bluuebunny/crossref_metadata_2025 huggingface dataset:
Plasma‐derived extracellular vesicles (EVs) as biomarkers of sepsis in burn patients via label‐free Raman spectroscopy
Authors: O'Toole Hannah J. , Lowe Neona M. , Arun Vishalakshi , Kolesov Anna V. , Palmieri Tina L. , Tran Nam K. , Carney Randy P.
Published: September 2024

Sepsis following burn trauma is a global complication with high mortality, with ∼60% of burn patient deaths resulting from infectious complications. Diagnosing sepsis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers lack the sensitivity and specificity required for prompt treatment. There is a strong rationale to assess circulating extracellular vesicles (EVs) from patient liquid biopsy as sepsis biomarkers due to their release by pathogens from bacterial biofilms and roles in the subsequent immune response. This study applies Raman spectroscopy to patient plasma‐derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non‐septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care and establishes Raman analysis as a fast, label‐free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.
Risk of dyslipidaemia in people living with HIV who are taking tenofovir alafenamide: a systematic review and meta‐analysis
Authors: Yoo Jeong‐Ju , Jung Eun Ae , Kim Sang Gyune , Kim Young Seok , Kim Min Jae
Published: September 2024

Introduction

Among many antiretroviral drugs, tenofovir alafenamide is used extensively in combination regimens of tenofovir/emtricitabine or tenofovir/emtricitabine/bictegravir. However, concerns have arisen about the potential of tenofovir alafenamide to exacerbate hyperlipidaemia. This meta‐analysis evaluates the relationship between tenofovir alafenamide use and lipid‐profile alterations in people living with HIV.

Methods

We searched PubMed, Ovid MEDLINE, EMBASE and the Cochrane Library to identify studies on changes in cholesterol levels (e.g. total cholesterol, low‐density and high‐density lipoprotein cholesterol, and triglycerides) in people living with HIV who received treatment with a regimen containing tenofovir alafenamide (data collected 31 March 2023, review completed 30 July 2023). Potential risk factors for worsening lipid profile during treatment with tenofovir alafenamide were also evaluated.

Results

Sixty‐five studies involving 39,713 people living with HIV were selected. Significant increases in total cholesterol, low‐density and high‐density lipoprotein cholesterol, and triglycerides were observed after treatment with tenofovir alafenamide. Specifically, low‐density lipoprotein cholesterol (+12.31 mg/dl) and total cholesterol (+18.86 mg/dl) increased markedly from the third month of tenofovir alafenamide use, with significant elevations observed across all time points up to 36 months. Comparatively, tenofovir alafenamide regimens resulted in higher lipid levels than tenofovir disoproxil fumarate regimens at 12 months of use. Notably, discontinuation of the tenofovir alafenamide regimen led to significant decreases in low‐density lipoprotein cholesterol (–9.31 mg/dl) and total cholesterol (–8.91 mg/dl). Additionally, tenofovir alafenamide use was associated with increased bodyweight (+1.38 kg; 95% confidence interval: 0.92–1.84), which became more pronounced over time. Meta‐regression analysis identified young age, male sex and low body mass index as risk factors for worsening cholesterol levels in individuals treated with tenofovir alafenamide.

Conclusions

Tenofovir alafenamide use in people living with HIV is associated with significant alterations in lipid profile.
Persistently high HIV incidence among men who have sex with men and people who inject drugs attending integrated care centres in India: a longitudinal assessment of clinic‐based data
Authors: McFall Allison M. , Gunaratne Mihili P. , Ganapathi Lakshmi , Srikrishnan A. K. , Vasudevan C. K. , Anand Santhanam , Celentano David D. , Solomon Sunil S. , Mehta Shruti H. , Lucas Gregory M.
Published: September 2024

Introduction

Globally, there have been significant declines in HIV incidence over the past two decades, but this decline is slowing, and in some settings, declines have stalled or are growing—particularly where epidemics are concentrated in key populations (KPs). Understanding temporal changes in HIV incidence among KP is critical yet, due to logistical constraints, there are few sources of longitudinal incidence data, particularly among KP.

Methods

We present HIV incidence rates from June 2014 to December 2022 among cisgender men who have sex with men (MSM) and people who inject drugs (PWID) attending community‐based integrated care centres (ICCs) in 15 Indian cities. ICCs, established between 2014 and 2017, provide HIV testing and other services to MSM (eight sites) or PWID (eight sites). Client HIV testing data were included in the analysis if they had ≥2 tests and were not positive on the first test. We calculated incidence rates per 100 person‐years (PY), stratified by KP, city/site and year. Poisson regression explored associations of incidence with time, age, gender (PWID only) and ICC use.

Results

From June 2014 to December 2022, 13,501 clients (5722 MSM, 7779 PWID) had ≥2 HIV tests over a median of 1.8 years. There were a total of 1093 incident HIV acquisitions. Overall incidence rates for MSM and PWID were 1.9/100 PY (95% CI: 1.7−2.2) and 4.1 (3.9−4.4), respectively. Among MSM sites, incidence ranged from 0.4 to 3.5 and in PWID sites from 0.6 to 17.9. From adjusted models, incidence increased by 17% annually among MSM. Among PWID, incidence increased by 11% annually up until 2020 and then decreased by 29% after 2020; when excluding the outlier of New Delhi, incidence was stable among PWID. MSM and PWID 21−25 years old had the highest risk of HIV and among PWID, those more consistently engaged in medication for opioid use disorder were at the lowest risk.

Conclusions

While there was substantial geographic variability, MSM and PWID engaged in a free community‐based clinic experienced persistently high HIV incidence (>2/100 PY). KP in low‐ and middle‐income countries should be a focus when considering novel strategies such as long‐acting pre‐exposure prophylaxis to curtail incidence.
Creative Minds in a Polarized World
Authors: Iannello Paola , Cancer Alice , Zmigrod Leor , Antonietti Alessandro , Salvi Carola
Published: September 2024

In today's digital milieu, characterized by pervasive media exposure, the intricate interplay between individual differences and cognitive processes has garnered significant scholarly interest. A notable facet of this interrelation pertains to the nexus between cognitive flexibility and individuals' engagement with online information. Recognized as essential for creative cognition, cognitive flexibility encompasses various facets of human reasoning vital for adapting to new information and challenging entrenched perspectives. This review expands the traditional “out‐of‐the‐box” thinking paradigm beyond creative cognition to encompass a comprehensive reasoning framework that questions entrenched viewpoints and fixed cognitive schemas. By analyzing recent scholarly discussions on fake news and polarization, we underscore the pivotal role of cognitive flexibility in guiding individuals' critical navigation of the digital information landscape, promoting nuanced and less polarized perspectives. Introducing the concept of Socio‐Cognitive Polarization, we elucidate how individuals with differing ideological backgrounds can exhibit escalating cognitive divisions and absolutist tendencies. Leveraging insights from existing literature, we present methodological strategies for combating misinformation and nurturing critical thinking to counteract the detrimental impact of deceptive narratives on societal discourse. This scholarly exposition advances the understanding of the challenges associated with misinformation dissemination and offers empirically grounded approaches for enhancing critical cognition in the digital era.
Sugar/sugar alcohol with glycerol as co‐plasticizers for high‐content starch/PBAT blown films: from fine structure to physicochemical properties
Authors: Wu Yuntong , Gao Shan , Zhao Jiajun , Kong Shuai , Wang Hao , Wang Wentao , Hou Hanxue
Published: September 2024

BACKGROUND

Glycerol is a well‐known plasticizer for starch‐based materials, but it easily migrates during starch retrogradation, thereby deteriorating the films' properties. We hypothesized that the performance of high‐content starch/poly(butylene adipate‐co‐terephthalate) (PBAT) films could be enhanced by using sugar/sugar alcohol (glucose, sucrose and sorbitol) as natural, green and edible co‐plasticizers with glycerol.

RESULTS

The employment of co‐plasticizers reduced the melt fluidity of the blends, established intermolecular hydrogen bonds with starch and resulted in a brittle film structure. The presence of sucrose contributed to the formation of more B‐type starch crystals. Glucose and sucrose promoted the conversion of bound water to entrapped water, while sorbitol contributed to more bound water. The co‐plasticizers enhanced films' thermal stability, moisture permeability (from 3.61 to 3.72 × 10−11 g m m−2 s−1 Pa−1), and oxygen barrier (from 12.84 to 8.74 × 10−13 cm3 cm cm−2 s−1 Pa−1). Glucose/glycerol co‐plasticized film had the maximum tensile strength (10.12 MPa), and sucrose/glycerol co‐plasticized film showed the highest Young's modulus (380.31 MPa).

CONCLUSION

Sorbitol with linear structure and the lowest melting point exhibited a plasticizing capacity similar to glycerol. The molecular structure (linear or cyclic), hydroxyl group proportion and melting point of the sugar/sugar alcohol were the key factors to regulate the fine structure and properties of starch/PBAT films. © 2024 Society of Chemical Industry.
Fiber complex‐stabilized high‐internal‐phase emulsion for allicin encapsulation: microstructure, stability, and thermal‐responsive properties
Authors: Zhu Yuqing , Peng Shengfeng , Peng Sixian , Chen Xing , Zou Liqiang , Liang Ruihong , Ruan Roger , Dai Leilei , Liu Wei
Published: September 2024

BACKGROUND

Stimuli‐responsive emulsions have garnered significant attention for their ability to enhance sensory qualities and control the release of encapsulated nutrient in emulsion‐based products. However, the characteristics of synthetic materials of fabricating stimuli‐responsive emulsions have been a crucial limitation in the food industry. Regulating the behavior of molecules at the interface could potentially achieve the desired stimuli‐responsive behavior, but currently there is limited information available.

RESULTS

High‐internal‐phase emulsions (HIPEs) were fabricated for the encapsulation of allicin, stabilized by a complex of 20 g kg−1 whey protein amyloid fibrils (WPF) and 20 g kg−1 glycyrrhizin fibers (GA). The intermolecular interactions between WPF and GA in the fiber complexes were predominantly governed by hydrophobic and electrostatic forces. These complexes adsorbed and stacked around the oil droplets, forming a protective interfacial film that enhanced droplet stability. An increased proportion of WPF (WPF = 3:1 or 4:1) surrounding the oil droplets enhanced the accelerated storage stability of HIPEs, with instability indexes approaching 0.2. Additionally, HIPEs displayed a temperature‐dependent modulus, with the emulsion stabilized by a WPF ratio of 3:1 showing the highest modulus at 85 °C. The encapsulation efficiency of allicin in HIPEs ranged from 88.69 ± 6.62% to 101 ± 1.37% at 25 °C, and from 31.95 ± 1.92% to 78.69 ± 4.63% after incubation at 85 °C for 8 h. The release profile of allicin from the HIPEs exhibited thermal responsiveness, depending on the interfacial content of GA.

CONCLUSION

These findings indicated that the thermal‐responsive properties of HIPEs can be strategically engineered by manipulating their interfacial characteristics. © 2024 Society of Chemical Industry.
The impact of item order on the factor structure of the PTSD Checklist for DSM‐5
Authors: Franklin C. Laurel , Raines Amanda M. , Clauss Kate E. , Koscinski Brandon , Saulnier Kevin , Allan Nicholas P. , Villarosa‐Hurlocker Margo C. , Chambliss Jessica L. , Walton Jessica L. , McCormick Michael
Published: September 2024

The PTSD Checklist for DSM‐5 (PCL‐5) is the most widely used self‐report measure of posttraumatic stress disorder (PTSD) and is frequently modeled as having four correlated factors consistent with the DSM‐5 symptom structure. Some researchers have argued that item order may influence factor structure. Although two studies have examined this, they were both based on DSM‐IV criteria, and neither utilized a randomized design. Thus, this study aimed to determine whether item order impacts the factor structure of the PCL‐5, using two independent samples of community participants (N = 347, 67.7% female, 85.3% White) and veterans (N = 409, 83.6% male, 61.9% Black/African American). Approximately half of each sample was randomized to receive the PCL‐5 in the original fixed order, whereas the other half received a uniquely randomized version. We compared the DSM‐5 four‐factor model to several theoretically relevant models and found improved model fit in the seven‐factor hybrid model, community sample: ∆χ2 = 153.87, p < .001; veterans: ∆χ2 = 152.61, p < . 001. Consequently, the DSM‐5 four‐factor and seven‐factor hybrid models were retained for invariance testing. Across both samples, measurement invariance was examined between the randomized and fixed‐order groups. Configural invariance, partial metric invariance, and partial scalar invariance were achieved in both samples, ps = .054–.822, suggesting that the fit of the DSM‐5 four‐factor structure and the seven‐factor hybrid model, as measured using the PCL‐5, are not due to order effects. These findings support the continued use of the PCL‐5 in a fixed fashion.
High‐throughput determination of total lipids from North Pacific marine fishes via the sulfo‐phospho‐vanillin microplate assay
Authors: Pinger Cody , Porter Drew , Cormack Bryan , Fugate Corey , Rogers Matthew
Published: September 2024

Total lipid content is a valuable indicator of fish health, prey quality, survival potential, stock health, and ecosystem status. Here, we demonstrate an accurate method for measuring total lipids in fish tissues using the spectrophotometric sulfo‐phospho‐vanillin (SPV) assay, adapted to a 96‐well plate format. Samples of dried homogenate were cross‐analyzed via the SPV assay and standard gravimetric lipid analysis. Initial measurements of whole fish homogenates analyzed include Pacific herring (Clupea pallasii), Pacific cod (Gadus macrocephalus), walleye pollock (G. chalcogrammus), Pacific capelin (Mallotus villosus), Chinook (Oncorhynchus tshawytscha), and coho (O. kisutch) salmon. Samples of muscle tissue were analyzed from Chinook, pink (O. gorbuscha), sockeye (O. nerka), and chum (O. keta) salmon. All SPV measurements were calibrated using menhaden oil. The mean absolute and relative difference between gravimetric and SPV analysis was 0.5 and ~ 16.4%, respectively (n = 121). To improve the accuracy of SPV assay results, linear calibration models specific to taxa and tissue matrix type were developed, enabling calculation of corrected SPV assay values. The accuracy of using these calibration models was tested by analyzing additional fish samples (n = 16). The results of the corrected SPV assay were not statistically different (p > 0.05) from gravimetric analysis for any samples measured, and the mean absolute and relative difference between the two assays improved to 0.2% and 4.6%, respectively. The SPV assay provides a rapid (2 h), high‐throughput (25 samples processed in triplicate), precise (interassay coefficient of variation = 5.6%), and accurate method for quantifying the total lipid content of homogenized fish tissue.
Enhancing Transcranial Blood Flow Visualization with Dynamic Light Scattering Technologies: Advances in Quantitative Analysis
Authors: Zherebtsov Evgeny , Sdobnov Anton , Sieryi Oleksii , Kaakinen Mika , Eklund Lauri , Myllylä Teemu , Bykov Alexander , Meglinski Igor
Published: September 2024

A comparative application of major dynamic light scattering (DLS)‐based image methodologies applied to transcranial cerebral blood flow imaging is presented. In particular, the study delves into assessing capability of Laser Doppler Flowmetry (LDF), Laser Speckle Contrast Imaging (LSCI), and Diffuse Correlation Spectroscopy (DCS) in enhancing the spatial and temporal resolution of transcranial blood flow imaging. An integral part of the study is focused on the modulation of blood flow through the administration of the vasodilator drug, Sodium Nitroprusside (SNP). This pharmacological intervention facilitated a direct observation of cerebral vasculature's responsiveness to external stimuli, illuminating the physiological adaptations within the brain's microvascular architecture. Advanced LSCI processing techniques are incorporated, notably entropy and principal component analysis (PCA). Entropy is providing a quantifiable measure of the randomness and complexity within the speckle patterns of transcranial blood flow images, revealing remarkably similar outcomes with DSC approach in terms of blood flow dynamics and its quantitative evaluation. The application of PCA approach is provided a more nuanced understanding of blood flow dynamics, facilitating the identification of subtle changes induced by drug administration. This method proved instrumental in enhancing the visualization and detection of nuanced blood flow dynamics, thereby allowing for a more detailed examination of cerebral circulation alterations induced by SNP administration. The study seeks to offer a wider‐ranging insight into comprehending the translating further the concept of DLS into transcrainial blood flow vizualization and explore its practical applications, considering hardware, advanced quantitative image processing, and data acquisition.
Mechanisms of radiation‐induced tissue damage and response
Authors: Zhou Lin , Zhu Jiaojiao , Liu Yuhao , Zhou Ping‐Kun , Gu Yongqing
Published: September 2024

Radiation‐induced tissue injury (RITI) is the most common complication in clinical tumor radiotherapy. Due to the heterogeneity in the response of different tissues to radiation (IR), radiotherapy will cause different types and degrees of RITI, which greatly limits the clinical application of radiotherapy. Efforts are continuously ongoing to elucidate the molecular mechanism of RITI and develop corresponding prevention and treatment drugs for RITI. Single‐cell sequencing (Sc‐seq) has emerged as a powerful tool in uncovering the molecular mechanisms of RITI and for identifying potential prevention targets by enhancing our understanding of the complex intercellular relationships, facilitating the identification of novel cell phenotypes, and allowing for the assessment of cell heterogeneity and spatiotemporal developmental trajectories. Based on a comprehensive review of the molecular mechanisms of RITI, we analyzed the molecular mechanisms and regulatory networks of different types of RITI in combination with Sc‐seq and summarized the targeted intervention pathways and therapeutic drugs for RITI. Deciphering the diverse mechanisms underlying RITI can shed light on its pathogenesis and unveil new therapeutic avenues to potentially facilitate the repair or regeneration of currently irreversible RITI. Furthermore, we discuss how personalized therapeutic strategies based on Sc‐seq offer clinical promise in mitigating RITI.
Extensive infiltration of CD8+ T cells and M1 macrophages is beneficial for multiple cancer patients undergoing chemotherapy
Authors: Bai Yuquan , Xu He , Guo Minzhang , Xia Liang , Deng Senyi
Published: September 2024

Not all patients can benefit from chemotherapy due to the various of tumor type, stage, location, and the different distribution of immune cells in tumor immune microenvironment (TIME). Immune cells are widely involved in every step of cancer progression, including immune escape, metastasis, drug response, and prognosis. In this study, we explored the transcriptome data of 10 solid tumors treated with chemotherapy to identify the role of immune cells. We downloaded the transcriptome and mutation data of 10 cancers from TCGA databases, and used ESTIMATE and CIBERSORT algorithms to assess the proportion of immune cells in the TIME. According to the proportion of specific immune cell infiltration (SICI) of CD8+ T cells and M1 macrophages to group the patients, we found that compared with the SICI low and medium groups, the SICI high group had a larger tumor mutation burden, more gene mutations with targeted drugs, more activation of immune checkpoints (PD‐1, PD‐L1, CTLA‐4, LAG‐3, TIM‐3, and TIGIT), and immune molecules (CD8a, CD80, CD86, TLR2, HLA‐A, HLA‐B, and CD11a) (p < 0.05). Therefore, we can select an appropriate treatment for patients by clarifying the proportion of immune infiltration of CD8+ T cells and M1 macrophages in the TIME.
A physics‐informed deep learning framework for dynamic susceptibility contrast perfusion MRI
Authors: Rotkopf Lukas T. , Ziener Christian H. , von Knebel‐Doeberitz Nikolaus , Wolf Sabine D. , Hohmann Anja , Wick Wolfgang , Bendszus Martin , Schlemmer Heinz‐Peter , Paech Daniel , Kurz Felix T.
Published: September 2024

Background

Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization.

Purpose

We propose a physics‐informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with high accuracy.

Methods

The framework uses physics‐informed neural networks (PINNs) to learn the voxel‐wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov‐regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high‐grade gliomas.

Results

The simulation results demonstrate that PINN‐derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast‐to‐noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN‐predicted perfusion parameters (Pearson's rho for nCBF: and nCBV: ) and very high indices of image similarity (structural similarity index for nCBF: and for nCBV: ).

Conclusions

PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy.
Plasticized PVC composites comprising MWCNT‐RGO hybrid filler–the synergetic effect of hybrids on the dielectric and mechanical properties
Authors: Akhina H. , Reghunadhan Arunima , Nair M. R. Gopinathan , Thomas Sabu
Published: September 2024

Modification of polymers with hybrid fillers has sought much attention in the past few years. The addition of hybrid fillers will lead to a synergistic effect in reinforcing and is advantageous in polymer nanocomposites. One such attempt is presented here that explores the effect of incorporation of hybrid derived from reduced graphene oxide (RGO) and multiwalled carbon nanotubes (MWCNT) on the dielectric and mechanical properties of plasticized polyvinyl chloride (PPVC). This system of hybrid composite incorporating RGO‐CNT and PPVC has not been prepared and reported yet. Melt mixing techniques were effectively used for the fabrication of PPVC nanocomposites containing the hybrid filler in different ratios. The prepared composites showed enhanced dielectric strength in the lower frequency region up to 450 and high AC conductivity. The mutual stacking and penetration of the 1D‐2D nanomaterials lead to effective dispersion and enhancement in the electronic conduction. Among the hybrids, the R1C5 (with the filler ration RGO 1 wt% and CNT 5%) was having the optimum properties. The tensile modulus showed a cumulative variation with the addition of CNT. Even though the tensile strength decreased slightly because of the immobilization of the PPVC chains by the hybrid effect, the modulus value showed a marked increase of over 90% from the virgin polymer. The PPVC composite with R1C5 hybrid filler exhibited an EMI shielding efficiency of ⁓23 dB and it was frequency independent. The results suggest the composite R1C5 should be used as a cost‐effective material for applications in the L‐band and S‐band regions.
Game Theoretic Approach Toward Detection of Input‐Driven Evasive Malware in the IoT
Authors: Ho Jun‐Won
Published: September 2024

Numerous researchers have scrutinized evasive malware that exerts influences on various systems. Although these studies have examined a variety of aspects on evasive malware in terms of attack and defense, they have mainly focused on evasive malware driven by the environment and have rarely explored input‐driven evasive malware, which is regarded as a new type of evasive malware exhibiting malicious activities under the characteristics of incoming inputs. To mollify these limitations of existing studies, we dissect input‐driven evasive malware in the IoT from the perspective of game theoretic security analysis. More specifically, we first formulate an input‐driven evasive malware problem in the IoT, then provide a Bayesian signaling game theoretic security analysis of that problem and evaluate the proposed game theoretic security analysis, taking into consideration the diverse facets. In our game theoretic security analysis, we define two players: the input provider with both dynamic analyzer and human user types, and the input receiver with the input‐driven evasive malware type. Then, we derive the pure‐strategy and mixed‐strategy Bayesian Nash equilibria for a single‐stage game and weak sequential equilibrium with the aid of the sequential probability ratio test for a multiple‐stage game. The main evaluation result demonstrates that the input receiver with input‐driven evasive malware type begins to disclose its malicious activities between 6.0234th and 9.6663th stages on an average, under the condition that both the input provider with the dynamic analyzer type and the input receiver with the input‐driven evasive malware type adhere to their multi‐stage strategies of weak sequential equilibrium, contributing to successful discernment of input receiver with input‐driven evasive malware type.
Observing systemic values in a tax avoidance environment: Using systems theory to examine the Ireland–Apple tax relationship
Authors: Hunt Emer , Doyle Gerardine
Published: September 2024

Tax has been the site of many battles between ethical behaviour and formal compliance with tax law, particularly in an international context. This paper accepts the functional differentiation of society and explores the amorality of systems. The environmental noise about unfair tax competition by states and tax avoidance by multinational enterprises is extensive. The autopoietic legal system cannot be relied upon to deliver a concurring view, instead producing its self‐referential and sometimes contradictory communications. The empirical setting considers Ireland and Apple and the legal system's verdict on the legality of their tax relationship. This legal verdict changes as the case progresses from European Commission to one court after another, a contingent and uncertain outcome that may be prompted by systemic irritation but which is not co‐ordinated with moral commentary on Ireland or Apple. The absence of coordination by the legal system with environmental commentary on fairness means that, as the legal verdict changes from court to superior court, moral commentary is not a reliable predictor of legal outcome.
Recent Advances in Graphene‐Based Membranes with Nanochannels and Nanopores
Authors: Lin Tongxi , Wen Xinyue , Ren Xiaojun , Quintano Vanesa , Andreeva Daria V. , Novoselov Kostya S. , Joshi Rakesh
Published: September 2024

Understating the mass transport via nanopores and nanochannels plays a vital role in membrane‐based separation systems and applications. Graphene and its derivates, such as graphene oxide (GO) and reduced GO, are one‐atom‐thick, 2D materials that exhibit outstanding physical and chemical properties. Based on their fascinating features, graphene‐based materials have become important building blocks for 2D separation membranes that with well‐defined nanopores or nanochannels. In this review, the mass transport through in‐plane nanopores on nanoporous graphene membranes and 2D nanochannels in GO‐based laminar membranes are discussed. The recent advances in nanoporous graphene engineering, as well as separation applications, are discussed. The discussion of GO‐based membranes is unfolded from the structure and properties of GO and nanochannels formed by GO laminates. Through the understanding of GO‐based membrane structures and separation performance, the mechanisms of mass transport in the nanochannels of GO‐based membranes are revealed, and strategies for GO membrane modification are introduced and summarized from both mechanism and application perspectives. This study may provide a highly desirable guideline for future graphene‐based nanostructures and their applications in mass transport.
Mesoporous Silica Nanoparticle Grafted Polypropylene Membrane toward Long‐Term Efficient Oxygenation
Authors: Feng Aoxing , Lin Yakai , Sun Dayin , Wu Fangyu , Wu Huanhuan , Tang Yuanhui , Zhang Fanchen , Jia Wei , Yu Lixin , Wang Xiaolin , Yang Zhenzhong
Published: September 2024

The trade‐off between gas permeability and resistance to plasma leakage imposes a great challenge for the practical use of membranes in extracorporeal membrane oxygenation (ECMO). Herein, a polypropylene (PP) hollow‐fiber composite membrane is fabricated by simply grafting mesoporous silica nanoparticles onto the commercial PP membrane, which shows a significantly enhanced gas permeability and superior resistance to plasma leakage. The performance metrics such as gas permeability, bubble point, surface hydrophobicity, and plasma leakage resistance are largely influenced by the type of functional groups on the silica nanoparticles (hydroxyl, vinyl, or trifluoropropyl). It is shown that the trifluoropropyl‐group functionalized mesoporous silica nanoparticle grafted composite membrane demonstrates a superior performance than the commercial ECMO membrane of poly(4‐methyl‐1‐pentene) (PMP). The bubble point is greatly elevated from 0.36 to 1.20 MPa while the decrease in gas flux is negligible within 4%. And the leakage resistance time is significantly prolonged from 600 to 4140 min. The gained benefits are originated from the enhanced mass transfer area and diminished surface pores of the composite membrane are grafted with the mesoporous nanoparticles. The high‐performance PP‐based composite membranes are cost‐effective and promising in practical applications of ECMO.
High‐Efficiency Flexible GaAs/InGaAs Dual‐Junction Solar Cells Fabricated by Metallic Nanoparticle‐Based Wafer Bonding
Authors: Kim Yeongho , Park Suho , Nguyen Thuy Thi , Jeon Jiyeon , Chun Byong Sun , Lee Sang Jun
Published: September 2024

Multijunction solar cells made from highly lattice‐mismatched (LMM) material systems offer an optimal bandgap combination for the ultrahigh conversion of solar energy to electricity. Conventional fabrication techniques for multijunction cells, such as metamorphic epitaxy, direct wafer bonding, and adhesive wafer bonding, are still expensive and produce low yields owing to the use of complex process steps and sophisticated equipment. Herein, flexible GaAs/In0.53Ga0.47As dual‐junction solar cells with a large lattice mismatch of 3.7% between the subcells are fabricated using an indium‐tin‐oxide (ITO) nanoparticle‐based wafer bonding process. The ITO bonding layer electrically and optically connecting the GaAs top and InGaAs bottom cells shows a low series resistance of 5.7 × 10−2 Ω cm−2 and a high optical transmission of 90% in the infrared range of 870–1800 nm. The dark current characteristic of the ITO‐bonded dual‐junction cell exhibits a good rectifying behavior with a high on–off ratio of ≈105 at ±2 V. Under 1 sun AM 1.5G illumination, a high power conversion efficiency of 28.5% is achieved for the two‐terminal series‐connected dual‐junction cell, with an average external radiative efficiency of 2.6%. In these findings, it is suggested that ITO nanoparticle‐based wafer bonding may be a facile and cost‐effective route for fabricating LMM solar cells, potentially overcoming the Shockley–Queisser efficiency limit.
Generation of Regression Tests From Logs With Clustering Guided by Usage Patterns
Authors: Tamagnan Frédéric , Vernotte Alexandre , Bouquet Fabrice , Legeard Bruno
Published: September 2024

Clustering is increasingly being used to select the appropriate test suites. In this paper, we apply this approach to regression testing. Regression testing is the practice of verifying the robustness and reliability of software by retesting after changes have been made. Creating and maintaining functional regression tests is a laborious and costly activity. To be effective, these tests must represent the actual user journeys of the application. In addition, an optimal number of test cases is critical for the rapid execution of the regression test suite to stay within the time and computational resource budget as it is re‐run at each major iteration of the software development. Therefore, the selection and maintenance of functional regression tests based on the analysis of application logs has gained popularity in recent years. This paper presents a novel approach to improve regression testing by automating the creation of test suites using user traces fed into clustering pipelines. Our methodology introduces a new metric based on pattern mining to quantify the statistical coverage of prevalent user paths. This metric helps to determine the optimal number of clusters within a clustering pipeline, thus addressing the challenge of suboptimal test suite sizes. Additionally, we introduce two criteria, to systematically evaluate and rank clustering pipelines. Experimentation involving 33 variations of clustering pipelines across four datasets demonstrates the potential effectiveness of our automated approach compared with manually crafted test suites. (All the experiments and data on Scanner, Spree and Booked Scheduler are available at https://github.com/frederictamagnan/STVR2024.) Then, we analyse the semantics of the clusters based on their principal composing patterns.
Phylogenetic relationships of the African Lorantheae (Loranthaceae, Santalales): Insights from plastome, mitochondrial, and nuclear ribosomal sequences
Authors: Omollo Wyckliffe Omondi , Zhang Qiang , Lai Yang‐Jun , Chen Zhi‐Duan , Le Chi Toan , Lu Li‐Min , Liu Bing
Published: September 2024

Lorantheae is the largest tribe within the Loranthaceae family. However, the relationships among genera of African Lorantheae have been poorly understood due to limited molecular data and taxon sampling in previous studies. Understanding phylogenetic relationships and developing a stable classification of the African Lorantheae requires a robust molecular phylogeny. To address this, we obtained complete plastomes, nuclear ribosomal DNA 18S‐ITS1‐5.8S‐ITS2‐26S, and mitochondrial sequences for 61 accessions using genome skimming data to attempt resolving recalcitrant relationships of this tribe within the African lineage. The sampling included representatives of all genera from the previous subtribal classification. Analysis of the plastome dataset resulted in a well‐supported phylogeny with four clades. The topologies based on the plastome and mitochondrial datasets were generally congruent, only with minor discordance at the position of some genera. Topology of the nuclear ribosomal DNA was, however, not congruent with those from other datasets, with a few incongruent nodes detected. Our phylogenomic analyses largely resolve relationships in the tribe Lorantheae, supporting the monophyly of its subtribes and most of its genera, except for Agelanthus and Oncocalyx. Based on the well‐resolved phylogenies, we propose two new subtribes and provide circumscriptions for the previous subtribes.
Governance of the RCVS: is there an acceptable solution?
Authors: Jones Bruce Vivash
Published: September 2024

Bruce Vivash Jones argues that RCVS council must address the question ‘By what authority can they scrap elections?‘ before this proposal proceeds any further.
New approaches must be taken to control rabies in low‐ and middle‐income countries
Authors: Acharya Krishna Prasad , Phuyal Sarita
Published: September 2024

Krishna Prasad Acharya and Sarita Phuyal argue that while low‐ to middle‐income countries may have some rabies control initiatives in place, these are not fully effective and strategies must be improved to reach the global goal of eliminating human deaths from dog‐transmitted rabies by 2030.
Catalysis in the digital age: Unlocking the power of data with machine learning
Authors: Abraham Bokinala Moses , Jyothirmai Mullapudi V. , Sinha Priyanka , Viñes Francesc , Singh Jayant K. , Illas Francesc
Published: September 2024

The design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data‐driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML‐guided catalyst development.

This article is categorized under:

Structure and Mechanism > Reaction Mechanisms and Catalysis

Data Science > Artificial Intelligence/Machine Learning

Electronic Structure Theory > Density Functional Theory
Well‐posedness and large‐time behavior for the two‐phase system with magnetic field in critical Besov spaces
Authors: Zhu Limin
Published: September 2024

This paper is dedicated to the study of the two‐phase system with magnetic field in multi‐dimensional spaces . We investigate the local and global well‐posedness of strong solutions to the Cauchy problem in critical Besov spaces. Moreover, a Lyapunov‐type energy argument can be developed, which leads to the time‐decay estimates of solutions without additional smallness assumptions. We improve the previous effects obtained by Wen and Zhu (JDE‐2018), Wang and Cheng (AMS‐2022), where they established the global well‐posedness of strong solutions in the ‐norm Sobolev spaces.
Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
Authors: Liebel Grischa , Sigurðardóttir Steinunn Gróa
Published: September 2024

Grischa Liebel and Steinunn Gróa Sigurðardóttir
Software Engineering Through Community-Engaged Learning and an Inclusive Network
Authors: Arony Nowshin Nawar , Devathasan Kezia , Li Ze Shi , Damian Daniela
Published: September 2024

Nowshin Nawar Arony, Kezia Devathasan, Ze Shi Li, and Daniela Damian
Strategies for Reporting and Centering Marginalized Developer Experiences
Authors: Ford Denae , Johnson Brittany
Published: September 2024

Denae Ford and Brittany Johnson
Developers’ Perspective of Diverse End User Requirements
Authors: Grundy John , Kanij Tanjila , McIntosh Jennifer , Khalijah Hourieh , Mueller Ingo
Published: September 2024

John Grundy, Tanjila Kanij, Jennifer McIntosh, Hourieh Khalajzadeh, and Ingo Mueller
Beta deformed sigma model and strong deformation coupling limit
Authors: Viana Eggon
Published: September 2024

Abstract

We study the beta deformation of the superstring in AdS5 × S5 at all orders in the deformation parameter, employing the pure spinor formalism. This is necessary in order to study the regime of strong deformation parameter, which in the field side is related to fishnet theories. We compare the pure spinor sigma model approach to the previously known supergravity description. We find a complete agreement. Moreover, the BRST structure of the worldsheet model provides a natural explanation of the peculiar features of the worldsheet model in the fishnet limit. In particular, we study the degeneracy of the sigma model Lagrangian. We show that the BRST structure is responsible for a particularly “tame” degeneration of the fishnet sigma-model.
Nicolai maps and uniqueness in the light-cone gauge
Authors: Bhave Nipun , Pant Saurabh
Published: September 2024

Abstract

We compute the Nicolai map for the supersymmetric Yang-Mills theory, in the light-cone gauge, to the second order in the coupling constant for all critical dimensions (d = 3, 4, 6, 10). The process of integrating out unphysical degrees of freedom in this gauge, produces a four fermion interaction term. We show that, to the order investigated here, this term is harmless. We demonstrate the existence of a particularly ‘simple’ map in d = 4 in the light-cone gauge and address the issue of uniqueness in the context of the map. We also investigate the map in the light-cone superspace in d = 4.
Deep learning to improve the sensitivity of Di-Higgs searches in the 4b channel
Authors: Chiang Cheng-Wei , Hsieh Feng-Yang , Hsu Shih-Chieh , Low Ian
Published: September 2024

Abstract

The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four b-quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (Spa-Net) — a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the Spa-Net can enhance the experimental reach over baseline methods such as the cut-based and the Dense Neural Network-based analyses. At the Large Hadron Collider, with a 14-TeV center-of-mass energy and an integrated luminosity of 300 fb−1, the Spa-Net allows us to establish 95% C.L. upper limits in resonant production cross-sections that are 10% to 45% stronger than baseline methods. For non-resonant di-Higgs production, Spa-Net enables us to constrain the self-coupling that is 9% more stringent than the baseline method.
Gluonic evanescent operators: negative-norm states and complex anomalous dimensions
Authors: Jin Qingjun , Ren Ke , Yang Gang , Yu Rui
Published: September 2024

Abstract

In this paper, we build on our previous work to further investigate the role of evanescent operators in gauge theories, with a particular focus on their contribution to violations of unitarity. We develop an efficient method for calculating the norms of gauge-invariant operators in Yang-Mills (YM) theory by employing on-shell form factors. Our analysis, applicable to general spacetime dimensions, reveals the existence of negative-norm states among evanescent operators. We also explore the one-loop anomalous dimensions of these operators and find complex anomalous dimensions. We broaden our analysis by considering YM theory coupled with scalar fields and we observe similar patterns of non-unitarity. The presence of negative-norm states and complex anomalous dimensions across these analyses provides compelling evidence that general gauge theories are non-unitary in non-integer spacetime dimensions.
Exploring the role of orexins in the modulation of social reward
Authors: Amaral Inês M. , Ouaidat Sara , Scheffauer Laura , Granza Anna E. , Monteiro Diogo G. , Salti Ahmad , Hofer Alex , El Rawas Rana
Published: September 2024

Rationale

positive social interactions are essential for mental health, by offering emotional support, reducing stress levels, and promoting resilience against drugs of abuse effects. However, not all individuals perceive social interaction as rewarding.

Objectives

the goal of this study was to investigate whether the modulation of the orexin system can shift passive coping and non-social behavior (vulnerable) to active coping and social behavior (resilient). This knowledge is primordial for stress- and addiction-related disorders, and for other psychiatric disorders involving impairment in social interaction.

Methods

male C57/BL6N mice categorized into social and non-social groups, received injections of SB334867, a selective orexin 1 receptor (OX1R) antagonist, before the conditioning sessions with a male conspecific of the same weight and age.

Results

our results from the conditioned place preference test (CPP) show that SB334867 has no effect on social preference in non-social mice, but it reduces their stress levels and depression-like behavior. These effects appear to be due to a higher OX1R expression in the basolateral amygdala (BLA), a stress-related brain area, of non-social mice compared to their social counterparts.

Conclusions

these data suggest that the orexin system may be a target to alleviate stress and depression-like behavior in non-social individuals rather than to promote social reward.
KI-Kompetenz zu Studienbeginn als Prädiktor für die Nutzung von KI-Werkzeugen zur Unterstützung von Lernprozessen
Authors: Heil Joana , Delcker Jan , Ifenthaler Dirk , Seufert Sabine , Spirgi Lukas
Published: September 2024

Der Einfluss von Künstlicher Intelligenz (KI) auf die Hochschulbildung nimmt rasant zu. Generative KI-Werkzeuge, wie Übersetzer, Paraphrasierer und Chatbots können die Lernprozesse von Studierenden unterstützen. Die Wahrnehmungen und Erwartungen von Studienanfänger:innen in Bezug auf KI-Werkzeuge sind jedoch bisher nicht eingehend erforscht. Gleiches gilt für die notwendigen Voraussetzungen und Fähigkeiten für den gezielten Einsatz von KI-Werkzeugen in der Hochschulbildung. Diese Arbeit untersucht den Zusammenhang zwischen den Kenntnissen, Fähigkeiten und Einstellungen von Studierenden in den ersten zwei Semestern und ihrer Nutzung von KI-Werkzeugen für Lernprozesse. Die Analyse der Daten von N = 634 Studienanfänger:innen ergab, dass die Einstellung zu KI die beabsichtigte Nutzung von KI-Werkzeugen signifikant erklärt. Überdies sind die wahrgenommenen Vorteile der KI-Anwendungen Prädiktoren für die Wahrnehmung von KI-Werkzeugen als Kooperationspartner des Menschen durch die Studierenden. Lehrende in der Hochschulbildung müssen die KI-Kompetenzen der Studierenden fördern und KI-Werkzeuge in die Unterrichtsgestaltung integrieren, um Lehr-Lern-Prozesse zu verbessern.