bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patient‐related factors associated with patient retention and non‐completion in psychosocial treatment of borderline personality disorder: A systematic review
Authors: Lau Parky , Amestoy Maya E. , Roth Maya , Monson Candice 
Published: May 2024

AbstractThe potential efficacy of psychosocial interventions in the treatment of borderline personality disorder (BPD) is impacted by significant treatment non‐completion (TNC), with meta‐analytic studies reporting rates of attrition of between 25% and 28%. Increasing patient retention could facilitate outcomes and improve resource utilization, given limited healthcare services. A systematic search of PsycINFO, CINAHL, EMBASE, CENTRAL, and Web of Science Core Collection identified 33 articles that met the criteria for inclusion. Although substantial heterogeneity in terms of methodology and quality of analysis limited conclusions that could be drawn in the narrative review, a few consistent patterns of findings were elucidated, such as Cluster B personality disorder comorbidities and lower therapeutic alliance were associated with TNC. Interestingly, the severity of BPD symptoms was not a predictor of TNC. These findings are discussed in terms of their potential theoretical contribution to TNC. Clinically, there may be value in applying mindfulness and motivational interviewing strategies early on in treatment for individuals who present uncertainty about engaging in treatment. Further research to develop this empirical landscape includes focusing on high‐powered replications, examining burgeoning lines of research, and investigating dynamic predictors of TNC.
Proteomics is advancing the understanding of stallion sperm biology
Authors: Peña Fernando J. , Martín‐Cano Francisco Eduardo , Becerro‐Rey Laura , Ortega‐Ferrusola Cristina , Gaitskell‐Phillips Gemma , da Silva‐Álvarez Eva , Gil María Cruz 
Published: May 2024

AbstractThe mammalian ejaculate is very well suited to proteomics studies. As such, research concerning sperm proteomics is offering a huge amount of new information on the biology of spermatozoa. Among domestic animals, horses represent a species of special interest, in which reproductive technologies and a sizeable market of genetic material have grown exponentially in the last decade. Studies using proteomic approaches have been conducted in recent years, showing that proteomics is a potent tool to dig into the biology of the stallion spermatozoa. The aim of this review is to present an overview of the research conducted, and how these studies have improved our knowledge of stallion sperm biology. The main outcomes of the research conducted so far have been an improved knowledge of metabolism, and its importance in sperm functions, the impact of different technologies on the sperm proteome, and the identification of potential biomarkers. Moreover, proteomics of seminal plasma and phosphoproteomics are identified as areas of major interest.
Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences
Authors: Kombo David C. , LaMarche Matthew J. , Konkankit Chilaluck C. , Rackovsky S. 
Published: May 2024

AbstractWe apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue‐specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.
Discovery of novel Propionamide‐Pyrazole‐Carboxylates as Transketolase‐inhibiting herbicidal candidates
Authors: Sun Susu , Li Yaze , Wang Wenfei , Kou Song , Huo Jinqian , An Zexiu , Zhu Lin , Li Kaiwen , Chen Lai , Zhang Jinlin 
Published: May 2024

AbstractBACKGROUNDTransketolase (TKL, EC 2.2.1.1) is a key enzyme in the pentose phosphate pathway and Calvin cycle, and is expected to act as a herbicidal site‐of‐action. On the basis of TKL, we designed and synthesized a series of 1‐oxy‐propionamide‐pyrazole‐3‐carboxylate analogues and evaluated their herbicidal activities.RESULTSMethyl 1‐methyl‐5‐((1‐oxo‐1‐((4‐(trifluoromethyl)phenyl)amino)propan‐2‐yl)oxy)‐1H‐pyrazole‐3‐carboxylate (C23) and methyl 1‐methyl‐5‐((1‐oxo‐1‐((perfluorophenyl)amino)propan‐2‐yl)oxy)‐1H‐pyrazole‐3‐carboxylate (C33) were found to provide better growth‐inhibition activities against Digitaria sanguinalis root than those of nicosulfuron, mesotrione and pretilachlor at 200 mg L−1 using the small‐cup method. These compounds were also identified as promising compounds in pre‐emergence and postemergence herbicidal‐activity experiments, with relatively good inhibitory effects toward Amaranthus retroflexus and D. sanguinalis at 150 g ai ha−1. In addition, enzyme inhibition assays and molecular docking studies revealed that C23 and C33 interact favourably with SvTKL (Setaria viridis TKL).CONCLUSIONC23 and C33 are promising lead TKL inhibitors for the optimization of new herbicides. © 2024 Society of Chemical Industry.
Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation‐maximization algorithm
Authors: Liu Zhuangyu , Zhao Shunyi , Wan Haiying , Luan Xiaoli , Liu Fei 
Published: May 2024

AbstractThe article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation‐maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E‐step, it calculates the Q‐function recursively within the maximum likelihood framework, while the PF estimates the system states. The M‐step involves local maximization of the recursive Q‐function to online estimate the UIs. The effectiveness of the PF‐based recursive EM algorithm is demonstrated with a numerical example, and comparisons with the augmented state PF are made to highlight its advantages. Finally, the proposed algorithm is implemented in a real application for the estimation of the continuous fermentation process.
Self‐Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators
Authors: Zhang Maoqin , Yan Wei , Ma Weiting , Deng Yuheng , Song Weixing 
Published: May 2024

AbstractTriboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self‐powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG‐based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non‐implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG‐based biomedical sensors are also indicated, highlighting the associated challenges.
Browser‐in‐the‐middle attacks: A comprehensive analysis and countermeasures
Authors: Jagannath Raut Omprakash , Jain Ankit Kumar 
Published: May 2024

AbstractA browser‐in‐the‐middle (BITM) attack occurs when an attacker intercepts and manipulates communication between a user's web browser and the website they are attempting to visit. This approach is risky, as it allows the attacker to eavesdrop on crucial information, such as login passwords or financial details, leading to unauthorized access and potential data breaches. Moreover, the attacker can inject malicious content into the conversation, compromising the integrity of the data shared between the user's browser and the website. The article delves into the BITM attack as a strategy employed in phishing assaults. The article emphasizes precautionary measures to safeguard against BITM attacks. Additionally, it offers insights into how users can defend themselves and respond to an attack. The significance of user awareness in avoiding phishing attempts is a key highlight of the article.
Model selection for generalized linear models with weak factors
Authors: Zhou Xin , Dong Yan , Yu Qin , Zheng Zemin 
Published: May 2024

AbstractThe literature has witnessed an upsurge of interest in model selection in diverse fields and optimization applications. Despite the substantial progress, model selection remains a significant challenge when covariates are highly correlated, particularly within economic and financial datasets that exhibit cross‐sectional and serial dependency. In this paper, we introduce a novel methodology named factor augmented regularized model selection with weak factors (WeakFARM) for generalized linear models in the presence of correlated covariates with weak latent factor structure. By identifying weak latent factors and idiosyncratic components and employing them as predictors, WeakFARM converts the challenge from model selection with highly correlated covariates to that with weakly correlated ones. Furthermore, we develop a variable screening method based on the proposed WeakFARM method. Comprehensive theoretical guarantees including estimation consistency, model selection consistency and sure screening property are also provided. We demonstrate the effectiveness of our approach by extensive simulation studies and a real data application in economic forecasting.
Revolutionizing digital healthcare networks with wearable strain sensors using sustainable fibers
Authors: Zhang Junze , Xu Bingang , Chen Kaili , Li Yi , Li Gang , Liu Zekun 
Published: May 2024

AbstractWearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health.
Detection and characterization of micro‐ and nano‐plastics in wastewater: Current status of preparatory & analytical techniques
Authors: Saha Nilanjana , Shourie Abhilasha 
Published: May 2024

AbstractMicro‐ and nano‐plastics (MNPs) are ubiquitous emerging pollutants that have gained global traction due to their persistent nature and potential risks to the environment and human health. The physico‐chemical properties of MNPs such as size, shape, density, surface charge and hydrophobicity are quite different from macro‐plastics, which render them unique behavior exemplified by high rate of diffusion, high mobility, easy uptake by organisms, high toxicity, and potential of bioaccumulation. Human exposure to MNPs causes serious health impacts including apoptosis, necrosis, inflammation, oxidative stress, metabolic disorders, and hampered immune responses. The hazardous nature and high persistence of MNPs in the environment necessitates accelerated studies focused upon their detection, quantification, impact analysis, and risk assessment. All kinds of wastewaters whether municipal, agricultural, or industrial, consist of significant loads of MNPs and act as secondary source of MNP pollution in groundwater and soil. Wastewater is highly complex in composition and there are several impediments in the accurate detection and identification of MNPs in this matrix owing to the in‐efficient sampling methods, lack of standardized methodologies, and non‐reproducibility of data. This review provides an overview of currently available techniques for sampling, detection, identification, and characterization of MNPs in wastewater. A critical commentary on the challenges and limitations of these techniques has been given and strategic combinatorial methods have been suggested for efficient and reliable MNP analysis in wastewater samples. The article also highlights novel sensor‐based methods enabling rapid and accurate detection of MNPs in wastewater.
Non‐invasive imaging techniques for diagnosis of pelvic deep endometriosis and endometriosis classification systems: an International Consensus Statement
Authors: Condous G. , Gerges B. , Thomassin‐Naggara I. , Becker C. , Tomassetti C. , Krentel H. , van Herendael B. J. , Malzoni M. , Abrao M. S. , Saridogan E. , Keckstein J. , Hudelist G. ,   Collaborators
Published: May 2024

ABSTRACTThe International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) and International Deep Endometriosis Analysis (IDEA) group, the European Endometriosis League (EEL), the European Society for Gynaecological Endoscopy (ESGE), the European Society of Human Reproduction and Embryology (ESHRE), the International Society for Gynecologic Endoscopy (ISGE), the American Association of Gynecologic Laparoscopists (AAGL) and the European Society of Urogenital Radiology (ESUR) elected an international, multidisciplinary panel of gynecological surgeons, sonographers and radiologists, including a steering committee, which searched the literature for relevant articles in order to review the literature and provide evidence‐based and clinically relevant statements on the use of imaging techniques for non‐invasive diagnosis and classification of pelvic deep endometriosis. Preliminary statements were drafted based on review of the relevant literature. Following two rounds of revisions and voting orchestrated by chairs of the participating societies, consensus statements were finalized. A final version of the document was then resubmitted to the society chairs for approval.Twenty statements were drafted, of which 14 reached strong and three moderate agreement after the first voting round. The remaining three statements were discussed by all members of the steering committee and society chairs and rephrased, followed by an additional round of voting. At the conclusion of the process, 14 statements had strong and five statements moderate agreement, with one statement left in equipoise. This consensus work aims to guide clinicians involved in treating women with suspected endometriosis during patient assessment, counseling and planning of surgical treatment strategies. © 2024 The Authors. Published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology, by Universa Press, by The International Society for Gynecologic Endoscopy, by Oxford University Press on behalf of European Society of Human Reproduction and Embryology, by Elsevier Inc. on behalf of American Association of Gynecologic Laparoscopists and by Elsevier B.V.
Owners’ experiences of administering meglumine antimoniate injections to dogs with leishmaniosis: An online questionnaire study
Authors: de Jong Marja K. , van Eijk Demy , Broere Femke , Piek Christine J. 
Published: May 2024

AbstractBackgroundThis study examined the experiences of owners of dogs with leishmaniosis who treated their dogs with daily subcutaneous meglumine antimoniate injections. The owners' perceived ease of administering the injections, the occurrence of problems and the effects on the owners and on the dog‒owner bond were evaluated.MethodsDogs prescribed meglumine antimoniate as a treatment for leishmaniosis were identified using the database of the veterinary pharmacy of the Faculty of Veterinary Medicine, Utrecht University. An online questionnaire was sent to the owners of these dogs to evaluate the perceived ease of administering the injections, the occurrence of problems and the effects on the owner and the dog‐owner bond.ResultsResponses were received from 64 dog owners. Most respondents (78%) reported that administering the injections was not difficult. Pain or the development of nodules at the injection site was reported in 50% and 40% of the dogs, respectively. Polyuria was reported in 44% of the dogs. Some owners reported that administering the injections had a negative impact on their psychological wellbeing (20%), and some would have liked more veterinary support (11%).LimitationsSome questions were answered by a limited number of people, and their responses may not be representative.ConclusionDog owners remain highly motivated to persevere with meglumine antimoniate treatment and are willing to administer the injections themselves. The availability of active support when needed during the therapy cycle may further improve their acceptance of and confidence in giving the injections.
Effects of dynamic capillarity on the shear strength of sandy soils during transient two‐phase flow: Insights from non‐equilibrium triaxial simulations
Authors: Daman Shokouh Alireza , Nikooee Ehsan , Habibagahi Ghassem , Hassanizadeh S. Majid 
Published: May 2024

AbstractModeling two‐phase flow in unsaturated porous media is not only important to vadose zone hydrology but also of great value in diverse disciplines. Common approaches use a simplified relationship between fluid pressure difference and saturation, neglecting the influence of saturation change rates. However, many studies have suggested that the applicability of this approach is limited to situations where the rate of change in saturation is insignificant. Despite several studies highlighting the importance of non‐equilibrium capillarity effects in unsaturated flow modeling, its significance in the mechanical response of the porous medium remains unclear. This study thus aims to address this gap by comparing the simulation results of the traditional static approach and an advanced approach that incorporates dynamic capillarity effects. The comparison is conducted under various flow boundary conditions to assess the magnitude of the differences between the two approaches. The results indicate that as the hydraulic boundary conditions’ absolute values increase, the contrast between the mechanical response of the two simulation scenarios (dynamic and static) becomes more significant. For instance, the dynamic model can predict shear strengths up to 50% higher than the static model. This highlights the importance of considering non‐equilibrium effects while modeling the mechanical behavior of an unsaturated porous medium. Finally, the parametric study of the effect of dynamic coefficient, air entry value, and saturated conductivity reveals the more pronounced effect of the dynamic coefficient on the mechanical response.
Doing the Work: Institutional Policy, Research, and Practice for Closing the White-BAME Awarding Gap
Authors: De La Torre Alexandra , Buswell Elizabeth , Dowie Ellen , Moriarty Jan 
Published: May 2024

Abstract
This chapter explores the journey of Student Success as an institutional research and practice initiative that, over almost ten years, has provided a platform to host, develop, and sustain initiatives and interventions to investigate and address the white-BAME awarding gap at the University of Kent. By bringing together academics, students, and a team of practitioners, Student Success has contributed to the understanding of the complex dynamics of attainment differentials. The evolution of the Student Success team is presented from the perspective of Theory of Change, as a planning and evaluation methodology that provides a linear and unifying framework (Mayne, 2017) to examine the different phases of this initiative. Across three distinct phases, Student Success has sought to address many of the challenges common to institutional EDI work, including achieving buy-in from academic staff, balancing central leadership with devolved responsibility for academic schools, developing effective models for monitoring and evaluation, and negotiating organisational changes within the institution. Overall, we believe the lessons learnt and experiences shared in this chapter can inform other higher education practitioners when planning or delivering similar projects to address attainment differentials through the lens of students’ and academics’ engagement, institutional research, monitoring, and evaluation.
Subregion independence in gravity
Authors: Folkestad Åsmund 
Published: May 2024

Abstract
In gravity, spacelike separated regions can be dependent on each other due to the constraint equations. In this paper, we give a natural definition of subsystem independence and gravitational dressing of perturbations in classical gravity. We find that extremal surfaces, non-perturbative lumps of matter, and generic trapped surfaces are structures that enable dressing and subregion independence. This leads to a simple intuitive picture for why extremal surfaces tend to separate independent subsystems. The underlying reason is that localized perturbations on one side of an extremal surface contribute negatively to the mass on the other side, making the gravitational constraints behave as if there exist both negative and positive charges. Our results support the consistency of islands in massless gravity, shed light on the Python’s lunch, and provide hints on the nature of the split property in perturbatively quantized general relativity. We also prove a theorem bounding the area of certain surfaces in spherically symmetric asymptotically de Sitter spacetimes from above and below in terms of the horizon areas of de Sitter and Nariai. This theorem implies that it is impossible to deform a single static patch without also deforming the opposite patch, provided we assume spherical symmetry and an energy condition.
Massive covariant colour-kinematics in 3D
Authors: Moynihan Nathan 
Published: May 2024

Abstract
We explore topologically massive gauge theories using the covariant colour kinematics duality recently introduced in [1]. We show that the massive bi-adjoint scalar field is simply related to topologically massive gauge theory by the duality, and that enacting the same duality on the gauge theory produces topologically massive gravity coupled to a scalar or, equivalently, an antisymmetric field. We also show that different choices for the replacement of the colour structure constants with kinematic structure constants lead to different theories, including a topologically massive generalisation of Born-Infeld theory.