bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Timing on Post-Operative Outcomes in Esophagectomy for Squamous Cell Carcinoma: A Prospective Observational Study Comparing Surgery Within 8 Weeks vs. After 8 Weeks Post-Neoadjuvant Chemotherapy in a North Indian Tertiary Care Centre
Authors: Singh Lovepreet , Tandup Cherring , Thakur Manish , Sekar Aravind , Samanta Jayanta , S.N. Satish , Sahu Swapnesh , Sakaray Yashwant , Santosh RN Naga , Kurdia Kailash , Thakur Vipul 
Published: September 2024

Abstract

Purpose-: Neoadjuvant chemotherapy followed by esophagectomy is the usual approach to manage esophageal squamous cell carcinoma (ESCC). The optimal interval to operate after completion of Neoadjuvant Chemo-radiotherapy (NACRT) still remains controversial.
Methods:A prospective study was conducted to observe and compare post-operative complications and pathological outcomes in patients with squamous cell carcinoma of the esophagus who underwent NACRT followed by surgery within 8 weeks or after 8 weeks of NACRT completion. The pathological complete response was assessed using Mandard tumour regression grade. Morbidity and mortality were compared and were graded using Clavien-Dindo scale.
Results: The Study included 50 patients, 19 patients in within <8 week group and 31 in >8 week group study. Patients underwent thoracoscopy assisted esophagectomy with neoesophagus formation using gastric conduit. There was a significant difference between mortality between the two groups, with three mortalities in <8 week group and none in the other group (p=0.022). Postoperative complications and pathological outcomes did not have a statistically significant difference between the two groups.
Conclusion: The rate of post-operative complications and pathological response in ESCC cases do not appear to be impacted by the interval between NACRT and surgery; nevertheless, early surgery was associated with a higher risk of mortality.
Leveraging class-balancing techniques for predicting c-MET Inhibitors: Descriptor Calculation, Selection, and QSAR Model Optimization using Machine Learning
Authors: Mishra Gauri , Acharya Malika , Pandit Akansha , Mohbey Krishna Kumar , Sawant Devesh Madhukar 
Published: September 2024

Abstract

The rapid emergence of resistance in cancer chemotherapy is a major challenge in the drug discovery of cancer, restricting the action of various important classes of inhibitors against EGFR, VEGF, BRAF, alkylating agents, and DNA damaging agents. c-MET plays an important role in the development of resistance to cancer. Identifying a potent c-MET inhibitor can improve therapeutic access to existing anti-cancer agents. In the current study, we propose a novel technique for the prediction of drug activity class by using class balancing and ML classifiers. This study utilizes 3091 molecules with c-MET inhibitory concentration value (IC50) publicly available from the ChEMBL Database. Using 14 descriptors and random oversampling for class balancing, we investigated seven classical ML models, i.e., decision tree (DT), Adaboost decision tree (ABDT), K-nearest neighbors (K-NN), support vector machine (SVM), Bernoulli Naïve Bayes (BNB), random forest (RF), and linear logistic regression (LLR) for activity prediction against c-MET. Of which SVM, LR, and RF were the top three models providing high predictability after applying balancing techniques and performing rigorous.hyperparameter tuning. Even though SVM, LR, and RF showed exemplary performance in terms of ROC-AUC and recall metrics, their validation on seven FDA-approved drugs demonstrated their susceptibility to high false negatives. Owing to this, we developed a consensus mechanism based on these three models. The consensus mechanism can work on a large, diverse database and screen potential inhibitors, prioritizing which molecule to be considered further for experimental testing. The consensus model proved beneficial as a drug design algorithm for c-MET inhibitor drug discovery and development.
Context-aware Learning and Background Activation Suppression for Weakly Supervised Semantic Segmentation
Authors: Mi Aizhong , Huang Xianru , Huo Zhanqiang , Liu Luyao 
Published: September 2024

Abstract

Weakly supervised semantic segmentation (WSSS) using only image-level labels has gradually become an emerging research hotspot in the field of computer vision in recent years due to its low annotation cost. Existing methods rely on Class Activation Maps (CAMs) from specific classification models to locate target regions. However, the classifiers tend to focus on the most discriminative regions of the input image and assign higher weights to these areas, leading to the problem of incomplete CAM target regions. To address this issue, we design a Siamese feature aggregation network, named SFA-Net, which introduces contextual information to activate more complete target regions while suppressing the similarly adjacent background regions. Specifically, the context-aware module in the SFA-Net is consisting of a multi-scale adaptive aggregation sub-module and a contextual linkage sub-module, which can uncover potential target features and identify global target areas. A background activation suppression loss is designed to minimize false activations in the background regions by measuring the similarity between the target object and background regions at the boundary. Extensive experiments on the challenging PASCAL VOC 2012 and COCO 2014 datasets show that our SFA-Net outperforms other state-of-the-art methods.
Post-transcriptional regulation in early cell fate commitment of germ layers
Authors: Gomes-Júnior Rubens , Horinouchi Cintia Delai Silva , Hansel-Fröse Aruana Fiuza Fagundes , Ribeiro Annanda Lyra , Pereira Isabela Tiemy , Spangenberg Lucia , Dallagiovanna Bruno 
Published: September 2024

Abstract

Background:Cell differentiation during development is orchestrated by precisely coordinated gene expression programs. While mechanisms such as the maintenance of pluripotent states are well-understood, others like lineage choice and cell-fate decisions remain poorly comprehended. Given that gene expression is influenced not only by transcriptional control but also by post-transcriptional events, we employed monolayer differentiation protocols to delineate early transcriptional and post-transcriptional events in human embryonic stem cell specification. This involved obtaining representative populations of the three germ layers, followed by sequencing of polysome-bound and total RNAs.Results:We observed a consistent similar distribution of gene upregulation and downregulation when comparing the transcriptome and translatome during the differentiation of all three germ layers. Notably, certain differentially expressed genes were exclusively detected in the polysome fractions, suggesting active post-transcriptional regulation. Upregulated genes in the translatome more accurately reflected the differentiation process. Additionally, genes such as DLX3, DHFR2, and UNC13D were identified as differentially expressed solely in the polysome fraction, indicating their post-transcriptional regulation during ectoderm commitment. Recruitment of these genes to polysomes was also confirmed.Conclusions:Substantial post-transcriptional modulation was found during germ layer commitment, emphasizing the translatome reliability in capturing nuanced gene expression regulation. These findings highlight the post-transcriptional regulation's critical role in early embryonic development, offering new insights into the molecular mechanisms of cell differentiation.
Systematic Review and Meta Analysis on Prevalence of Opticospinal Multiple Sclerosis in Asian Population
Authors: Rayan Benson , Swethakumar Mangaiyarkarasy , J Poongothai 
Published: September 2024

Abstract

Multiple sclerosis is a chronic, inflammatory disorder of the central nervous system which is prevalent across the Western population. Employing current technologies, it has been discovered that a subtype of MS which results in severe damage of spinal cord and optic nerves exists in Asian populations. A binary random-effect meta-analysis carried out in the scope of this study illuminates the mean prevalence rate of OSMS in Asia, including studies from regions East Asia, Sound-East Asia, Central Asia, Middle Eastern. The findings estimated a mean prevalence ratio of around 0.340 of the selected 25 studies out of 117 screened studies with a confidence level of 95%. Regarding regional disparities in MS prevalence, Japan serves as an example of a country with moderate rates and a significant portion of the data used in this study is derived from the Japanese population. In contrast, countries within the Indian subcontinent, such as India and Pakistan, as well as certain Middle Eastern nations Iran and Oman, tend to exhibit lower MS prevalence rates.
Magnetic-Based Integrated Sensing and In/Near-Sensor Processing:A Comprehensive Survey and Future Outlook
Authors: Tabrizchi Sepehr , Amin Md Hasibul , Najafi Deniz , Angizi Shaahin , Zand Ramtin , Roohi Arman 
Published: September 2024

Abstract

In recent years, spintronic devices and non-Von Neuman architectures have emerged as two promising approaches to overcome the power, performance, and efficiency limitations of conventional computing architectures. Spintronic devices, such as Magneto-Electric FETs (MEFETs) and Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM), offer unique advantages, including non-volatility, high-speed operation, and low-power characteristics, making them well-suited for various data and compute -intensive applications. On the other hand, in-sensor processing, which integrates computation and sensing into a single platform, offers significant improvements in energy efficiency, latency, and overall system performance by reducing data movement and offloading computation tasks to the sensor level. In this survey paper, we provide a comprehensive overview of recent advancements in magnetic-based integrated sensing and processing schemes, highlighting their key features, benefits, and potential applications. We discuss the challenges associated with integrating spintronic devices into sensing/processing platforms, including power management, reliability, and design complexity. We also review various design approaches and methodologies to optimize the performance and energy efficiency of these systems. Furthermore, we explore the impact of spintronic and in-sensor processing on various application domains, such as edge computing and artificial intelligence. Finally, we discuss future research directions and potential opportunities in this emerging field, emphasizing the need for further exploration and collaboration among researchers in the areas of device technology, circuit design, and system architecture.
Concrete Jungle to Urban Oasis: Scale, Greenspace Size and Patchiness Influence Wildlife in Cities of Eastern Los Angeles County, California
Authors: Elihu Adrianna J. , Ortiz Janel L. 
Published: September 2024

Abstract

Urban greenspaces are a haven for wildlife in densely populated cities. Wildlife use greenspaces for resource acquisition, shelter, and traveling across urbanized landscapes. Greenspace characteristics such as presence of woody or herbaceous landcover, size, edge density, and patchiness influence species richness. The goals of this study was to: 1) identify and quantify greenspace metrics to determine relationships with wildlife and 2) determine differences in greenspace patterns at various spatial scales. To monitor wildlife, twenty-six camera traps were set in eastern Los Angeles County, California; greenspace metrics were gathered using 3m land cover supervised classification. We used a generalized linear mixed model to determine the influence of greenspace metrics on richness at four scales (200m, 500m, 1km, and 2km). At larger scales, 1km and 2km, high herbaceous cover, whether as increasing aggregated patches or increased patchiness, and moderate levels of woody cover positively influence species richness. At smaller scales, 200m and 500m, low to moderate levels of herbaceous cover and high levels of woody cover strongly and positively influence species. These results suggest that wildlife are able to utilize urban areas with increasing fragmentation of greenspace habitat and require greenspace, either as a few, less fragmented patches or as many patches with high herbaceous cover in the urban matrix. From the perspective of urban planning, developing greenspaces from a broader ecological scale is important to ensure they function as stepping stones in the urban matrix. Understanding these patterns can improve greenspaces that support wildlife and therefore, ecological functions.
Novel Pediatrics Chief Program Utilizing Near-peer Teaching and Mentoring to Enhance Clerkship Curricula
Authors: Johnson Victoria , Craven Sarah , Pohren Lindsey Beard , Braukmann Nicholas , Hoburg Joslynn , Leyden Emily , Schmidt John 
Published: September 2024

Abstract

Introduction:
Near-peer teachers add many benefits to the academic, clinical performance, and enjoyment of near-peer learners in medical education. We describe and examine how the Creighton University School of Medicine Chief Program fills a gap in medical education by offering an organized and formal methodology for near-peer teaching. The Creighton University M4 Pediatrics Chief Program, launched in 2016, utilizes select 4th-year medical students (colloquially referred to as the “M4 Peds Chiefs”) to orient, teach basic clinical skills, mentor, and participate in curriculum development for the third-year pediatrics clerkship students.
Methods:
Third-year students (n = 43) in their pediatrics clerkship from September 2020 to February 2021 completed surveys at the end of the clerkship to assess the quality and effectiveness of the M4 Pediatrics Chief Program. Students rated effectiveness of the chiefs most highly as clerkship guides, teachers, and mentors.
Results:
Near-peer perspectives, education, provision of pertinent content, serving as a clerkship resource, and being a source of encouragement were the most beneficial aspects of the program. Students reported strong agreement all clerkships should have an M4 Chief Program.
Discussion:
Due to the positive reviews by the third-years students, the M4 Pediatrics Chief Program is a permanent part of the pediatrics clerkship. Additionally, other third-year clerkships at Creighton University School of Medicine have adopted this model. Finally, the M4 students acting as the chiefs gain valuable leadership and educational skills. Based on largely positive perceptions of the “M4 Chief Program”, the authors recommend other medical schools consider initiating similar programs within their third-year clerkships.
Self-Knowledge Distillation Enhanced Universal Framework for Physics-Informed Neural Networks
Authors: Li Ying , Yang Jiawei , Wang Dong 
Published: September 2024

Abstract

With the advancement of machine learning and deep learning, Physics-Informed Neural Networks (PINNs) have emerged as a prominent approach for solving partial differential equation (PDE) problems. In this article, we introduce a novel distillation framework specifically designed for PINNs, termed Self-Knowledge Distillation for PINNs (SKD-PINNs). Within this framework, knowledge distillation techniques are integrated into PINNs. Unlike traditional knowledge distillation approaches, the method is focused on regression tasks and transfers knowledge only when the predictions of the teacher model are reliable. Additionally, the SKD-PINNs framework utilizes self-distillation techniques, which iteratively extract knowledge from the model. This process not only enhances supervised information during training but also significantly speeds up network convergence. To our best knowledge, the proposed method is the first to incorporate a knowledge distillation mechanism into physics-informed learning. Notably, the method provides a versatile approach that enhances various existing PINN methodologies. The experimental results show that the method can significantly improve prediction accuracy and accelerate network convergence.
Two-Mode Subharmonic Generating System with Coherent and Thermal Light
Authors: Tirfie Lemma , Hirpo Deribe 
Published: September 2024

Abstract

The statistical and squeezing characteristics of the light generated by a two-mode subharmonic generating system in a cavity pumped by two-mode coherent light and coupled to a two-mode thermal reservoir through a singleport mirror have been studied in this study. We construct the c-number Langevin equations linked to the normal ordering using the master equation for the system under discussion, and we establish the correlation characteristics of the noise forces. The mean and variance of the photon number of the cavity light are then determined using the solutions of the resulting differential equations. We have discovered that the cavity light mode’s photon statistics are super-Poissonian because the variance of the photon number is more significant than the mean of the photon number. Additionally, using the same solutions, we were able to extract the antinormally ordered characteristic function, which is used to calculate the Q function. In addition, the photon number distribution for the cavity mode is computed using the Q function. The variance of the plus and minus quadrature of the two-mode cavity light generated by the two-mode subharmonic generating system is also obtained. It is discovered that the cavity mode’s squeezing characteristics are unaffected by the two-mode driving coherent light, however, the thermal light has the effect of decreasing the squeezing of the cavity light and the squeezing takes place in the plus quadrature. Finally, we discovered that when the cavity is coupled to a vacuum reservoir, there is a 50 % maximum squeezing of the two-mode cavity at a steady state and at the threshold. The nondegenerate subharmonic generator generates a two-mode cavity light that is 50 % entangled.
Single-cell characterization of intra-tumoral heterogeneity and the immunosuppressive microenvironment in uterine serous carcinoma
Authors: Bai Fan , You Xuewu , Dong Yangyang , Liu Xuerun , Li He , Li Liwei , Zhang Xiaobo , Wang Jiaqi , Zhou Jingyi , Zhang Guo , Deng Hao , Wang Yiqin , Wang Jianliu 
Published: September 2024

Abstract

Uterine serous carcinoma (USC) has a much worse chemotherapy response and prognosis than endometrioid endometrial carcinoma (EEC). A comprehensive understanding of the intra-tumoral heterogeneity and tumor-specific ecosystem of USC is essential for improving diagnosis, treatment, and prognosis. In this study, we performed 5′ single-cell RNA sequencing, along with T-cell receptor and B-cell receptor sequencing, on 16 USC samples and 5 normal endometrial samples. We then integrated data from 15 EEC samples obtained from public databases for comparative analysis. USC malignant cells exhibited novel subclonal genomic complexities and unique transcriptional states, with significantly activated MYC signaling and cell cycle-related pathways. Treatment with the CDK12 inhibitor (CDK12-IN-3) effectively inhibited cell viability and increased the chemotherapy sensitivity of USC cell lines. Additionally, we characterized the stromal niche and immune environment of USC and EEC, finding a higher degree of CD8+ T-cell exhaustion and SPP1+ macrophage infiltration in USC. USC-derived fibroblasts were found to promote malignant cell proliferation through CNTN1-NRCAM, THBS2-CD47, and THBS2-SDC4 interactions. In summary, our study provides a comprehensive single-cell transcriptome analysis of USC and its tumor microenvironment in comparison with EEC, explores intra-tumoral heterogeneity, and identifies CDK12 and CAF-CNTN1 as potential therapeutic targets for USC.
A data-consistent model of the last glaciation in the Alps achieved with physics-driven AI
Authors: Leger Tancrède , Jouvet Guillaume , Kamleitner Sarah , Mey Jürgen , Herman Frederic , Finley Brandon , Ivy-Ochs Susan , Vieli Andreas , Henz Andreas , Nussbaumer Samuel 
Published: September 2024

Abstract

25 thousand years ago, the European Alps were covered by a kilometre-thick body of ice, commonly described as the Alpine Ice Field. Numerical modelling of this glaciation has been challenged by persistent model-data disagreements, including large overestimations of its former thickness. Here, we tackle this issue by applying the Instructed Glacier Model, a three-dimensional, high-order, and thermo-mechanically coupled model enhanced with physics-informed machine learning. This new approach allows us to produce an ensemble of 100, Alps-wide and 17 thousand-year-long (35-18 ka) simulations at 300 m spatial resolution. Unfeasible with traditional models due to computational costs, our experiment substantially increases model-data agreement in both ice extent and thickness. The model-data offset in ice thickness, for instance, is here reduced by between 200% and 450% relative to previous studies. The results yield implications for more accurately reconstructing former ice velocities, ice temperature, basal conditions, glacial erosion processes, glacial isostatic adjustment, and climate evolution in the Alps during the last glaciation. Furthermore, this study demonstrates that physics-informed AI-driven glacier evolution models can overcome the bottleneck of high-resolution continental-scale modelling required to accurately describe complex topographies and ice dynamics.
FitzHugh–Nagumo Model in Neutral Delay Differential Equation Representation
Authors: Elfouly M. A. , Sohaly M. A. , Fares M. E. 
Published: September 2024

Abstract

Action potentials are a crucial physiological event for body regulation and sensory information detection. This study represented a modification of FitzHugh-Nagumo model as a neutral delay differential equation. We proposed three nonlinear models and a linear model that explain the interaction between the time delays of the effects on the membrane potential and the stimulated current. To detect Hopf bifurcation, we determined fixed points and calculated eigenvalues. We identified regions of stability, instability, and oscillatory solutions for the proposed models. This paper shows how changing certain parameters can change the way a system works, how stable nerve firing is, and what role the time delay of the response to the stimulating current plays in nerve impulse firing. Gaining this understanding is critical for building diagnostic models and potential treatments for neurological disorders characterized by irregular firing patterns. We performed numerical simulations of the proposed models. Regular spike neurons have a strong tendency to adapt to large time delays of the stimulating current, whereas intrinsic bursting neurons make rapid spikes. Chattering neurons respond to the stimulating current with high-frequency bursts but exhibit moderate time delays in their responses. Fast-growing neurons, in contrast, show a rapid firing rate at high frequencies. Low-threshold neurons adapt significantly to high frequencies with only a low time delay to stimulation, which gives lots of insight into the dynamics of neuronal firing. Significantly, the time delay affects the stability or oscillatory solutions of the proposed models, and it is necessary for the critical role of synchronization between distinct brain loops and circuits.