bluuebunny/crossref_metadata_2025_split huggingface dataset:
IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data
Authors: Bi Xia-an , Li Lou , Wang Zizheng , Wang Yu , Luo Xun , Xu Luyun 
Published: March 2022

Abstract
Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
multi-type neighbors enhanced global topology and pairwise attribute learning for drug–protein interaction prediction
Authors: Xuan Ping , Zhang Xiaowen , Zhang Yu , Hu Kaimiao , Nakaguchi Toshiya , Zhang Tiangang 
Published: March 2022

Abstract

Motivation
Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods focused on integrating multisource data about drugs and proteins for predicting drug–target interactions (DTIs). There are both similarity connection and interaction connection between two drugs, and these connections reflect their relationships from different perspectives. Similarly, two proteins have various connections from multiple perspectives. However, most of previous methods failed to deeply integrate these connections. In addition, multiple drug-protein heterogeneous networks can be constructed based on multiple kinds of connections. The diverse topological structures of these networks are still not exploited completely.

Results
We propose a novel model to extract and integrate multi-type neighbor topology information, diverse similarities and interactions related to drugs and proteins. Firstly, multiple drug–protein heterogeneous networks are constructed according to multiple kinds of connections among drugs and those among proteins. The multi-type neighbor node sequences of a drug node (or a protein node) are formed by random walks on each network and they reflect the hidden neighbor topological structure of the node. Secondly, a module based on graph neural network (GNN) is proposed to learn the multi-type neighbor topologies of each node. We propose attention mechanisms at neighbor node level and at neighbor type level to learn more informative neighbor nodes and neighbor types. A network-level attention is also designed to enhance the context dependency among multiple neighbor topologies of a pair of drug and protein nodes. Finally, the attribute embedding of the drug-protein pair is formulated by a proposed embedding strategy, and the embedding covers the similarities and interactions about the pair. A module based on three-dimensional convolutional neural networks (CNN) is constructed to deeply integrate pairwise attributes. Extensive experiments have been performed and the results indicate GCDTI outperforms several state-of-the-art prediction methods. The recall rate estimation over the top-ranked candidates and case studies on 5 drugs further demonstrate GCDTI’s ability in discovering potential drug-protein interactions.
An Improved Hill Climbing Algorithm for Graph Partitioning
Authors: Li He , Liu Yanna , Yang Shuqi , Lin Yishuai , Yang Yi , Yoo Jaesoo 
Published: March 2022

Abstract
Graph partitioning is an NP-hard combinatorial optimization problem, and is a fundamental step in distributing workloads on parallel compute systems, circuit placement, and sparse matrix reordering. The proposed heuristic algorithms such as streaming graph partitioning provide solutions to large-scale graph in a reasonable amount of time. However, the ability of breaking out of local minima in existing these methods is very limited as they are simple in reflecting the connectivity between vertices in real graphs with power-law distribution characteristic. As hill climbing algorithm is a local search method, it can be adopted to improve the result of graph partitioning. However, directly adopting the existing hill climbing algorithm to graph partitioning will result in local minima and poor convergence speed during the iterative process. In this paper, we propose an improved hill climbing graph partitioning algorithm based on clustering. Instead of taking a single vertex as a basic unit, the proposed method considers a cluster consisting of a series of vertices as a hill to move during each iteration. The method uses a new metric that considers both balance and edgecuts to look for the most beneficial cluster as the hill. With these improvements, the method provides a strong power to break out of local minima and achieve an adaptive tradeoff between balance and edgecuts. Experimental results on real-world graphs show that the proposed algorithm substantially reduces edgecuts within a controlled imbalance range.
Large-Scale Emulation Network Topology Partition Based on Community Detection With the Weight of Vertex Similarity
Authors: Yan Jianen , Xu Haiyan , Li Ning , Zhang Zhaoxin 
Published: March 2022

Abstract
Due to the limitations of physical resources, if a large-scale emulation network environment composed of millions of vertices and edges is constructed by virtualization technology, the whole network topology should be partitioned into a set of subnets. The topology partition is a work of graph partition. The existing topology partition methods have shortcomings, such as low efficiency and poor practicability, especially for large-scale network topology. The emulation network is a kind of complex network and has the characteristics of community structure. Therefore, we proposed LENTP (large-scale emulation network topology partition) based on the community detection with the weight of the vertex similarity for large-scale topology partition. In the first stage, the tree-structured area compression reduces the topology scales significantly to improve partition efficiency. And then, the improved Louvain algorithm is used to topology partitioning and obtain an initial set of subnets with the minimum number of subnets and remote links. Finally, after repartitioning and merging for the initial subnets, the result of subnets is the final topology partition that reaches the optimization objectives with the conditions of the virtual resources. In the experiment, the method is tested in five groups of network topology with different scales. The results demonstrate that LENTP can partition the network topology over 1 000 000 nodes and significantly improve the running-time efficiency of the network topology partition.
Severe hyponatraemia in a patient with a large left atrial myxoma: a case report
Authors: Bhat Srushti , Awad Wael I 
Published: March 2022

Abstract

Background
Atrial myxomas (AMs) are the most commonly encountered cardiac tumours. They can be genetically inherited and are commonly found in the left atrium. They usually present with dyspnoea, syncopal episodes, heart failure from mitral valve obstruction, and constitutional symptoms including weight loss, fatigue, and fever. We present a rare case of severe symptomatic hyponatraemia secondary to a large AM and discuss possible aetiology.

Case summary
A 75-year-old Caucasian female presented with acute nausea, vomiting, confusion, and drowsiness. She had a background of palpitations for about 20 years. Her blood test results revealed severe hyponatraemia (serum sodium—103 mmol/L). Further investigations for hyponatraemia including serum cortisol and urine biochemistry suggested Syndrome of Inappropriate Anti-Diuretic Hormone (SIADH) secretion. Computer tomography scan revealed an incidental large left AM. Echocardiography confirmed the AM attached to the left side of the inter-atrial septum and occupying the majority of the left atrium. She was treated medically for hyponatraemia and referred for excision of myxoma. She underwent urgent resection of the myxoma once sodium levels were optimized. Postoperatively, her serum sodium remained low but gradually returned to normal on postoperative Day 11.

Conclusion
This is only the third reported case of significant hyponatraemia associated with a large AM. It has been previously hypothesized that large left AM stretch the atrium causing release of atrial natriuretic peptide and subsequent hyponatraemia. The excision of myxoma and reduction in left atrial size postoperatively with an improvement in sodium levels suggests an association between the two pathologies.
Using item response theory to develop and refine patient-reported outcome measures
Authors: Nguyen Tam H , Lee Christopher S , Kim Miyong T 
Published: March 2022

Abstract
There is a long tradition of incorporating patient-reported outcomes (PROs) into nursing research and practice. Classical Test Theory (CTT) has been the traditional approach used to develop and refine PROs. Item response theory (IRT) offers promise for addressing measurement problems that have been difficult to solve using CTT. This paper presents foundational concepts in IRT to illustrate how it can be used to improve the development and refinement of PRO measures, with emphasis on interpreting key IRT parameters.
Physical activity attenuates but does not eliminate coronary heart disease risk amongst adults with risk factors: EPIC-CVD case-cohort study
Authors: Fortuin-de Smidt Melony C , Sewe Maquins Odhiambo , Lassale Camille , Weiderpass Elisabete , Andersson Jonas , Huerta José María , Ekelund Ulf , Aleksandrova Krasimira , Tong Tammy YN , Dahm Christina C , Tjønneland Anne , Kyrø Cecilie , Steindorf Karen , Schulze Matthias B , Katzke Verena , Sacerdote Carlotta , Agnoli Claudia , Masala Giovanna , Tumino Rosario , Panico Salvatore , Boer Jolanda MA , Onland-Moret N Charlotte , Wendel-Vos GC Wanda , van der Schouw Yvonne T , Borch Kristin Benjaminsen , Agudo Antonio , Petrova Dafina , Chirlaque María Dolores , Conchi Moreno Iribas , Amiano Pilar , Melander Olle , Heath Alicia K , Aune Dagfinn , Forouhi Nita G , Langenberg Claudia , Brage Soren , Riboli Elio , Wareham Nicholas J , Danesh John , Butterworth Adam S , Wennberg Patrik 
Published: March 2022

Abstract

Aims
This study aimed to evaluate the association between physical activity and the incidence of coronary heart disease (CHD) in individuals with and without CHD risk factors.

Methods and results
EPIC-CVD is a case-cohort study of 29 333 participants that included 13 582 incident CHD cases and a randomly selected sub-cohort nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Self-reported physical activity was summarized using the Cambridge physical activity index (inactive, moderately inactive, moderately active, and active). Participants were categorized into sub-groups based on the presence or the absence of the following risk factors: obesity (body mass index ≥30 kg/m2), hypercholesterolaemia (total cholesterol ≥6.2 mmol/L), history of diabetes, hypertension (self-reported or ≥140/90 mmHg), and current smoking. Prentice-weighted Cox regression was used to assess the association between physical activity and incident CHD events (non-fatal and fatal).
Compared to inactive participants without the respective CHD risk factor (referent), excess CHD risk was highest in physically inactive and lowest in moderately active participants with CHD risk factors. Corresponding excess CHD risk estimates amongst those with obesity were 47% [95% confidence interval (CI) 32–64%] and 21% (95%CI 2–44%), with hypercholesterolaemia were 80% (95%CI 55–108%) and 48% (95%CI 22–81%), with hypertension were 80% (95%CI 65–96%) and 49% (95%CI 28–74%), with diabetes were 142% (95%CI 63–260%), and 100% (95%CI 32–204%), and amongst smokers were 152% (95%CI 122–186%) and 109% (95%CI 74–150%).

Conclusions
In people with CHD risk factors, moderate physical activity, equivalent to 40 mins of walking per day, attenuates but does not completely offset CHD risk.
Targaryen Thought Experiments: Do Science Fiction and Fantasy Examples Aid or Obfuscate Student Learning?
Authors: Irish Adam , Sherman Nicole , Watts Levi 
Published: March 2022

AbstractSeveral recently published international relations and criminal justice writings, textbooks, and supplements focus on using science fiction and fantasy texts to teach social science theories. This article investigates how science fiction and fantasy examples affect student learning compared to documentary films. We use mixed methods to conduct two studies on the use of science fiction and fantasy in political science and criminal justice classrooms. In the first study, we divide up a large lecture class, comparing how examples from the HBO series Game of Thrones versus documentary films affect student learning about the United Nations and peacekeeping. Our second study uses a combination of interviews and surveys to assess how students apply various criminological theories to the 1997 film Gattaca versus a documentary. Our research reveals that science fiction and fantasy examples neither help nor harm student learning. However, incorporating examples from fictional worlds may alienate some students, affecting their enjoyment of class and perception of preparedness. Most importantly, we find that the use of fictional examples creates an interpretive barrier in the classroom. Thus, we recommend judicious use of science fiction and fantasy examples. When fictional examples are used, we encourage student preparation and extensive debriefing.
Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?
Authors: Junqueira Vinícius Silva , Lourenco Daniela , Masuda Yutaka , Cardoso Fernando Flores , Lopes Paulo Sávio , Silva Fabyano Fonseca e , Misztal Ignacy 
Published: March 2022

Abstract
Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H−1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1–9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G−1 and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.
The Evolution Toward Designer Benzodiazepines in Drug-Facilitated Sexual Assault Cases
Authors: Pérez Orts Mireia , van Asten Arian , Kohler Isabelle 
Published: March 2022

AbstractDrug-facilitated sexual assault (DFSA) is a crime where the victim is unable to provide sexual consent due to incapacitation resulting from alcohol or drug consumption. Due to the large number of substances possibly used in DFSA, including illicit, prescription and over-the-counter drugs, DFSA faces many toxicological challenges. Benzodiazepines (BZDs) are ideal candidates for DFSA, as they are active at low doses, have a fast onset of action and can be easily administered orally. The last decade has seen the emergence of designer benzodiazepines (DBZDs), which show slight modifications compared with BZDs and similar pharmacological effects but are not controlled under the international drug control system. DBZDs represent an additional challenge due to the number of new entities regularly appearing in the market, their possibly higher potency and the limited knowledge available on their pharmacokinetic and pharmacodynamics properties. Many BZDs and DBZDs have a short half-life, leading to rapid metabolism and excretion. The low concentrations and short time windows for the detection of BZD in body fluids require the use of highly sensitive analysis methods to enable the detection of drugs and their respective metabolites. This review discusses the current state of the toxicological analysis of BZDs and DBZDs in forensic casework and their pharmacokinetic properties (i.e., absorption, distribution, metabolism, and elimination), as well as their analysis in biosamples typically encountered in DFSA (i.e., blood, urine and hair).
Drone Laying Honey Bee Workers in Queen Monitoring Cages
Authors: Fine Julia D , Litsey Eliza M 
Published: March 2022

Abstract
Techniques to monitor honey bee (Apis mellifera) egg production in cages allow researchers to study how different environmental factors contribute to reproduction. However, although the conditions required to facilitate queen egg production in a laboratory setting have been established, limited work has addressed the requirements for stimulating and monitoring worker egg laying. Here, we documented that drone laying workers will lay eggs in Queen Monitoring Cages (QMC), specialized cages designed to facilitate queen egg laying under controlled conditions. Egg production and worker mortality were compared between QMCs containing queens and those containing drone laying workers. High-definition images of the last abdominal segments of living first-instar larvae hatched from worker laid eggs and those putatively laid by queens were qualitatively compared to identify candidate characteristics to determine their sex.
Accuracy of Estimated Ratios as Affected by Dynamic Classification Errors
Authors: van Delden Arnout , Scholtus Sander , Burger Joep , Meertens Quinten 
Published: March 2022

AbstractWithin the field of official statistics, we are often interested in the relative change of statistical indicators through time. A typical example is economic growth measured as the ratio of gross domestic product at two consecutive time periods. In this article, we investigate the accuracy of such ratios when the numerator and denominator describe a subpopulation. Subpopulations are always identified by a classification variable, which might contain classification errors. Our main objective is to quantify the bias and variance of statistical estimates of ratios of statistical indicators that are affected by classification errors. Previous studies have already shown how to estimate the bias and variance of statistical indicators for a single subpopulation as affected by classification errors. For estimated ratios, such results are not yet available in the academic literature. In this article, we will provide those results. More specifically, we will investigate three situations: the classification errors for the numerator and denominator are (1) completely dependent, (2) completely independent, or (3) partially dependent. For these three cases, we derive estimators of the bias and variance using a Taylor expansion and a bootstrap approach. By means of a simulation study, we show that the Taylor expansion is valid unless the distribution of the target variable of the numerator or the denominator is highly skewed. The methods are applied to two widely differing case studies: one on classification errors made by a machine learning classifier (independent errors) and one on classification errors in register data (partially dependent errors). This illustrates that our method can be useful for a wide range of applications.
Monitoring Female Fertility Through ‘Femtech’: The Need for a Whole-System Approach to Regulation
Authors: McMillan Catriona 
Published: March 2022

Abstract
Concurrent with the rise of digital health and personal health tracking technologies, a market has also emerged of products targeted specifically at women: ‘femtech’. This article is motivated by the concern that insufficient regulatory attention has been devoted to this growing market, and that extant ambiguity in the regulation of femtech leaves its users at risk of relying on technologies of as-yet unproven worth. It is posited that femtech profoundly disrupts well-established regulatory mechanisms of protection in ways that mean that these silos of protection will not be adequate. This is because regulation, as it is currently constructed, is insufficiently sensitive to feminist perspectives regarding what these technologies mean for women. As a result, the regulatory sphere in which femtech operates fundamentally fails to ensure that the health and safety of femtech users are protected as this market continues to expand. To counteract this, the argument is made that an appropriate regulatory response to femtech must respond to the distinctive unmet need in the regulation of this technological realm and the acute risk that femtech poses. This must include a multidimensional whole-system approach grounded in feminist perspectives on health, fertility, and technology.