bluuebunny/crossref_metadata_2025_split huggingface dataset:
BLINK: Ultrafast tandem mass spectrometry cosine similarity scoring
Authors: Harwood Thomas , Treen Daniel G.C. , Wang Mingxun , de Jong Wibe , Northen Trent R. , Bowen Benjamin P. 
Published: February 2023

AbstractSummaryMetabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. BLINK calculates substantially equivalent cosine similarity scores (>99% identification agreement) over 1000 times faster than commonly used loop-based implementations by bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources.Availability and ImplementationBLINK is implemented in Python3 and is published under a modified open source license. Code and license are available on Github:https://github.com/biorack/blink
A Systematic Benchmark of Machine Learning Methods for Protein-RNA Interaction Prediction
Authors: Horlacher Marc , Cantini Giulia , Hesse Julian , Schinke Patrick , Goedert Nicolas , Londhe Shubhankar , Moyon Lambert , Marsico Annalisa 
Published: February 2023

AbstractRNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile binding sites of RBPsin vivoare limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods forin vivoRBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.
Uni-Fold MuSSe: De Novo Protein Complex Prediction with Protein Language Models
Authors: Zhu Jinhua , He Zhenyu , Li Ziyao , Ke Guolin , Zhang Linfeng 
Published: February 2023

AbstractAccurately solving the structures of protein complexes is crucial for understanding and further modifying biological activities. Recent success of AlphaFold and its variants shows that deep learning models are capable of accurately predicting protein complex structures, yet with the painstaking effort of homology search and pairing. To bypass this need, we present Uni-Fold MuSSe (Multimer with Single Sequence inputs), which predicts protein complex structures from their primary sequences with the aid of pre-trained protein language models. Specifically, we built protein complex prediction models based on the protein sequence representations of ESM-2, a large protein language model with 3 billion parameters. In order to adapt the language model to inter-protein evolutionary patterns, we slightly modified and further pre-trained the language model on groups of protein sequences with known interactions. Our results highlight the potential of protein language models for complex prediction and suggest room for improvements.
Structural Optimization of siRNA Conjugates for Albumin Binding Achieves Effective MCL1-Targeted Cancer Therapy
Authors: Hoogenboezem Ella N. , Patel Shrusti S. , Cavnar Ashley B. , Lo Justin H. , Babb Lauren M. , Francini Nora , Patil Prarthana , Colazo Juan M. , Michell Danielle L. , Sanchez Violeta M. , McCune Joshua T. , Ma Jinqi , DeJulius Carlisle R. , Lee Linus H. , Rosch Jonah C. , Allen Ryan M. , Stokes Larry D. , Hill Jordan L. , Vickers Kasey C. , Cook Rebecca S. , Duvall Craig L. 
Published: February 2023

AbstractThe high potential for therapeutic application of siRNAs to silence traditionally undruggable oncogenic drivers remains largely untapped due to the challenges of tumor cell delivery. Here, siRNAs were optimized forin situbinding to albumin through C18 lipid modifications to improve pharmacokinetics and tumor delivery. Systematic variation of siRNA conjugates revealed a lead structure with divalent C18lipids each linked through three repeats of hexaethylene glycol connected by phosphorothioate bonds. Importantly, we discovered that locating the branch site of the divalent lipid structure proximally (adjacent to the RNA) rather than at a more distal site (after the linker segment) promotes association with albumin, while minimizing self-assembly and lipoprotein association. Comparison to higher albumin affinity (diacid) lipid variants and siRNA directly conjugated to albumin underscored the importance of conjugate hydrophobicity and reversibility of albumin binding for siRNA delivery and bioactivity in tumors. The lead conjugate increased tumor siRNA accumulation 12-fold in orthotopic mouse models of triple negative breast cancer over the parent siRNA. When applied for silencing of the anti-apoptotic oncogene MCL-1, this structure achieved approximately 80% MCL1 silencing in orthotopic breast tumors. Furthermore, application of the lead conjugate structure to target MCL1 yielded better survival outcomes in three independent, orthotopic, triple negative breast cancer models than an MCL1 small molecule inhibitor. These studies provide new structure-function insights on optimally leveraging siRNA-lipid conjugate structures that associatein situwith plasma albumin for molecular-targeted cancer therapy.
Aging-related changes in expression and function of glutamate transporters in rat spinal cord astrocytes
Authors: Sharan Shiksha , Tewari Bhanu Prakash , Joshi Preeti G. 
Published: February 2023

AbstractAstrocytes are abundant and heterogeneous cell types in the CNS [1]. They promote neuronal health and survival and protect neurons from glutamate-induced excitotoxicity. In the spinal cord, astrocytes are present in both the dorsal horn (DH) and ventral horns (VH). However, only motor neurons in the VH are highly vulnerable to glutamate excitotoxicity. We hypothesize that the preferential vulnerability of motor neurons may underlie their spatial confinement to the VH, where astrocytes are differentially equipped with glutamate-handling machinery. With aging, glutamate excitotoxicity increases, suggesting compromised astrocytic glutamate handling. We tested our hypotheses by comparing astrocytic morphology, expression of glutamate transporters, and glutamate uptake function in the DH and VH using immunohistochemical staining, western blotting, and whole-cell patch-clamp electrophysiology. We found a global reduction in the numerical density and astroglial coverage in the spinal cord, with a prominent decline in the VH with aging. Astrocytic glutamate transporters, Excitatory amino acid transporter 1 (EAAT1) and Excitatory amino acid transporter 2 (EAAT2), show an overall reduction in expression with aging, with a more progressive decrease in the VH than DH. We performed whole-cell patch-clamp studies on DH and VH astrocytes to assess the functional outcome of the altered expression of EAATs with aging. Both VH and DH astrocytes showed a dramatic decline in glutamate uptake currents with aging, suggesting compromised glutamate handling in aging astrocytes. Our study suggests that astrocytes in the DH and VH undergo differential changes with aging, exhibiting compromised glutamate handling properties, which may be one of the key contributors to the late onset of neurodegenerative disorders.
Psychophysical measurement of perceived motion flow of naturalistic scenes
Authors: Yang Yung-Hao , Fukiage Taiki , Sun Zitang , Nishida Shin’ya 
Published: February 2023

AbstractThe neural and computational mechanisms underlying visual motion perception have been extensively investigated over several decades, but most studies have used simple artificial stimuli such as random-dot kinematograms. Thus, it remains difficult to predict how human observers perceive optical flows in complex natural scenes. Here, we report a novel method to measure, psychophysically, optical flows perceived by human observers watching naturalistic movies, and to reveal the characteristics of human motion perception via comparison of the measured perceived flow to the ground truths and model predictions. We selected movie clips from the MPI Sintel Flow Dataset, which contains open-source computer graphics animations with ground truths. To measure the perceived vectors at a spatiotemporal point, we flashed a small dot during presentation of a brief clip and asked the observers to adjust the speed and direction of a matching random-noise stimulus, to reproduce the vector at the flashed point. The proposed method adequately estimated perceived flow, and the estimated perceived vector also indicated flow illusions, i.e., consistent deviations from the ground truths, in various ways, depending on the stimulus patterns. Comparisons with the predictions of biologically motivated models and machine vision algorithms indicated that some flow illusions were attributable to lower-level factors such as spatiotemporal pooling and signal loss, but others reflected higher-level computations including coordinate transformations that cannot be precisely predicted by existing flow estimation models. Psychophysical measurement of the optical flows that humans perceive in realistic environments constitutes a promising paradigm for advancing our understanding of visual motion perception.Significance StatementThe basic approach to studying human vision is to analyze relationships among subjective perceptual experiences, responses of neural mechanisms, and predictions of computational models. Recent technical advances have enabled researchers to access large-scale neuronal responses and model predictions for complex sensory inputs. The data on human visual perception, however, cannot be easily scaled up. Accurate measurement of rich visual experiences when viewing natural scenes remains challenging. We thus devised a novel psychophysical method to measure optical motion flows perceived by humans. We successfully visualized human-perceived flows of complex naturalistic movies and show, for the first time, ways in which human-perceived naturalistic flows agree with, and deviate from, the physical ground truths and the predictions of various visual motion models.
Predicting CIN rates from single-cell whole genome sequencing data using anin silicomodel
Authors: Bakker Bjorn , Schubert Michael , Bolhaqueiro Ana C.F. , Kops Geert J.P.L. , Spierings Diana. C.J. , Foijer Floris 
Published: February 2023

AbstractChromosomal instability (CIN) drives the formation of karyotype aberrations in cancer cells and is a major contributor to intra-tumour heterogeneity, metastasis, and therapy resistance. Understanding how CIN contributes to tumour karyotype evolution requires quantification of CIN rates in primary tumours. Single-cell sequencing-based technologies enable the detection of karyotype heterogeneity, however deducing the actual CIN rates that underlie intra-tumour heterogeneity is still complicated. We have developed anin-silicomodel, calledCINsim, to simulate the karyotype dynamics and validated our model in a murine mouse model for T-cell lymphoma (T-ALL) in which CIN is introduced by mutation of the Mps1 spindle assembly checkpoint protein.CINsimcan simulate karyotype evolution within physiologically relevant timescales, across a range of CIN rates, and across a range of karyotype-imposed survival and proliferation effects. We find thatCINsimcan accurately predict the CIN rates in chromosomal instable mouse T-ALLs as well as in human colon cancer organoids as observed by live-cell time-lapse imaging. We conclude thatCINsimis a powerful tool to estimate CIN rates from static single-cell DNA sequencing data by finding the most likely path from euploid founder cell to a heterogeneous tumour cell population.
Disentangled deep generative models reveal coding principles of the human face processing network
Authors: Soulos Paul , Isik Leyla 
Published: February 2023

AbstractDespite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that “disentangles” different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model’s learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigated the representation of different latent dimensions across face-selective voxels. We find a gradient from low- to high-level face feature representations along posterior to anterior face-selective regions, corroborating prior models of human face recognition. Interestingly, though, we find no spatial segregation between identity-relevant and irrelevant face features. Finally, we provide new insight into the few “entangled” (uninterpretable) dimensions in our model by showing that they match responses across the ventral stream and carry significant information about facial identity. Disentangled face encoding models provide an exciting alternative to standard “black box” deep learning approaches for modeling and interpreting human brain data.
Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer
Authors: Ko Kyung-Pil , Zhang Shengzhe , Huang Yuanjian , Kim Bongjun , Zou Gengyi , Jun Sohee , Zhang Jie , Martin Cecilia , Dunbar Karen J. , Efe Gizem , Rustgi Anil K. , Zhang Haiyang , Nakagawa Hiroshi , Park Jae-Il 
Published: February 2023

SummaryDespite the promising outcomes of immune checkpoint blockade (ICB), resistance to ICB presents a new challenge. Therefore, selecting patients for specific ICB applications is crucial for maximizing therapeutic efficacy. Herein we curated 69 human esophageal squamous cell cancer (ESCC) patients’ tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network transcriptional signatures of T cells, myeloid cells, and fibroblasts define distinct ESCC subtypes characterized by T cell exhaustion, Interferon (IFN) a/b signaling, TIGIT enrichment, and specific marker genes. Furthermore, this approach classifies ESCC patients into ICB responders and non-responders, as validated by liquid biopsy single-cell transcriptomics. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and predicting ICB responses in ESCC patients.
Classification of dog breeds using convolutional neural network models and support vector machine
Authors: Cui Ying , Tang Bixia , Wu Gangao , Li Lun , Zhang Xin , Du Zhenglin , Zhao Wenming 
Published: February 2023

AbstractConvolutional neural network (CNN) has been widely used for fine-grained image classification, which has proven to be an effective approach for the classification and identification of specific species. For breed classification of dog, there are several proposed methods based on dog images, however, the highest accuracy rate for dogs (about 93%) is still below expectations compared to other animals or plants (more than 95% on birds and more than 97% on flowers). In this study, we used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). Eventually, the classification accuracy rate reached 95.24% for 120 breeds and 99.34% for 76 selected breeds, respectively, demonstrating a significant improvement over existing methods using the same Stanford Dog Dataset. It is expected that our proposed method will further serve as a fundamental framework for accurate classification of a wider range of species.
Specialized actin nanoscale layers control focal adhesion turnover
Authors: Kumari Reena , Ven Katharina , Chastney Megan , Peränen Johan , Aaron Jesse , Almeida-Souza Leonardo , Kremneva Elena , Poincloux Renaud , Chew Teng-Leong , Gunning Peter W. , Ivaska Johanna , Lappalainen Pekka 
Published: February 2023

SUMMARYFocal adhesions (FAs) connect inner workings of the cell to the extracellular matrix to control cell adhesion, migration, and mechanosensing1,2. Previous studies demonstrated that FAs contain three vertical layers, which connect extracellular matrix to the cytoskeleton3,4,5. However, cellular processes rely on precisely-regulated FA turnover, but the molecular machineries that control FA assembly and disassembly have remained elusive. By using super-resolution iPALM microscopy, we identified two unprecedented nanoscale layers within FAs, specified by actin filaments bound to tropomyosin isoforms Tpm1.6 and Tpm3.2. The Tpm1.6-actin filaments beneath the previously identified ‘actin-regulatory layer’ are critical for adhesion maturation and controlled cell motility, whereas the Tpm3.2-actin filament layer towards the bottom of FA facilitates adhesion disassembly. Mechanistically, Tpm3.2 stabilizes KANK-family proteins at adhesions, and hence targets microtubule plus-ends to FAs to catalyse their disassembly. Loss of Tpm3.2 leads to disorganized microtubule network, abnormally stable FAs, and defects in tail retraction during cell migration. Thus, FAs are composed of at least three distinct actin filament layers, each having specific roles in coupling of adhesion to the cytoskeleton, or in controlling adhesion dynamics. In a broader context, these findings demonstrate how distinct actin filament populations can co-exist and perform specific functions within a defined cellular compartment.
Multivariate Residualization in Medical Imaging Analysis
Authors: Donovan Kevin , Tustison Nicholas J , Linn Kristin A. , Shinohara Russell T. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2023

AbstractNuisance variables in medical imaging research are common, complicating association and prediction studies based on image data. Medical image data are typically high dimensional, often consisting of many highly correlated features. As a result, computationally efficient and robust methods to address nuisance variables are difficult to implement. By-region univariate residualization is commonly used to remove the influence of nuisance variables, as are various extensions. However, these methods neglect multivariate properties and may fail to fully remove influence related to the joint distribution of these regions. Some methods, such as functional regression and others, do consider multivariate properties when controlling for nuisance variables. However, the utility of these methods is limited for data with many image regions due to computational and model complexity. We develop a multivariate residualization method to estimate the association between the image and nuisance variable using a machine learning algorithm and then compute the orthogonal projection of each subject’s image data onto this space. We illustrate this method’s performance in a set of simulation studies and apply it to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Barley shows reduced Fusarium Head Blight under drought and modular expression of differential expressed genes under combined stress
Authors: Hoheneder Felix , Steidele Christina E. , Messerer Maxim , Mayer Klaus , Köhler Nikolai , Wurmser Christine , Heß Michael , Gigl Michael , Dawid Corinna , Stam Remco , Hückelhoven Ralph 
Published: February 2023

AbstractPlants often face simultaneous abiotic and biotic stress conditions. However, physiological and transcriptional responses of plants under combined stress situations are little understood. Spring barley is susceptible to Fusarium Head Blight (FHB), which is strongly affected by weather conditions. We therefore studied the potential influence of drought on FHB severity and responses in three differently susceptible spring barley varieties and found strongly reduced FHB severity in susceptible varieties under drought. Quantity of differentially expressed genes (DEGs) and strength of transcriptomic regulation reflected the concentration of physiological stress markers such as abscisic acid or fungal DNA contents. Infection-related gene expression associated rather with susceptibility than resistance. Weighted gene correlation network analysis uncovered 18 modules of co-expressed genes, which reflect the pathogen or drought response in the used varieties. A generally infection-related module contained co-expressed genes for defence, programmed cell death and mycotoxin-detoxification indicating that diverse genotypes use a similar defence strategy towards FHB albeit with different success. Further DEGs showed co-expression in drought or genotype-associated modules correlating with measured phytohormones or the osmolyte proline. The combination of drought stress with infection lead to highest numbers of DEGs and provoked a modular composition of single stress responses rather than a specific transcriptional readout.HighlightCo-expression network analysis reveals association of physiological stress markers and gene expression modules under biotic and abiotic stress