ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
HIV Care Outcomes among Hispanics/Latinos with Diagnosed HIV in the United States by Place of Birth-2015-2018, Medical Monitoring Project

Relocation from one's birthplace may affect human immunodeficiency virus (HIV) outcomes, but national estimates of HIV outcomes among Hispanics/Latinos by place of birth are limited. We analyzed Medical Monitoring Project data collected in 2015-2018 from 2564 HIV-positive Hispanic/Latino adults and compared clinical outcomes between mainland US-born (referent group), Puerto Rican (PR-born), and those born outside the United States (non-US-born). We reported weighted percentages of characteristics and used logistic regression with predicted marginal means to examine differences between groups (p < 0.05). PR-born Hispanics/Latinos were more likely to be prescribed antiretroviral therapy (ART) (94%) and retained in care (94%) than mainland-US-born (79% and 77%, respectively) and non-US-born (91% and 87%, respectively) Hispanics/Latinos. PR-born Hispanics/Latinos were more likely to have sustained viral suppression (75%) than mainland-US-born Hispanics/Latinos (57%). Non-US-born Hispanics/Latinos were more likely to be prescribed ART (91% vs. 79%), retained in care (87% vs. 77%), and have sustained viral suppression (74% vs. 57%) than mainland-US-born Hispanics/Latinos. Greater Ryan White HIV/AIDS-funded facility usage among PR-born, better mental health among non-US-born, and less drug use among PR-born and non-US-born Hispanics/Latinos may have contributed to better HIV outcomes. Expanding programs with comprehensive HIV/AIDS services, including for mental health and substance use, may reduce HIV outcome disparities among Hispanics/Latinos.
Nitrogen Fertilizer Application Alters the Root Endophyte Bacterial Microbiome in Maize Plants, but Not in the Stem or Rhizosphere Soil

Plant-associated microorganisms that affect plant development, their composition, and their functionality are determined by the host, soil conditions, and agricultural practices. How agricultural practices affect the rhizosphere microbiome has been well studied, but less is known about how they might affect plant endophytes. In this study, the metagenomic DNA from the rhizosphere and endophyte communities of root and stem of maize plants was extracted and sequenced with the "diversity arrays technology sequencing," while the bacterial community and functionality (organized by subsystems from general to specific functions) were investigated in crops cultivated with or without tillage and with or without N fertilizer application. Tillage had a small significant effect on the bacterial community in the rhizosphere, but N fertilizer had a highly significant effect on the roots, but not on the rhizosphere or stem. The relative abundance of many bacterial species was significantly different in the roots and stem of fertilized maize plants, but not in the unfertilized ones. The abundance of N cycle genes was affected by N fertilization application, most accentuated in the roots. How these changes in bacterial composition and N genes composition might affect plant development or crop yields has still to be unraveled. IMPORTANCE We investigated the bacterial community structure in the rhizosphere, root, and stem of maize plants cultivated under different agricultural techniques, i.e., with or without N fertilization, and with or without tillage. We found that the bacterial community was defined mostly by the plant compartment and less by agricultural techniques. In the roots, N fertilizer application affected the bacterial community structure, the microbiome functionality, and the abundance of genes involved in the N cycle, but the effect in the rhizosphere and stem was much smaller. Contrary, tillage did not affect the maize microbiome. This study enriches our knowledge about the plant-microbiome system and how N fertilization application affected it.
A Parent-Focused Creative Approach as a Treatment for a High-Functioning Child with Autism Spectrum Disorder (ASD) in Korea: A Case Study

This study was conducted on a 6-year-old girl with autism spectrum disorder (ASD) in Korea. The case was initiated in February 2015, and intensive treatment was provided for one year. Then, the case was monitored over the course of 6 years until December 2021. The intervention plan was an art therapy-based treatment plan (Individual Therapeutic Education Plan: ITEP) with two integral foci: (1) creative arts-based parent counseling and education and (2) didactic art therapy with the child. This was a new type of integral approach that was not a standard of care practice in Korea, acknowledging the importance of including parents in therapy and the notion of creative arts therapy. There was no scientific evidence supporting this qualitive approach; however, the intervention was a notable success, sustaining a positive outcome-the intervention (1) reduced the anxiety levels of both the mother and the child in the short term; (2) enhanced the child-parent relationship as well as the home environment of the child while the art therapy-based counseling and education increased the mother's competence; and (3) enhanced the communicative and adaptive functioning of the child and the mother, with art becoming the supportive breakthrough for their emotional obstacles. The findings suggest that a parent-focused creative approach impacts parental changes and child development: the evidence indicates that parent-driven interventions are a viable option for parents and children with ASD to build a better home environment that supports the child's development.
Visual Target TRACTOR: Tracker and Detector

In this paper, we focus on developing a novel visual target tracking system (TRACTOR) that enables reliable operation in realistic tracking scenarios. Although much progress has been made in the field of visual target tracking, there are still challenging scenarios in which even state-of-the-art trackers do not operate reliably. For instance, most trackers are prone to drift if a target moves abruptly in unexpected directions, or reappears after being fully occluded by the clutters or disappeared from the field of view of a camera. To cope with these scenarios effectively, the proposed tracking system subdivides the task of visual target tracking into two subtasks, i.e., tracking and detection. 1) For target-visible frames, the tracker builds a collaborative framework with the proposed appearance, observation, and motion models thereby achieving robust performance against unexpected motions and appearance changes of a target and 2) for target-invisible frames, the detector verifies continuously whether a target candidate is the lost target or clutter thereby reducing false target alarms effectively. In extensive experiments, the proposed tracking system shows very promising performance in comparison with state-of-the-art tracking methods.
[Development and Application of Medical Device Recall Event Tracking System]

A medical device recall event tracking system was designed, which can enable the users to obtain the recall, early warning and other information related to medical devices in time. The tracking system can timely obtain and release the recall information of medical devices, effectively improve the quality control of hospital medical devices, reduce the use risk of medical devices, and ensure the life safety of patients.
Scope-Aware Useful Cache Block Calculation for Cache-Related Pre-Emption Delay Analysis With Set-Associative Data Caches

Timing analysis of real-time systems must consider cache-related pre-emption delay (CRPD) costs when pre-emptive scheduling is used. While most previous work on CRPD analysis only considers instruction caches, the CRPD incurred on data caches is actually more significant. The state-of-the-art CRPD analysis methods are based on useful cache block (UCB) calculation. Unfortunately, as shown in this article, directly extending the existing UCB calculation techniques from instruction caches to data caches will lead to both unsoundness and significant imprecision. To solve these problems, we develop a new UCB calculation technique for data caches, which redefines the analysis unit (to address the unsoundness in the existing method) and precisely captures the dynamic cache access behavior by taking the temporal scopes of memory blocks into consideration. The experimental results show that our new technique yields substantially tighter CRPD estimations comparing with the state-of-the-art.
Enterococcus faecium Clade Competition in the Presence of β-Lactam Antibiotics in a Mouse GI Tract Colonization Model

Previously, we showed that Enterococcus faecium clade B strains outcompeted health care-associated clade A1 strains in murine gastrointestinal colonization. Here, parenterally administered piperacillin-tazobactam and ceftriaxone significantly promoted colonization by clade A1 over clade B strains except that ceftriaxone, at the dose used, did not favor the least β-lactam-resistant A1 strain. The advantage that β-lactam administration gives to more highly ampicillin-resistant E. faecium over ampicillin-susceptible strains mirrors what occurs in hospitalized patients administered these antibiotics.
FoveaBox: Beyound Anchor-Based Object Detection

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox.
Coexistence in diverse communities with higher-order interactions

A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However, two species may interactively affect the growth of a focal species. Although interactions among three or more species, called higher-order interactions, have the potential to modify our theoretical understanding of coexistence, ecologists lack clear expectations for how these interactions shape community structure. Here we analytically predict and numerically confirm how the variability and strength of higher-order interactions affect species coexistence. We found that as higher-order interaction strengths became more variable across species, fewer species could coexist, echoing the behavior of pairwise models. If interspecific higher-order interactions became too harmful relative to self-regulation, coexistence in diverse communities was destabilized, but coexistence was also lost when these interactions were too weak and mutualistic higher-order effects became prevalent. This behavior depended on the functional form of the interactions as the destabilizing effects of the mutualistic higher-order interactions were ameliorated when their strength saturated with species' densities. Last, we showed that more species-rich communities structured by higher-order interactions lose species more readily than their species-poor counterparts, generalizing classic results for community stability. Our work provides needed theoretical expectations for how higher-order interactions impact species coexistence in diverse communities.
An ultra-performance LC-MS/MS method for determination of JRF103 in human plasma: application in first in-patient study

Background: JRF103, a novel pan-HER inhibitor, has shown potent activity against HER1, HER2, HER4 and EGFR in vitro. To support its first in-patient trial, a sensitive and rapid method was developed and validated using ultra-performance LC-MS/MS. Materials & methods: JRF103 was extracted from plasma using protein precipitation. Extracts were subjected to ultra-performance LC-MS/MS with electrospray ionization. Results: Separation of analyte was achieved using a 1.7-μm C18 column (2.1 × 50-mm internal diameter) with a gradient elution. The developed method was fully validated following the international guides. Conclusion: The developed method was sensitive, specific and suitable for measuring JRF103 concentration in patients with advanced solid tumors in the first in-patient study of JRF103.
Realising potentials for arts-based sustainability science

In recent years, a profusion of methods, practices, and experiences has emerged in the interface between arts and sustainability science. Drawing from two strong currents within sustainability science, namely, the emphasis on transdisciplinary approaches and the need to move towards societal transformations, such hybrid approaches seemingly contribute with unique methods to sustainability research. Despite repeated claims from sustainability scientists about art's role in sustainability transformations, joint analyses with artists and practitioners are still rare. We conveyed a collaborative and exploratory workshop with scientists, artists, and practitioners from the fields of education, public engagement, and activism to identify the potentials for arts-based sustainability research. Participants were invited to facilitate and trial various artistic practices from disciplines of performative, literary, narrative, audio-visual and plastic arts. In this paper, we present five key areas identified in the workshop, where arts-based methods can significantly contribute to sustainability research: embracing more-than-cognitive aspects of knowledge, improving communication, grappling with power dynamics, shifting relationships to nature, and facilitating futures visioning. Workshop participants also identified challenges related to power dynamics, tensions across paradigms, and implementation conditions, providing insights into how to leverage arts' potential to respond to global environmental challenges while boosting societal transformations. We then discuss research questions identified that address challenges and limitations for arts-based research in sustainability. Overall, these results suggest there are yet untapped resources and experiences within the field of arts-based sustainability science. (Audio-visual abstract available on S1)
3-D + t Human Sperm Flagellum Tracing in Low SNR Fluorescence Images

Tracing tubular structures from biomedical images is important for a wide range of applications. Particularly, the spermatozoon is an essential cell whose flagella have a tubular form. Its main function is to fertilize the egg, and the flagellum is fundamental to achieve this task which depends importantly on the dynamics of intracellular calcium ([Ca2+]i). Measuring [Ca2+] i along the flagellum in 3-D is not a simple matter since it requires: 1) sophisticated fluorescence imaging techniques dealing with low intensity and signal to noise ratio (SNR) and 2) tracing the flagellum's centerline. Most of the algorithms proposed to trace tubular structures have been developed for multi-branch structures not being adequate for single tubular structures with low SNR. Taking into account the prior knowledge that the flagellum is constituted by a single tubular structure, we propose an automatic method to trace and track multiple single tubular structures from 3-D + t images. First, an algorithm based on one-class classification allows enhancement of the flagellum. This enhanced 3-D image permits guiding an iterative centerline algorithm toward the flagellum's centerline. Each sperm is assigned an ID to keep track of it in 3-D + t. Our algorithm was quantitatively evaluated using a ground truth 564 semi-manual traces (six 3-D + t image stacks) comparing them to those obtained from state-of-the-art tubular structure centerline extraction algorithms. The qualitative and quantitative results show that our algorithm is extracting similar traces as compared with ground truth, and it is more robust and accurate to trace the flagellum's centerline than multibranch algorithms.
Discovery of malathion resistance QTL in Drosophila melanogaster using a bulked phenotyping approach

Drosophila melanogaster has proved an effective system with which to understand the evolutionary genetics and molecular mechanisms of insecticide resistance. Insecticide use has left signatures of selection in the fly genome, and both functional and quantitative genetic studies in the system have identified genes and variants associated with resistance. Here, we use D. melanogaster and leverage a bulk phenotyping and pooled sequencing "extreme quantitative trait loci" approach to genetically dissect variation in resistance to malathion, an organophosphate insecticide. We resolve 2 quantitative trait loci, one of which implicates allelic variation at the cytochrome P450 gene Cyp6g1, a strong candidate based on previous work. The second shows no overlap with hits from a previous genome-wide association study for malathion resistance, recapitulating other studies showing that different strategies for complex trait dissection in flies can yield apparently different architectures. Notably, we see no genetic signal at the Ace gene. Ace encodes the target of organophosphate insecticide inhibition, and genome-wide association studies have identified strong Ace-linked associations with resistance in flies. The absence of quantitative trait locus implicating Ace here is most likely because our mapping population does not segregate for several of the known functional polymorphisms impacting resistance at Ace, perhaps because our population is derived from flies collected prior to the widespread use of organophosphate insecticides. Our fundamental approach can be an efficient, powerful strategy to dissect genetic variation in resistance traits. Nonetheless, studies seeking to interrogate contemporary insecticide resistance variation may benefit from deriving mapping populations from more recently collected strains.
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure

In this paper, we introduce a novel and robust approach to quantized matrix completion. First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: first, it is differentiable; and second, it is less sensitive to outliers than the quadratic lass. A smooth rank approximation is utilized to endorse lower rank on the genuine data matrix. Thus, an unconstrained optimization problem with differentiable objective function is obtained allowing us to advantage from gradient descent technique. Novel and firm theoretical analysis of the problem model and convergence of our algorithm to the global solution are provided. Another contribution of this letter is that our method does not require projections or initial rank estimation, unlike the state-of-the-art. In the Numerical Experiments section, the noticeable outperformance of our proposed method in learning accuracy and computational complexity compared to those of the state-of-the-art literature methods is illustrated as the main contribution.
Neural representational geometry underlies few-shot concept learning

Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing that it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to predictions about behavioral outcomes by delineating several fundamental and measurable geometric properties of neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. This theory reveals, for instance, that high-dimensional manifolds enhance the ability to learn new concepts from few examples. Intriguingly, we observe striking mismatches between the geometry of manifolds in the primate visual pathway and in trained DNNs. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.
Anomaly Detection in Self-Organizing Networks: Conventional Versus Contemporary Machine Learning

This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks. While deep learning has gained significant traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong statistical alternatives, especially when using proper learning representations. For instance, support vector machines have previously demonstrated state-of-the-art potential in many binary classification applications and can be further exploited with different representations, such as one-class learning and data augmentation. We demonstrate for the first time, on a previously published and publicly available dataset, that conventional machine learning can outperform the previous state-of-the-art using deep learning by 15% on average across four different application scenarios. Our results further indicate that with nearly two orders of magnitude improvement in computational speed and an order of magnitude reduction in trainable parameters, conventional machine learning provides a robust alternative for 5G self-organizing networks especially when the execution and detection times are critical.