bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interactions between amphiphilic nanoparticles coated with striped hydrophilic/hydrophobic ligands and a lipid bilayer
Authors: Sun Lijuan , Cao Yu , Chen Xiaojie , Liang Qing 
Published: April 2023

Abstract
Due to their unique physicochemical properties, nanoparticles play an important role in the fields of nanomedicine and so on. In this paper, the interactions between the nanoparticles coated with striped hydrophilic and hydrophobic ligands and a lipid bilayer are investigated by using the coarse-grained molecular dynamics simulation. This study focuses on the effects of the density of the ligands, the ratio of the hydrophilic ligands to the hydrophobic ligands, and the rigidity of the ligands on the interactions of the nanoparticles with the lipid bilayer. The results show that the nanoparticles interact with the lipid bilayer in two different ways. The nanoparticle with a small size, a large ratio of hydrophilic ligands to the hydrophobic ligands, and flexible ligands can readily be inserted into the lipid bilayer, and the nanoparticle rotation is very crucial to the insertion of the nanoparticle into the bilayer. However, the nanoparticle with a large size, a small ratio of hydrophilic ligands to hydrophobic ligands, and rigid ligands can only be adsorbed on the surface of the lipid bilayer. This work provides an effective method to modulate the interactions of the amphiphilic nanoparticles with the lipid bilayers and some insights into the applications of the nanoparticles in drug delivery, cell imaging, etc.
Finding optimal basis states for variation after projection nuclear wave functions*
Authors: Lu Xiao , Lian Zhan-Jiang , Li Xue-Wei , Gao Zao-Chun , Chen Yong-Shou 
Published: April 2023

Abstract
The variation after projection (VAP) method is expected to be an efficient way of obtaining the optimized nuclear wave functions, which can be as close as possible to the exact shell model ones. However, we found that there are two additional problems that may seriously affect the convergence of the VAP iteration. The first problem is the existence of irrelevant projected basis states. At a VAP iteration, the Hill-Wheeler (HW) equation is composed of all updated projected basis states. If one of these projected basis states does not mix with a calculated wave function of interest, which is obtained by solving this HW equation, it is likely that this basis state will never mix with this wave function even after the VAP iteration converges. The other problem is the poor orthonormality among the projected basis states, which seriously affects the accuracy of the calculated VAP wave function. In the present work, solutions for these two problems are proposed, and examples are presented to test the validity. With the present solutions, the most important projected basis states can be reliably obtained, and the fully optimized VAP wave functions can be accurately and efficiently calculated.
Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain–machine interfaces
Authors: Nason-Tomaszewski Samuel R , Mender Matthew J , Kennedy Eric , Lambrecht Joris M , Kilgore Kevin L , Chiravuri Srinivas , Ganesh Kumar Nishant , Kung Theodore A , Willsey Matthew S , Chestek Cynthia A , Patil Parag G 
Published: April 2023

Abstract

Objective. Brain–machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user’s own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. Approach. We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI’s predictions to control FES of the monkey’s finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI’s predictions to control movements of virtual fingers, with no FES. Main results. In the BCFES task, the monkey improved his success rate to 83% (1.5 s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5 s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time. Significance. These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.
Double scaling limit of the prismatic tensor model
Authors: Krajewski T , Muller T , Tanasa A 
Published: April 2023

Abstract
In Giombi et al (2018 Phys. Rev. D 98 105005), a prismatic tensor model was introduced. We study here the diagrammatics and the double scaling limit of this model, using the intermediate field method. We explicitly exhibit the next-to-leading order Feynman graphs in the

1

/

N

expansion. Using appropriate combinatorial tools, we further study the general term of the

1

/

N

expansion and we compute the two-point function in the double scaling limit. We find that the double scaling limit mechanism implemented here is similar to the one implemented for various quartic tensor models.
Effect of Spraying with Amino Acids (Tryptophan and Glycine) and Nano-Potassium on some Vegetative Growth Characteristics of Stevia Plant (Stevia rebaudiana Bertoni)
Authors: Mahdi Amal Najeh , Abdullah Kadum Mohammed , Almyali Ali Ahmed Hussein 
Published: April 2023

Abstract
Researchers from the College of Agriculture at the University of Kerbala in the Karbala province of Iraq used a greenhouse to examine the effects of nano-potassium and the amino acids tryptophan and glycine on the development of the stevia plant and the concentration of its active substances in the local climate. The experiment had three replicates and was carried out using a factorial trial design within a randomized complete block design (R.C.B.D). The study had three variables. The first component was tryptophan, with three concentrations (0, 25, and 50 mg. L-1), the second factor was glycine, with three concentrations (0, 500, and 1000 mg. L-1), and the third factor was nano-potassium, with two values (0, and 100 nmol L-1) (0, 2 g. L-1). The findings demonstrated that the variables under study, whether acting separately or in combination, significantly improved all characteristics of vegetative development. The triple interaction treatment (50 mg. L-1 tryptophan + 1000 mg. L-1 glycine + 2 g. L-1 potassium) gave the highest percentages for plant height, number of main branches, leaf dry weight, number of total leaves, and total chlorophyll content, where recorded (89.68 cm, 12.67 main branches. plant-1, 48.43 g. plant-1, 189.09 leaves. plant-1, 12,609 mg. 100 g-1 fresh weight, respectively). It may be concluded that the application of an integrated fertilization program can sustain or enhance stevia production in both quantity and quality under challenging environmental circumstances.
Effects of growth temperature on structural and electrical properties of in-rich InAlN–GaN heterostructures by radio-frequency metal–organic molecular beam epitaxy
Authors: Chen Wei-Chun , Chiu Kun-An , Chen Hung-Pin , Lin Yu-Wei , Chen Che-Chin , Chen Fong-Zhi 
Published: April 2023

Abstract
In-rich InAlN is a promising nitride semiconductor alloy for high-efficiency solar cells and wide-range light-emitting diodes due to its tunable bandgap from 0.7 to 6.2 eV. However, incomplete characterization has led to inconsistent fundamental properties in some studies. The aim of this study was to comprehensively investigate the structural, optical, and electrical properties of In-rich InAlN films grown on GaN/Al2O3 templates by RF-MOMBE at various temperatures. The methodology involved state-of-the-art metrology techniques, such as high-resolution x-ray diffraction (HRXRD), scanning electron microscopy (FE-SEM), Hall effect measurements, and transmission electron microscopy (TEM). The results showed that all InxAl1-xN films were epitaxially grown on the GaN/Al2O3 template, with the indium composition (x) decreasing with increasing growth temperature. Furthermore, phase separation of the In-rich InAlN films occurred at high growth temperatures(>550 °C), resulting in a relatively smooth surface. The optical absorption method measured the band-gap of the InxAl1-xN films, which ranged from 1.7 to 1.9 eV for x values between 0.77 and 0.91. The mobility and carrier concentrations of all In-rich InAlN films were measured at ∼60−277 cm2 V−1-s−1 and 2–7 × 1019 cm3 in the growth temperature of range 450 °C–610 °C, respectively. In conclusion, our comprehensive characterization using advanced metrology methods provides valuable insights into the properties of In-rich InAlN films, which can inform future optimization of these materials for various applications.
Progression of Hodgkin lymphoma and plasma cell neoplasms: Report from the 2021 SH/EAHP Workshop
Authors: Nejati Reza , Amador Catalina , Czader Magdalena , Thacker Elizabeth , Thakkar Devang , Dave Sandeep S , Dogan Ahmet , Duffield Amy , Goodlad John R , Ott German , Wasik Mariusz A , Xiao Wenbin , Cook James R 
Published: April 2023

Abstract

Objectives
To summarize cases submitted to the 2021 Society for Hematopathology/European Association for Haematopathology Workshop under the categories of progression of Hodgkin lymphoma, plasmablastic myeloma, and plasma cell myeloma.

Methods
The workshop panel reviewed 20 cases covered in this session. In addition, whole-exome sequencing (WES) and whole-genome RNA expression analysis were performed on 10 submitted cases, including 6 Hodgkin lymphoma and 4 plasma neoplasm cases.

Results
The cases of Hodgkin lymphoma included transformed cases to or from various types of B-cell lymphoma with 1 exception, which had T-cell differentiation. The cases of plasma cell neoplasms included cases with plasmablastic progression, progression to plasma cell leukemia, and secondary B-lymphoblastic leukemia. Gene variants identified by WES included some known to be recurrent in Hodgkin lymphoma and plasma cell neoplasm. All submitted Hodgkin lymphoma samples showed 1 or more of these mutations: SOCS1, FGFR2, KMT2D, RIT1, SPEN, STAT6, TET2, TNFAIP3, and ZNF217.

Conclusions
Better molecular characterization of both of these neoplasms and mechanisms of progression will help us to better understand mechanisms of progression and perhaps develop better prognostic models, as well as identifying novel therapeutic targets.
Longitudinal Associations Between Neighborhood Child Opportunity and Physical Fitness for New York City Public School Youth
Authors: Zewdie Hiwot Y , Zhao Amy Y , Ogletree S Scott , Messiah Sarah E , Armstrong Sarah C , Skinner Asheley C , Neshteruk Cody D , Hipp J Aaron , Day Sophia E , Konty Kevin J , D’Agostino Emily M 
Published: April 2023

Abstract
Neighborhood environments can support fitness-promoting behavior, yet little is known about their influence on youth physical fitness outcomes over time. We examined longitudinal associations between neighborhood opportunity and youth physical fitness among New York City (NYC) public school youth. The Child Opportunity Index (COI), a composite index of 29 indicators measuring neighborhood opportunity at the census-tract level, along with scores on 4 selected COI indicators were linked to NYC FITNESSGRAM youth data at baseline. Fitness outcomes (measured annually, 2011–2018) included body mass index, curl-ups, push-ups, and Progressive Aerobic Cardiovascular Endurance Run (PACER) laps. Unstratified and age-stratified, adjusted, 3-level generalized linear mixed models, nested by census tract and time, estimated the association between COI and fitness outcomes. The analytical sample (n = 204,939) lived in very low (41%) or low (30%) opportunity neighborhoods. Unstratified models indicated that overall COI is modestly associated with improved youth physical fitness outcomes. The strongest opportunity-fitness associations were observed for PACER. Stratified models show differences in associations across younger vs. older youth. We find that neighborhood factors are associated with youth fitness outcomes over time, with the strength of the associations dependent on age. Future implications include better informed place-based interventions tailored to specific life stages to promote youth health.
Graph deep learning enabled spatial domains identification for spatial transcriptomics
Authors: Liu Teng , Fang Zhao-Yu , Li Xin , Zhang Li-Ning , Cao Dong-Sheng , Yin Ming-Zhu 
Published: April 2023

Abstract
Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL.
DEGGs: an R package with shiny app for the identification of differentially expressed gene–gene interactions in high-throughput sequencing data
Authors: Sciacca Elisabetta , Alaimo Salvatore , Silluzio Gianmarco , Ferro Alfredo , Latora Vito , Pitzalis Costantino , Pulvirenti Alfredo , Lewis Myles J 
Published: April 2023

Abstract

Summary
The discovery of differential gene–gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene–gene link, differential statistical significance is provided through robust linear regression with an interaction term.

Availability and implementation
DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.
matchRanges: generating null hypothesis genomic ranges via covariate-matched sampling
Authors: Davis Eric S , Mu Wancen , Lee Stuart , Dozmorov Mikhail G , Love Michael I , Phanstiel Douglas H 
Published: April 2023

Abstract

Motivation
Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non-trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic features including genes, enhancers, and transcription factor binding sites. Propensity score-based covariate matching methods allow the selection of null sets from a pool of possible items while controlling for multiple covariates; however, existing packages do not operate on genomic data classes and can be slow for large data sets making them difficult to integrate into genomic workflows.

Results
To address this, we developed matchRanges, a propensity score-based covariate matching method for the efficient and convenient generation of matched null ranges from a set of background ranges within the Bioconductor framework.

Availability and implementation
Package: https://bioconductor.org/packages/nullranges, Code: https://github.com/nullranges, Documentation: https://nullranges.github.io/nullranges.
FAS: assessing the similarity between proteins using multi-layered feature architectures
Authors: Dosch Julian , Bergmann Holger , Tran Vinh , Ebersberger Ingo 
Published: April 2023

Abstract

Motivation
Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations.

Results
Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications.

Availability and implementation
FAS is available as python package: https://pypi.org/project/greedyFAS/.
AcrNET: predicting anti-CRISPR with deep learning
Authors: Li Yunxiang , Wei Yumeng , Xu Sheng , Tan Qingxiong , Zong Licheng , Wang Jiuming , Wang Yixuan , Chen Jiayang , Hong Liang , Li Yu 
Published: April 2023

Abstract

Motivation
As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.

Results
On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.

Availability and implementation
Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.
Predicting the pathogenicity of missense variants using features derived from AlphaFold2
Authors: Schmidt Axel , Röner Sebastian , Mai Karola , Klinkhammer Hannah , Kircher Martin , Ludwig Kerstin U 
Published: April 2023

Abstract

Motivation
Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. This raises the question of whether AlphaFold2 wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants.

Results
To address this, we first engineered a set of features for each amino acid from these structures. We then trained a random forest to distinguish between relatively common (proxy-benign) and singleton (proxy-pathogenic) missense variants from gnomAD v3.1. This yielded a novel AlphaFold2-based pathogenicity prediction score, termed AlphScore. Important feature classes used by AlphScore are solvent accessibility, amino acid network related features, features describing the physicochemical environment, and AlphaFold2’s quality parameter (predicted local distance difference test). AlphScore alone showed lower performance than existing in silico scores used for missense prediction, such as CADD or REVEL. However, when AlphScore was added to those scores, the performance increased, as measured by the approximation of deep mutational scan data, as well as the prediction of expert-curated missense variants from the ClinVar database. Overall, our data indicate that the integration of AlphaFold2-predicted structures can improve pathogenicity prediction of missense variants.

Availability and implementation
AlphScore, combinations of AlphScore with existing scores, as well as variants used for training and testing are publicly available.