bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
Authors: Lupo Umberto , Sgarbossa Damiano , Bitbol Anne-Florence 
Published: October 2022

AbstractSelf-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold’s EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer’s row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer’s column attentions strongly correlate with Hamming distances between sequences in MSAs. Therefore, MSA-based language models encode detailed phylogenetic relationships. We further show that these models can separate coevolutionary signals encoding functional and structural constraints from phylogenetic correlations reflecting historical contingency. To assess this, we generate synthetic MSAs, either without or with phylogeny, from Potts models trained on natural MSAs. We find that unsupervised contact prediction is substantially more resilient to phylogenetic noise when using MSA Transformer versus inferred Potts models.
Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions
Authors: Eswaran Sudha cheranma devi , Subramaniam Senthil , Sanyal Udishnu , Rallo Robert , Zhang Xiao 
Published: October 2022

AbstractLignin is one of the most abundant biopolymers in nature and has great potential to be transformed into high-value chemicals. However, the limited availability of molecular structure data hinders its potential industrial applications. Herein, we present the Lignin Structural (LGS) Dataset that includes the molecular structure of milled wood lignin focusing on two major monomeric units (coniferyl and syringyl), and the six most common interunit linkages (phenylpropane β-aryl ether, resinol, phenylcoumaran, biphenyl, dibenzodioxocin, and diaryl ether). The dataset constitutes a unique resource that covers a part of lignin’s chemical space characterized by polymer chains with lengths in the range of 3 to 25 monomer units. Structural data were generated using a sequence-controlled polymer generation approach that was calibrated to match experimental lignin properties. The LGS dataset includes 60 K newly generated lignin structures that match with high accuracy (~90%) the experimentally determined structural compositions available in the literature. The LGS dataset is a valuable resource to advance lignin chemistry research, including computational simulation approaches and predictive modelling.
Density-of-states similarity descriptor for unsupervised learning from materials data
Authors: Kuban Martin , Rigamonti Santiago , Scheidgen Markus , Draxl Claudia 
Published: October 2022

AbstractWe develop a materials descriptor based on the electronic density-of-states (DOS) and investigate the similarity of materials based on it. As an application example, we study the Computational 2D Materials Database (C2DB) that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We introduce additional descriptors to characterize these clusters in terms of crystal structures, atomic compositions, and electronic configurations of their members. This allows us to rationalize the found (dis)similarities and to perform an automated exploratory and confirmatory analysis of the C2DB data. From this analysis, we find that the majority of clusters consist of isoelectronic materials sharing crystal symmetry, but we also identify outliers, i.e., materials whose similarity cannot be explained in this way.
Comparative sensitivity of social media data and their acceptable use in research
Authors: Hemphill Libby , Schöpke-Gonzalez Angela , Panda Anmol 
Published: October 2022

AbstractSocial media data offer a rich resource for researchers interested in public health, labor economics, politics, social behaviors, and other topics. However, scale and anonymity mean that researchers often cannot directly get permission from users to collect and analyze their social media data. This article applies the basic ethical principle of respect for persons to consider individuals’ perceptions of acceptable uses of data. We compare individuals’ perceptions of acceptable uses of other types of sensitive data, such as health records and individual identifiers, with their perceptions of acceptable uses of social media data. Our survey of 1018 people shows that individuals think of their social media data as moderately sensitive and agree that it should be protected. Respondents are generally okay with researchers using their data in social research but prefer that researchers clearly articulate benefits and seek explicit consent before conducting research. We argue that researchers must ensure that their research provides social benefits worthy of individual risks and that they must address those risks throughout the research process.
A comprehensive ultra-wideband dataset for non-cooperative contextual sensing
Authors: Bocus Mohammud J. , Piechocki Robert 
Published: October 2022

AbstractNowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target’s location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications.
Thirty Years of Nutrient Biogeochemistry in the Barents Sea and the adjoining Arctic Ocean, 1990–2019
Authors: Gundersen Kjell , Møgster Jane S. , Lien Vidar S. , Ershova Elizaveta , Lunde Linda F. , Arnesen Hilde , Olsen Ann-Kristin 
Published: October 2022

AbstractThis dataset contains biogeochemical samples from the Barents Sea and Arctic region analyzed by the Plankton Chemistry Laboratory at the Institute of Marine Research (IMR). Number of surveys and stations visited in the Barents Sea and Arctic has varied over the last 30 years. One major effort is the annual Ecosystem Survey in the fall, with multiple trawl surveys, net tows and CTD water sampling. Additionally, two transects are visited multiple times each year (Fugløya-Bjørnøya and Vardø-North). Only samples collected from water bottles are reported here. Bottle samples from each CTD cast were collected for dissolved inorganic nutrients (nitrate, nitrite, phosphate, silicate) and phytoplankton chlorophyll-a and phaeopigments (ChlA, PHAEO) at predetermined depths and for later analysis at IMR. On occasion, short-term projects have performed Winkler dissolved oxygen titrations (DOW) and particulate organic carbon and nitrogen (POC, PN) determinations. This unique data set has seen limited use over the years but is a great contribution towards global ocean research and climate change investigations.
Highly efficient hybridoma generation and screening strategy for anti-PD-1 monoclonal antibody development
Authors: Phakham Tanapati , Boonkrai Chatikorn , Wongtangprasert Tossapon , Audomsun Thittaya , Attakitbancha Chadaporn , Saelao Pijitra , Muanwien Phijitra , Sooksai Sarintip , Hirankarn Nattiya , Pisitkun Trairak 
Published: October 2022

AbstractProgrammed cell death protein 1 (PD-1) plays a significant role in suppressing antitumor immune responses. Cancer treatment with immune checkpoint inhibitors (ICIs) targeting PD-1 has been approved to treat numerous cancers and is the backbone of cancer immunotherapy. Anti-PD-1 molecule is necessary for next-generation cancer immunotherapy to further improve clinical efficacy and safety as well as integrate into novel treatment combinations or platforms. We developed a highly efficient hybridoma generation and screening strategy to generate high-potency chimeric anti-PD-1 molecules. Using this strategy, we successfully generated several mouse hybridoma and mouse/human chimeric clones that produced high-affinity antibodies against human PD-1 with high-quality in vitro PD-1/PD-L1 binding blockade and T cell activation activities. The lead chimeric prototypes exhibited overall in vitro performance comparable to commercially available anti-PD-1 antibodies and could be qualified as promising therapeutic candidates for further development toward immuno-oncology applications.
Large-volume and deep brain imaging in rabbits and monkeys using COMPACT two-photon microscopy
Authors: Lu Yuqing , Wei Xiangzan , Li Wei , Wu Xujun , Chen Chao , Li Ge , Huang Zhongqiang , Li Yunfeng , Zhang Yu , Gan Wen-Biao 
Published: October 2022

AbstractIn vivo imaging has been widely used for investigating the structure and function of neurons typically located within ~ 800 μm below the cortical surface. Due to light scattering and absorption, it has been difficult to perform in-vivo imaging of neurons in deep cortical and subcortical regions of large animals with two-photon microscopy. Here, we combined a thin-wall quartz capillary with a GRIN lens attached to a prism for large-volume structural and calcium imaging of neurons located 2 mm below the surface of rabbit and monkey brains. The field of view was greatly expanded by rotating and changing the depth of the imaging probe inside a quartz capillary. Calcium imaging of layer 5/6 neurons in the rabbit motor cortex revealed differential activity of these neurons between quiet wakefulness and slow wave sleep. The method described here provides an important tool for studying the structure and function of neurons located deep in the brains of large animals.
Noxa and Mcl-1 expression influence the sensitivity to BH3-mimetics that target Bcl-xL in patient-derived glioma stem cells
Authors: Vera Mariana Belén , Morris-Hanon Olivia , Nogueiras Germán Ignacio , Ripari Luisina Belén , Esquivel Myrian Inés , Perez-Castro Carolina , Romorini Leonardo , Sevlever Gustavo Emilio , Scassa María Elida , Videla-Richardson Guillermo Agustín 
Published: October 2022

AbstractThe recurrence of Glioblastoma is partly attributed to the highly resistant subpopulation of glioma stem cells. A novel therapeutic approach focuses on restoring apoptotic programs in these cancer stem cells, as they are often deregulated. BH3-mimetics, targeting anti-apoptotic Bcl-2 family members, are emerging as promising compounds to sensitize cancer cells to antineoplastic treatments. Herein, we determined that the most abundantly expressed anti-apoptotic Bcl-2 family members, Bcl-xL and Mcl-1, are the most relevant in regulating patient-derived glioma stem cell survival. We exposed these cells to routinely used chemotherapeutic drugs and BH3-mimetics (ABT-263, WEHI-539, and S63845). We observed that the combination of BH3-mimetics targeting Bcl-xL with chemotherapeutic agents caused a marked increase in cell death and that this sensitivity to Bcl-xL inhibition correlated with Noxa expression levels. Interestingly, whereas co-targeting Bcl-xL and Mcl-1 led to massive cell death in all tested cell lines, down-regulation of Noxa promoted cell survival only in cell lines expressing higher levels of this BH3-only. Therefore, in glioma stem cells, the efficacy of Bcl-xL inhibition is closely associated with Mcl-1 activity and Noxa expression. Hence, a potentially effective strategy would consist of combining Bcl-xL inhibitors with chemotherapeutic agents capable of inducing Noxa, taking advantage of this pro-apoptotic factor.
Personalized ctDNA micro-panels can monitor and predict clinical outcomes for patients with triple-negative breast cancer
Authors: Barnell Erica K. , Fisk Bryan , Skidmore Zachary L. , Cotto Kelsy C. , Basu Anamika , Anand Aparna , Richters Megan M. , Luo Jingqin , Fronick Catrina , Anurag Meenakshi , Fulton Robert , Ellis Matthew J. , Griffith Obi L. , Griffith Malachi , Ademuyiwa Foluso O. 
Published: October 2022

AbstractCirculating tumor DNA (ctDNA) in peripheral blood has been used to predict prognosis and therapeutic response for triple-negative breast cancer (TNBC) patients. However, previous approaches typically use large comprehensive panels of genes commonly mutated across all breast cancers. Given the reduction in sequencing costs and decreased turnaround times associated with panel generation, the objective of this study was to assess the use of custom micro-panels for tracking disease and predicting clinical outcomes for patients with TNBC. Paired tumor-normal samples from patients with TNBC were obtained at diagnosis (T0) and whole exome sequencing (WES) was performed to identify somatic variants associated with individual tumors. Custom micro-panels of 4–6 variants were created for each individual enrolled in the study. Peripheral blood was obtained at baseline, during Cycle 1 Day 3, at time of surgery, and in 3–6 month intervals after surgery to assess variant allele fraction (VAF) at different timepoints during disease course. The VAF was compared to clinical outcomes to evaluate the ability of custom micro-panels to predict pathological response, disease-free intervals, and patient relapse. A cohort of 50 individuals were evaluated for up to 48 months post-diagnosis of TNBC. In total, there were 33 patients who did not achieve pathological complete response (pCR) and seven patients developed clinical relapse. For all patients who developed clinical relapse and had peripheral blood obtained ≤ 6 months prior to relapse (n = 4), the custom ctDNA micro-panels identified molecular relapse at an average of 4.3 months prior to clinical relapse. The custom ctDNA panel results were moderately associated with pCR such that during disease monitoring, only 11% of patients with pCR had a molecular relapse, whereas 47% of patients without pCR had a molecular relapse (Chi-Square; p-value = 0.10). In this study, we show that a custom micro-panel of 4–6 markers can be effectively used to predict outcomes and monitor remission for patients with TNBC. These custom micro-panels show high sensitivity for detecting molecular relapse in advance of clinical relapse. The use of these panels could improve patient outcomes through early detection of relapse with preemptive intervention prior to symptom onset.
Geometric learning of functional brain network on the correlation manifold
Authors: You Kisung , Park Hae-Jeong 
Published: October 2022

AbstractThe correlation matrix is a typical representation of node interactions in functional brain network analysis. The analysis of the correlation matrix to characterize brain networks observed in several neuroimaging modalities has been conducted predominantly in the Euclidean space by assuming that pairwise interactions are mutually independent. One way to take account of all interactions in the network as a whole is to analyze the correlation matrix under some geometric structure. Recent studies have focused on the space of correlation matrices as a strict subset of symmetric positive definite (SPD) matrices, which form a unique mathematical structure known as the Riemannian manifold. However, mathematical operations of the correlation matrix under the SPD geometry may not necessarily be coherent (i.e., the structure of the correlation matrix may not be preserved), necessitating a post-hoc normalization. The contribution of the current paper is twofold: (1) to devise a set of inferential methods on the correlation manifold and (2) to demonstrate its applicability in functional network analysis. We present several algorithms on the correlation manifold, including measures of central tendency, cluster analysis, hypothesis testing, and low-dimensional embedding. Simulation and real data analysis support the application of the proposed framework for brain network analysis.
A data driven approach in less expensive robust transmitting coverage and power optimization
Authors: Parnianifard Amir , Mumtaz Shahid , Chaudhary Sushank , Imran Muhammad Ali , Wuttisittikulkij Lunchakorn 
Published: October 2022

AbstractThis paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).
Communication skills in children aged 6–8 years, without cerebral palsy cooled for neonatal hypoxic-ischemic encephalopathy
Authors: Robb Thomas J. , Tonks James , Spencer Arthur P. C. , Jary Sally , Whitfield Charlotte K. , Thoresen Marianne , Cowan Frances M. , Chakkarapani Ela 
Published: October 2022

AbstractWe assessed communication skills of 48 children without cerebral palsy (CP) treated with therapeutic hypothermia (TH) for neonatal hypoxic-ischemic encephalopathy (HIE) (cases) compared to 42 controls at early school-age and examined their association with white matter diffusion properties in both groups and 18-month Bayley-III developmental assessments in cases. Parents completed a Children’s Communication Checklist (CCC-2) yielding a General Communication Composite (GCC), structural and pragmatic language scores and autistic-type behavior score. GCC ≤ 54 and thresholds of structural and pragmatic language score differences defined language impairment. Using tract-based spatial statistics (TBSS), fractional anisotropy (FA) was compared between 31 cases and 35 controls. Compared to controls, cases had lower GCC (p = 0.02), structural (p = 0.03) and pragmatic language score (p = 0.04) and higher language impairments (p = 0.03). GCC correlated with FA in the mid-body of the corpus callosum, the cingulum and the superior longitudinal fasciculus (p < 0.05) in cases. Bayley-III Language Composite correlated with GCC (r = 0.34, p = 0.017), structural (r = 0.34, p = 0.02) and pragmatic (r = 0.32, p = 0.03) language scores and autistic-type behaviors (r = 0.36, p = 0.01).
Neural interactions in working memory explain decreased recall precision and similarity-based feature repulsion
Authors: Johnson Jeffrey S. , van Lamsweerde Amanda E. , Dineva Evelina , Spencer John P. 
Published: October 2022

AbstractOver the last several years, the study of working memory (WM) for simple visual features (e.g., colors, orientations) has been dominated by perspectives that assume items in WM are stored independently of one another. Evidence has revealed, however, systematic biases in WM recall which suggest that items in WM interact during active maintenance. In the present study, we report two experiments that replicate a repulsion bias between metrically similar colors during active storage in WM. We also observed that metrically similar colors were stored with lower resolution than a unique color held actively in mind at the same time. To account for these effects, we report quantitative simulations of two novel neurodynamical models of WM. In both models, the unique behavioral signatures reported here emerge directly from laterally-inhibitory neural interactions that serve to maintain multiple, distinct neural representations throughout the WM delay period. Simulation results show that the full pattern of empirical findings was only obtained with a model that included an elaborated spatial pathway with sequential encoding of memory display items. We discuss implications of our findings for theories of visual working memory more generally.
Effect of aspirin on primary prevention of cardiovascular disease and mortality among patients with chronic kidney disease
Authors: Haim-Pinhas Hadar , Yoskovitz Gil , Lishner Michael , Pereg David , Kitay-Cohen Yona , Topaz Guy , Sela Yaron , Wand Ori , Rozenberg Ilan , Benchetrit Sydney , Cohen-Hagai Keren 
Published: October 2022

AbstractChronic kidney disease is associated with an increased risk for cardiovascular and bleeding events. Data regarding the effectiveness and risks of aspirin therapy for primary prevention in the high-risk group of patients with chronic kidney disease are scant and controversial. This retrospective study included patients with chronic kidney disease. Participants were divided according to aspirin use. Outcomes included non-fatal cardiovascular events, major bleeding events and all-cause mortality. Among 10,303 patients, 2169 met the inclusion criteria and 1818 were included after 1:1 propensity-score matching. Our final cohort included patients with mean age of 73.4 ± 11.6 years, estimated glomerular filtration rate of 31.5 ± 10.5 ml/min/1.73m2 with follow up of 4.9 ± 1.5 years. There were no significant differences in all-cause mortality and bleeding events (odds ratio = 1.03, confidence interval [0.62, 1.84], p = .58 and odds ratio = 1.09, confidence interval [0.65, 1.72], p = .87 respectively). The incidence of cardiovascular events was higher in aspirin users versus non-users on univariate analysis (p < 0.01) and was comparable after controlling for possible risk-factors (OR = 1.05, CI [0.61, 3.14], p = .85). Chronic aspirin use for primary prevention of cardiovascular disease was not associated with lower mortality, cardiovascular events or increased bleeding among patients with chronic kidney disease. Those results were unexpected and should prompt further research in this field.