Pharmacological inhibition of receptors during disease development reverses the symptoms of PD and halts disease progression. We found that P2Y12R regulate ROCK and p38 MAPK activity and control cytokine production. Understanding protective and detrimental P2Y12 receptor-mediated actions in the CNS may reveal novel approaches to control neuroinflammation and modify disease progression in PD.
A biomechanical switch regulates the transition towards homeostasis in esophageal epithelium
Authors: McGinn Jamie , Hallou Adrien , Han Seungmin , Krizic Kata , Ulyanchenko Svetlana , Iglesias-Bartolome Ramiro , England Frances J. , Verstreken Christophe , Chalut Kevin J. , Jensen Kim B. , Simons Benjamin D. , Alcolea Maria P. 
Published: February 2021

AbstractEpithelial cells are highly dynamic and can rapidly adapt their behavior in response to tissue perturbations and increasing tissue demands. However, the processes that finely control these responses and, particularly, the mechanisms that ensure the correct switch to and from normal tissue homeostasis are largely unknown. Here we explore changes in cell behavior happening at the interface between postnatal development and homeostasis in the epithelium of the mouse esophagus, as a physiological model exemplifying a rapid but controlled tissue growth transition. Single cell RNA sequencing and histological analysis of the mouse esophagus reveal significant mechanical changes in the epithelium upon tissue maturation. Organ stretching experiments further indicate that tissue strain caused by the differential growth of the mouse esophagus relative to the entire body promotes the emergence of a defined committed population in the progenitor compartment as homeostasis is established. Our results point to a simple mechanism whereby the mechanical changes experienced at the whole tissue level are integrated with those “sensed” at the cellular level to control epithelial cell behavior and tissue maintenance.
Visualizing cellular and tissue ultrastructure using Ten-fold Robust Expansion Microscopy (TREx)
Authors: Damstra Hugo G.J. , Mohar Boaz , Eddison Mark , Akhmanova Anna , Kapitein Lukas C. , Tillberg Paul W. 
Published: February 2021

ABSTRACTExpansion microscopy (ExM) is a powerful technique to overcome the diffraction limit of light microscopy that can be applied in both tissues and cells. In ExM, samples are embedded in a swellable polymer gel to physically expand the sample and isotropically increase resolution in x, y and z. The maximum resolution increase is limited by the expansion factor of the gel, which is four-fold for the original ExM protocol. Variations on the original ExM method have been reported that allow for greater expansion factors but at the cost of ease of adoption or versatility. Here, we systematically explore the ExM recipe space and present a novel method termed Ten-fold Robust Expansion Microscopy (TREx) that, like the original ExM method, requires no specialized equipment or procedures. We demonstrate that TREx gels expand ten-fold, can be handled easily, and can be applied to both thick mouse brain tissue sections and cultured human cells enabling high-resolution subcellular imaging with a single expansion step. Furthermore, we show that TREx can provide ultrastructural context to subcellular protein localization by combining antibody-stained samples with off-the-shelf small molecule stains for both total protein and membranes.
Improved alpha-beta power reduction via combined Electrical and Ultrasonic stimulation in a Parkinsonian Cortex-Basal Ganglia-Thalamus Computational Model
Authors: Tarnaud Thomas , Joseph Wout , Schoeters Ruben , Martens Luc , Tanghe Emmeric 
Published: February 2021

AbstractObjectiveTo investigate computationally the interaction of combined electrical and ultrasonic modulation of isolated neurons and of the Parkinsonian cortex-basal ganglia-thalamus loop.MethodsContinuous-wave or pulsed electrical and ultrasonic neuromodulation is applied to isolated Otsuka plateau-potential generating subthalamic nucleus (STN) and Pospischil regular, fast and low-threshold spiking cortical cells in a temporally-alternating or simultaneous manner. Similar combinations of electrical/ultrasonic waveforms are applied to a Parkinsonian biophysical cortex-basal ganglia-thalamus neuronal network. Ultrasound-neuron interaction is modelled respectively for isolated neurons and the neuronal network with the NICE and SONIC implementations of the bilayer sonophore underlying mechanism. Reduction inα—βspectral energy is used as a proxy to express improvement in Parkinson’s disease by insonication and electrostimulation.ResultsSimultaneous electro-acoustic stimulation achieves a given level of neuronal activity at lower intensities compared to the separate stimulation modalities. Conversely, temporally alternating stimulation with 50 Hz electrical and ultrasound pulses is capable of eliciting 100 Hz STN firing rates. Furthermore, combination of ultrasound with hyperpolarizing currents can alter cortical cell relative spiking regimes. In the Parkinsonian neuronal network, high-frequency pulsed separated electrical and ultrasonic deep brain stimulation (DBS) reduce pathologicalα—βpower by entraining STN-neurons. In contrast, continuous-wave ultrasound reduces pathological oscillations by silencing the STN. Compared to the separated stimulation modalities, temporally simultaneous or alternating electro-acoustic stimulation can achieve higher reductions inα—βpower for the same contraints on electrical/ultrasonic intensity.ConclusionContinuous-wave and pulsed ultrasound reduce pathological oscillations by different mechanisms. Electroacoustic stimulation further improvesα—βpower for given safety limits and is capable of altering cortical relative spiking regimes.Significancefocused ultrasound has the potential of becoming a non-invasive alternative of conventional DBS for the treatment of Parkinson’s disease. Here, we elaborate on proposed benefits of combined electro-acoustic stimulation in terms of improved dynamic range, efficiency, resolution, and neuronal selectivity.
Effects of awareness and task relevance on neurocomputational models of mismatch negativity generation
Authors: Schlossmacher Insa , Lucka Felix , Peters Antje , Bruchmann Maximilian , Straube Thomas 
Published: February 2021

AbstractDetection of regularities and their violations in sensory input is key to perception. Violations are indexed by an early EEG component called the mismatch negativity (MMN) – even if participants are distracted or unaware of the stimuli. On a mechanistic level, two dominant models have been suggested to contribute to the MMN: adaptation and prediction. Whether and how context conditions, such as awareness and task relevance, modulate the mechanisms of MMN generation is unknown. We conducted an EEG study disentangling influences of task relevance and awareness on the visual MMN. Then, we estimated different computational models for the generation of single-trial amplitudes in the MMN time window. Amplitudes were best explained by a prediction error model when stimuli were task-relevant but by an adaptation model when task-irrelevant and unaware. Thus, mismatch generation does not rely on one predominant mechanism but mechanisms vary with task relevance of stimuli.
Compensatory variability in network parameters enhances memory performance in theDrosophilamushroom body
Authors: Abdelrahman Nada Y. , Vasilaki Eleni , Lin Andrew C. 
Published: February 2021

AbstractNeural circuits use homeostatic compensation to achieve consistent behaviour despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual, and what computational benefits might result from such compensation. We address these questions in theDrosophilamushroom body, the fly’s olfactory memory center. In a computational model, we show that memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability, because the resulting inter-KC variability in average activity levels makes odor representations less separable. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in theDrosophilahemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.Significance statementHow does variability between neurons affect neural circuit function? How might neurons behave similarly despite having different underlying features? We addressed these questions in neurons called Kenyon cells, which store olfactory memories in flies. Kenyon cells differ among themselves in key features that affect how active they are, and in a model of the fly’s memory circuit, adding this inter-neuronal variability made the model fly worse at learning the values of multiple odors. However, memory performance was rescued if compensation between the variable underlying features allowed Kenyon cells to be equally active on average, and we found the hypothesized compensatory variability in real Kenyon cells’ anatomy. This work reveals the existence and computational benefits of compensatory variability in neural networks.
DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data
Authors: Chen Jiaxing , Cheong Chinwang , Lan Liang , Zhou Xin , Liu Jiming , Lyu Aiping , Cheung William K , Zhang Lu 
Published: February 2021

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).