Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure to Drought: Duration, Severity and Intensity (Java, Bali and Nusa Tenggara)

Occurrence of drought is a slow process lasted for a long time until the rainy season come. This natural disaster has broad and severe impact. This research was conducted to examine the level of severity and intensity of drought in Java, Bali and Nusa Tenggara using Standardized Precipitation Evapotranspiration Index (SPEI). SPEI is drought index used for quantifying drought occurrence, and can be used to analyze duration, severity and intensity. Drought is a climatological phenomenon which is difficult how to determine its onset, duration, magnitude, intensity, spatial extent, etc. Therefore, this information of exposure to drought could be describe the characteristics of drought events. To summarise the calculation, for 30 years (1985-2014) from 22 stations BMKG obtained the longest and strongest drought occurred at meteorology Serang – Banten, with long duration is 11 months and severity - 16.816 at October 2002 until August 2003. In one period of drought event, not always the longest it’s mean the strongest.
Incidental carcinoma of the prostate in cystoprostatectomy specimens – is it always a toothless lion?

Incidental prostate carcinoma (iPC) is a subject of debate concerning its definition, incidence, biology, diagnosis, staging, and treatment. The present study aimed to assess the incidence and main clinical-morphological characteristics of iPC identified in radical cystoprostatectomy (RCP) specimens over a 5-year period. Using the database of the Urology and Pathology Departments, we identified all patients with bladder carcinomas (BCs) who underwent RCP within a 5-year frame time. We selected only those patients with synchronous BC and prostate carcinoma (PC). The following parameters were analyzed for these patients: age, type of bladder and prostate tumor, degree of differentiation, pathological stage, and other prognostic parameters. We identified 91 men with bladder tumors treated by RCP among whom 43, aged between 53 and 84 years (mean age: 69.2 years), presented synchronous PC. iPC was more prevalent in older individuals (>65 years: 30 patients, 69.8%), with only six out of the 43 (12.8%) patients with iPC being aged ≤60 years. All iPC cases were conventional adenocarcinoma. Well-differentiated prostate adenocarcinomas (grade group 1) predominated (65.1%). Among the 43 iPCs, 16 (37.2%) were clinically significant PCs. iPC is frequently identified in patients with BC when inclusion and evaluation of all or most of the prostate tissue are performed. Although more than half of iPCs were well-differentiated tumors confined to the prostate, a significant number of cases met the criteria of clinically significant PC. All men over the age of 50 who are candidates for RCP, should undergo evaluation through serum prostate specific antigen determination.
Proinflammatory CD14highCD16low monocytes/macrophages prevail in Treponema phagedenis-associated bovine digital dermatitis

ABSTRACT Digital dermatitis (DD) is a skin disease in cattle characterized by painful inflammatory ulcerative lesions in the feet, mostly associated with local colonization by Treponema spp. , including Treponema phagedenis. The reason why most DD lesions remain actively inflamed and progress to chronic conditions despite antibiotic treatment remains unknown. Herein, we show an abundant infiltration of proinflammatory (CD14highCD16low) monocytes/macrophages in active DD lesions, a skin response that was not mitigated by topical treatment with oxytetracycline. The associated bacterium, T. phagedenis, isolated from DD lesions in cattle, when injected subcutaneously into mice, induced abscesses with a local recruitment of Ly6G+ neutrophils and proinflammatory (Ly6ChighCCR2+) monocytes/macrophages, which appeared at infection onset (4 days post challenge) and persisted for at least 7 days post challenge. When exploring the ability of macrophages to regulate inflammation, we showed that bovine blood-derived macrophages challenged with live T. phagedenis or its structural components secreted IL-1β via a mechanism dependent on the NLRP3 inflammasome. This study shows that proinflammatory characteristics of monocytes/macrophages and neutrophils dominate active non-healing ulcerative lesions in active DD, thus likely impeding wound healing after antibiotic treatment.
Serum vitamin C levels and their correlation with chronic kidney disease in adults: a nationwide study

Abstract Introduction Inflammation and oxidative stress play significant roles in the development of chronic kidney disease (CKD). Given the recognized antioxidant properties of vitamin C, our study aimed to explore the correlation between CKD and serum vitamin C levels. Methods Data were gathered from the 2017–2018 National Health and Nutrition Examination Survey. Participants below 18 years of age, pregnant individuals, those lacking essential data for CKD diagnosis, or individuals with incomplete serum vitamin C data were excluded. Subgroup and weighted multivariable logistic regression analyses were performed to assess the potential correlation between serum vitamin C and CKD. Results Our study comprised 4969 participants, revealing an overall CKD prevalence of 15.0%. The results indicated that individuals with reduced serum vitamin C levels were more likely to be male, possess lower educational attainment, have a diminished poverty-income ratio, engage in heavy drinking, and be current smokers. Additionally, they exhibited a higher prevalence of obesity and diabetes. Significantly, participants in the third quartile group experienced a 37.0%, 47.0%, and 46.6% decrease in the risk of developing albuminuria, low estimated glomerular filtration rate (eGFR), and CKD, respectively. Subgroup analysis demonstrated that individuals between 65 and 80 years of age showed a statistically reduced risk of developing CKD and low eGFR when their serum vitamin C levels fell in the third and fourth quartile groups. Conclusions Our findings reveal a correlation between elevated serum vitamin C levels and a decreased risk of developing albuminuria, low eGFR, and CKD. Appropriately increasing serum vitamin C levels may hold promise in protecting renal function, particularly among older individuals.
Independent component analysis reveals 49 independently modulated gene sets within the global transcriptional regulatory architecture of multidrug-resistant Acinetobacter baumannii

ABSTRACT Acinetobacter baumannii causes severe infections in humans, resists multiple antibiotics, and survives in stressful environmental conditions due to modulations of its complex transcriptional regulatory network (TRN). Unfortunately, our global understanding of the TRN in this emerging opportunistic pathogen is limited. Here, we apply independent component analysis, an unsupervised machine learning method, to a compendium of 139 RNA-seq data sets of three multidrug-resistant A. baumannii international clonal complex I strains (AB5075, AYE, and AB0057). This analysis allows us to define 49 independently modulated gene sets, which we call iModulons. Analysis of the identified A. baumannii iModulons reveals validating parallels to previously defined biological operons/regulons and provides a framework for defining unknown regulons. By utilizing the iModulons, we uncover potential mechanisms for a RpoS-independent general stress response, define global stress-virulence trade-offs, and identify conditions that may induce plasmid-borne multidrug resistance. The iModulons provide a model of the TRN that emphasizes the importance of transcriptional regulation of virulence phenotypes in A. baumannii. Furthermore, they suggest the possibility of future interventions to guide gene expression toward diminished pathogenic potential. IMPORTANCE The rise in hospital outbreaks of multidrug-resistant Acinetobacter baumannii infections underscores the urgent need for alternatives to traditional broad-spectrum antibiotic therapies. The success of A. baumannii as a significant nosocomial pathogen is largely attributed to its ability to resist antibiotics and survive environmental stressors. However, there is limited literature available on the global, complex regulatory circuitry that shapes these phenotypes. Computational tools that can assist in the elucidation of A. baumannii’s transcriptional regulatory network architecture can provide much-needed context for a comprehensive understanding of pathogenesis and virulence, as well as for the development of targeted therapies that modulate these pathways. The rise in hospital outbreaks of multidrug-resistant Acinetobacter baumannii infections underscores the urgent need for alternatives to traditional broad-spectrum antibiotic therapies. The success of A. baumannii as a significant nosocomial pathogen is largely attributed to its ability to resist antibiotics and survive environmental stressors. However, there is limited literature available on the global, complex regulatory circuitry that shapes these phenotypes. Computational tools that can assist in the elucidation of A. baumannii’s transcriptional regulatory network architecture can provide much-needed context for a comprehensive understanding of pathogenesis and virulence, as well as for the development of targeted therapies that modulate these pathways.
Stress Detection of Autistic Adults during Simulated Job Interviews using a Novel Physiological Dataset and Machine Learning

The interview process has been identified as one of the major barriers to employment of autistic individuals, which contributes to the staggering rate of under and unemployment of autistic adults. Decreasing stress during the interview has been shown to improve interview performance. However, in order to effectively provide insights on stress to both interviewees and interviewers, it is necessary to first effectively measure stress. This work explores physiological stress detection through wearable sensing as a means of obtaining quantitative stress measures from young autistic adults undergoing a virtual simulated interview using supervised machine learning techniques. Several supervised learning models were explored and it was found that Elastic Net Regression had the best accuracy with individual models with an accuracy of 84.8% while Support Vector Regression models evaluated with leave-one-out cross validation had a group accuracy of 75.4%. The predictions from the stress model were used with data visualization techniques in order to provide insights on the interview process from both a group and individual viewpoint, showing that stress trends can be found and evaluated using the stress model. This work also addresses a major gap in physiological stress detection literature by presenting a novel dataset of physiological data and ground truth labels for 15 autistic young adults undergoing a simulated interview.
Integration of cervical cancer screening into healthcare facilities in low- and middle-income countries: a scoping review protocol

Background Cervical cancer is the fourth most common malignancy in women, with 90% of deaths in low- and middle-income countries. Integrating cervical cancer screening services into healthcare facilities is crucial for overcoming the disease. Thus, this review aims to map existing evidence and identify barriers and facilitators in low- and middle-income countries. Methods The scoping review will employ a five-step framework as proposed by Arksey and O’Malley. These are (1) formulating the research questions, (2) identifying relevant studies, (3) selecting eligible studies, (4) charting the data, and (5) collating, summarising and reporting the results. Five databases (MEDLINE, Maternity and Infant Care, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science) will be systematically searched. Grey literature will also be searched. Data will be extracted, charted, synthesised and summarised. Ethics and dissemination This review does not require ethics approval. Findings will be disseminated through peer-reviewed publications, policy briefs and conference presentations.
Revisiting the Fréchet distance between piecewise smooth curves

Since its introduction to computational geometry by Alt and Godau in 1992, the Fr\'echet distance has been a mainstay of algorithmic research on curve similarity computations. The focus of the research has been on comparing polygonal curves, with the notable exception of an algorithm for the decision problem for planar piecewise smooth curves due to Rote (2007). We present an algorithm for the decision problem for piecewise smooth curves that is both conceptually simpler and naturally extends to the first algorithm for the problem for piecewise smooth curves in $\mathbb{R}^d$. We assume that the algorithm is given two continuous curves, each consisting of a sequence of $m$, resp. \ $n$, smooth pieces, where each piece belongs to a sufficiently well-behaved class of curves, such as the set of algebraic curves of bounded degree. We introduce a decomposition of the free space diagram into a controlled number of pieces that can be used to solve the decision problem similarly to the polygonal case, in $O(mn)$ time, leading to a computation of the Fr\'echet distance that runs in $O(mn\log(mn))$ time. Furthermore, we study approximation algorithms for piecewise smooth curves that are also $c$-packed for some fixed value $c$. We adapt the existing framework for $(1+\epsilon)$-approximations and show that an approximate decision can be computed in $O(cn/\epsilon)$ time for any $\epsilon>0$.
Reversible ternary logic with Laguerre-Gaussian modes

The need set by a computational industry to increase processing power, while simultaneously reducing the energy consumption of data centers became a challenge for modern computational systems. In this work, we propose an optical communication solution, that could serve as a building block for future computing systems, due to its versatility. The solution arises from Landauer principle and utilizes reversible logic, manifested as an optical logical gate with structured light, here represented as Laguerre-Gaussian modes. We introduced an information encoding technique that employs phase shift as an information carrier and incorporates multi-valued logic in the form of a ternary system. In the experimental validation, the free space communication protocol is implemented to determine the similarity between two images. Obtained results are compared with their binary counterparts, illustrating denser information capacity and enhanced information security, which underscores its capability to transmit and process both quantum and classical information.
Quantumness of gravitational field: A perspective on monogamy relation

Understanding the phenomenon of quantum superposition of gravitational fields induced by massive quantum particles is an important starting point for quantum gravity. The purpose of this study is to deepen our understanding of the phenomenon of quantum superposition of gravitational fields. To this end, we consider a trade-off relation of entanglement (monogamy relation) in a tripartite system consisting of two massive particles and a gravitational field that may be entangled with each other. Consequently, if two particles cannot exchange information mutually, they are in a separable state, and the particle and gravitational field are always entangled. Furthermore, even when two particles can send information to each other, there is a trade-off between the two particles and the gravitational field. We also investigate the behavior of the quantum superposition of the gravitational field using quantum discord. We find that quantum discord increases depending on the length scale of the particle superposition. Our results may help understand the relationship between the quantization of the gravitational field and the meaning of the quantum superposition of the gravitational field.
Large Language Models in Mental Health Care: a Scoping Review

Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts. This scoping review aims to critically analyze the existing development and applications of LLMs in mental health care, highlighting their successes and identifying their challenges and limitations in these specialized fields. Materials and Methods: A broad literature search was conducted in November 2023 using six databases (PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv) following the 2020 version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 313 publications were initially identified, and after applying the study inclusion criteria, 34 publications were selected for the final review. Results: We identified diverse applications of LLMs in mental health care, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. Conclusion: LLMs show promising potential in advancing mental health care, with applications in diagnostics, and patient support. Continued advancements depend on collaborative, multidisciplinary efforts focused on framework enhancement, rigorous dataset development, technological refinement, and ethical integration to ensure the effective and safe application of LLMs in mental health care.
UMIE: Unified Multimodal Information Extraction with Instruction Tuning

Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-specific model structures, which results in limited generalizability across tasks and underutilizes shared knowledge across MIE tasks. To address these issues, we propose UMIE, a unified multimodal information extractor to unify three MIE tasks as a generation problem using instruction tuning, being able to effectively extract both textual and visual mentions. Extensive experiments show that our single UMIE outperforms various state-of-the-art (SoTA) methods across six MIE datasets on three tasks. Furthermore, in-depth analysis demonstrates UMIE's strong generalization in the zero-shot setting, robustness to instruction variants, and interpretability. Our research serves as an initial step towards a unified MIE model and initiates the exploration into both instruction tuning and large language models within the MIE domain. Our code, data, and model are available at https://github.com/ZUCC-AI/UMIE.
Automated construction of effective potential via algorithmic implicit bias

We introduce a novel approach for decomposing and learning every scale of a given multiscale objective function in $\mathbb{R}^d$, where $d\ge 1$. This approach leverages a recently demonstrated implicit bias of the optimization method of gradient descent by Kong and Tao, which enables the automatic generation of data that nearly follow Gibbs distribution with an effective potential at any desired scale. One application of this automated effective potential modeling is to construct reduced-order models. For instance, a deterministic surrogate Hamiltonian model can be developed to substantially soften the stiffness that bottlenecks the simulation, while maintaining the accuracy of phase portraits at the scale of interest. Similarly, a stochastic surrogate model can be constructed at a desired scale, such that both its equilibrium and out-of-equilibrium behaviors (characterized by auto-correlation function and mean path) align with those of a damped mechanical system with the original multiscale function being its potential. The robustness and efficiency of our proposed approach in multi-dimensional scenarios have been demonstrated through a series of numerical experiments. A by-product of our development is a method for anisotropic noise estimation and calibration. More precisely, Langevin model of stochastic mechanical systems may not have isotropic noise in practice, and we provide a systematic algorithm to quantify its covariance matrix without directly measuring the noise. In this case, the system may not admit closed form expression of its invariant distribution either, but with this tool, we can design friction matrix appropriately to calibrate the system so that its invariant distribution has a closed form expression of Gibbs.
Inverse Reinforcement Learning with Sub-optimal Experts

Inverse Reinforcement Learning (IRL) techniques deal with the problem of deducing a reward function that explains the behavior of an expert agent who is assumed to act optimally in an underlying unknown task. In several problems of interest, however, it is possible to observe the behavior of multiple experts with different degree of optimality (e.g., racing drivers whose skills ranges from amateurs to professionals). For this reason, in this work, we extend the IRL formulation to problems where, in addition to demonstrations from the optimal agent, we can observe the behavior of multiple sub-optimal experts. Given this problem, we first study the theoretical properties of the class of reward functions that are compatible with a given set of experts, i.e., the feasible reward set. Our results show that the presence of multiple sub-optimal experts can significantly shrink the set of compatible rewards. Furthermore, we study the statistical complexity of estimating the feasible reward set with a generative model. To this end, we analyze a uniform sampling algorithm that results in being minimax optimal whenever the sub-optimal experts' performance level is sufficiently close to the one of the optimal agent.
Hybrid Vector Message Passing for Generalized Bilinear Factorization

In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity.
Effect of chemical treatments on the microbial load of fruits and vegetables

Full Length Research Article Effect of chemical treatments on the microbial load of fruits and vegetables was investigated. Fruits and vegetables purchased from street vendors were analyzed to determine their microbial load. Bacteria load ranged from 1.3×10 4 ‒1.8×10 6 cfu/g while fungi load ranged from 3.0×10 4 ‒5.2×10 4 cfu/g. Carrots had the highest bacterial and fungal load. Bacteria isolated included Staphylococcus, Streptococcus, Bacillus, Pseudomonas, Escherichia, Salmonella and Klebsiella while fungi were Aspergillus, Rhizopus and Saccharomyces with various percentage of occurrences. Staphylococcus was the most frequently isolated bacteria genera, varying between 60 and 100% in all cases. Treatment of fruits and vegetables with vinegar was found to significantly reduce their respective microbial load when compared with other solvent. This study revealed that fruits and vegetables were contaminated with different bacteria and fungi genera and the microbial load can be reduced when properly washed especially with vinegar. ©2013 BluePen Journals Ltd. All rights reserved