Miracle-dz/UniFAR-FTUs huggingface dataset:
Racial disparities in Black men with prostate cancer: A literature review

Black men are disproportionately affected by prostate cancer (PCa), with earlier presentation, more aggressive disease, and higher mortality rates versus White men. Furthermore, Black men have less access to PCa treatment and experience longer delays between diagnosis and treatment. In this review, the authors discuss the factors contributing to racial disparities and present solutions to improve access to care and increase clinical trial participation among Black men with PCa. Racial disparities observed among Black men with PCa are multifaceted, evolving from institutional racism. Cultural factors include generalized mistrust of the health care system, poor physician‐patient communication, lack of information on PCa and treatment options, fear of PCa diagnosis, and perceived societal stigma of the disease. In the United States, geographic trends in racial disparities have been observed. Economic factors, e.g., cost of care, recovery time, and cancer debt, play an important role in racial disparities observed in PCa treatment and outcomes. Racial diversity is often lacking in genomic and precision medicine studies. Black men are largely underrepresented in key phase 3 PCa trials and may be less willing to enroll in clinical trials due to lack of awareness, lack of diversity in clinical trial research teams, and bias of health care providers to recommend clinical research. The authors propose solutions to address these factors that include educating clinicians and institutions on the barriers Black men experience, increasing the diversity of health care providers and clinical research teams, and empowering Black men to be involved in their treatment, which are keys to creating equity for Black men with PCa.
Effects of different fluid management on lung and kidney during pressure‐controlled and pressure‐support ventilation in experimental acute lung injury

Optimal fluid management is critical during mechanical ventilation to mitigate lung damage. Under normovolemia and protective ventilation, pulmonary tensile stress during pressure‐support ventilation (PSV) results in comparable lung protection to compressive stress during pressure‐controlled ventilation (PCV) in experimental acute lung injury (ALI). It is not yet known whether tensile stress can lead to comparable protection to compressive stress in ALI under a liberal fluid strategy (LF). A conservative fluid strategy (CF) was compared with LF during PSV and PCV on lungs and kidneys in an established model of ALI. Twenty‐eight male Wistar rats received endotoxin intratracheally. After 24 h, they were treated with CF (minimum volume of Ringer's lactate to maintain normovolemia and mean arterial pressure ≥70 mmHg) or LF (~4 times higher than CF) combined with PSV or PCV (VT = 6 ml/kg, PEEP = 3 cmH2O) for 1 h. Nonventilated animals (n = 4) were used for molecular biology analyses. CF‐PSV compared with LF‐PSV: (1) decreased the diffuse alveolar damage score (10 [7.8–12] vs. 25 [23–31.5], p = 0.006), mainly due to edema in axial and alveolar parenchyma; (2) increased birefringence for occludin and claudin‐4 in lung tissue and expression of zonula‐occludens‐1 and metalloproteinase‐9 in lung. LF compared with CF reduced neutrophil gelatinase‐associated lipocalin and interleukin‐6 expression in the kidneys in PSV and PCV. In conclusion, CF compared with LF combined with PSV yielded less lung epithelial cell damage in the current model of ALI. However, LF compared with CF resulted in less kidney injury markers, regardless of the ventilatory strategy.
Resolvin D2 uses multiple Ca2+‐dependent signaling pathways to stimulate mucin secretion in rat and human conjunctival goblet cells

The mucin layer of the tear film is produced by goblet cells in the conjunctiva to protect the ocular surface and maintain homeostasis. The pro‐resolving lipid mediator resolvin D2 (RvD2) biosynthesized from an omega 3 fatty acid actively terminates inflammation and regulates mucin secretion from conjunctival goblet cells. Our objective was to determine which Ca2+‐dependent signaling pathways RvD2 uses to stimulate conjunctival goblet cell function (CGC). We hypothesize that RvD2 activates multiple intracellular Ca2+ signaling pathways to stimulate CGC secretion. Rat and human CGCs were cultured from conjunctival explants. The amount of RvD2 receptor GPR18/DRV2 message and protein were determined. The intracellular concentration of Ca2+ ([Ca2+]i) was measured in CGCs using a fluorescent Ca2+ dye and mucin secretion was determined by measuring protein secretion enzymatically with a lectin. Goblet cells were incubated with signaling pathway inhibitors before stimulation with RvD2 and [Ca2+]i or secretion was measured. In rat and human CGCs RvD2 receptor and in rat CGCs IP3 (a molecule that releases Ca2+ from intracellular organelles) receptors 1–3 were detected. In both species of CGC RvD2 increased [Ca2+]i similarly to RvD1. In rat CGCs, the increase in [Ca2+]i and secretion stimulated by RvD2 was significantly blocked by inhibitors to phospholipase (PL‐) C and IP3‐receptor, but not protein kinase C. Increase in [Ca2+]i was blocked by the PLD inhibitor, but not the PLA2 inhibitor. Secretion was blocked by PLA2 inhibitor, but not the PLD inhibitor. An inhibitor of the epidermal growth factor receptor blocked the increase in [Ca2+]i by RvD2 in both species of CGCs. In CGCs RvD2 activates multiple intracellular signaling pathways that are Ca2+‐dependent, along with one Ca2+‐independent and one cAMP/protein kinase A‐dependent pathway. Activation of these pathways stimulate mucin secretion from rat and human CGCs into the tear film contributing to ocular surface homeostasis and health.
Coherent structures in turbulent mixing layers

Coherent structures and the similarity characteristics o f the turbulent mixing layer were the subjects o f this experimental sun'ey. The results achieved highlighted the fundam ental changes in the mixing layer similarity characteristics beyond the mixing transition. The mixing transition was observed to depend on the achievement o f a sufficiently high local Reynolds number and the completion o f a minimum number o f vortex amalgamations. The experiments carried out confirmed and extended to a wider range o f experimental conditions the changes observed by Pedley (1990) and Coats (1992) beyond the mixing transition. In this region the growth o f the individual structures present in the flow switched from the pre-transition instability driven mechanism to a process in which the individual vortices grow in a continuous and linear manner. In the post transition region o f the flo w the entrainment o f irrotational flu id from the free-stream s was seen to occur not in association with amalgamations but continuously as coherent structures grew linearly in a sort o f procession along the length o f the test section. The experimental results on the entrainment ratio were also obtained from flo w visualisation records, applying a developed version o f geometrical method, originally suggested by Dimotakis (1986). Comparisons were made between the measured similarity characteristics o f the mixing layers studied and predictions from theoretical models. Statistical data on the observed intermittency o f coherent structures in the post­ transition layer were carried out. The study o f the intermittency suggested that two distinctive flow conditions could occur in the post-transition mixing layer, one associated to the coherently structured flow, the other to the unstructured mixing layer. Each o f these must have their own similarity characteristics. The key to the understanding o f this result was the observed intermittency o f the coherent structures. When the mixing layer studied switched from its structured to unstructured condition a shift o f the mixing layer centreline towards the high speed stream and a smaller growth were observed.
A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.
Cognitive decline and hippocampal functional connectivity within older Black adults

While there has been a proliferation of neuroimaging studies on cognitive decline in older non‐Hispanic White adults, there is a dearth of knowledge regarding neuroimaging correlates of cognitive decline in Black adults. Resting‐state functional neuroimaging approaches may be particularly sensitive to early cognitive decline, but there are no studies that we know of that apply this approach to examining associations of brain function to cognition in older Black adults. We investigated the association of cognitive decline with whole‐brain voxel‐wise functional connectivity to the hippocampus, a key brain region functionally implicated in early Alzheimer's dementia, in 132 older Black adults without dementia participating in the Minority Aging Research Study and Rush Memory and Aging Project, two longitudinal studies of aging that include harmonized annual cognitive assessments and magnetic resonance imaging brain imaging. In models adjusted for demographic factors (age, education, sex), global cognitive decline was associated with functional connectivity of the hippocampus to three clusters in the right and left frontal regions of the dorsolateral prefrontal cortex. In domain‐specific analyses, decline in semantic memory was associated with functional connectivity of the hippocampus to bilateral clusters in the precentral gyrus, and decline in perceptual speed was inversely associated with connectivity of the hippocampus to the bilateral intracalcarine cortex and the right fusiform gyrus. These findings elucidate neurobiological mechanisms underlying cognitive decline in older Black adults and may point to specific targets of intervention for Alzheimer's disease.
Superconducting flux qubits: Quantum chains and tunable qubits

This thesis presents results of theoretical and experimental work on superconducting persistent-current flux quantum bits. These qubits are promising candidates for the implementation of scalable quantum information processing. This work focuses on the study of one dimensional chains of inductively coupled flux qubits and on qubits with a tunable energy gap. Chains of flux qubits can be used as models of quantum spin chains, one of the most basic systems in many-body physics that has been extensively studied theoretically. The ability to design and tune the qubit and coupling parameters enables exploration of different phase regimes during measurements, in parameter regimes that are not accessible with magnetic materials. The study of the dynamics of quantum waves in an artificial spin chain can also be used to explore novel quantum phenomena with possible applications in quantum computing. Tunability of the minimal energy splitting (the gap) enables one to rapidly bring the flux qubit in and out of resonance with other quantum systems, including a harmonic oscillator. With tunable qubits it also becomes possible to create inter-qubit couplings of different vector nature, using magnetic fluxes. This permits the design of various interaction Hamiltonians for multiple qubit systems. These operations can be performed at the degeneracy point of the qubit, where coherence properties are optimal. Therefore the tunable flux qubit provides an attractive component for the implementation of scalable quantum computation.
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Abstract. The modelling of small-scale processes is a major source of error in weather and climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a recurrent neural network within a probabilistic framework (L96-RNN). Our model is competitive and often superior to both a bespoke baseline and an existing probabilistic machine learning approach (GAN, generative adversarial network) when applied to the Lorenz 96 atmospheric simulation. This is due to its superior ability to model temporal patterns compared to standard first-order autoregressive schemes. It also generalizes to unseen scenarios. We evaluate it across a number of metrics from the literature and also discuss the benefits of using the probabilistic metric of hold-out likelihood.
High Dynamic Range Image Quality Assessment Based on Frequency Disparity

In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that the human visual system (HVS) is highly adapted for extracting structural information and partial frequencies when perceiving the visual scene, the Gabor and the Butterworth filters are applied to the luminance component of the HDR image to extract the local and global frequency features, respectively. The similarity measurement and feature pooling strategy are sequentially performed on the frequency features to obtain the predicted single quality score. The experiments evaluated on four widely used benchmarks demonstrate that the proposed LGFM can provide a higher consistency with the subjective perception compared with the state-of-the-art HDR IQA methods. Our code is available at: https://github.com/eezkni/LGFM.
Johari–Goldstein β relaxation in glassy dynamics originates from two-scale energy landscape

Significance When liquids are cooled while avoiding crystallization, their dynamics dramatically slow down. This is a major problem in the glass transition phenomena. Vast experiments broadly observed two processes of dynamics: a faster Johari–Goldstein (JG) β relaxation and a slower α relaxation. However, most theoretical and numerical works have intensively focused on the α relaxation, whereas only limited attention has been given to the JG β relaxation. Here, we perform extensive MD simulations of an asymmetric dimer system, which shows both relaxation processes. We report the first detailed analyses on JG β relaxation, including the real-space dynamics of molecules and hierarchical potential energy landscape. Our results contribute to closing a gap between theoretical and experimental sides of the glass transition studies.
Avoiding the Great Filter: A Simulation of Important Factors for Human Survival

Humanity’s path to avoiding extinction is a daunting and inevitable challenge which proves difficult to solve, partially due to the lack of data and evidence surrounding the concept. We aim to address this confusion by addressing some of the most dangerous threats to humanity, in hopes of providing a direction to approach this problem. Using a probabilistic model, we observed the effects of nuclear war, climate change, asteroid impacts, artificial intelligence and pandemics, which are among the most harmful disasters in terms of their extent of destruction on the length of human survival. We consider the starting point of the predicted average number of survival years as the present calendar year. Nuclear war, when sampling from an artificial normal distribution, results in an average human survival time of 60 years into the future starting from the present, before a civilization-ending disaster. While climate change results in an average human survival time of 193 years, the simulation based on impact from asteroids results in an average of 1754 years. Since the risks from asteroid impacts could be considered to reside mostly in the far future, it can be concluded that nuclear war, climate change, and pandemics are presently the most prominent threats to humanity. Additionally, the danger from superiority of artificial intelligence over humans, although still somewhat abstract, is worthy of further study as its potential for impeding humankind’s progress towards becoming a more advanced civilization cannot be confidently dismissed.
Source counts at 7.7–21 μm in CEERS field with JWST

Source counts – the number density of sources as a function of flux density – represent one of the fundamental metrics in observational cosmology due to their straightforward and simple nature. It is an important tool that provides information on galaxy formation and evolution. Source counting is a direct measurement. Compared to advanced analyses that require more observational input such as luminosity/mass functions, it is less affected by any cosmological parameter assumptions or any errors propagated from luminosities. In this study, we present source counts at the six mid-infrared (MIR) bands, i.e. 7.7, 10, 12.8, 15, 18, and 21 μm from the MIR instrument of the JWST. Contrasted with the infrared source counts achieved by prior generations of infrared space telescopes, our source counts delve up to ∼100 times deeper, showcasing the exceptional sensitivity of the JWST, and aligning with the model predictions based on preceding observations. In a follow-up study, we utilize our source counts to establish a new IR galaxy population evolutionary model that provides a physical interpretation.
Entanglement dynamics of photon pairs and quantum memories in the gravitational field of the earth

We investigate the effect of entanglement dynamics due to gravity – the basis of a mechanism of universal decoherence – for photonic states and quantum memories in Mach-Zehnder and Hong-Ou-Mandel interferometry setups in the gravitational field of the earth. We show that chances are good to witness the effect with near-future technology in Hong-Ou-Mandel interferometry. This would represent an experimental test of theoretical modeling combining a multi-particle effect predicted by the quantum theory of light and an effect predicted by general relativity. Our article represents the first analysis of relativistic gravitational effects on space-based quantum memories which are expected to be an important ingredient for global quantum communication networks.
Aspects of Modelling Requirements in Very-Large Agile Systems Engineering

Using models for requirements engineering (RE) is uncommon in systems engineering, despite the widespread use of model-based engineering in general. One reason for this lack of use is that formal models do not match well the trend to move towards agile developing methods. While there exists work that investigates challenges in the adoption of requirements modeling and agile methods in systems engineering, there is a lack of work studying successful approaches of using requirements modelling in agile systems engineering. To address this gap, we conducted a case study investigating the application of requirements models at Ericsson AB, a Swedish telecommunications company. We studied a department using requirements models to bridge agile development and plan-driven development aspects. We find that models are used to understand how requirements relate to each other, and to keep track with the product's evolution. To cope with the effort to maintain models over time, study participants suggest to rely on text-based notations that bring the models closer to developers and allow integration into existing software development workflows. This results in tool trade-offs, e.g., losing the possibility to control diagram layout.
PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection

Recent years have witnessed a trend of applying context frames to boost the performance of object detection as video object detection. Existing methods usually aggregate features at one stroke to enhance the feature. These methods, however, usually lack spatial information from neighboring frames and suffer from insufficient feature aggregation. To address the issues, we perform a progressive way to introduce both temporal information and spatial information for an integrated enhancement. The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i.e., the frame to be detected). Meanwhile, we employ a Spatial Transition Awareness Model (STAM) to convey the location transition information between each context frame and target frame. Built upon a transformer-based detector DETR, our PTSEFormer also follows an end-to-end fashion to avoid heavy post-processing procedures while achieving 88.1% mAP on the ImageNet VID dataset. Codes are available at https://github.com/Hon-Wong/PTSEFormer.
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection

The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications. Prior work has demonstrated the benefits of effective architecture design and integration of relevant cues for more accurate HOI detection. However, the design of an appropriate pre-training strategy for this task remains underexplored by existing approaches. To address this gap, we propose Relational Language-Image Pre-training (RLIP), a strategy for contrastive pre-training that leverages both entity and relation descriptions. To make effective use of such pre-training, we make three technical contributions: (1) a new Parallel entity detection and Sequential relation inference (ParSe) architecture that enables the use of both entity and relation descriptions during holistically optimized pre-training; (2) a synthetic data generation framework, Label Sequence Extension, that expands the scale of language data available within each minibatch; (3) mechanisms to account for ambiguity, Relation Quality Labels and Relation Pseudo-Labels, to mitigate the influence of ambiguous/noisy samples in the pre-training data. Through extensive experiments, we demonstrate the benefits of these contributions, collectively termed RLIP-ParSe, for improved zero-shot, few-shot and fine-tuning HOI detection performance as well as increased robustness to learning from noisy annotations. Code will be available at https://github.com/JacobYuan7/RLIP.