bluuebunny/crossref_metadata_2025 huggingface dataset:
Localized ion-acoustic solitons in collision with dusty plasma
Authors: Pakzad H R , Javidan K
Published: April 2022

In this research, we investigate the characters of localized waves arriving from an electron-ion plasma into an electron-ion-dusty plasma region. For this purpose, we consider the presence of dust particles as a weak perturbation in the medium. It is shown that the ion-acoustic wave propagates with greater speed and higher energy when it interacts with dust particles. In addition, an oscillatory shock wave creates at the border of two different plasma environments that propagates in the opposite direction of the initial wave velocity. Time evolution of solitary wave and characters of created shock profile is also studied.
Excited states of holographic superconductors from massive gravity
Authors: Xiang Qian , Zhao Li , Wang Yong-Qiang
Published: April 2022

In this paper, we generalize the study of the model of holographic superconductors in excited states to the framework of massive gravity at the probe limit. By taking into account the effect of a massive graviton, we numerically present a family of solutions for holographic superconductors in excited states and find that the critical temperatures can be higher due to the effect of the massive graviton, in comparison with the superconductor in Einstein gravity. We also investigate the condensates and conductivities in the ground state and the excited states by studying various parameters that determine the framework of gravity background.
Charge density wave states in phase-engineered monolayer VTe2
Authors: Zhu Zhi-Li , Liu Zhong-Liu , Wu Xu , Li Xuan-Yi , Shi Jin-An , Liu Chen , Qian Guo-Jian , Zheng Qi , Huang Li , Lin Xiao , Wang Jia-Ou , Chen Hui , Zhou Wu , Sun Jia-Tao , Wang Ye-Liang , Gao Hong-Jun
Published: April 2022

Charge density wave (CDW) strongly affects the electronic properties of two-dimensional (2D) materials and can be tuned by phase engineering. Among 2D transitional metal dichalcogenides (TMDs), VTe2 was predicted to require small energy for its phase transition and shows unexpected CDW states in its T-phase. However, the CDW state of H-VTe2 has been barely reported. Here, we investigate the CDW states in monolayer (ML) H-VTe2, induced by phase-engineering from T-phase VTe2. The phase transition between T- and H-VTe2 is revealed with x-ray photoelectron spectroscopy (XPS) and scanning transmission electron microscopy (STEM) measurements. For H-VTe2, scanning tunneling microscope (STM) and low-energy electron diffraction (LEED) results show a robust

2

3

×
2

3

CDW superlattice with a transition temperature above 450 K. Our findings provide a promising way for manipulating the CDWs in 2D materials and show great potential in its application of nanoelectronics.
High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors
Authors: Lian Xiao-Juan , Fu Jin-Ke , Gao Zhi-Xuan , Gu Shi-Pu , Wang Lei
Published: April 2022

Threshold switching (TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage (0.38 V) and current (200 nA), an extremely steep slope (< 0.1 mV/dec), and a relatively large off/on ratio (> 103). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.
Large positive magnetoresistance in photocarrier-doped potassium tantalites
Authors: Yang Rui-Shu , Wang Ding-Bang , Zhao Yang , Wang Shuan-Hu , Jin Ke-Xin
Published: April 2022

We report on the high-field magnetotransport of KTaO3 single crystals, which are a promising candidate for study in the extreme quantum limit. By photocarrier doping with 360 nm light, we observe a significant positive, non-saturating, and linear magnetoresistance at low temperatures accompanied by a decreasing Hall coefficient. When cooling down to 10 K, the magnetoresistance value of KTaO3 (100) reaches ∼ 433% at a magnetic field of 12 T. Such behavior can be attributed to all the electrons occupying only the lowest Landau level in the extreme quantum limit. Light inhomogeneity may also contribute to large linear magnetoresistance. These results provide insights into novel magnetic devices based on complex materials and add a new family of materials with positive magnetoresistance.
The Hybrid Drive: a chronic implant device combining tetrode arrays with silicon probes for layer-resolved ensemble electrophysiology in freely moving mice
Authors: Guardamagna Matteo , Eichler Ronny , Pedrosa Rafael , Aarts Arno , Meyer Arne F , Battaglia Francesco P
Published: April 2022

Objective. Understanding the function of brain cortices requires simultaneous investigation at multiple spatial and temporal scales and to link neural activity to an animal’s behavior. A major challenge is to measure within- and across-layer information in actively behaving animals, in particular in mice that have become a major species in neuroscience due to an extensive genetic toolkit. Here we describe the Hybrid Drive, a new chronic implant for mice that combines tetrode arrays to record within-layer information with silicon probes to simultaneously measure across-layer information. Approach. The design of our device combines up to 14 tetrodes and 2 silicon probes, that can be arranged in custom arrays to generate unique areas-specific (and multi-area) layouts. Main results. We show that large numbers of neurons and layer-resolved local field potentials can be recorded from the same brain region across weeks without loss in electrophysiological signal quality. The drive’s lightweight structure (≈3.5 g) leaves animal behavior largely unchanged, compared to other tetrode drives, during a variety of experimental paradigms. We demonstrate how the data collected with the Hybrid Drive allow state-of-the-art analysis in a series of experiments linking the spiking activity of CA1 pyramidal layer neurons to the oscillatory activity across hippocampal layers. Significance. Our new device fits a gap in the existing technology and increases the range and precision of questions that can be addressed about neural computations in freely behaving mice.
Front-end hybrid designs for the CMS Phase-2 Upgrade towards the production phase
Authors: Kovacs M. , Blanchot G. , Gadek T. , Hollos A. , Dominguez I. , Zografos A.
Published: April 2022

Sixteen thousand 2S front-end hybrids and twelve thousand PS front-end hybrids will be produced for the CMS Tracker Phase-2 upgrade. The hybrids consist of flip-chips, passives and mechanical components mounted on a flexible substrate, laminated onto carbon-fibre stiffeners with thermal expansion compensators. In the prototyping phase, several critical issues have been solved to manufacture these complex circuits. Final designs are now reaching readiness for the full-scale production. Lessons learned during the prototyping phase and different improvements implemented for manufacturability will be presented in this contribution.
Production and quality control of NICA-MPD shashlik electromagnetic calorimeter in Tsinghua University
Authors: Li Y. , Shen C. , Zhang Z. , Wang Y. , Deng Z. , Ran X. , Zhang X. , Li L. , Liang S. , Han D. , Golovatyuk V. , Tyapkin I. , Krechetov Y. , Li Y.
Published: April 2022

The Multi Purpose Detector (MPD) is one of the detectors at
the NICA collider and the Electromagnetic Calorimeter (ECal) is an
important part of MPD. The cylindrical ECal system consists of 2400
ECal modules. In the first stage (2020–2024), Tsinghua University
(THU) will produce 460 ECal modules for MPD project. This article
mainly introduces the production, quality control and cosmic ray
test of ECal modules in THU. The process flow, quality control
standards, and testing system of ECal production have been
established, which guarantees the high-quality completion of mass
production on schedule. Up to now, nearly 360 modules have been
manufactured, and a total of 460 modules will be produced by June
2022.
Physics-aware nonparametric regression models for Earth data analysis
Authors: Cortés-Andrés Jordi , Camps-Valls Gustau , Sippel Sebastian , Székely Enikő , Sejdinovic Dino , Diaz Emiliano , Pérez-Suay Adrián , Li Zhu , Mahecha Miguel , Reichstein Markus
Published: April 2022

Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning (ML). Despite great success in many areas, ML algorithms in the Earth and climate sciences, and more broadly in physical sciences, are not explicitly designed to be physically-consistent and may, therefore, violate the most basic laws of physics. In this work, motivated by the field of algorithmic fairness, we reconcile data-driven ML with physics modeling by illustrating a nonparametric and nonlinear physics-aware regression method. By incorporating a dependence-based regularizer, the method leads to models that are consistent with domain knowledge, as reflected by either simulations from physical models or ancillary data. The idea can conversely encourage independence of model predictions with other variables that are known to be uncertain either in their representation or magnitude. The method is computationally efficient and comes with a closed-form analytic solution. Through a consistency-vs-accuracy path diagram, one can assess the consistency between data-driven models and physical models. We demonstrate in three examples on simulations and measurement data in Earth and climate studies that the proposed ML framework allows us to trade-off physical consistency and accuracy.
Manakov system with parity symmetry on nonzero background and associated boundary value problems
Authors: Abeya Asela , Biondini Gino , Prinari Barbara
Published: April 2022

We characterize initial value problems for the defocusing Manakov system (coupled two-component nonlinear Schrödinger equation) with nonzero background and well-defined spatial parity symmetry (i.e., when each of the components of the solution is either even or odd), corresponding to boundary value problems on the half line with Dirichlet or Neumann boundary conditions at the origin. We identify the symmetries of the eigenfunctions arising from the spatial parity of the solution, and we determine the corresponding symmetries of the scattering data (reflection coefficients, discrete spectrum and norming constants). All parity induced symmetries are found to be more complicated than in the scalar (i.e., one-component) case. In particular, we show that the discrete eigenvalues giving rise to dark solitons arise in symmetric quartets, and those giving rise to dark–bright solitons in symmetric octets. We also characterize the differences between the purely even or purely odd case (in which both components are either even or odd functions ofx) and the ‘mixed parity’ cases (in which one component is even while the other is odd). Finally, we show how, in each case, the spatial symmetry yields a constraint on the possible existence of self-symmetric eigenvalues, corresponding to stationary solitons, and we study the resulting behavior of solutions.
Detecting genuine multipartite entanglement in three-qubit systems with eternal non-Markovianity
Authors: Vaishy Ankit , Mitra Subhadip , Bhattacharya Samyadeb
Published: April 2022

We devise a novel protocol to detect genuinely multipartite entangled states by harnessing quantum non-Markovian operations. We utilize a particular type of non-Markovianity known as the eternal non-Markovianity to construct a non-complete positive map to filter out the bi-separable states and detect genuine multipartite entanglement. We further propose a witness operator to detect genuinely multipartite entangled states experimentally based on this theory. Our study sheds light on a hitherto unexplored connection between entanglement theory and quantum non-Markovianity.
Anomalous diffusion originated by two Markovian hopping-trap mechanisms
Authors: Vitali S , Paradisi P , Pagnini G
Published: April 2022

We show through intensive simulations that the paradigmatic features of anomalous diffusion are indeed the features of a (continuous-time) random walk driven by two different Markovian hopping-trap mechanisms. If p ∈ (0, 1/2) and 1 − p are the probabilities of occurrence of each Markovian mechanism, then the anomalousness parameter β ∈ (0, 1) results to be β ≃ 1 − 1/{1 + log[(1 − p)/p]}. Ensemble and single-particle observables of this model have been studied and they match the main characteristics of anomalous diffusion as they are typically measured in living systems. In particular, the celebrated transition of the walker’s distribution from exponential to stretched-exponential and finally to Gaussian distribution is displayed by including also the Brownian yet non-Gaussian interval.
Geometric tilt-to-length coupling in precision interferometry: mechanisms and analytical descriptions
Authors: Hartig Marie-Sophie , Schuster Sönke , Wanner Gudrun
Published: April 2022

Tilt-to-length (TTL) coupling is a technical term for the cross-coupling of angular or lateral jitter into an interferometric phase signal. It is an important noise source in precision interferometers and originates either from changes in the optical path lengths or from wavefront and clipping effects. Within this paper, we focus on geometric TTL coupling and categorise it into a number of different mechanisms for which we give analytic expressions. We then show that this geometric description is not always sufficient to predict the TTL coupling noise within an interferometer. We, therefore, discuss how understanding the geometric effects allows TTL noise reduction already by smart design choices. Additionally, they can be used to counteract the total measured TTL noise in a system. The presented content applies to a large variety of precision interferometers, including space gravitational wave detectors like LISA.
Wideband radar cross-section reduction using plasma-based checkerboard metasurface
Authors: ZHAO Zhiming , LI Xiaoping , DONG Guoxiang , LIU Yanming
Published: April 2022

For stealth technology, in order to overcome the limitations of thin-layer plasma for electromagnetic waves attenuation and further broaden the radar cross-section (RCS) reduction (RCSR) band of the metasurface, the plasma-based checkerboard metasurface composed of plasma and checkerboard metasurface is investigated to achieve better RCSR. We designed a checkerboard metasurface which can achieve abnormal reflection to reduce RCS and whose −10 dB RCSR bandwidth is from 8.1 to 14.5 GHz, the RCSR principle of it lies in the backscattering cancellation, which depends on the phase difference of artificial magnetic conductor (AMC) units. The designed plasma-based checkerboard metasurface is a thin composite structure, including a checkerboard metasurface, a plasma layer, and an air gap which is between them. Full wave simulations confirm that the plasma-based checkerboard metasurface’s ‒10 dB RCS reduction bandwidth and RCS reduction amplitude, are both increased under different polarized waves compared with the only single plasma or the only metasurface. We also introduced the reason and mechanism of the interaction between plasma and the checkerboard metasurface to improve the RCSR effect in detail. As plasma-based checkerboard metasurface does not need the plasma to be too thick for plasma stealth, its application in practical scenarios is easier to implement.
Air quality monitoring and management system model of vehicles based on the internet of things
Authors: Khan Angshuman , Chandra Saurabh , Parameshwara M C
Published: April 2022

The design of an air quality monitoring system is a recent trend and emerging research topic using IoT and wireless sensor technology. The massive number of automobiles entering metropolitan areas contributes to increased air pollution and lower air quality, which leads to serious health problems. The paper’s primary goal is to introduce a vehicular pollution monitoring system model that can detect and measure pollutants like carbon monoxide and smoke produced by automobiles. The proposed module consists of sensors that can detect the pollutants, carbon monoxide, and smoke released by a vehicle. A Node Micro-Controller Unit (NodeMCU) will work as the brain of the sensor node and communicator with the server through wireless fidelity. The suggested system model can monitor automobile pollution, and if any vehicle exceeds a certain threshold value, it will be reported to the traffic department and the owner of the vehicle. The proposed system model is straightforward and simple, and it is predicted to be inexpensive.
Signed, sealed, and delivered: RNA localization and translation at centrosomes
Authors: Lerit Dorothy A.
Published: April 2022

Protein localization is intrinsic to cellular function and specialized activities, such as migration or proliferation. Many localized proteins enrich at defined organelles, forming subdomains of functional activity further specified by interacting protein assemblies. One well-studied organelle showing dynamic, functional changes in protein composition is the centrosome. Centrosomes are microtubule-organizing centers with diverse cellular functions largely defined by the composition of the pericentriolar material, an ordered matrix of proteins organized around a central pair of centrioles. Also localizing to the pericentriolar material are mRNAs. Although RNA was identified at centrosomes decades ago, the characterization of specific RNA transcripts and their functional contributions to centrosome biology remained largely unstudied. While the identification of RNA localized to centrosomes accelerated with the development of high-throughput screening methods, this discovery still outpaces functional characterization. Recent work indicates RNA localized to centrosomes is biologically significant and further implicates centrosomes as sites for local protein synthesis. Distinct RNA localization and translational activities likely contribute to the diversity of centrosome functions within cells.
An update on genetically encoded lipid biosensors
Authors: Hammond Gerald R. V. , Ricci Morgan M. C. , Weckerly Claire C. , Wills Rachel C.
Published: April 2022

Specific lipid species play central roles in cell biology. Their presence or enrichment in individual membranes can control properties or direct protein localization and/or activity. Therefore, probes to detect and observe these lipids in intact cells are essential tools in the cell biologist’s freezer box. Herein, we discuss genetically encoded lipid biosensors, which can be expressed as fluorescent protein fusions to track lipids in living cells. We provide a state-of-the-art list of the most widely available and reliable biosensors and highlight new probes (circa 2018–2021). Notably, we focus on advances in biosensors for phosphatidylinositol, phosphatidic acid, and PI 3-kinase lipid products.
Misinformation Across Digital Divides: Theory And Evidence From Northern Ghana
Authors: Gadjanova Elena , Lynch Gabrielle , Saibu Ghadafi
Published: April 2022

Social media misinformation is widely recognized as a significant and growing global problem. Yet, little is known about how misinformation spreads across broader media ecosystems, particularly in areas with varying internet access and connectivity. Drawing on research in northern Ghana, we seek to address this gap. We argue that ‘pavement media’—the everyday communication of current affairs through discussions in marketplaces, places of worship, bars, and the like and through a range of non-conversational and visual practices such as songs, sermons, and graffiti—is a key link in a broader media ecosystem. Vibrant pavement and traditional media allow for information from social media to quickly cross into offline spaces, creating a distinction not of the connected and disconnected but of first-hand and indirect social media users. This paper sets out how social, traditional, and pavement media form a complex and deeply gendered and socio-economically stratified media ecosystem and investigates its implications for how citizens differentially encounter, process, and respond to misinformation. Based on the findings, we argue that efforts intended to combat the spread of misinformation need to move beyond the Western-centred conception of what constitutes media and take different local modalities of media access and fact-checking into account.
State Weakness, a Fragmented Patronage-Based System, and Protracted Local Conflict in the Central African Republic
Authors: Duursma Allard
Published: April 2022

There is an academic consensus that addressing the local cleavages that drive armed conflict through local peacemaking is crucial to building peace. However, several studies also suggest that local peacemaking is often unsustainable without the conclusion of a national-level elite pact. This article moves this debate forward by arguing that even if an elite-level pact is in place, a lack of connections between national-level elites and local conflict parties can prevent peace from trickling down from the national level to the local level. This argument is demonstrated by looking at national-level and local peacemaking efforts in the Central African Republic. Although several national-level peace agreements have been concluded in the Central African Republic, extreme state weakness and a highly fragmented patronage-based system have prevented national-level peacemaking efforts from having a positive impact on local peacemaking efforts in the Central African Republic. This article contributes to the literature on conflict resolution in Africa. Many studies have focused on the local peace process, yet the national-level context in which these local peace processes take place is only rarely considered.
Patterns of unplanned hospital admissions among people with dementia: from diagnosis to the end of life
Authors: Yorganci Emel , Stewart Robert , Sampson Elizabeth L , Sleeman Katherine E
Published: April 2022

Background

hospitalisations are sentinel events for people with dementia. How patterns of unplanned hospital admissions change among people with dementia after diagnosis is relatively unknown.

Objective

to describe patterns of unplanned hospital admissions of people with dementia from diagnosis until death/study end.

Methods

retrospective cohort study using mental healthcare provider data of people diagnosed with dementia in London, UK (1995–2017), linked to mortality and hospital data. The primary outcome was the rate of unplanned hospital admissions after diagnosis until death/study end. We calculated the cumulative incidence of unplanned hospital admissions. The rates of unplanned hospital admissions and the percentage of time spent as an inpatient were stratified by time from first dementia diagnosis.

Results

for 19,221 people with dementia (61.4% female, mean age at diagnosis 81.0 years (standard deviation, SD 8.5)), the cumulative incidence of unplanned hospital admissions (n = 14,759) was 76.8% (95% CI 76.3%–77.3%). Individuals remained in the study for mean 3.0 (SD 2.6) years, and 12,667 (65.9%) died. Rates and lengths of unplanned hospital admissions remained relatively low and short in the months after the dementia diagnosis, increasing only as people approached the end of life. Percentage of time spent as an inpatient was <3% for people who were alive at the study end but was on average 19.6 and 13.3% for the decedents in the last 6 and 12 months of life, respectively.

Conclusions

the steep rise in hospitalisations before death highlights the need for improved community care and services for people with dementia who are approaching the end of life.
The Quantification and Significance of Extramedullary Hematopoiesis Seen on Liver Biopsy Specimens
Authors: Tremblay Douglas , Saberi Shahram , Mascarenhas John , Schiano Thomas D , Fiel M Isabel
Published: April 2022

Objectives

Extramedullary hematopoiesis (EMH) may occur in the liver and is often considered pathologic in adults. Many hematologic and nonhematologic disorders are associated with the development of EMH. However, it is unclear whether the presence of EMH is always pathologic. At present, no formal grading system for EMH in the liver exists.

Methods

We reviewed 42 liver biopsy specimens with EMH and developed a novel grading system to quantify the degree of EMH from 1 to 3 based on the number of EMH foci in 10 high-power fields.

Results

Most patients had nonhematologic conditions (n = 25). Seventeen patients had a hematologic condition, most frequently a myeloproliferative neoplasm (n = 9). Patients with an underlying hematologic condition had a significantly higher EMH grade compared with those without a hematologic condition (P < .0001). All patients with grade 3 EMH had an underlying hematologic diagnosis, and most (86%) patients with grade 1 EMH had a nonhematologic disorder.

Conclusions

Our data suggest that EMH grading in the liver is feasible and can identify patients who may have an underlying hematologic condition, which can guide further diagnostic workup.
The Origins of the Market for Corporate Law
Authors: Sanga Sarath
Published: April 2022

I study the origins of the market for corporate charters and the emergence of Delaware as the leader of this market. Specifically, I assemble new data on 19th- and 20th-century corporations to evaluate two widely held beliefs: (1) the U.S. Supreme Court is responsible for enabling a national market for corporate charters in the 19th century and (2) Delaware became the leader in this market only because New Jersey (the initial leader) repealed its extremely liberal corporate laws in 1913. I argue against both claims: The Supreme Court always opposed a national market for corporate charters, and New Jersey’s decline began a decade before its 1913 repeal. It is more likely that the market for corporate charters emerged as a collateral consequence of interstate commerce and that New Jersey declined because Delaware and other states simply copied its laws.
Ability of Thermal Work Limit (TWL) to Assess Sustainable Heat Stress Exposures
Authors: Bernard Thomas E , Ashley Candi D , Kapanowski Danielle
Published: April 2022

Thermal Work Limit (TWL) recommends a maximum metabolic rate for a given environmental condition, clothing ensemble, and acclimatization state so that thermal equilibrium can be sustained at or below the limiting metabolic rate. The purpose of this paper was to assess the ability of TWL to recommend maximum sustainable levels of heat stress using an existing database of progressive heat stress trials using four levels of clothing (woven clothing, particle barrier, water barrier, and vapor barrier), three levels of relative humidity, and three levels of metabolic rate. Each trial had a compensable and an uncompensable observation plus and observation at the transition point from compensable to uncompensable. Each observation was classified as a case (steady increase in rectal temperature) or non-case (steady rectal temperature). The data were used to compare the difference between the observed metabolic rate (Mobs) and the limiting metabolic rate of TWL (i.e., ∆LimitTWL = Mobs − TWL), where ∆LimitTWL > 0 was above the TWL limit. The sensitivity and specificity for each of the four clothing ensembles were about 0.96 and about 0.20, respectively. Logistic regression for all the data found that ∆LimitTWL, clothing, metabolic rate, and water vapor pressure were significant predictors of outcome. The ln(odds) equations for each clothing ensemble predicted a probability of an uncompensable exposure. The probability of an uncompensable outcome (case) when ∆LimitTWL = 0 was 0.14 for work clothes and particle barrier, and 0.22 for water barrier and vapor barrier. The probability of a case at ∆LimitTWL = 0 was greater than the probability of a case for the wet bulb globe temperature-based exposure limits where the probability of a case was 0.01. That is, the TWL was less restrictive than WBGT but with higher risk.
Validating the Short-form Foreign Language Classroom Anxiety Scale
Authors: Botes Elouise , van der Westhuizen Lindie , Dewaele Jean-Marc , MacIntyre Peter , Greiff Samuel
Published: April 2022

Foreign language classroom anxiety (FLCA) is a popular construct in applied linguistics research, traditionally measured with the 33-item Foreign Language Classroom Anxiety Scale (FLCAS). However, recent studies have started utilizing the eight-item Short-Form FLCAS (S-FLCAS). There is therefore a need, which this study addressed in five sequential steps, to validate the S-FLCAS in order to ensure the validity and reliability of the scale. A sample of n = 370 foreign language learners was utilized in the validation efforts, which included exploratory and confirmatory factor analyses, the establishment of convergent and discriminant validity, and invariance testing. The S-FLCAS was found to have a unidimensional structure with the eight items loading on a single latent variable. Evidence was provided of the internal consistency and the convergent and discriminate validity of the S-FLCAS. In addition, the measure was found to be fully invariant across age, gender, educational levels, and L1 groups. It is, therefore, with some considerable confidence that we can recommend the future use of the S-FLCAS in peer-reviewed research.
Associations between Intra-Individual Neurocognitive Variability and Prospective Memory in Veterans with Mild Traumatic Brain Injury History and Posttraumatic Stress Disorder
Authors: Sheppard David P , Rau Holly K , Werhane Madeleine L , Fonseca Luciana Mascarenhas , Chaytor Naomi S , Peskind Elaine R , Pagulayan Kathleen F
Published: April 2022

Objective

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) frequently co-occur and are associated with neurocognitive intra-individual variability (IIV) and difficulty with prospective memory (PM). The current study aimed to examine associations between IIV and PM in this comorbid group.

Method

Fifty veterans with a history of blast mTBI and current comorbid PTSD completed a standardized neurocognitive battery to measure IIV, and the Memory for Intentions Screening Test measuring PM.

Results

Adjusting for age, education, and race, higher IIV was associated with poorer time-based PM (p < .001, f2 = .34), but not event-based PM. In a subset of the sample with self-report data, higher IIV was associated with poorer self-reported retrospective memory, but not PM.

Conclusions

Cognitive variability on a standardized neuropsychological battery was associated with strategically demanding PM, which is an ecologically relevant ability and highlights the possible connection between subtle cognitive difficulties in-clinic and those experienced in daily life.
Neuropsychological Profile of Hereditary Ataxias: Study of 38 Patients
Authors: García Maitane , Rouco-Axpe Idoia , Amayra Imanol , Rodríguez-Antigüedad Alfredo , Catalli Claudio , Cabrera-Zubizarreta Alberto , Rodríguez Alicia Aurora , Pérez Manuel
Published: April 2022

Hereditary ataxias are a heterogeneous group of disorders characterized by degeneration of the cerebellum and its connections. It is known that patients with ataxia can manifest a broad spectrum of motor symptoms; however, current research has emphasized the relevance of cognitive disturbances. The aim of this study is to analyze the presence of cognitive impairment in a heterogeneous cohort of patients with hereditary ataxia (HA). A group of 38 patients with HA of different etiologies and a matched group of 38 healthy controls were recruited and evaluated through a comprehensive battery of neuropsychological tests. The findings show a worse performance in ataxic patients on planning, visuospatial skills, naming, and Theory of Mind tasks, regardless their physical and psychological symptomatology. The influence of clinical status as well as functional ability-related variables on their performance were analyzed, showing that the level of disability and motor disturbances have a significant effect on verbal memory, verbal fluency, and working memory. These findings suggest that patients with HA can manifest cognitive and neuropsychiatric symptoms as part of their clinical features, which demands its inclusion for the diagnosis and management of the disease.
A quality control portal for sequencing data deposited at the European genome–phenome archive
Authors: Fernández-Orth Dietmar , Rueda Manuel , Singh Babita , Moldes Mauricio , Jene Aina , Ferri Marta , Vasallo Claudia , Fromont Lauren A , Navarro Arcadi , Rambla Jordi
Published: April 2022

Since its launch in 2008, the European Genome–Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/).
Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer’s disease
Authors: Bi Xia-an , Zhou Wenyan , Luo Sheng , Mao Yuhua , Hu Xi , Zeng Bin , Xu Luyun
Published: April 2022

The roles of brain regions activities and gene expressions in the development of Alzheimer’s disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region–gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.
On the influence of several factors on pathway enrichment analysis
Authors: Mubeen Sarah , Tom Kodamullil Alpha , Hofmann-Apitius Martin , Domingo-Fernández Daniel
Published: April 2022

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.
D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19
Authors: Yang Yanqing , Zhou Deshan , Zhang Xinben , Shi Yulong , Han Jiaxin , Zhou Liping , Wu Leyun , Ma Minfei , Li Jintian , Peng Shaoliang , Xu Zhijian , Zhu Weiliang
Published: April 2022

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
Directed graph attention networks for predicting asymmetric drug–drug interactions
Authors: Feng Yi-Yang , Yu Hui , Feng Yue-Hua , Shi Jian-Yu
Published: April 2022

It is tough to detect unexpected drug–drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction. Such an asymmetry is crucial to poly-drug treatments since it determines drug priority in co-prescription. This paper designs a directed graph attention network (DGAT-DDI) to predict asymmetric DDIs. First, its encoder learns the embeddings of the source role, the target role and the self-roles of a drug. The source role embedding represents how a drug influences other drugs in DDIs. In contrast, the target role embedding represents how it is influenced by others. The self-role embedding encodes its chemical structure in a role-specific manner. Besides, two role-specific items, aggressiveness and impressionability, capture how the number of interaction partners of a drug affects its interaction tendency. Furthermore, the predictor of DGAT-DDI discriminates direction-specific interactions by the combination between two proximities and the above two role-specific items. The proximities measure the similarity between source/target embeddings and self-role embeddings. In the designated experiments, the comparison with state-of-the-art deep learning models demonstrates the superiority of DGAT-DDI across a direction-specific predicting task and a direction-blinded predicting task. An ablation study reveals how well each component of DGAT-DDI contributes to its ability. Moreover, a case study of finding novel DDIs confirms its practical ability, where 7 out of the top 10 candidates are validated in DrugBank.
NSCGRN: a network structure control method for gene regulatory network inference
Authors: Liu Wei , Sun Xingen , Yang Li , Li Kaiwen , Yang Yu , Fu Xiangzheng
Published: April 2022

Accurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies’ specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology’s specific forms and cooperation mode. The method is carried out in a cooperative mode of ‘global topology dominates and local topology refines’. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola–Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.
A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction
Authors: Yaish Ofir , Asif Maor , Orenstein Yaron
Published: April 2022

CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this editing technique is quite accurate in the target region, there may be many unplanned off-target sites (OTSs). Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of OTSs) produced by experimental techniques to detect OTSs with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect OTSs, was used to produce a dataset of unprecedented scale and quality (>200 000 OTS over 110 guide RNAs). In addition, the same study included in cellula GUIDE-seq experiments for 58 of the guide RNAs. Here, we fill the gap in previous computational methods by utilizing these data to systematically evaluate data processing and formulation of the CRISPR OTSs prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive OTSs to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between guide RNAs and their OTSs as a feature. Finally, we present predictive off-target in cellula models based on both in vitro and in cellula data and compare them to state-of-the-art methods in predicting true OTSs. Our conclusions will be instrumental in any future development of an off-target predictor based on high-throughput datasets.
Biological activities of drug inactive ingredients
Authors: Zhang Chenyang , Mou Minjie , Zhou Ying , Zhang Wei , Lian Xichen , Shi Shuiyang , Lu Mingkun , Sun Huaicheng , Li Fengcheng , Wang Yunxia , Zeng Zhenyu , Li Zhaorong , Zhang Bing , Qiu Yunqing , Zhu Feng , Gao Jianqing
Published: April 2022

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs’ activity data, which was the first evaluation on the possibility to predict DIG’s activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.
Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary
Authors: Blecua Pedro , Davalos Veronica , de Villasante Izar , Merkel Angelika , Musulen Eva , Coll-SanMartin Laia , Esteller Manel
Published: April 2022

High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package ‘conumee’, to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as ‘conumee-KCN’, improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.