bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differentiable programming of isometric tensor networks
Authors: Geng Chenhua , Hu Hong-Ye , Zou Yijian 
Published: January 2022

Abstract
Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation. This concept emerges from deep learning, and has also been generalized to tensor network optimizations. Here, we extend the differentiable programming to tensor networks with isometric constraints with applications to multiscale entanglement renormalization ansatz (MERA) and tensor network renormalization (TNR). By introducing several gradient-based optimization methods for the isometric tensor network and comparing with Evenbly–Vidal method, we show that auto-differentiation has a better performance for both stability and accuracy. We numerically tested our methods on 1D critical quantum Ising spin chain and 2D classical Ising model. We calculate the ground state energy for the 1D quantum model and internal energy for the classical model, and scaling dimensions of scaling operators and find they all agree with the theory well.
Drug–target interactions prediction via deep collaborative filtering with multiembeddings
Authors: Chen Ruolan , Xia Feng , Hu Bing , Jin Shuting , Liu Xiangrong 
Published: January 2022

AbstractDrug–target interactions (DTIs) prediction research presents important significance for promoting the development of modern medicine and pharmacology. Traditional biochemical experiments for DTIs prediction confront the challenges including long time period, high cost and high failure rate, and finally leading to a low-drug productivity. Chemogenomic-based computational methods can realize high-throughput prediction. In this study, we develop a deep collaborative filtering prediction model with multiembeddings, named DCFME (deep collaborative filtering prediction model with multiembeddings), which can jointly utilize multiple feature information from multiembeddings. Two different representation learning algorithms are first employed to extract heterogeneous network features. DCFME uses the generated low-dimensional dense vectors as input, and then simulates the drug–target relationship from the perspective of both couplings and heterogeneity. In addition, the model employs focal loss that concentrates the loss on sparse and hard samples in the training process. Comparative experiments with five baseline methods show that DCFME achieves more significant performance improvement on sparse datasets. Moreover, the model has better robustness and generalization capacity under several harder prediction scenarios.
Accelerating the discovery of antifungal peptides using deep temporal convolutional networks
Authors: Singh Vishakha , Shrivastava Sameer , Kumar Singh Sanjay , Kumar Abhinav , Saxena Sonal 
Published: January 2022

Abstract
The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism’s intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal–Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.
Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities
Authors: Xiao Di , Kim Hani Jieun , Pang Ignatius , Yang Pengyi 
Published: January 2022

Abstract

Motivation
The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored.

Results
Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs.

Availability and implementation
The source code for data analysis in this study is available from Github repository https://github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8).

Supplementary information
Supplementary data are available at Bioinformatics online.
Effect of bariatric surgery on cancer risk: results from an emulated target trial using population-based data
Authors: Lazzati Andrea , Epaud Salomé , Ortala Matthieu , Katsahian Sandrine , Lanoy Emilie 
Published: January 2022

Abstract

Background
The impact of weight loss induced by bariatric surgery on cancer occurrence is controversial. To study the causal effect of bariatric surgery on cancer risk from an observational database, a target-trial emulation technique was used to mimic an RCT.

Methods
Data on patients admitted between 2010 and 2019 with a diagnosis of obesity were extracted from a national hospital discharge database. Criteria for inclusion included eligibility criteria for bariatric surgery and the absence of cancer in the 2 years following inclusion. The intervention arms were bariatric surgery versus no surgery. Outcomes were the occurrence of any cancer and obesity-related cancer; cancers not related to obesity were used as negative controls.

Results
A total of 1 140 347 patients eligible for bariatric surgery were included in the study. Some 288 604 patients (25.3 per cent) underwent bariatric surgery. A total of 48 411 cancers were identified, including 4483 in surgical patients and 43 928 among patients who did not receive bariatric surgery. Bariatric surgery was associated with a decrease in the risk of obesity-related cancer (hazard ratio (HR) 0.89, 95 per cent c.i. 0.83 to 0.95), whereas no significant effect of surgery was identified with regard to cancers not related to obesity (HR 0.96, 0.91 to 1.01).

Conclusion
When emulating a target trial from observational data, a reduction of 11 per cent in obesity-related cancer was found after bariatric surgery.
Performance of non-invasive biomarkers compared with invasive methods for risk prediction of posthepatectomy liver failure in hepatocellular carcinoma
Authors: Hobeika Christian , Guyard Clémence , Sartoris Riccardo , Maino Cesare , Rautou Pierre-Emmanuel , Dokmak Safi , Bouattour Mohamed , Durand François , Weiss Emmanuel , Vilgrain Valérie , Beaufrère Aurélie , Sepulveda Ailton , Farges Olivier , Paradis Valérie , Luciani Alain , Lim Chetana , Sommacale Daniele , Scatton Olivier , Laurent Alexis , Nault Jean-Charles , Soubrane Olivier , Ronot Maxime , Cauchy François 
Published: January 2022

Abstract

Background
Posthepatectomy liver failure (PHLF) is a rare but dreaded complication. The aim was to test whether a combination of non-invasive biomarkers (NIBs) and CT data could predict the risk of PHLF in patients who underwent resection of hepatocellular carcinoma (HCC).

Methods
Patients with HCC who had liver resection between 2012 and 2020 were included. A relevant combination of NIBs (NIB model) to model PHLF risk was identified using a doubly robust estimator (inverse probability weighting combined with logistic regression). The adjustment variables were body surface area, ASA fitness grade, male sex, future liver remnant (FLR) ratio, difficulty of liver resection, and blood loss. The reference invasive biomarker (IB) model comprised a combination of pathological analysis of the underlying liver and hepatic venous pressure gradient (HVPG) measurement. Various NIB and IB models for prediction of PHLF were fitted and compared. NIB model performances were validated externally. Areas under the curve (AUCs) were corrected using bootstrapping.

Results
Overall 323 patients were included. The doubly robust estimator showed that hepatitis C infection (odds ratio (OR) 4.33, 95 per cent c.i. 1.29 to 9.20; P = 0.001), MELD score (OR 1.26, 1.04 to 1.66; P = 0.001), fibrosis-4 score (OR 1.36, 1.06 to 1.85; P = 0.001), liver surface nodularity score (OR 1.55, 1.28 to 4.29; P = 0.031), and FLR volume ratio (OR 0.99, 0.97 to 1.00; P = 0.014) were associated with PHLF. Their combination (NIB model) was fitted externally (2-centre cohort, 165 patients) to model PHLF risk (AUC 0.867). Among 129 of 323 patients who underwent preoperative HVPG measurement, NIB and IB models had similar performances (AUC 0.753 versus 0.732; P = 0.940). A well calibrated nomogram was drawn based on the NIB model (AUC 0.740). The risk of grade B/C PHLF could be ruled out in patients with a cumulative score of less than 160 points.

Conclusion
The NIB model provides reliable preoperative evaluation with performance at least similar to that of invasive methods for PHLF risk prediction.
Dopaminergic medication normalizes aberrant cognitive control circuit signalling in Parkinson’s disease
Authors: Cai Weidong , Young Christina B , Yuan Rui , Lee Byeongwook , Ryman Sephira , Kim Jeehyun , Yang Laurice , Henderson Victor W , Poston Kathleen L , Menon Vinod 
Published: January 2022

AbstractDopaminergic medication is widely used to alleviate motor symptoms of Parkinson’s disease, but these medications also impact cognition with significant variability across patients. It is hypothesized that dopaminergic medication impacts cognition and working memory in Parkinson’s disease by modulating frontoparietal-basal ganglia cognitive control circuits, but little is known about the underlying causal signalling mechanisms and their relation to individual differences in response to dopaminergic medication. Here we use a novel state-space computational model with ultra-fast (490 ms resolution) functional MRI to investigate dynamic causal signalling in frontoparietal-basal ganglia circuits associated with working memory in 44 Parkinson’s disease patients ON and OFF dopaminergic medication, as well as matched 36 healthy controls.Our analysis revealed aberrant causal signalling in frontoparietal-basal ganglia circuits in Parkinson’s disease patients OFF medication. Importantly, aberrant signalling was normalized by dopaminergic medication and a novel quantitative distance measure predicted individual differences in cognitive change associated with medication in Parkinson’s disease patients. These findings were specific to causal signalling measures, as no such effects were detected with conventional non-causal connectivity measures. Our analysis also identified a specific frontoparietal causal signalling pathway from right middle frontal gyrus to right posterior parietal cortex that is impaired in Parkinson’s disease. Unlike in healthy controls, the strength of causal interactions in this pathway did not increase with working memory load and the strength of load-dependent causal weights was not related to individual differences in working memory task performance in Parkinson’s disease patients OFF medication. However, dopaminergic medication in Parkinson’s disease patients reinstated the relation with working memory performance.Our findings provide new insights into aberrant causal brain circuit dynamics during working memory and identify mechanisms by which dopaminergic medication normalizes cognitive control circuits.
Commentary: Role and communications of cancer hazard determinations
Authors: Samet Jonathan M , Berrington de Gonzalez Amy , Lunn Ruth M , Schubauer-Berigan Mary K 
Published: January 2022

Abstract
This commentary is written in response to a recent commentary in Carcinogenesis that provides several viewpoints on the International Agency for Research on Cancer’s (IARC) Monographs program on cancer hazard identification. This commentary offers an alternative viewpoint of the role of cancer hazard identification derived from cancer epidemiology studies in risk characterization, as well as clarification on the previous commentary’s interpretation of the purpose of the Monographs and other programs of cancer hazard identification and how IARC communicates the findings of the Monographs.
Validated Smart Different Chromatographic Methods for Selective Quantification of Acefylline Piperazine, Phenobarbital Sodium and Methylparaben Additive in Bulk and Pharmaceutical Dosage Form
Authors: Boltia Shereen A , Algmaal Shrouk E , Mostafa Nadia M , El Saharty Yasser S 
Published: January 2022

Abstract
Two chromatographic methods have been proposed for the simultaneous determination of acefylline piperazine (ACEF) and phenobarbital (PHENO) in presence of methylparaben as additive in pharmaceutical dosage form. The first method was thin-layer chromatography. The separation was achieved using silica gel as stationary phase and chloroform: methanol: glacial acetic acid (2.0, 8.0 and 0.1, by volume) as a developing system at 254 nm. Accurate determination of both drugs was attained over the concentration range of 0.5–25 μg/band. The second method was based on the use of reversed phase liquid chromatography with diode array detection, by which the proposed components were separated on a reversed phase C18 analytical column using methanol: water (60: 40, by volume) as a mobile phase with flow rate of 0.8 mL/min at 214 nm in a concentration range of 0.5–100 μg/mL. The proposed chromatographic methods were practiced successfully for the determination of ACEF and PHENO in pharmaceutical dosage form. Both methods were validated according to the International Conference on Harmonization guidelines and statistically compared with a reported high-performance liquid chromatograph method. Planar chromatography has never been proposed in the literature for ACEF and PHENO determination besides the proposed columnar chromatographic method using an isocratic eco-friendly mobile phase.
A Multicancer Malignant Pleural Effusion Diagnostic Test Using Hexokinase 2 and Single-Cell Sequencing
Authors: Chen Jie , Yang Yibin , Wang Zhuo , Shen Xiaohan , Zhang Ziyuan , Wang Chunying , Xu Haimiao , Shi Qihui 
Published: January 2022

Abstract

Background
Malignant pleural effusion (MPE) represents advanced malignant disease with poor prognosis. To date, pleural effusion cytology remains the best test to diagnose MPE but suffers from limited diagnostic sensitivity and high variation. We report a hexokinase 2-based method (HK2-seq) as a novel diagnostic method for multicancer MPE diagnosis.

Methods
HK2-seq employed HK2 as a new metabolic function-associated marker to detect disseminated tumor cells engaging increased glycolysis in pleural effusion from many cancer types. Single-cell sequencing was used to confirm the malignancy of HK2-derived high glycolytic tumor cells (hgTCs) at the single-cell level via surveying genome-wide copy number alterations (CNAs), leading to establishment of definitive MPE diagnosis.

Results
In a prospective cohort study including 111 patients with pleural effusion, the HK2 test showed diagnostic sensitivity, diagnostic specificity, positive predictive value, and negative predictive value of 91% (95% CI: 80%–97%), 84% (95% CI: 68%–93%), 90% (95% CI: 79%–96%), and 86% (95% CI: 70%–95%), respectively, in MPE diagnosis across 12 different cancer types. In contrast, pleural effusion cytology exhibits an overall diagnostic sensitivity of 45%. In addition to confirming the tumor origin of hgTCs, single-cell sequencing allowed identification of prognostic or targetable CNAs in hgTCs, especially CNAs found in liquid biopsies but absent in solid biopsies.

Conclusions
HK2-seq establishes definitive MPE diagnosis across many cancer types with high diagnostic performance. It has the potential to be used for multicancer detection of circulating tumor cells in blood and other types of body fluids, as well as liquid biopsy-based genomic characterization for informative diagnosis.
Targeting RUNX1 as a novel treatment modality for pulmonary arterial hypertension
Authors: Jeong Euy-Myoung , Pereira Mandy , So Eui-Young , Wu Keith Q , Del Tatto Michael , Wen Sicheng , Dooner Mark S , Dubielecka Patrycja M , Reginato Anthony M , Ventetuolo Corey E , Quesenberry Peter J , Klinger James R , Liang Olin D 
Published: January 2022

Abstract

Aims
Pulmonary arterial hypertension (PAH) is a fatal disease without a cure. Previously, we found that transcription factor RUNX1-dependent haematopoietic transformation of endothelial progenitor cells may contribute to the pathogenesis of PAH. However, the therapeutic potential of RUNX1 inhibition to reverse established PAH remains unknown. In the current study, we aimed to determine whether RUNX1 inhibition was sufficient to reverse Sugen/hypoxia (SuHx)-induced pulmonary hypertension (PH) in rats. We also aimed to demonstrate possible mechanisms involved.

Methods and results
We administered a small molecule specific RUNX1 inhibitor Ro5-3335 before, during, and after the development of SuHx-PH in rats to investigate its therapeutic potential. We quantified lung macrophage recruitment and activation in vivo and in vitro in the presence or absence of the RUNX1 inhibitor. We generated conditional VE-cadherin-CreERT2; ZsGreen mice for labelling adult endothelium and lineage tracing in the SuHx-PH model. We also generated conditional Cdh5-CreERT2; Runx1(flox/flox) mice to delete Runx1 gene in adult endothelium and LysM-Cre; Runx1(flox/flox) mice to delete Runx1 gene in cells of myeloid lineage, and then subjected these mice to SuHx-PH induction. RUNX1 inhibition in vivo effectively prevented the development, blocked the progression, and reversed established SuHx-induced PH in rats. RUNX1 inhibition significantly dampened lung macrophage recruitment and activation. Furthermore, lineage tracing with the inducible VE-cadherin-CreERT2; ZsGreen mice demonstrated that a RUNX1-dependent endothelial to haematopoietic transformation occurred during the development of SuHx-PH. Finally, tissue-specific deletion of Runx1 gene either in adult endothelium or in cells of myeloid lineage prevented the mice from developing SuHx-PH, suggesting that RUNX1 is required for the development of PH.

Conclusion
By blocking RUNX1-dependent endothelial to haematopoietic transformation and pulmonary macrophage recruitment and activation, targeting RUNX1 may be as a novel treatment modality for pulmonary arterial hypertension.
Virus Infection and Host Plant Suitability Affect Feeding Behaviors of Cannabis Aphid (Hemiptera: Aphididae), a Newly Described Vector of Potato Virus Y
Authors: Pitt William Jacob , Kairy Lisa , Villa Emily , Nalam Vamsi J , Nachappa Punya 
Published: January 2022

AbstractAphids are the most prolific vectors of plant viruses resulting in significant yield losses to crops worldwide. Potato virus Y (PVY) is transmitted in a non-persistent manner by 65 species of aphids. With the increasing acreage of hemp (Cannabis sativa L.) (Rosales: Cannabaceae) in the United States, we were interested to know if the cannabis aphid (Phorodon cannabis Passerini) (Hemiptera: Aphididae) is a potential vector of PVY. Here, we conduct transmission assays and utilize the electrical penetration graph (EPG) technique to determine whether cannabis aphids can transmit PVY to hemp (host) and potato (non-host) (Solanum tuberosum L.) (Solanales: Solanaceace). We show for the first time that the cannabis aphid is an efficient vector of PVY to both hemp (96% transmission rate) and potato (91%) using cohorts of aphids. In contrast, individual aphids transmitted the virus more efficiently to hemp (63%) compared to potato (19%). During the initial 15 min of EPG recordings, aphids demonstrated lower number and time spent performing intracellular punctures on potato compared to hemp, which may in part explain low virus transmission to potato using individual aphids. During the entire 8-hour recording, viruliferous aphids spent less time ingesting phloem compared to non-viruliferous aphids on hemp. This reduced host acceptance could potentially cause viruliferous aphids to disperse thereby increasing virus transmission. Overall, our study shows that cannabis aphid is an efficient vector of PVY, and that virus infection and host plant suitability affect feeding behaviors of the cannabis aphid in ways which may increase virus transmission.
The impact of the coronavirus lockdown on mental health: evidence from the United States
Authors: Adams-Prassl Abi , Boneva Teodora , Golin Marta , Rauh Christopher 
Published: January 2022

Abstract
The coronavirus outbreak has caused significant disruptions to people’s lives. We exploit variation in lockdown measures across states to document the impact of stay-at-home orders on mental health using real-time survey data in the United States. We find that the lockdown measures lowered mental health by 0.083 standard deviations. This large negative effect is entirely driven by women. As a result of the lockdown measures, the existing gender gap in mental health has increased by 61%. The negative effect on women’s mental health cannot be explained by an increase in financial worries or caring responsibilities.