bluuebunny/crossref_metadata_2025 huggingface dataset:
Key performance evaluation of commercialized multiplex rRT-PCR kits for respiratory viruses: implications for application and optimization
Authors: Feng Wanyu , Chen Yuqing , Han Yanxi , Diao Zhenli , Zhao Zihong , Zhang Yuanfeng , Huang Tao , Ma Yu , Li Ziqiang , Jiang Jian , Li Jing , Li Jinming , Zhang Rui
Published: October 2024

Respiratory tract infections (RTIs) caused by viruses are prevalent and significant conditions in clinical settings. Accurate and effective detection is of paramount importance in the diagnosis, treatment, and prevention of viral RTIs. With technological advancements, multiplex real-time reverse transcription polymerase chain reaction (rRT-PCR) assays have been developed and extensively adopted for the diagnosis of viral RTIs. Given the potential challenges in the detection performance of multiplex assays, this study evaluated the analytical sensitivity and competitive interference of the six most commonly used multiplex rRT-PCR kits for detection of respiratory viruses in China. The results revealed that the limits of detection were variable across the viruses and kits. Most of the evaluated multiplex kits demonstrated comparable or enhanced analytical sensitivity compared with singleplex kits for clinically significant viruses, including human adenovirus (HAdV)-3, HAdV-7, Omicron BA.5, H1N1pdm09, H3N2, B/Victoria, respiratory syncytial virus subtype A, and respiratory syncytial virus subtype B, whereas multiplex kits showed relatively less analytical sensitivity for human rhinovirus-B72, human metapneumovirus-A2, parainfluenza virus (PIV)-1, and PIV-3. In addition, most multiplex kits successfully identified co-infections when one analyte was present at a low concentration and another analyte was present at a high concentration.

IMPORTANCE

The complexity and severity of viral respiratory tract infections (RTIs) emphasize the pivotal role of precise diagnosis for viral RTIs in guiding effective public health responses and ensuring appropriate medical interventions, given the substantial population at risk. This study highlights the necessity and importance of evaluating the analytical validity of multiplex real-time reverse transcription polymerase chain reaction assays, offering valuable insights into their optimization and application.
Effectiveness of paediatric asthma hubs: a clinical pilot study
Authors: Hakizimana Ali , Lo David K H , Roland Damian , Rai Vinayak K , Danvers Lesley , Rowlands Rachel , Ahmed Molla Imaduddin , Herzallah Reeta , Gaillard Erol A
Published: October 2024

Background

Children and young people (CYP) with asthma in the UK are at higher risk of poor outcomes compared with other high-income European countries due to factors including poor access to high-quality asthma reviews, diagnostic testing and inconsistent postattack reviews. The Leicester Integrated Care Board funded the first UK pilot asthma hub for CYP, to investigate the feasibility and effectiveness of hubs, in providing postattack reviews along with providing asthma education, the opportunity to carry out diagnostic lung function tests and optimise treatment.

Methods

Clinical pilot study including CYP aged 4–17 years referred to the hub with uncontrolled asthma or postattack from November 2021 to April 2022. CYP received a structured clinical assessment including National Institute for Health and Care Excellence (NICE) first-line diagnostic investigations for asthma including spirometry, bronchodilator reversibility (BDR) and fraction of exhaled nitric oxide (FeNO).

Results

Of 312 CYP referred (mean age 8.6±3.2 years; 42% women), 266 (85.3%) attended their appointment. Median time from referral to review was 2 days (IQR 1–3). Three CYP (1.1%) were severely unwell at review and required further hospital treatment. In the 231 CYP who completed first-line tests, asthma was confirmed for 73 (31.6%) based on NICE diagnostic criteria for CYP. Twenty-two per cent of children with normal baseline spirometry had ≥12% BDR.

Conclusion

Paediatric asthma hubs are a feasible model of care to deliver CYP postasthma attack reviews and identify high-risk patients requiring further treatment. Spirometry, BDR and FeNO testing allowed diagnostic confirmation in a significant proportion of CYP.
Best practice in COPD care: a health equity approach
Authors: Moore Anna , Hylton Hannah , Long Alex , Taylor Catherine , Pennington Patricia , Patel Irem
Published: October 2024

There is a strong evidence base for the best care in chronic obstructive pulmonary disease (COPD) as outlined by the National Institute for Health and Care Excellence in its five fundamentals of COPD care: offer treatment and support to stop smoking; offer pneumococcal and influenza vaccinations; offer pulmonary rehabilitation if indicated; codevelop a personalised self-management plan; and optimise treatment for comorbidities. However, only a minority of those living with COPD receive or engage with all five interventions, with clear implications for outcomes. Further, barriers which prevent people who live with COPD accessing necessary care are unequally experienced. In order to overcome many of these barriers, clinicians caring for people with COPD must prioritise personalised treatment and continuity of care.
Bulk viscosity analysis with new holographic dark energy in f(R, T) theory
Authors: Kaur Simran , Srivastava Milan , Mangku Bengia
Published: October 2024

This paper represents the viscous circumstances to imitate new holographic dark energy behaviour in modified f( R, T) gravity theory with a flat Friedmann–Robertson–Walker model with bulk viscosity. Here, we assume bulk viscosity to be directly proportional to Hubble’s parameter (ζ∝ H), i.e., ζ = ζ0 H, where ζ is assumed to be bulk viscosity coefficient, ζ0 is a positive constant, and H is Hubble’s parameter. In this particular model, we consider f( R, T) = R + λ T, where R represents the Ricci scalar, λ as a constant, and T as trace of the energy–momentum tensor. The focus of this article majorly revolves around the concept that the negative pressure induced by bulk viscosity could potentially act as the driving force behind dark energy, contributing to the expansion of the universe. We precisely determine the scale factor’s primary solution and examine all possible conditions: deceleration as well as acceleration, which contributes to the universe’s evolution. Depending on the values of the viscous terms, it has been observed that the model shows the decelerated period as well as the accelerated period. Later, we examine the diagnostic parameters, statefinder { r, s} to distinguish our model from the other existing dark energy models. Thus, a modified theory of gravity can explain the dark energy phenomenon in the presence of bulk viscosity.
Epigenetic factors related to recalcitrance in plant biotechnology
Authors: Hesami Mohsen , Pepe Marco , Spitzer-Rimon Ben , Eskandari Milad , Jones Andrew Maxwell Phineas
Published: October 2024

This review explores the challenges and potential solutions in plant micropropagation and biotechnology. While these techniques have proven successful for many species, certain plants or tissues are recalcitrant and do not respond as desired, limiting the application of these technologies due to unattainable or minimal in vitro regeneration rates. Indeed, traditional in vitro culture techniques may fail to induce organogenesis or somatic embryogenesis in some plants, leading to classification as in vitro recalcitrance. This paper focuses on recalcitrance to somatic embryogenesis due to its promise for regenerating juvenile propagules and applications in biotechnology. Specifically, this paper will focus on epigenetic factors that regulate recalcitrance as understanding them may help overcome these barriers. Transformation recalcitrance is also addressed, with strategies proposed to improve transformation frequency. The paper concludes with a review of CRISPR-mediated genome editing's potential in modifying somatic embryogenesis-related epigenetic status and strategies for addressing transformation recalcitrance.
Asp/ASPM phospho-regulation throughout the cell cycle
Authors: Burns Maria C. , Borgal Lori
Published: October 2024

In mammals and Drosophila melanogaster, Asp/ASPM proteins contribute to cell proliferation and spindle formation. Recent evidence also suggests interphase roles for Asp/ASPM proteins, but little is known about the regulation allowing distinct roles in different cell cycle phases. In this review, we consider a cross-species comparison of Asp/ASPM protein sequences in light of cyclin–CDK literature, and suggest Asp/ASPM proteins to be prime candidates for cyclin–CDK regulation. Conserved regulatory features include an N-terminal proline directed serine/threonine (S/T-P) “supershift” phosphorylation domain common to proteins with bistable interphase and mitotic roles, as well as putative cyclin-binding sites positioned to allow multisite phosphorylation by cyclin–CDK complexes. Human, mouse, and Drosophila Asp/ASPM protein structural predictions show that multisite phosphorylation of the N-term supershift domain could alter the availability of CH-domains and HEAT-motifs, which can contribute to microtubule binding and protein aggregation likely required for spindle formation. Structural predictions of the smallest reported microcephaly patient truncation also emphasize the importance of the arrangement of these motifs. We position this in silico analysis within recent literature to build new hypotheses for Asp/ASPM regulation in interphase and mitosis, as well as de-regulation in microcephaly and cancer. We also highlight the utility of comparing structural/functional differences between human ASPM and Drosophila Asp to gain further insight.
Cavity-based bunch length measurement study for the Super Tau-Charm Facility transport line
Authors: Wang Qian , Huang Letian , Luo Qing
Published: October 2024

In this paper, a nondestructive cavity-based scheme is proposed to achieve high-resolution bunch length measurement in the Super Tau-Charm Facility (STCF) transport line. By extracting the TM010 and TM020 mode signals excited by the beam within a single pillbox cavity, a beam spectrum can be obtained to deduce the Root-Mean-Square (RMS) bunch length. This scheme is more spatially compact compared to the traditional method using double cavities. In this study, the basic principles of the cavity-based bunch length measurement method are explained, the monitor layout is introduced, and the design methods for the cavity and electronics are described. A prototype cavity was machined. Offline tests demonstrate that the eigenmodes in the cavity meet the design requirements and can be used for the bunch length measurements. Based on the online signal characteristics of the Cavity Beam Position Monitors (CBPMs) and the beam parameters of the STCF, the performance of this measurement method is evaluated. For picosecond-level bunches in the STCF transport line, this scheme can provide a bunch length measurement resolution of 233.5 fs at 1.5 nC.
Multi-kink solitons, resonant soliton molecules and multi-lumps solutions to the (3+1)-dimensional shallow water wave equation
Authors: Shi Feng , Qi Meng , Wang Kang-Jia
Published: October 2024

The objective of this research is to seek some new exact solutions to the [Formula: see text]-dimensional shallow water wave equation (SWWE). By means of the Cole–Hopf transformation, the Hirota bilinear expression of the considered equation is developed. Then, taking advantage of the Hirota bilinear approach, the multi-kink solitons solutions are developed. Based on this, the resonant soliton molecules are derived via imposing certain resonant conditions (RCs). Furthermore, the new homoclinic technique is applied to explore the multi-lump solutions. To reveal the performances of the extracted solutions, the corresponding profiles are unveiled graphically through the Maple. For all we know, the derived solutions in this paper have not been probed in other literature and can expand the exact solutions of the considered equation.
Hydrodynamization and resummed viscous hydrodynamics
Authors: Strickland Michael
Published: October 2024

In this paper, I review our current understanding of the applicability of hydrodynamics to modeling the quark–gluon plasma (QGP), focusing on the question of hydrodynamization/thermalization of the QGP and the anisotropic hydrodynamics (aHydro) far-from-equilibrium hydrodynamic framework. I discuss the existence of far-from-equilibrium hydrodynamic attractors and methods for determining attractors within different hydrodynamical frameworks. I also discuss the determination of attractors from exact solutions to the Boltzmann equation in relaxation time approximation and effective kinetic field theory applied to quantum chromodynamics. I then present comparisons of the kinetic attractors with the attractors obtained in standard second-viscous hydrodynamics frameworks and aHydro. I demonstrate that, due to the resummation of terms to all orders in the inverse Reynolds number, the aHydro framework can describe both the weak- and strong-interaction limits. I then review the phenomenological application of aHydro to relativistic heavy-ion collisions using both quasiparticle aHydro and second-order viscous aHydro. The phenomenological results indicate that aHydro provides a controlled extension of dissipative relativistic hydrodynamics to the early-time far-from-equilibrium stage of heavy-ion collisions. This allows one to better describe the data and to extract the temperature dependence of transport coefficients at much higher temperatures than linearized second-order viscous hydrodynamics.
INTEGRATING OPTIMAL DEEP LEARNING WITH NATURAL LANGUAGE PROCESSING FOR ARABIC SPAM AND HAM TWEETS RECOGNITION
Authors: AL-SHATHRY NAJLA I. , ALGHAMDI MOHAMMED , AL-DOBAIAN ABDULLAH SAAD , DAREM ABDULBASIT A. , ALOTAIBI SHOAYEE DLAIM , ALMANEA MANAR , ALGHAMDI BANDAR M. , SOROUR SHAYMAA
Published: October 2024

Natural language processing (NLP) is a domain of artificial intelligence (AI) that concentrates on the communication between human and computer language. Detection of Arabic spam and ham tweets involves leveraging deep learning (DL) models, mainly NLP techniques such as brain-like computing and AI-driven tweets recognition, to mechanically differentiate between spam and ham messages dependent upon content semantics, linguistic patterns, and contextual data within the Arabic text. This study presents an optimal deep learning with natural language processing for Arabic spam and ham tweets recognition (ODLNLP-ASHTR) technique in various complex systems platforms. In the ODLNLP-ASHTR technique, the data pre-processing is initially performed to alter the input tweets into a compatible format, and a BERT word embedding process is used. For Arabic ham and spam tweet recognition, the ODLNLP-ASHTR technique makes use of the self-attention bidirectional gated recurrent unit (SA-BiGRU) model. At last, the detection performance of the SA-BiGRU model can be boosted by the design of an improved salp swarm algorithm (ISSA). The experimental evaluation of the ODLNLP-ASHTR technique takes place using the Arabic tweets dataset. The experimental results pointed out the improved performance of the ODLNLP-ASHTR model compared to recent approaches with a maximum accuracy of 98.11%.
The zero-divisor conjecture and Mealy automata
Authors: Bondarenko Ievgen , Juschenko Kate
Published: October 2024

To prove Kaplansky’s zero-divisor conjecture over [Formula: see text], it suffices to prove it for a certain class of finitely presented groups where the relations are given by a pairing of generators. We associate Mealy automata to such pairings, and prove that the zero-divisor conjecture holds for groups corresponding to invertible automata with three states. In particular, there cannot be zero-divisors arising from an invertible pairing such that one of them has exactly three elements in its support.
Phthalocyanine-based solid-state electrolyte for high-capacity and long-life lithium-ion batteries
Authors: Lucente Leah , Lawson Jacob , Feld William , Firsich David , Kichambare Padmakar , Jenkins Thomas , Rodrigues Stanley , Scanlon Lawrence
Published: October 2024

A new solid-state electrolyte (SSE) with low energy activation for lithium-ion conduction was designed and tested in an all-solid-state battery configuration with a lithium metal anode and a lithium iron phosphate (LiFePO[Formula: see text] cathode. The aromatic character of the phthalocyanine-based SSE facilitates cell stabilization against lithium metal dendrites, and the low energy of activation enables battery cycling at -20 °C. The use of the phthalocyanine-based SSE necessitated the development of an in-situ process for the formation of an effective solid electrolyte interphase (SEI) layer between the anode and the SSE. Such a procedure enabled battery cycling over a 12-to-16-month time frame. The cells delivered high discharge capacities of up to 291.3 mAh/g at 23 °C and 194.5 mAh/g at -20 °C. All cells were fabricated in a dry room via simple solution casting of the soluble SSE onto either an aerosol-jet-deposited or tape-cast LiFePO4 cathode.
Deep Reinforcement Learning-Enhanced Target Encirclement of Multiple Unmanned Surface Vessels
Authors: Yang Caipei , Liu Jie , Cai Xuan , Wei Wei , Ge Ming-Feng
Published: October 2024

Target encirclement of multiple unmanned surface vessels (USVs) is a prevalent tactical strategy in maritime defense missions, significantly enhancing defense efficiency. However, challenges arise due to the limited collaborative capacity stemming from underutilized information within multi-USV systems during such tasks. In this context, a multi-USV target encirclement based on an improved multi-head attention Q-value mixing network is proposed. Initially, a reinforcement learning model tailored for USVs is designed, taking into account the complexities inherent in encirclement tasks. Subsequently, by incorporating distinct action semantics for value calculation, USVs are empowered to accurately assess action values, thereby improving decision-making processes. Leveraging the Qatten algorithm framework, a multi-USV target encirclement method with weighted action semantic-assisted value function decomposition is introduced. Comparative and ablation experiments conducted in uniform-speed target encirclement scenarios validate the effectiveness and high success rate of the proposed method.
Qualitatively Analyzing Optimization Objectives in the Design of HPC Resource Manager
Authors: Boëzennec Robin , Dufossé Fanny , Pallez Guillaume
Published: October 2024

A correct evaluation of scheduling algorithms and a good understanding of their optimization criteria are key components of resource management in HPC. In this work, we discuss bias and limitations of the most frequent optimization metrics from the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling.

We experimentally demonstrate these limitations by focusing on two use-cases: a study on the impact of runtime estimates on scheduling performance, and the reproduction of a recent high-impact work that designed an HPC batch scheduler based on a network trained with reinforcement learning. We demonstrate that focusing on quantitative optimization criterion (“our work improves the literature by X%”) may hide extremely important caveat, to the point that the results obtained are opposed to the actual goals of the authors.

Key findings show that mean bounded slowdown and mean response time are hazardous for a purely quantitative analysis in the context of HPC. Despite some limitations, utilization appears to be a good objective. We propose to complement it with the standard deviation of the throughput in some pathological cases. Finally, we argue for a larger use of area-weighted response time, that we find to be a very relevant objective.
Disentangled Dynamic Graph Attention Network for Out-of-Distribution Sequential Recommendation
Authors: Zhang Zeyang , Wang Xin , Chen Haibo , Li Haoyang , Zhu Wenwu
Published: October 2024

Sequential recommendation, leveraging user-item interaction histories to provide personalized and timely suggestions, has drawn significant research interest recently. With the power of exploiting spatio-temporal dynamics, Dynamic Graph Neural Networks (DyGNNs) show great potential in sequential recommendation by modeling the dynamic relationship between users and items. However, spatio-temporal distribution shifts naturally exist in out-of-distribution sequential recommendation, where both user-item relationships and temporal sequences demonstrate pattern shifts. The out-of-distribution scenarios may lead to the failure of existing DyGNNs in handling spatio-temporal distribution shifts in sequential recommendation, given that the patterns they exploit tend to be variant w.r.t labels under distribution shifts. In this article, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (
I-DIDA
) to handle spatio-temporal distribution shifts in sequential recommendation by discovering and utilizing
invariant patterns
, i.e., structures and features whose predictive abilities are stable across distribution shifts. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. By utilizing the disentangled patterns, we design a spatio-temporal intervention mechanism to create multiple interventional distributions and an environment inference module to infer the latent spatio-temporal environments, and minimize the invariance loss to leverage the invariant patterns with stable predictive abilities under distribution shifts. Extensive experiments demonstrate the superiority of our method over state-of-the-art sequential recommendation baselines under distribution shifts.
Iterating Pointers: Enabling Static Analysis for Loop-based Pointers
Authors: Lepori Andrea , Calotoiu Alexandru , Hoefler Torsten
Published: October 2024

Pointers are an integral part of C and other programming languages. They enable substantial flexibility from the programmer’s standpoint, allowing the user fine, unmediated control over data access patterns. However, accesses done through pointers are often hard to track, and challenging to understand for optimizers, compilers, and sometimes, even for the developers themselves because of the direct memory access they provide. We alleviate this problem by exposing additional information to analyzers and compilers. By separating the concept of a pointer into a data container and an offset, we can optimize C programs beyond what other state-of-the-art approaches are capable of, in some cases even enabling auto-parallelization. Using this process, we are able to successfully analyze and optimize code from OpenSSL, the Mantevo benchmark suite, and the Lempel–Ziv–Oberhumer compression algorithm. We provide the only automatic approach able to find all parallelization opportunities in the HPCCG benchmark from the Mantevo suite the developers identified and even outperform the reference implementation by up to 18%, as well as speed up the PBKDF2 algorithm implementation from OpenSSL by up to 11x.
PhysFiT: Physical-aware 3D Shape Understanding for Finishing Incomplete Assembly
Authors: Wang Weihao , You Mingyu , Zhou Hongjun , He Bin
Published: October 2024

Understanding the part composition and structure of 3D shapes is crucial for a wide range of 3D applications, including 3D part assembly and 3D assembly completion. Compared to 3D part assembly, 3D assembly completion is more complicated, which involves repairing broken or incomplete furniture that miss several parts with a toolkit. Given an incomplete assembly, 3D assembly completion seeks to identify its missing parts from multiple candidates, determine their poses, and produce complete assembly that is well-connected, structurally stable, and aesthetically pleasing. This task necessitates not only specialized knowledge of part composition but, more importantly, an awareness of physical constraints, i.e., connectivity, stability, and symmetry. Neglecting these constraints often results in assemblies that, although visually plausible, are impractical. To address this challenge, we propose PhysFiT, a physical-aware 3D shape understanding framework. This framework is built upon attention-based part relation modeling and incorporates connection modeling, simulation-free stability optimization and symmetric transformation consistency. We evaluate its efficacy on 3D part assembly and 3D assembly completion, a novel assembly task presented in this work. Extensive experiments demonstrate the effectiveness of PhysFiT in constructing geometrically sound and physically compliant assemblies.
Towards a Rust-Like Borrow Checker for C
Authors: Silva Tiago , Correia Pedro Gonçalo , Sousa Luís , Bispo João , Carvalho Tiago
Published: October 2024

Memory safety issues in C are the origin of various vulnerabilities that can compromise a program’s correctness or safety from attacks. We propose an approach to tackle memory safety by replicating Rust’s Mid-level Intermediate Representation (MIR) Borrow Checker. Our solution uses static analysis and successive source-to-source code transformations to be composed upstream of the compiler, ensuring maximal compatibility with existing build systems. This allows us to apply the memory safety guarantees of the rustc compiler to C code with fewer changes than a rewrite in Rust. In this work, we present a comprehensive study of Rust’s efforts towards ensuring memory safety, and describe the theoretical basis for a C borrow checker, alongside a proof-of-concept that was developed to demonstrate its potential. We have evaluated the prototype on the CHStone and bzip2 benchmarks. This prototype correctly identified violations of the ownership and aliasing rules, and exposed incompatibilities between such rules and common C patterns, which can be addressed in future work.
Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Authors: Vasconcelos Helena , Bansal Gagan , Fourney Adam , Liao Q. Vera , Vaughan Jennifer Wortman
Published: October 2024

Large-scale generative models have enabled the development of AI-powered code completion tools to assist programmers in writing code. Like all AI-powered tools, these code completion tools are not always accurate and can introduce bugs or even security vulnerabilities into code if not properly detected and corrected by a human programmer. One technique that has been proposed and implemented to help programmers locate potential errors is to highlight uncertain tokens. However, little is known about the effectiveness of this technique. Through a mixed-methods study with 30 programmers, we compare three conditions: providing the AI system's code completion alone, highlighting tokens with the lowest likelihood of being generated by the underlying generative model, and highlighting tokens with the highest predicted likelihood of being edited by a programmer. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits, and is subjectively preferred by study participants. In contrast, highlighting tokens according to their probability of being generated does not provide any benefit over the baseline with no highlighting. We further explore the design space of how to convey uncertainty in AI-powered code completion tools and find that programmers prefer highlights that are granular, informative, interpretable, and not overwhelming. This work contributes to building an understanding of what uncertainty means for generative models and how to convey it effectively.
Predicting Recurrence after Sublobar Resection in Patients with Lung Adenocarcinoma Using Preoperative Chest CT Scans
Authors: Yun Jae Kwang , Kim Ji Yong , Ahn Yura , Kim Mi Young , Lee Geon Dong , Choi Sehoon , Kim Yong-Hee , Kim Dong Kwan , Park Seung-Il , Kim Hyeong Ryul
Published: October 2024

Increased consolidation-to-tumor ratio and maximal diameter of the solid tumor component on preoperative chest CT images were associated with an increased risk of recurrence after sublobar resection in patients with stage IA lung adenocarcinoma.
Impact of Patient-reported Symptom Information on the Interpretation of MRI of the Lumbar Spine
Authors: Balza Rene , Mercaldo Sarah F. , Huang Ambrose J. , Husseini Jad S. , Jarraya Mohamed , Simeone F. Joseph , Vicentini Joao R. T. , Palmer William E.
Published: October 2024

Radiologists achieved higher levels of diagnostic certainty and agreement with clinical specialists when they interpreted lumbar spine MRI using patient-reported symptom information.
AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer
Authors: Yang David D. , Lee Leslie K. , Tsui James M. G. , Leeman Jonathan E. , McClure Heather M. , Sudhyadhom Atchar , Guthier Christian V. , Taplin Mary-Ellen , Trinh Quoc-Dien , Mouw Kent W. , Martin Neil E. , Orio Peter F. , Nguyen Paul L. , D’Amico Anthony V. , Shin Kee-Young , Lee Katie N. , King Martin T.
Published: October 2024

The volume of artificial intelligence–segmented intraprostatic tumors was an independent prognostic factor for outcomes of localized prostate cancer treated with radical prostatectomy and radiation therapy.
Comparing Commercial and Open-Source Large Language Models for Labeling Chest Radiograph Reports
Authors: Dorfner Felix J. , Jürgensen Liv , Donle Leonhard , Al Mohamad Fares , Bodenmann Tobias R. , Cleveland Mason C. , Busch Felix , Adams Lisa C. , Sato James , Schultz Thomas , Kim Albert E. , Merkow Jameson , Bressem Keno K. , Bridge Christopher P.
Published: October 2024

Privacy-preserving open-source large language models show potential as an alternative to OpenAI’s GPT-4 for accurate chest radiograph report classification.
Novel Ball-Type Phthalocyanines with Eight Benzotriazole Groups: Synthesis, Computational DFT Studies, and Supercapacitor Properties
Authors: Koçyiğit Nilüfer , Ramzy Ahmed Taalat Anwar , Yazar Sibel , Arvas Melih Beşir , Acar-Selçuki Nursel , Özer Metin , Bekaroğlu Özer
Published: October 2024

Novel four MBTOB-bridged ball-type metallophthalocyanines were obtained from 4,4′-((methylenebis(6-(2H-benzo[d][1, 2, 3]triazol-2-yl)−4-(2,4,4-trimethylpentan-2-yl)−2, 1-phenylene))bis(oxy))diphthalonitrile by means of transition metal (II) acetate salts in 2-dimethylaminoethanol. The new starting bisphthalonitrile compound was accomplished from 2,2′-methylenebis[6-(benzotriazol-2-yl)−4-tert-octylphenol] and 4-nitrophthalonitrile in dimethylformamide under the catalysis of potassium carbonate at 50 °C. The structural characterization of the compounds was accomplished by infrared, proton-nuclear magnetic resonance, matrix-assisted laser desorption/ionisation time-of-flight mass, and ultraviolet–visible spectroscopic methods. The supercapacitor performances of the electrodes were examined by cyclic voltammetry, galvanostatic charge discharge, and electrochemical impedance spectroscopy analyses. The specific capacitances obtained from the GCD measurements were calculated as 320.4 ± 15.1 F g−1 for ball-type zinc (II) phthalocyanine in three electrode systems. The highest specific capacitance value was found in the electrode containing ball-type nickel (II) phthalocyanine as 929.8 ± 32.8 F g−1 at a scan rate of 100 mV s−1. In symmetric supercapacitor measurements, the capacitance retention value was 100.7% after 5000 cycles.
Single-Ended Surface Acoustic Wave Pressure Resonators
Authors: Hu Yusong , Yuan Rui , Fu Hao , Xu Zhangliang
Published: October 2024

Surface acoustic wave (SAW) sensors exhibit advantageous attributes for pressure detection, including compact dimensions, cost-effectiveness, facile integration, elevated sensitivity, and a high quality factor (Q value). In this study, the single-ended resonant configuration of SAW pressure sensing element based on ST-X cut quartz was simulated and fabricated. The influence of the interdigitated transducer (IDT) and applied pressure on the resonance frequency of SAW were simulated and analyzed. The designed phase velocity (3159.344 m s−1) without IDT is closest to the theoretical phase velocity (3158 m s−1) of SAW propagation in the substrate, and the relative error is about 0.043%. The designed phase velocity of SAW dropped to 3149.198 m s−1 due to the loading of the IDT mass. With an increase in applied pressure from 0 to 350 kPa, the resonance frequency of the SAW decreases from 157.05 to 154.02 MHz, yielding a maximum linear pressure sensitivity of approximately 8.6 kHz kPa−1. The measured center frequencies of the fabricated single-ended SAW devices are predominantly clustered around 157 MHz, exhibiting a deviation of 0.46 MHz from the simulated results. The present work establishes a foundation for subsequent experimental investigations into pressure sensing.
The effect of adrenalectomy on bleomycin-induced pulmonary fibrosis in mice
Authors: McGovern John , Perry Carrighan , Ghincea Alexander , Herzog Erica L. , Shao Shuai , Sun Huanxing
Published: October 2024

The lung-adrenal axis plays a significant role in pulmonary fibrosis. Adrenalectomy provides protection against lung fibrotic ECM remodeling and lung inflammation by reducing the levels of lymphocytes in BAL and macrophages in lung of bleomycin-treated mice. Although compared with sham surgery, adrenalectomy raised collagen concentration in uninjured mice, there was no discernible difference in bleomycin-induced collagen accumulation. However, adrenalectomy significantly reversed the enhanced expression and colocalization of αSMA and fibronectin induced by bleomycin.
A Comprehensive Review on Potential In Silico Screened Herbal Bioactive Compounds and Host Targets in the Cardiovascular Disease Therapy
Authors: Zarenezhad Elham , Hadi Ali Tareq , Nournia Ensieh , Rostamnia Sadegh , Ghasemian Abdolmajid
Published: October 2024

Herbal medicines (HMs) have deciphered indispensable therapeutic effects against cardiovascular disease (CVD) (the predominant cause of death worldwide). The conventional CVD therapy approaches have not been efficient and need alternative medicines. The objective of this study was a review of herbal bioactive compound efficacy for CVD therapy based on computational and in silico studies. HM bioactive compounds with potential anti‐CVD traits include campesterol, naringenin, quercetin, stigmasterol, tanshinaldehyde, Bryophyllin A, Bryophyllin B, beta‐sitosterol, punicalagin, butein, eriodyctiol, butin, luteolin, and kaempferol discovered using computational studies. Some of the bioactive compounds have exhibited therapeutic effects, as followed by in vitro (tanshinaldehyde, punicalagin, butein, eriodyctiol, and butin), in vivo (gallogen, luteolin, chebulic acid, butein, eriodyctiol, and butin), and clinical trials (quercetin, campesterol, and naringenin). The main mechanisms of action of bioactive compounds for CVD healing include cell signaling and inhibition of inflammation and oxidative stress, decrease of lipid accumulation, and regulation of metabolism and immune cells. Further experimental studies are required to verify the anti‐CVD effects of herbal bioactive compounds and their pharmacokinetic/pharmacodynamic features.
Penicillium citrinum CFAM 521 Isolated From the Amazon Region: A Novel Source of a Fibrinolytic Enzyme
Authors: Souza Thayana Cruz de , Schwarz Marcos Gustavo Araujo , Silva Daniela Marinho da , Maia Carolina Rabelo , Araújo Cláudia Patrícia Mendes de , Balieiro Antônio Alcirley da Silva , Oliveira Luiz Antonio de , Degrave Wim Maurits Sylvain , Fernandes Ormezinda Celeste Cristo , Mendonça-Lima Leila
Published: October 2024

Fibrinolytic agents are essential in treating thrombosis, playing a critical role in improving survival rates in cardiovascular diseases. Microbial fibrinolytic proteases have emerged as promising alternatives due to their affordability, specificity, lower toxicity, and reduced side effects. Consequently, the search for microorganisms capable of producing these enzymes has gained significant economic importance in the pharmaceutical industry. This study reports and characterizes a novel fibrinolytic enzyme produced by Penicillium citrinum CFAM 521, a strain isolated from the Amazon region. The enzyme was purified using a polyethylene glycol (PEG)–phosphate salt aqueous two‐phase system (ATPS). The effects of PEG molecular weight, PEG concentration, and phosphate concentration on the protease partition coefficient (K) were evaluated through a 22 full factorial design. The enzyme exhibited both fibrinolytic and fibrinogenolytic activities. After partitioning in a two‐phase system with 10% (w/w) PEG and 15% (w/w) sodium phosphate, the fibrinolytic proteases were predominantly retained in the salt‐rich bottom phase (K = 0.33). The enzyme has a molecular weight of 34 kDa, with optimal pH and temperature at 9°C and 37°C, respectively. Inhibitory analysis confirmed that it is a serine protease, and its activity was enhanced by the addition of Mn2+. Notably, the enzyme exhibited no hemolytic activity. Therefore, P. citrinum CFAM 521 represents a novel source of fibrinolytic enzymes, highlighting its potential as an alternative for the development of thrombolytic agents.
AdaFNDFS: An AdaBoost Ensemble Model with Fast Nondominated Feature Selection for Predicting Enterprise Credit Risk in the Supply Chain
Authors: Yao Gang , Hu Xiaojian , Song Pingfan , Zhou Taiyun , Zhang Yue , Yasir Ammar , Luo Suizhi
Published: October 2024

Early warnings of enterprise credit risk based on supply chain scenarios are helpful for preventing enterprise credit deterioration and resolving systemic risk. Enterprise credit risk data in the supply chain are characterized by higher‐dimension information and class imbalance. The class imbalance influences the feature selection effect, and the feature subset is closely related to the predictive performance of subsequent learning algorithms. Therefore, ensuring the adaptivity of feature selection and the subsequent class imbalance–oriented classification model is a key issue. We propose an AdaBoost ensemble model with fast nondominated feature selection (AdaFNDFS). AdaFNDFS uses the FNDFS method in the AdaBoost algorithm to iteratively select features and uses the classifier to evaluate the performance of feature subsets to train the class imbalance–oriented classifier and the best‐matched feature subset, ensuring the adaptivity of feature selection and subsequent classifiers. The further use of the differential sampling rate (DSR) method enables AdaFNDFS to integrate more training models with different knowledge and to obtain higher accuracy and better generalization ability for prediction tasks facing high‐dimensional information and class imbalance. A test using credit risk data from Chinese listed enterprises containing supply chain information demonstrates that the prediction scoring indicators, such as AUC, KS, AP, and accuracy, of the AdaFNDFS are better than those of basic models such as LR, LDA, DT, and SVM and multiple hybrid models that use SMOTE, feature selection, and ensemble methods. AdaFNDFS outperforms the basic models by at least 0.0073 (0.0344, 0.0349, and 0.0071) in terms of the AUC (KS, AP, and accuracy). AdaFNDFS has outstanding advantages in predicting enterprise credit risk in the supply chain and can support interested decision‐makers.
The Impact of COVID‐19 Pandemic on Agricultural, Livestock, Poultry and Fish Sectors
Authors: Singh Malik Yashpal , Ikram Ansari Mohd , Gharieb Rasha , Ghosh Souvik , Kumar Chaudhary Ratan , Gomaa Hemida Maged , Torabian Dayan , Rahmani Farzad , Ahmadi Hadis , Hajipour Pouneh , Salajegheh Tazerji Sina
Published: October 2024

COVID‐19 pandemic is considered a global crisis that adversely impacted the world economy. The virus possessed a serious threat to different sectors including agricultural, livestock, poultry and fish sectors in both developing and developed countries. COVID‐19 pandemic and the associated lockdown for a long period have not only caused enormous distress to the millions of poor and marginal farmers for saving their crops and/or livestock but also affected livestock, poultry production systems and associated value chains, nutrition, health care and labour availability. In addition, COVID‐19 pandemic significantly impacted the fishery sector through disruption in fish supply and value chains and had noteworthy effects on income of fish stakeholders, especially in developing countries. In this regard, the current review discussed the impact of COVID‐19 pandemic on agricultural, livestock, poultry and fish sectors.
Product Recommendation System With Machine Learning Algorithms for SME Banking
Authors: Met Ilker , Erkoc Ayfer , Seker Sadi Evren , Erturk Mehmet Ali , Ulug Baha
Published: October 2024

In the present era, where competition pervades across all domains, profitability holds crucial economic importance for numerous companies, including the banking industry. Offering the right products to customers is a fundamental problem that directly affects banks’ net revenue. Machine learning (ML) approaches can address this issue using customer behavior analysis from historical customer data. This study addresses the issue by processing customer transactions using a bank’s current account debt (CAD) product with state‐of‐the‐art ML approaches. In the first step, exploratory data analysis (EDA) is performed to examine the data and detect patterns and anomalies. Then, different regression methods (tree‐based methods) are tested to analyze the model’s performance. The obtained results show that the light gradient boosting machine (LGBM) algorithm outperforms other methods with an 84% accuracy rate in the light gradient boosting algorithm, which is the most accurate of the three methods used.
Optimal Control Strategies and Cost‐Effectiveness Analysis of Tuberculosis Dynamics With Multidrug‐Resistant Compartment
Authors: Kitaro Damtew Bewket , Bole Boka Kumsa , Rao Koya Purnachandra
Published: October 2024

In this current study, the theory of optimal control is applied to the model of tuberculosis with a multidrug‐resistant compartment. It is assumed that latent case finding, case holding, and case management are the main controls for TB disease. The objective of optimal controls is to reduce the number of infections that result from contact with infectious individuals while minimizing the cost associated with control measures. To achieve this goal, Pontryagin’s maximum principle is applied and the existence of optimal controls is verified. Moreover, numerical simulations have been carried out using forward and backward sweeps in time based on fourth‐order Runge–Kutta schemes. Simulation of the model, by considering different strategies, is carried out to examine which alternative might yield the most favorable results. The findings indicate that a strategy that combines all three controls (latent case finding, case holding, and case management) is more beneficial in eliminating tuberculosis than the other strategies. In addition to this, cost‐effectiveness analysis is performed using the incremental cost‐effectiveness ratio (ICER). The ICER findings demonstrate that a strategy that incorporates all three controls (latent case finding, case holding, and case management) is the most cost‐effective of all other strategies considered in this study.
Decision‐Making and Path Planning for Head‐On Collision Avoidance on Curved Roads
Authors: Abdollahinia Masoud , Ghaffari Ali , Azadi Shahram
Published: October 2024

Deviating to the left on two‐way roads can result in fatal head‐on collisions. This article presents an intelligent decision‐making and path‐planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path‐planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path‐planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision‐making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision‐making process. Once the maneuver direction is determined, the collision‐free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left‐ and right‐hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.
Comprehensive Genome Analysis of Colistin‐Only–Sensitive KPC‐2 and NDM1‐1‐Coproducing Klebsiella pneumoniae ST11 and Acinetobacter baumannii ST2 From a Critically Ill Patient With COVID‐19 in Saudi Arabia
Authors: Al-Zahrani Ibrahim A. , Brek Thamer M.
Published: October 2024

The COVID‐19 pandemic has intensified the issue of multidrug‐resistant (MDR) infections, particularly in intensive care units (ICUs). This study documents the first known case of coinfection with two extensively drug‐resistant (XDR) bacterial isolates in a critically ill patient with COVID‐19 in Saudi Arabia. Both XDR isolates were recovered from blood and were resistant to all tested antimicrobial agents except colistin. Whole genome sequencing (WGS) revealed that the K. pneumoniae isolate KP‐JZ107 had sequence type 11 (ST11) and core genome MLST (cgMLST 304742), while the A. baumannii isolate AB‐JZ67 had ST2 and cgMLST 785. KP‐JZ107 was found to possess the virulence plasmid KpVP‐type‐1, carbapenemase genes blaNDM and blaKPC, and numerous antimicrobial‐resistant genes (ARGs). The AB‐JZ67 isolate had several biofilm‐related genes, including biofilm‐associated protein (BAP), csuE, and pgaB, and multiple ARGs, including blaADC−25, blaOXA−23, and blaOXA−66. Our findings suggest that the coexistence of KP‐JZ107 and AB‐JZ67 isolates may indicate their widespread presence in ICUs, requiring comprehensive surveillance studies across all hospitals.
Table S3 from Phase 1 Trial of ALRN-6924, a Dual Inhibitor of MDMX and MDM2, in Patients with Solid Tumors and Lymphomas Bearing Wild-type TP53
Authors: Saleh Mansoor N. , Patel Manish R. , Bauer Todd M. , Goel Sanjay , Falchook Gerald S. , Shapiro Geoffrey I. , Chung Ki Y. , Infante Jeffrey R. , Conry Robert M. , Rabinowits Guilherme , Hong David S. , Wang Judy S. , Steidl Ulrich , Naik Gurudatta , Guerlavais Vincent , Vukovic Vojislav , Annis D. Allen , Aivado Manuel , Meric-Bernstam Funda
Published: October 2024

All treatment-emergent adverse events that occurred in {greater than or equal to}10% of patients regardless of relationship with ALRN-6924 (no grade 4 treatment-emergent adverse events occurred in {greater than or equal to}10% of patients; no grade 5 events were observed). G1, grade 1; G2, grade 2; G3, grade 3; CTCAE, Common Terminology Criteria for Adverse Events.
Table S2 from Phase 1 Trial of ALRN-6924, a Dual Inhibitor of MDMX and MDM2, in Patients with Solid Tumors and Lymphomas Bearing Wild-type TP53
Authors: Saleh Mansoor N. , Patel Manish R. , Bauer Todd M. , Goel Sanjay , Falchook Gerald S. , Shapiro Geoffrey I. , Chung Ki Y. , Infante Jeffrey R. , Conry Robert M. , Rabinowits Guilherme , Hong David S. , Wang Judy S. , Steidl Ulrich , Naik Gurudatta , Guerlavais Vincent , Vukovic Vojislav , Annis D. Allen , Aivado Manuel , Meric-Bernstam Funda
Published: October 2024

Table S2 shows TP53 Status and Concordance Between Local and Central Laboratory Results