White Matter Integrity And Age Related Differences In Reaction Time Components

Reduced speed in information processing is a well-documented phenomenon associated with advanced aging. Age-related deterioration in white matter integrity might play a role in age-related increase in reaction time (RT). However, the association between microstructural differences in particular white matter regions or tracts with RT is unclear. Decomposing RT into parts might be a better way to understand the relationship due to multiple processes involved in RT. In a lifespan sample of 90 healthy normotensive participants, this study examined the association between RT components derived from the Ratcliff diffusion model with age related difference in DTI indices of a wide variety of white matter tracts in both normal-appearing and whole white matter. The results revealed that advanced age was associated with lower drift rate, greater response conservativeness and longer non-decision time. Age-related reduction in FA and increase in MD was observed in most examined white matter tracts in both normal-appearing and whole white matter. Even in healthy normotensive adults, WMH burden could account for part of variance between age and DTI indices. Greater age-related difference in white matter integrity was observed in normotensive men than in normotensive women. Increased axial diffusivity of the superior corona radiata in normal appearing white matter was associated with longer non-decision time. However, there was no association between age-related differences in DTI indices of examined white matter tracts and both drift rate and response conservativeness in healthy normotensive participants

Smart supply chain management in Industry 4.0

The emerging information and communication technologies (ICT) related to Industry 4.0 play a critical role to enhance supply chain performance. Employing the smart technologies has led to so-called smart supply chains. Understanding how Industry 4.0 and related ICT affect smart supply chains and how smart supply chains evolve with the support of the advanced technologies are vital to practical and academic communities. Existing review works on smart supply chains with ICT mainly rely on the academic literature alone. This paper presents an integrated approach to explore the effects of Industry 4.0 and related ICT on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well

Volume of white matter hyperintensities in healthy adults: Contribution of age, vascular risk factors, and inflammation-related genetic variants

AbstractAging is associated with appearance of white matter hyperintensities (WMH) on MRI scans. Vascular risk and inflammation, which increase with age, may contribute to white matter deterioration and proliferation of WMH. We investigated whether circulating biomarkers and genetic variants associated with elevated vascular risk and inflammation are associated with WMH volume in healthy adults (144 volunteers, 44–77years of age). We examined association of WMH volume with age, sex, hypertension, circulating levels of total plasma homocysteine (tHcy), cholesterol (low-density lipoprotein), and C-reactive protein (CRP), and four polymorphisms related to vascular risk and inflammation: Apolipoprotein ε (ApoE ε2,3,4), Angiotensin-Converting Enzyme insertion/deletion (ACE I/D), methylenetetrahydrofolate reductase (MTHFR) C677T, C-reactive protein (CRP)-286C>A>T, and interleukin-1β (IL-1β) C-511T. We found that larger WMH volume was associated with advanced age, hypertension, and elevated levels of homocysteine and CRP but not with low-density lipoprotein levels. Homozygotes for IL-1β-511T allele and carriers of CRP-286T allele that are associated with increased inflammatory response had larger WMH than the other allelic combinations. Carriers of the APOE ε2 allele had larger frontal WMH than ε3 homozygotes and ε4 carriers did. Thus, in healthy adults, who are free of neurological and vascular disease, genetic variants that promote inflammation and elevated levels of vascular risk biomarkers can contribute to brain abnormalities. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Unsupervised Behavior Extraction via Random Intent Priors

Reward-free data is abundant and contains rich prior knowledge of human behaviors, but it is not well exploited by offline reinforcement learning (RL) algorithms. In this paper, we propose UBER, an unsupervised approach to extract useful behaviors from offline reward-free datasets via diversified rewards. UBER assigns different pseudo-rewards sampled from a given prior distribution to different agents to extract a diverse set of behaviors, and reuse them as candidate policies to facilitate the learning of new tasks. Perhaps surprisingly, we show that rewards generated from random neural networks are sufficient to extract diverse and useful behaviors, some even close to expert ones. We provide both empirical and theoretical evidence to justify the use of random priors for the reward function. Experiments on multiple benchmarks showcase UBER's ability to learn effective and diverse behavior sets that enhance sample efficiency for online RL, outperforming existing baselines. By reducing reliance on human supervision, UBER broadens the applicability of RL to real-world scenarios with abundant reward-free data.Comment: Thirty-seventh Conference on Neural Information Processing System

RLTF: Reinforcement Learning from Unit Test Feedback

The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, these RL methods have only used offline frameworks, limiting their exploration of new sample spaces. Additionally, current approaches that utilize unit test signals are rather simple, not accounting for specific error locations within the code. To address these issues, we proposed RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code can be found at: https://github.com/Zyq-scut/RLTF

Identification of new antibacterial targets in RNA polymerase of Mycobacterium tuberculosis by detecting positive selection sites

Bacterial RNA polymerase (RNAP) is an effective target for antibacterial treatment. In order to search new potential targets in RNAP of Mycobacterium, we detected adaptive selections of RNAP related genes in 13 strains of Mycobacterium by phylogenetic analysis. We first collected sequences of 17 genes including rpoA, rpoB, rpoC, rpoZ, and sigma factor A-M. Then maximum likelihood trees were constructed, followed by positive selection detection. We found that sigG shows positive selection along the clade (M. tuberculosis, M. bovis), suggesting its important evolutionary role and its potential to be a new antibacterial target. Moreover, the regions near 933Cys and 935His on the rpoB subunit of M. tuberculosis showed significant positive selection, which could also be a new attractive target for anti-tuberculosis drugs