bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractBACKGROUNDGlycerol 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.RESULTSThe 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).CONCLUSIONSorbitol 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

AbstractBACKGROUNDStimuli‐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.RESULTSHigh‐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.CONCLUSIONThese 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

AbstractThe 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

AbstractTotal 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

AbstractA 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

AbstractRadiation‐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

AbstractNot 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

AbstractBackgroundPerfusion 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.PurposeWe 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.MethodsThe 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.ResultsThe 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: ).ConclusionsPINNs 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

AbstractModification 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

ABSTRACTNumerous 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

AbstractTax 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.
Generation of Regression Tests From Logs With Clustering Guided by Usage Patterns
Authors: Tamagnan Frédéric , Vernotte Alexandre , Bouquet Fabrice , Legeard Bruno 
Published: September 2024

ABSTRACTClustering 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

AbstractLorantheae 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.