bluuebunny/crossref_metadata_2025_split huggingface dataset:
Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation
Authors: Rungratsameetaweemana Nuttida , Kim Robert , Chotibut Thiparat , Sejnowski Terrence J. 
Published: October 2022

AbstractRecurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neurons perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Critically, the training process becomes difficult when the synaptic decay time constant is not fixed to a large constant number for all the model neurons. Here, we show that introducing random noise to the RNNs not only speeds up the training but also produces stable models that can maintain information longer than the RNNs trained without internal noise. Importantly, this robust working memory performance induced by internal noise during training is attributed to an increase in synaptic decay time constants of a distinct subset of inhibitory units, resulting in slower decay of stimulus-specific activity critical for memory maintenance.
Development and validation of an AI/ML platform for the discovery of splice-switching oligonucleotide targets
Authors: Fronk Alyssa D , Manzanares Miguel A , Zheng Paulina , Geier Adam , Anderson Kendall , Frederick Vanessa , Smith Shaleigh , Gera Sakshi , Munch Robin , Are Mahati , Dhingra Priyanka , Arun Gayatri , Akerman Martin 
Published: October 2022

AbstractThis study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of novel and verifiable splice-switching oligonucleotide (SSO) targetsin-silico. SSOs are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). To leverage the potential of AS research for therapeutic development, we created SpliceLearn™, an AI/ML algorithm for the identification of modulatory SSO binding sites on pre-mRNA. SpliceLearn also predicts the identity of specific splicing factors whose binding to pre-mRNA is blocked by SSOs, adding considerable transparency to AI/ML-driven drug discovery and informing biological insights useful in further validation steps. Here we predictedNEDD4Lexon 13 (NEDD4Le13) as a novel target in triple negative breast cancer (TNBC) and computationally designed an SSO to modulateNEDD4Le13. TargetingNEDD4Le13with this SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data. SpliceLearn is part of the SpliceCore® platform, an AI/ML predictive ensemble for AS-based drug target discovery.
MAP4Ks inhibition promotes retinal neuron regeneration from Müller glia in adult mice
Authors: Zhang Houjian , Guo Yuli , Yang Yaqiong , Zhuang Jingbin , Wang Yuqian , Zhao Jiankai , Zhang Rongrong , Hu Jiaoyue , Li Wei , Wu Jianfeng , Xu Haiwei , Fliesler Steven J. , Liao Yi , Liu Zuguo 
Published: October 2022

AbstractMammalian Müller glia (MG) possess limited regenerative capacities. However, the intrinsic capacity of mammalian MG to transdifferentiate to generate mature neurons without transgenic manipulations remains speculative. Here we show that MAP4K4, MAP4K6 and MAP4K7, which are conserved Misshapen subfamily of ste20 kinases homologs, repress YAP activity in mammalian MG and therefore restrict their ability to be reprogrammed. However, by treating with a small molecule inhibitor of MAP4K4/6/7, mouse MG regain their ability to proliferate and enter into a retinal progenitor cell (RPC)-like state after NMDA-induced retinal damage; such plasticity was lost in YAP knockout MG. Moreover, spontaneous trans-differentiation of MG into retinal neurons expressing both amacrine and retinal ganglion cell (RGC) markers occurs after inhibitor withdrawal. Taken together, these findings suggest that MAP4Ks block the reprogramming capacity of MG in a YAP-dependent manner in adult mammals, which provides a novel avenue for the pharmaceutical induction of retinal regenerationin vivo.
Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier
Authors: Winters Drew E. , Guha Anika , Sakai Joseph T. 
Published: October 2022

AbstractEmpathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n= 29) and those that do not (n= 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.
An Extensible Vector Toolkit and Parts Library for Advanced Engineering of Plant Genomes
Authors: Chamness James C. , Kumar Jitesh , Cruz Anna J. , Rhuby Elissa , Holum Mason J. , Cody Jon P. , Tibebu Redeat , Gamo Maria Elena , Starker Colby G. , Zhang Feng , Voytas Daniel F. 
Published: October 2022

AbstractPlant biotechnology is rife with new advances in transformation and genome engineering techniques. A common requirement for delivery and coordinated expression in plant cells, however, places the design and assembly of transformation constructs at a crucial juncture as desired reagent suites grow more complex. Modular cloning principles have simplified some aspects of vector design, yet many important components remain unavailable or poorly adapted for rapid implementation in biotechnology research. Here, we describe a universal Golden Gate cloning toolkit for vector construction. The toolkit chassis is compatible with the widely accepted Phytobrick standard for genetic parts, and supports assembly of arbitrarily complex T-DNAs through improved capacity, positional flexibility, and extensibility in comparison to extant kits. We also provision a substantial library of newly adapted Phytobricks, including regulatory elements for monocot and dicot gene expression, and coding sequences for genes of interest such as reporters, developmental regulators, and site-specific recombinases. Finally, we use a series of dual luciferase assays to measure contributions to expression from promoters, terminators, and from cross-cassette interactions attributable to enhancer elements in certain promoters. Taken together, these publicly available cloning resources can greatly accelerate the testing and deployment of new tools for plant engineering.
A critical analysis of plant science literature reveals ongoing inequities
Authors: Marks Rose A. , Amézquita Erik J. , Percival Sarah , Rougon-Cardoso Alejandra , Chibici-Revneanu Claudia , Tebele Shandry M. , Farrant Jill M. , Chitwood Daniel H. , VanBuren Robert 
Published: October 2022

ABSTRACTThe field of plant science has grown dramatically in the past two decades, but global disparities and systemic inequalities persist. Here, we analyzed ~300,000 papers published over the past two decades to quantify disparities across nations, genders, and taxonomy in the plant science literature. Our analyses reveal striking geographical biases—affluent nations dominate the publishing landscape and vast areas of the globe having virtually no footprint in the literature. Authors in Northern America are cited nearly twice as many times as authors based in Sub-Saharan Africa and Latin America, despite publishing in journals with similar impact factors. Gender imbalances are similarly stark and show remarkably little improvement over time. Some of the most affluent nations have extremely male biased publication records, despite supposed improvements in gender equality. In addition, we find that most studies focus on economically important crop and model species and a wealth of biodiversity is under-represented in the literature. Taken together, our analyses reveal a problematic system of publication, with persistent imbalances that poorly captures the global wealth of scientific knowledge and biological diversity. We conclude by highlighting disparities that can be addressed immediately and offer suggestions for long-term solutions to improve equity in the plant sciences.SIGNIFICANCE STATEMENTWe analyzed ~300,000 papers published over the past two decades to quantify global, gender, and taxonomic disparities in plant science. Our analyses reveal striking geographical biases that are correlated with national affluence. Gender imbalances were also evident, with far more papers led by authors with masculine names than authors with feminine names. Lastly, we identified substantial taxonomic sampling gaps. The vast majority of surveyed studies focused on major crop and model species and the remaining biodiversity accounted for only a fraction of publications. Taken together, our analyses represent an important addition to the growing conversation about diversifying and decolonizing science.
Predicting FDA approvability of small-molecule drugs
Authors: Huang Chih-Han , Hsu Justine , Yang Li-yen , Chen Tsai-Min , Shih Edward S.C. , Hwang Ming-Jing 
Published: October 2022

ABSTRACTA high rate of compound attrition makes drug discovery via conventional methods time-consuming and expensive. Here, we showed that machine learning models can be trained to classify compounds into distinctive groups according to their status in the drug development process, which can significantly reduce the compound attrition rate. Using molecular structure fingerprints and physicochemical properties as input, our models accurately predicted which drug compounds would proceed to trial, with an area under the receiver operating curve (AUC) of 0.94 ± 0.01 (mean ± standard deviation). Our models also identified which drugs in clinical trials would be approved by the US Food and Drug Administration (FDA) to go on the market, with an AUC of 0.73 ± 0.02. The predictive power of our models could reduce the attrition rate of preclinical compounds to enter clinical trials from 65%, as with conventional methods, to 12% (with 92% sensitivity) and the clinical trial failure rate from 80–90% to 29% (with 83% sensitivity). The results largely held in additional tests on new clinical trial compounds and new FDA-approved drugs, as well as on drugs uniquely approved for use in Europe and Japan.SIGNIFICANCE STATEMENTThe odds of developing a drug approved by the US Food and Drug Administration (FDA) are slim, meaning that the vast majority of drug candidates would fail tests for safety and efficacy in the drug discovery process, rendering it highly inefficient and costly. Here, we have developed machine learning models to predict drug compounds worthy of clinical trials with high accuracy, and clinical-trial compounds to receive FDA approval with a much higher success rate than that achieved by the traditional approach. Our computational prediction requires input of only the drug compound’s chemical structure and physicochemical properties. It can help mitigate the long-standing problem of drug discovery.
Comparison of Dimensionality Reduction and Clustering Methods for Single-Cell Transcriptomics Data
Authors: Pandit Vrushali , Swain Asish Kumar , Yadav Pankaj 
Published: October 2022

AbstractDimensionality reduction (DR) methods are applied to extract relevant features from inherently high dimensional and noisy single-cell RNA sequencing (scRNA-seq) data. Choice of DR method could influence the performance of clustering algorithm and subsequent analysis outcomes. We performed a benchmarking study of seven popular DR methods and four clustering algorithms widely used for scRNA-seq datasets. For this purpose, we used three publicly available real scRNA-seq datasets. The performance was evaluated using two clustering metrics viz. adjusted random index (ARI) and normalized mutual index (NMI). We also compared our results with a similar study published by Xiang and colleagues. Overall, we observed higher ARI and NMI scores for DR methods when compared with Xiang’s study. We also noticed several differences between our and Xiang’s study. Noteworthy, three methods, namely, Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) performed consistently well across three datasets. Linear method ICA was best performer onSegerstolpedataset, while nonlinear methods UMAP and t-SNE best performed onDengandChudatasets, respectively. Neural network-based methods Variational Autoencoder (VAE) and Deep Count Autoencoder (DCA) could not perform well probably due to their sensitivity to hyperparameters and overfitting. Among clustering methods, Gaussian Mixture Models (GMMs) performed consistently well across datasets. This might be because GMMs are the universal approximators of posterior probability densities. We conclude that performance of different DR methods is more dataset dependent and for various scRNA-seq datasets different algorithms are more suited and there is no one-fit-all method.
Fast and Accurate Prediction of Intrinsically Disordered Protein by Protein Language Model
Authors: Xu Shijie , Onoda Akira 
Published: October 2022

AbstractMotivationIntrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the last decades. Predicting IDPs from primary structures of proteins provides a very useful tool for protein analysis. However, most of the existing prediction methods heavily rely on multiple sequence alignments (MSAs) of homologous sequences which are formed by evolution over billions of years. Obtaining such information requires searching against the whole protein databases to find similar sequences and since this process becomes increasingly time-consuming, especially in large-scale practical applications, the alternative method is needed.ResultsIn this paper, we proposed a novel IDP prediction method named IDP-PLM, based on the protein language model (PLM). The method does not rely on MSAs or MSA-based profiles but leverages only the protein sequences, thereby achieving state-of-the-art performance even compared with predictors using protein profiles. The proposed IDP-PLM is composed of stacked predictors designed for several different protein-related tasks: secondary structure prediction, linker prediction, and binding predictions. In addition, predictors for the single task also achieved the highest accuracy. All these are based on PLMs thus making IDP-PLM not rely on MSA-based profiles. The ablation study reveals that all these stacked predictors contribute positively to the IDP prediction performance of IDP-PLM.AvailabilityThe method is available athttp://github.com/xu-shi-jie.Contactakira.onoda@ees.hokudai.ac.jpSupplementary informationSupplementary data are available atBioinformaticsonline.
Neural representation of goal direction in the monarch butterfly brain
Authors: Beetz M. Jerome , Kraus Christian , el Jundi Basil 
Published: October 2022

ABSTRACTNeural processing of a navigational goal requires the continuous comparison between the current heading and the intended goal direction. While the neural basis underlying the current heading is well-studied in insects, the coding of the goal direction is completely unexplored. Here, we identify for the first time neurons that encode goal direction in the brain of a navigating insect, the monarch butterfly. The spatial tuning of these neurons accurately correlates with the animal’s goal direction while being unaffected by compass perturbations. Thus, they specifically encode the goal direction similar to goal neurons described in the mammalian brain. Taken together, a navigation network based on goal-direction and heading-direction neurons generates steering commands that efficiently guides the monarch butterflies to their migratory goal.
Defining Multiple Layers of Intratumor Heterogeneity Based on Variations of Perturbations in Multi-omics Profiling
Authors: Ai Hongjing , Song Dandan , Wang Xiaosheng 
Published: October 2022

AbstractIntratumor heterogeneity (ITH) is associated with tumor progression, relapse, immunoevasion, and drug resistance. Existing algorithms for measuring ITH are limited to at a single molecular level. We proposed a set of algorithms for measuring ITH at the genome (somatic copy number alterations (CNAs) and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. These algorithms were designed based on a common concept: information entropy. By analyzing 33 TCGA cancer types, we demonstrated that these ITH measures had the typical properties of ITH, namely their significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. Furthermore, we showed that the correlations between ITH measures at identical molecular levels were stronger than those at different molecular levels. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Finally, we integrated the seven ITH measures into an ITH measure, which displayed more prominent properties of ITH than the ITH measures at a single molecular level. This analysis of multi-level ITH provides novel insights into tumor biology and potential values in clinical practice for pan-cancer.
Joint Variational Autoencoders for Multimodal Imputation and Embedding
Authors: Kalafut Noah Cohen , Huang Xiang , Wang Daifeng 
Published: October 2022

AbstractSingle-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.
Rhythms of Brain Impedance
Authors: Mivalt Filip , Kremen Vaclav , Sladky Vladimir , Cui Jie , Gregg Nicholas , Balzekas Irena , Marks Victoria , Louis Erik K. St , Croarkin Paul E. , Lundstrom Brian Nils , Nelson Noelle , Hermes Dora , Messina Steven , Worrell Samuel , Richner Thomas J. , Brinkmann Benjamin H. , Denison Timothy , Miller Kai J. , Van Gompel Jamie , Stead Matt , Worrell Gregory A. 
Published: October 2022

ABSTRACTBrain impedance is a fundamental electrical property that depends on tissue extracellular volume. We tracked impedance, behavioral state, and epileptiform activity in humans using an investigational device and identified behavioral state dependent impedance oscillations spanning hours to weeks in amygdala, hippocampus, and thalamus. Impedance reaches a minimum in slow wave sleep, is intermediate in rapid-eye-movement sleep and maximal during wakefulness consistent with previously observed extracellular volume changes in rodent glymphatic system.
Host independent deletion hotspots in the SARS-CoV-2 genome
Authors: Khalid Mohammad 
Published: October 2022

AbstractSARS-CoV-2 infects a wide range of hosts in varying degrees. The RNA genome of SARS-CoV-2 makes it prone to mutations. Advantageous mutations help the virus to evolve and the virus maintains such mutations across species. Here in this study, all non-human hosts-derived SARS-CoV-2 genomic sequences from the GISAID database were analyzed, and identified several deletion hotspots, which are maintained by the virus, across various host species, indicating their important role in the virus evolution. Several of these deletion hotspots are also found in human-derived SARS-CoV-2 genomic sequences. These deletion hotspots have the potential to affect the pathogenicity and virulence of the virus and have a role in molecular and serological diagnostics. Potentially, they can lead to immune escape, resulting in vaccine failure and drug-resistant variants.