Miracle-dz/UniFAR-FTUs huggingface dataset:
Pain and Its Association with Survival for Black and White Individuals with Advanced Prostate Cancer in the United States

Abstract Bone pain is a well-known quality-of-life detriment for individuals with prostate cancer and is associated with survival. This study expands previous work into racial differences in multiple patient-reported dimensions of pain and the association between baseline and longitudinal pain and mortality. This is a prospective cohort study of individuals with newly diagnosed advanced prostate cancer enrolled in the International Registry for Men with Advanced Prostate Cancer (IRONMAN) from 2017 to 2023 at U.S. sites. Differences in four pain scores at study enrollment by race were investigated. Cox proportional hazards models and joint longitudinal survival models were fit for each of the scale scores to estimate HRs and 95% confidence intervals (CI) for the association with all-cause mortality. The cohort included 879 individuals (20% self-identifying as Black) enrolled at 38 U.S. sites. Black participants had worse pain at baseline compared with White participants, most notably a higher average pain rating (mean 3.1 vs. 2.2 on a 10-point scale). For each pain scale, higher pain was associated with higher mortality after adjusting for measures of disease burden, particularly for severe bone pain compared with no pain (HR, 2.47; 95% CI: 1.44–4.22). The association between pain and all-cause mortality was stronger for participants with castration-resistant prostate cancer compared with those with metastatic hormone-sensitive prostate cancer and was similar among Black and White participants. Overall, Black participants reported worse pain than White participants, and more severe pain was associated with higher mortality independent of clinical covariates for all pain scales. Significance: Black participants with advanced prostate cancer reported worse pain than White participants, and more pain was associated with worse survival. More holistic clinical assessments of pain in this population are needed to determine the factors upon which to intervene to improve quality of life and survivorship, particularly for Black individuals.
Cytolethal distending toxin B inoculation leads to distinct gut microtypes and IBS-D-like microRNA-mediated gene expression changes in a rodent model

ABSTRACT Diarrhea-predominant irritable bowel syndrome (IBS-D), associated with increased intestinal permeability, inflammation, and small intestinal bacterial overgrowth, can be triggered by acute gastroenteritis. Cytolethal distending toxin B (CdtB) is produced by gastroenteritis-causing pathogens and may underlie IBS-D development, through molecular mimicry with vinculin. Here, we examine the effects of exposure to CdtB alone on gut microbiome composition, host intestinal gene expression, and IBS-D-like phenotypes in a rat model. CdtB-inoculated rats exhibited increased anti-CdtB levels, which correlated with increased stool wet weights, pro-inflammatory cytokines (TNFα, IL2) and predicted microbial metabolic pathways including inflammatory responses, TNF responses, and diarrhea. Three distinct ileal microbiome profiles (microtypes) were identified in CdtB-inoculated rats. The first microtype (most like controls) had altered relative abundance (RA) of genera Bifidobacterium, Lactococcus, and Rothia. The second had lower microbial diversity, higher Escherichia-Shigella RA, higher absolute E. coli abundance, and altered host ileal tissue expression of immune-response and TNF-response genes compared to controls. The third microtype had higher microbial diversity, higher RA of hydrogen sulfide (H2S)-producer Desulfovibrio, and increased expression of H2S-associated pain/serotonin response genes. All CdtB-inoculated rats exhibited decreased ileal expression of cell junction component mRNAs, including vinculin-associated proteins. Significantly, cluster-specific microRNA-mRNA interactions controlling intestinal permeability, visceral hypersensitivity/pain, and gastrointestinal motility genes, including several previously associated with IBS were seen. These findings demonstrate that exposure to CdtB toxin alone results in IBS-like phenotypes including inflammation and diarrhea-like stool, decreased expression of intestinal barrier components, and altered ileal microtypes that influenced changes in microRNA-modulated gene expression and predicted metabolic pathways consistent with specific IBS-D symptoms.
Novel signatures of prostate cancer progression and therapeutic resistance

ABSTRACT Introduction The extensive heterogeneity of prostate cancer (PCa) and multilayered complexity of progression to castration-resistant prostate cancer (CRPC) have contributed to the challenges of accurately monitoring advanced disease. Profiling of the tumor microenvironment with large-scale transcriptomic studies have identified gene signatures that predict biochemical recurrence, lymph node invasion, metastases, and development of therapeutic resistance through critical determinants driving CRPC. Areas Covered This review encompasses understanding of the role of different molecular determinants of PCa progression to lethal disease including the phenotypic dynamic of cell plasticity, EMT-MET interconversion, and signaling-pathways driving PCa cells to advance and metastasize. The value of liquid biopsies encompassing circulating tumor cells and extracellular vesicles to detect disease progression and emergence of therapeutic resistance in patients progressing to lethal disease is discussed. Relevant literature was added from PubMed portal. Expert Opinion Despite progress in the tumor-targeted therapeutics and biomarker discovery, distant metastasis and therapeutic resistance remain the major cause of mortality in patients with advanced CRPC. No single signature can encompass the tremendous phenotypic and genomic heterogeneity of PCa, but rather multi-threaded omics-derived and phenotypic markers tailored and validated into a multimodal signature.
Sociodemographic Correlates of Low Health Literacy Skills Among Cancer Survivors: National Findings From BRFSS 2016.

PURPOSE This study aimed to explore associations of sociodemographic factors with difficulties in three health literacy (HL) skills and the severity of low HL skills. DESIGN Cross-sectional secondary data analysis. Subjects: Data came from 17,834 adults who responded to the HL module with a response rate of 47% in the 2016 Behavioral Risk Factor Surveillance System. MEASURES Independent variables included sex, age, race/ethnicity, education, employment and income. Dependent variables are three HL skills: obtaining, understanding oral, and understanding written health information. ANALYSIS We conducted weighted Chi-square tests and multinominal logistic regressions. RESULTS Cancer survivors younger than 65 (aged 18-39: AOR = 4.46, P < .001; aged 40-64: AOR = 2.29, P < .001), Hispanic (AOR = 2.17, CI = 1.61-2.50, P < .01) had higher odds of difficulty obtaining health information. Female cancer survivors had lower odds of difficulty comprehending oral (AOR = .69, CI = .55-.87, P < .01) and written (AOR = .58, CI = .46-.74, P < .001) information. The relative risk ratio of having difficulties in three HL tasks was higher for those who were younger than 65 (aged 18-39: RRR = 10.18, CI = 2.41-4.3, P < .01; aged 40-64: RRR = 4.01, CI = 2.09-7.69, P < .001), Hispanic (RRR = 3.24, CI = 1.66-11.34, P < .01), unemployed (RRR = 6.1, CI = 2.88-12.76, P < .001), education levels lower than some college (some high school: RRR = 4.34, P < .01; high school: RRR = 2.62, P < .05) and household income under $25,000 (RRR = 6.99, CI = 2.8-17.5, P < .001). CONCLUSION Intervention and communication materials need to be tailored for patients with different HL skills considering age, gender, socioeconomic status and cultural backgrounds.
Anthropogenic carbon in the ocean—Surface to interior connections

Quantifying the surface to interior transport of anthropogenic carbon (CA) is critical for projecting future carbon uptake and for improved understanding of the role of the oceans in the global carbon cycle. Here we develop and apply a diagnostic tool that provides a volumetric stream function in (CA,σ0) coordinates to calculate the total diapycnal CA transport in the ocean, where σ0 is the surface referenced potential density anomaly. We combine this with air‐sea fluxes of CA to infer the internal ocean mixing of CA to obtain a closed globally integrated budget analyses of the ocean's CA transport. This diagnostic separates the contribution from the mean flow, seasonal cycles, trend, surface fluxes, and mixing in the distribution and the accumulation of CA in the ocean. We find that the redistribution of CA from the surface to the interior of the ocean is due to an interplay between circulation and mixing. The circulation component is dominated by the mean flow; however, effects due to seasonal cycles are significant for the CA redistribution. The two most important pathways for CA subduction are through the transformation of thermocline water (TW) into subantarctic mode water and by transformation of Circumpolar Deep Water (CDW) into lighter Antarctic Intermediate Water. The results suggest that an accurate representation of intermediate and mode water formation, deep water formation, and spatial and temporal distribution of ocean mixing in ocean models is essential to simulate and project the oceanic uptake of CA.
Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

ABSTRACT This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.
A Novel Question-Answering Framework for Automated Citation Screening Using Large Language Models

Objective: This paper aims to address the challenges in citation screening (a.k.a. abstract screening) within Systematic Reviews (SR) by leveraging the zero-shot capabilities of large language models, particularly ChatGPT. Methods: We employ ChatGPT as a zero-shot ranker to prioritize candidate studies by aligning abstracts with the selection criteria outlined in an SR protocol. Citation screening was transformed into a novel question-answering (QA) framework, treating each selection criterion as a question addressed by ChatGPT. The framework involves breaking down the selection criteria into multiple questions, properly prompting ChatGPT to answer each question, scoring and re-ranking each answer, and combining the responses to make nuanced inclusion or exclusion decisions. Results: Large-scale validation was performed on the benchmark of CLEF eHealth 2019 Task 2: Technology Assisted Reviews in Empirical Medicine. Across 31 datasets of four categories of SRs, the proposed QA framework consistently outperformed other zero-shot ranking models. Compared with complex ranking approaches with iterative relevance feedback and fine-tuned deep learning-based ranking models, our ChatGPT-based zero-shot citation screening approaches still demonstrated competitive and sometimes better results, underscoring their high potential in facilitating automated systematic reviews. Conclusion: Investigation justified the indispensable value of leveraging selection criteria to improve the performance of automated citation screening. ChatGPT demonstrated proficiency in prioritizing candidate studies for citation screening using the proposed QA framework. Significant performance improvements were obtained by re-ranking answers using the semantic alignment between abstracts and selection criteria. This further highlighted the pertinence of utilizing selection criteria to enhance citation screening.
Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.
Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

Abstract The purpose of this study is to present comparative performance analysis of different machine learning algorithms for large area crop classification. Ten Indian districts with significant rabi crops viz. wheat, mustard, gram, red lentils (masoor) have been selected for the study. Most popular classical ensemble models – bagging/ARCing, random forest (RF), gradient boosting and Importance Sampled Learning Ensemble (ISLE) with traditional single model (decision tree) have been selected for comparative analysis. To incorporate dependency of large area crop in different variables viz. parent material and soil, phenology, texture, topography, soil moisture, vegetation, climate etc., 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, Sentinental-1A) and climatic data (precipitation, temperature). In rabi season, field survey about crop type is carried out to prepare training data. Performance is evaluated on the basis of marginal rates, F-measure and Jaccard’s coefficient of community, Classification Success Index and Agreement Coefficients. Score is calculated to rank the algorithm. RF is best performer followed by gradient boosting for crop classification. Other ensemble methods ARCing, bagging and ISLE are in decreasing order of performance. Traditional non-ensemble method decision tree scored higher than ISLE.
Even-odd alternative dispersions and beyond. Part I. Oscillations on both sides of the (anti-)shock, shocliton and other indications

We have two basic observations: mathematically, the variational principle and Hamiltonian formulation of some models, such as the Korteweg-de Vries (KdV) equation, are preserved, \textit{mutatis mutandis}, if each mode of freedom is assigned a different dispersion coefficient; and, physically, (similar) dispersive oscillations appear on both sides of some ion-acoustic and quantum shocks, not generated by the dynamics of the models such as the KdV(-Burgers) equation. We thus consider assigning different types of dispersions for different dynamical modes, particularly with the alternation of the signs for alternative Fourier components, different to the two-sided KdV equations for head-on collisions of nonlinear waves. The KdV equation with periodic boundary condition and longest-wave sinusoidal initial field, as used by N. Zabusky and M. D. Kruskal, is chosen for our case study with such alternating-dispersion of the Fourier modes of (normalized) even and odd wavenumbers. Numerical results verify the capability of our model to produce two-sided (around the shock) similar oscillations and indicate even more, including the notion of (anti)shock-soliton duality and/or ``(anti)shocliton'', singular zero-dispersion limit or non-convergence to the classical shock (described by the entropy solution) and non-thermalization (of the Galerkin-truncated models). Extensions to other models and generalization of the mode-dependent dispersion models are also discussed, showcased respectively with Benjamin-Ono-type and the modified-KdV ones. A tentative physical application oriented towards modeling the ion-acoustic shock with the even-odd alternative dispersion model is made to compare against the traditional KdV-Burgers approach.
A multifidelity Bayesian optimization method for inertial confinement fusion design

Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cost remains too high to efficiently and effectively explore the numerous parameters required to describe a typical experiment. Traditionally, ICF design has relied on low-fidelity modeling to initially identify potentially interesting design regions, which are then subsequently explored via selected high-fidelity modeling. In this paper, we demonstrate that this two-step approach can be insufficient: even for simple design problems, a two-step optimization strategy can lead high-fidelity searching towards incorrect regions and consequently waste computational resources on parameter regimes far away from the true optimal solution. We reveal that a primary cause of this behavior in ICF design problems is the presence of low-fidelity optima in distinct regions of the parameter space from high-fidelity optima. To address this issue, we propose an iterative multifidelity Bayesian optimization method based on Gaussian Process Regression that leverages both low- and high-fidelity modelings. We demonstrate, using both two- and eight-dimensional ICF test problems, that our algorithm can effectively utilize low-fidelity modeling for exploration, while automatically refining promising designs with high-fidelity models. This approach proves to be more efficient than relying solely on high-fidelity modeling for optimization.
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines

Classification and probability estimation have broad applications in modern machine learning and data science applications, including biology, medicine, engineering, and computer science. The recent development of a class of weighted Support Vector Machines (wSVMs) has shown great values in robustly predicting the class probability and classification for various problems with high accuracy. The current framework is based on the $\ell^2$-norm regularized binary wSVMs optimization problem, which only works with dense features and has poor performance at sparse features with redundant noise in most real applications. The sparse learning process requires a prescreen of the important variables for each binary wSVMs for accurately estimating pairwise conditional probability. In this paper, we proposed novel wSVMs frameworks that incorporate automatic variable selection with accurate probability estimation for sparse learning problems. We developed efficient algorithms for effective variable selection for solving either the $\ell^1$-norm or elastic net regularized binary wSVMs optimization problems. The binary class probability is then estimated either by the $\ell^2$-norm regularized wSVMs framework with selected variables or by elastic net regularized wSVMs directly. The two-step approach of $\ell^1$-norm followed by $\ell^2$-norm wSVMs show a great advantage in both automatic variable selection and reliable probability estimators with the most efficient time. The elastic net regularized wSVMs offer the best performance in terms of variable selection and probability estimation with the additional advantage of variable grouping in the compensation of more computation time for high dimensional problems. The proposed wSVMs-based sparse learning methods have wide applications and can be further extended to $K$-class problems through ensemble learning.
Teenagers and Artificial Intelligence: Bootcamp Experience and Lessons Learned

Artificial intelligence (AI) stands out as a game-changer in today's technology landscape. However, the integration of AI education in classroom curricula currently lags behind, leaving teenagers inadequately prepared for an imminent AI-driven future. In this pilot study, we designed a three-day bootcamp offered in the summer of 2023 to a cohort of 60 high school students. The curriculum was delivered in person through animated video content, easy-to-follow slides, interactive playgrounds, and quizzes. These were packaged in the early version of an online learning platform we are developing. Results from the post-bootcamp survey conveyed a 91.4% overall satisfaction. Despite the short bootcamp duration, 88.5% and 71.4% of teenagers responded that they had an improved understanding of AI concepts and programming, respectively. Overall, we found that employing diverse modalities effectively engaged students, and building foundational modules proved beneficial for introducing more complex topics. Furthermore, using Google Colab notebooks for coding assignments proved challenging to most students. Students' activity on the platform and their answers to quizzes showed proficient engagement and a grasp of the material. Our results strongly highlight the need for compelling and accessible AI education methods for the next generation and the potential for informal learning to fill the gap of providing early AI education to teenagers.