The Effects of Chronic Sleep Deprivation on Sustained Attention: A Study of Brain Dynamic Functional Connectivity

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks\u27 adaptability, resulting in the disrupted dominance of small-world brain networks

RetouchUAA: Unconstrained Adversarial Attack via Image Retouching

Deep Neural Networks (DNNs) are susceptible to adversarial examples. Conventional attacks generate controlled noise-like perturbations that fail to reflect real-world scenarios and hard to interpretable. In contrast, recent unconstrained attacks mimic natural image transformations occurring in the real world for perceptible but inconspicuous attacks, yet compromise realism due to neglect of image post-processing and uncontrolled attack direction. In this paper, we propose RetouchUAA, an unconstrained attack that exploits a real-life perturbation: image retouching styles, highlighting its potential threat to DNNs. Compared to existing attacks, RetouchUAA offers several notable advantages. Firstly, RetouchUAA excels in generating interpretable and realistic perturbations through two key designs: the image retouching attack framework and the retouching style guidance module. The former custom-designed human-interpretability retouching framework for adversarial attack by linearizing images while modelling the local processing and retouching decision-making in human retouching behaviour, provides an explicit and reasonable pipeline for understanding the robustness of DNNs against retouching. The latter guides the adversarial image towards standard retouching styles, thereby ensuring its realism. Secondly, attributed to the design of the retouching decision regularization and the persistent attack strategy, RetouchUAA also exhibits outstanding attack capability and defense robustness, posing a heavy threat to DNNs. Experiments on ImageNet and Place365 reveal that RetouchUAA achieves nearly 100\% white-box attack success against three DNNs, while achieving a better trade-off between image naturalness, transferability and defense robustness than baseline attacks

The strong coupling gXJ/ψϕg_{X J/\psi\phi} of X(4700)→J/ψϕX(4700) \to J/\psi \phi in the light-cone sum rules

We assign the scalar tetraquark and the D-wave tetraquark state for $X(4700)$ and calculate the width of the decay $X(4700)$ $\to J/\psi \phi$ within the framework of light-cone sum rules. The strong coupling $g_{X J/\psi \phi}$ is obtained by considering the technique of soft-meson approximation. We also investigate the mass and the decay constant of $X(4700)$ in the framework of SVZ sum rules. Our prediction for the mass is in agreement with the experimental measurement, and that for the decay width of $X(4700)$ $\to J/\psi \phi$ support the possibility that $X(4700)$ could be a scalar tetraquark state if $X(4700)$ $\to J/\psi \phi$ is the predominant decay channel, or a D-wave tetraquark state if $X(4700)$ $\to J/\psi \phi$ is not the predominant one and there exist other decays

Can ShortWarning Messages Reduce Speeding Behaviour? Insights from A/B Testing

Speeding increases the likelihood and severity of an accident, and is the top cause of traffic fatalities. As such, it is important to study interventions such as warning signs and messages that may be able to reduce such behaviour. The main objective of this work was to study the effects of sending short warning messages on speeding behaviour. The study design was an A/B test – drivers who were detected to have sped were randomly assigned into treatment versus control groups. The treatment groups were sent a short warning message, while the control group did not receive any message. There were two types of messages sent – Harsh and Soft. Driver speeds were monitored in the subsequent weeks after the warning was sent out, and the number of repeat offenders and speeds in each group was tracked. We found that drivers who received a warning were 1.3 times less likely to speed in the subsequent week, with the Harsh warning message being 1.6 times more effective than the Soft message. We also found that the effects of harsh messages generally persisted longer than soft messages

Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey

Modern language models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based code intelligence, such as automated bug repair, and test case generation. Despite their great potential, language models for code intelligence (LM4Code) are susceptible to potential pitfalls, which hinder realistic performance and further impact their reliability and applicability in real-world deployment. Such challenges drive the need for a comprehensive understanding - not just identifying these issues but delving into their possible implications and existing solutions to build more reliable language models tailored to code intelligence. Based on a well-defined systematic research approach, we conducted an extensive literature review to uncover the pitfalls inherent in LM4Code. Finally, 67 primary studies from top-tier venues have been identified. After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, implications, current solutions, and challenges of different pitfalls for LM4Code systems. We developed a comprehensive classification scheme that dissects pitfalls across four crucial aspects: data collection and labeling, system design and learning, performance evaluation, and deployment and maintenance. Through this study, we aim to provide a roadmap for researchers and practitioners, facilitating their understanding and utilization of LM4Code in reliable and trustworthy ways

De novo Assembly and Transcriptome Characterization of Opisthopappus (Asteraceae) for Population Differentiation and Adaption

Opisthopappus Shih (Asteraceae), an endangered genus endemic to the Taihang Mountains of China, is a high-value ornamental and medicinal plant consisting of two species, Opisthopappus longilobus shih and Opisthopappus taihangensis (Ling) Shih. However, the evolutionary relationships and the taxonomic characteristics between the two species remain unknown. In this study, high-throughput transcriptome sequencing was used to analyze the differential metabolic activity and gene expression and screened special molecular markers for exploring the genetic variation and species differentiation in Opisthopappus Shih. The results showed that 33,974 unigenes with an average size of 801 bp were obtained with optimization of de novo assembly. The comprehensive functional annotation based on Gene Ontology (GO), Cluster of Orthologous Group (COG) and Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG) revealed that these unigenes were mainly related to many physiological, metabolic, and molecular processes. Furthermore, the comparative transcriptome analysis indicated that 3,410 differentially expressed genes were mainly involved in lipid, carbohydrate and amino acid metabolism, xenobiotics biodegradation and metabolism as well as environment adaptation via KEGG. Such as the CYP710A, GST, HSP90A and so on, could be the potential candidate genes for further investigating the molecular mechanism of physiological variations between O. taihangensis and O. longilobus. In addition, the potential 71,804 high quality single nucleotide polymorphisms (SNPs) and 1,444 simple sequence repeats (SSRs) were estimated. Based on the predicted SNP, we have developed eight SNP markers for population genetic analysis in Opisthopappus Shih. A significantly high level of genetic differentiation between the populations of O. longilobus and O. taihangensis were found, and they were clearly grouped into two distinct genetic clusters. These results conformed to the record of Flora Reipublicae Popularis Sinicae (FRPS) and unsupported the taxonomic status in the Flora of China. The transcriptome analysis of Opisthopappus Shih can contribute to in-depth exploring of internal mechanisms in species variation and differentiation based on molecular evidence. With the rich and valuable data resources, the more novel structural, functional, and comparative genomic studies will provide comprehensive insights into the evolutionary relationships between O. taihangensis and O. longilobus

Geographical disparities in the impacts of heat on diabetes mortality and the protective role of greenness in Thailand: A nationwide case-crossover analysis.

Diabetes is a major public health problem globally, and heat exposure may be a potential risk factor for death among diabetes. This study examines the association between heat and diabetes mortality in different regions of Thailand and investigates whether heat effects are modified by regional greenness. Daily temperature and daily diabetes deaths data were obtained for 60 provinces of Thailand during 2000-2008. A case-crossover analysis was conducted to quantify the odds of heat-related death among diabetes. Meta-regression was then used to examine potential modification effects of regional greenness (as represented by the Normalized Difference Vegetation Index) on heat-related mortality. A strong association between heat and diabetes mortality was found in Thailand, with important regional variations. Nationally, the pooled odds ratio of diabetes mortality was 1.10 (95% confidence interval (CI): 1.06-1.14) for heat (90th percentile of temperature) and 1.20 (95% CI: 1.10-1.30) for extreme heat (99th percentile of temperature) compared with the minimum mortality temperature, across lag 0-1 days. Central and northeast Thailand were the most vulnerable regions. Regional greenness modified the effects of heat, with lower mortality impacts in areas of higher levels of greenness. In conclusion, heat exposure increases mortality risk in diabetes, with large geographical variations in risk suggesting the need for region-specific public health strategies. Increasing greenness levels may help to reduce the burden of heat on diabetes in Thailand against the backdrop of a warming climate

Aberrant Expression of Proteins Involved in Signal Transduction and DNA Repair Pathways in Lung Cancer and Their Association with Clinical Parameters

Because cell signaling and cell metabolic pathways are executed through proteins, protein signatures in primary tumors are useful for identifying key nodes in signaling networks whose alteration is associated with malignancy and/or clinical outcomes. This study aimed to determine protein signatures in primary lung cancer tissues.We analyzed 126 proteins and/or protein phosphorylation sites in case-matched normal and tumor samples from 101 lung cancer patients with reverse-phase protein array (RPPA) assay. The results showed that 18 molecules were significantly different (p<0.05) by at least 30% between normal and tumor tissues. Most of those molecules play roles in cell proliferation, DNA repair, signal transduction and lipid metabolism, or function as cell surface/matrix proteins. We also validated RPPA results by Western blot and/or immunohistochemical analyses for some of those molecules. Statistical analyses showed that Ku80 levels were significantly higher in tumors of nonsmokers than in those of smokers. Cyclin B1 levels were significantly overexpressed in poorly differentiated tumors while Cox2 levels were significantly overexpressed in neuroendocrinal tumors. A high level of Stat5 is associated with favorable survival outcome for patients treated with surgery.Our results revealed that some molecules involved in DNA damage/repair, signal transductions, lipid metabolism, and cell proliferation were drastically aberrant in lung cancer tissues, and Stat5 may serve a molecular marker for prognosis of lung cancers

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe