bluuebunny/crossref_metadata_2025_split huggingface dataset:
An efficient implementation of analytical nuclear gradients for linear-response time-dependent density functional theory in the plane wave basis
Authors: Liu Jie , Hu Wei , Yang Jinlong 
Published: April 2023

Abstract
We present an efficient implementation of the analytical nuclear gradient of linear-response time-dependent density functional theory (LR-TDDFT) with the frozen core approximation (FCA). This implementation is realized based on the Hutter’s formalism and the plane wave pseudopotential method. Numerical results demonstrate that the LR-TDDFT/FCA method using a small subset of Kohn–Sham occupied orbitals are accurate enough to reproduce the LR-TDDFT results. Here, the FCA remarkably reduces the computational cost in solving the LR-TDDFT eigenvalue equation. Another challenge in the calculations of analytical nuclear gradients for LR-TDDFT is the solution of the Z-vector equation, for which the Davidson algorithm is a popular choice. While, for large systems the standard Davidson algorithm exhibits a low convergence rate. In order to overcome this problem, we generalize the two-level Davidson algorithm to solve linear equation problems. A more stable performance is achieved with this new algorithm. Our method should encourage further studies of excited-state properties with LR-TDDFT in the plane wave basis.
PEG functionalized ZnO nanoparticles by fusion of precipitation-cum-hydrothermal method with enhanced photocatalytic activity
Authors: Gaur Jyoti , Pal Mohinder , Kumar Sanjeev , Kaur Harpreet , Lotey Gurmeet Singh , Bala Kanchan , Supreet  , Kaur Arshdeep 
Published: April 2023

Abstract
In this study, polyethylene glycol (PEG)-aided zinc oxide (ZnO) nanoparticles (NPs) have been synthesized by fusion of precipitation-cum-hydrothermal method. The PEG/ZnO NPs were investigated by x-ray diffraction (XRD), Fourier-transform infrared (FTIR) transformation, UV-visible field emission scanning electron microscope (FESEM), energy dispersive x-ray, high resolution transmission electron microscope (HRTEM), and RAMAN techniques. XRD analysis confirms the formation of the wurtzite phase with a crystallite size of 8 nm of synthesized PEG/ZnO. While FESEM and HRTEM investigations reveal the formation of distinct structural forms, FTIR investigations show interactions between PEG and ZnO. High crystallinity of PEG/ZnO is observed in the selected area electron diffraction pattern. The Brunauer–Emmett–Teller (BET) study revealed that ZnO NPs have a mesoporous structure with a significant specific surface area of 42 m2 g−1. The evaluation of photocatalytic activity of PEG/ZnO-based photocatalyst was carried out via the degradation of typical azo dye (industrial methylene blue (MB) dye) along with total organic carbon (TOC) analysis. The PEG-ZnO (dose 200 mg l−1) was found to be an efficient photocatalyst for the degradation of MB dye. The degradation reaction exhibits pseudo-first-order kinetics. Additionally, TOC removal was monitored, elucidating almost complete mineralization.
Investigation of electrical parameters and temperature analysis of a dual-metal DG PNPN TFET with extended source
Authors: Baruah Karabi , Baishya Srimanta 
Published: April 2023

Abstract
In this article, a dual-metal double-gate extended-source PNPN tunnel-FET (DG-ES-DMG TFET) is proposed and investigated. The performance of conventional double-gate PNPN TFET (DG TFET) can be improved by extending a portion of the source to the channel side, which creates vertical tunneling along with the lateral tunneling, thereby enhancing the band-to-band tunneling rate and on-current in double-gate extended-source PNPN tunnel-FET (DG-ES TFET). The performance of this DG-ES TFET can be further enhanced by introducing a dual metal gate in DG-ES-DMG TFET. The performance of the TFETs is evaluated with the help of TCAD (Technology Computer-Aided Design) software. The proposed optimized DG-ES-DMG TFET provides very good performances; On-current (I
ON) of 1.39 × 10−3 A μm−1, On-Off current ratio (I
ON/I
OFF) of 1.10 × 1011, subthreshold swing (SS) of 20 mV/Decade, threshold voltage (V
TH) of 0.37 V, and a cut-off frequency (f
T) of 188 GHz. The temperature sensitivity of the TFETs is also analyzed in this work. The comparison of the proposed TFET with other existing TFETs reveals that the proposed TFET could be a good contender for low-power applications.
Plug & play directed evolution of proteins with gradient-based discrete MCMC
Authors: Emami Patrick , Perreault Aidan , Law Jeffrey , Biagioni David , St. John Peter 
Published: April 2023

Abstract
A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.
Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates
Authors: Su Mao , Yang Ji-Hui , Xiang Hong-Jun , Gong Xin-Gao 
Published: April 2023

Abstract
Despite the successes of machine learning methods in physical sciences, the prediction of the Hamiltonian, and thus the electronic properties, is still unsatisfactory. Based on graph neural network (NN) architecture, we present an extendable NN model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. The rotational equivariance of the Hamiltonian is achieved by our complete local coordinates (LCs). The LC information, encoded using a convolutional NN and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our NN model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, the present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials.
ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening
Authors: Chen Sikang , Gao Jian , Chen Jiexuan , Xie Yufeng , Shen Zheyuan , Xu Lei , Che Jinxin , Wu Jian , Dong Xiaowu 
Published: April 2023

Abstract
Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein–ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein–ligand complexes and cluster the ligands. In ClusterX, the graph was used to represent the protein–ligand complex, and the joint optimisation can be used efficiently for learning the cluster-friendly features. Experiments on the KLIFs database show that the model can distinguish well between the binding modes of different kinase inhibitors. To validate the effectiveness of the model, the clustering results on the virtual screening dataset further demonstrated that ClusterX achieved better or more competitive performance against traditional methods, such as SIFt and extended connectivity fingerprints. This framework may provide a unique tool for clustering analysis and prove to assist computational medicinal chemists in visual decision-making.
Modular response analysis reformulated as a multilinear regression problem
Authors: Borg Jean-Pierre , Colinge Jacques , Ravel Patrice 
Published: April 2023

AbstractMotivationModular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system, and results are sensitive to noise in the data and perturbation intensities. Due to noise propagation, applications to networks of 10 nodes or more are difficult.ResultsWe propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential additional perturbations in a larger, over-determined, and more stable system of equations. More relevant confidence intervals on network parameters can be obtained, and we show competitive performance for networks of size up to 1000. Prior knowledge integration in the form of known null edges further improves these results.Availability and implementationThe R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics
MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores
Authors: El Khili Mohamed Reda , Memon Safyan Aman , Emad Amin 
Published: April 2023

AbstractMotivationCombination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values.ResultsHere, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133 722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions.Availability and implementationAn implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY.
SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion
Authors: Canson Daffodil M , Davidson Aimee L , de la Hoya Miguel , Parsons Michael T , Glubb Dylan M , Kondrashova Olga , Spurdle Amanda B 
Published: April 2023

Abstract

Summary
SpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1212 single-nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance (≥84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins.

Availability and implementation
SAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values.
Polyethylene glycol-coated haemostatic patch for prevention of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: randomized clinical trial
Authors: Serradilla-Martín Mario , Paterna-López Sandra , Palomares-Cano Ana , Cantalejo-Díaz Miguel , Abadía-Forcén Teresa , Gutiérrez-Díez Marta L , Artigas-Marco Consuelo , Serrablo-Requejo Alejandro 
Published: April 2023

Abstract

Background
The potential of haemostatic patches to reduce the rate of postoperative pancreatic fistula remains unclear. The aim of this trial was to evaluate the impact of a polyethylene glycol-coated haemostatic patch on the incidence of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy.

Methods
In this randomized, single-centre, clinical trial, patients undergoing pancreatoduodenectomy were randomized 1 : 1 to receive pancreatojejunostomy reinforced with two polyethylene glycol-coated haemostatic patches (patch group) or without any reinforcement (control group). The primary outcome was clinically relevant postoperative pancreatic fistula, defined as grade B/C according to International Study Group of Pancreatic Surgery criteria, within 90 days. Key secondary outcomes were length of hospital stay, total rate of postoperative pancreatic fistula, and overall complication rate.

Results
From 15 May 2018 to 22 June 2020, 72 patients were randomized, and 64 were included in the analyses (31 in the patch group and 33 in the control group). The risk of clinically relevant postoperative pancreatic fistula was reduced by 90 per cent (OR 0.10, 95 per cent c.i. 0.01 to 0.89, P = 0.039). Moreover, the use of the polyethylene glycol-coated patch retained its protective effect on clinically relevant postoperative pancreatic fistula in a multivariable regression model, significantly reducing the risk of clinically relevant postoperative pancreatic fistula by 93 per cent (OR 0.07, 95 per cent c.i. 0.01 to 0.67, P = 0.021), regardless of patient age, sex, or fistula risk score. The incidence of secondary outcomes did not significantly differ between the groups. One patient died within 90 days in the patch group versus three patients in the control group.

Conclusions
A polyethylene glycol-coated haemostatic patch reduced the incidence of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy.

Registration number
NCT03419676 (http://www.clinicaltrials.gov).
Altered nucleus accumbens functional connectivity precedes apathy in Parkinson’s disease
Authors: Morris Lee-Anne , Harrison Samuel J , Melzer Tracy R , Dalrymple-Alford John C , Anderson Tim J , MacAskill Michael R , Le Heron Campbell J 
Published: April 2023

Abstract
Work in animal and human neuroscience has identified neural regions forming a network involved in the production of motivated, goal-directed behaviour. In particular, the nucleus accumbens and anterior cingulate cortex are recognized as key network nodes underlying decisions of whether to exert effort for reward, to drive behaviour. Previous work has convincingly shown that this cognitive mechanism, known as effort-based decision making, is altered in people with Parkinson’s disease with a syndrome of reduced goal-directed behaviour—apathy. Building on this work, we investigated whether the neural regions implementing effort-based decision-making were associated with apathy in Parkinson’s disease, and more importantly, whether changes to these regions were evident prior to apathy development.
We performed a large, multimodal neuroimaging analysis in a cohort of people with Parkinson’s disease (n = 199) with and without apathy at baseline. All participants had ∼2-year follow-up apathy scores, enabling examination of brain structure and function specifically in those with normal motivation who converted to apathy by ∼2-year follow-up. In addition, of the people with normal motivation, a subset (n = 56) had follow-up neuroimaging data, allowing for examination of the ‘rate of change’ in key nodes over time in those who did, and did not, convert to apathy. Healthy control (n = 54) data were also included to aid interpretation of findings.
Functional connectivity between the nucleus accumbens and dorsal anterior cingulate cortex was higher in people with normal motivation who later converted to apathy compared to those who did not, whereas no structural differences were evident between these groups. In contrast, grey matter volume in these regions was reduced in the group with existing apathy. Furthermore, of those with normal motivation who had undergone longitudinal neuroimaging, converters to apathy showed a higher rate of change in grey matter volume within the nucleus accumbens.
Overall, we show that changes in functional connectivity between nucleus accumbens and anterior cingulate cortex precedes apathy in people with Parkinson’s disease, with conversion to apathy associated with higher rate of grey matter volume loss in nucleus accumbens, despite no baseline differences. These findings significantly add to an accumulating body of transdiagnostic evidence that apathy arises from disruption to key nodes within a network in which normal goal-directed behaviour is instantiated, and raise the possibility of identifying those at risk for developing apathy before overt motivational deficits have arisen.
Lowering levels of reelin in entorhinal cortex layer II-neurons results in lowered levels of intracellular amyloid-β
Authors: Kobro-Flatmoen Asgeir , Battistin Claudia , Nair Rajeevkumar Raveendran , Bjorkli Christiana , Skender Belma , Kentros Cliff , Gouras Gunnar , Witter Menno P 
Published: April 2023

Abstract
Projection neurons in the anteriolateral part of entorhinal cortex layer II are the predominant cortical site for hyper-phosphorylation of tau and formation of neurofibrillary tangles in prodromal Alzheimer’s disease. A majority of layer II projection neurons in anteriolateral entorhinal cortex are unique among cortical excitatory neurons by expressing the protein reelin. In prodromal Alzheimer’s disease, these reelin-expressing neurons are prone to accumulate intracellular amyloid-β, which is mimicked in a rat model that replicates the spatio-temporal cascade of the disease. Two important findings in relation to this are that reelin-signalling downregulates tau phosphorylation, and that oligomeric amyloid-β interferes with reelin-signalling. Taking advantage of this rat model, we used proximity ligation assay to assess whether reelin and intracellular amyloid-β directly interact during early, pre-plaque stages in anteriolateral entorhinal cortex layer II reelin-expressing neurons. We next made a viral vector delivering micro-RNA against reelin, along with a control vector, and infected reelin-expressing anteriolateral entorhinal cortex layer II-neurons to test whether reelin levels affect levels of intracellular amyloid-β and/or amyloid precursor protein. We analysed 25.548 neurons from 24 animals, which results in three important findings. First, in reelin-expressing anteriolateral entorhinal cortex layer II-neurons, reelin and intracellular amyloid-β engage in a direct protein–protein interaction. Second, injecting micro-RNA against reelin lowers reelin levels in these neurons, amounting to an effect size of 1.3–4.5 (Bayesian estimation of Cohen’s d effect size, 95% credible interval). This causes a concomitant reduction of intracellular amyloid-β ranging across three levels of aggregation, including a reduction of Aβ42 monomers/dimers amounting to an effect size of 0.5–3.1, a reduction of Aβ prefibrils amounting to an effect size of 1.1–3.5 and a reduction of protofibrils amounting to an effect size of 0.05–2.1. Analysing these data using Bayesian estimation of mutual information furthermore reveals that levels of amyloid-β are dependent on levels of reelin. Third, the reduction of intracellular amyloid-β occurs without any substantial associated changes in levels of amyloid precursor protein. We conclude that reelin and amyloid-β directly interact at the intracellular level in the uniquely reelin-expressing projection neurons in anteriolateral entorhinal cortex layer II, where levels of amyloid-β are dependent on levels of reelin. Since amyloid-β is known to impair reelin-signalling causing upregulated phosphorylation of tau, our findings are likely relevant to the vulnerability for neurofibrillary tangle-formation of this entorhinal neuronal population.
Targeted Mass Azithromycin Distribution for Trachoma: A Community-Randomized Trial (TANA II)
Authors: Mahmud Hamidah , Haile Berhan A , Tadesse Zerihun , Gebresillasie Sintayehu , Shiferaw Ayalew , Zerihun Mulat , Liu Zijun , Callahan E Kelly , Cotter Sun Y , Varnado Nicole E , Oldenburg Catherine E , Porco Travis C , Lietman Thomas M , Keenan Jeremy D 
Published: April 2023

Abstract

Background
Current guidelines recommend annual community-wide mass administration of azithromycin for trachoma. Targeting treatments to those most likely to be infected could reduce the amount of unnecessary antibiotics distributed.

Methods
In a cluster-randomized trial conducted from 1 November 2010 through 8 November 2013, 48 Ethiopian communities previously treated with annual mass azithromycin distributions for trachoma were randomized in equal numbers to (1) annual azithromycin distributions targeted to children aged 0–5 years, (2) annual azithromycin distributions targeted to households with a child aged 0–5 years found to have clinically active trachoma, (3) continued annual mass azithromycin distributions to the entire community, or (4) cessation of treatment. The primary outcome was the community prevalence of ocular chlamydia infection among children aged 0–9 years at month 36. Laboratory personnel were masked to treatment allocation.

Results
The prevalence of ocular chlamydia infection among children aged 0–9 years increased from 4.3% (95% confidence interval [CI], .9%–8.6%) at baseline to 8.7% (95% CI, 4.2%–13.9%) at month 36 in the age-targeted arm, and from 2.8% (95% CI, .8%–5.3%) at baseline to 6.3% (95% CI, 2.9%–10.6%) at month 36 in the household-targeted arm. After adjusting for baseline chlamydia prevalence, the 36-month prevalence of ocular chlamydia was 2.4 percentage points greater in the age-targeted group (95% CI, −4.8% to 9.6%; P = .50; prespecified primary analysis). No adverse events were reported.

Conclusions
Targeting azithromycin treatment to preschool children was no different than targeting azithromycin to households with a child with clinically active trachoma. Neither approach reduced ocular chlamydia over the 3-year study.

Clinical Trials Registration
NCT01202331.