Estimating Optimal Depth of VGG Net with Tree-Structured Parzen Estimators

Deep convolutional neural networks (CNNs) have shown astonishingperformances in variety of fields. However, different architecturesof the networks are required for different datasets, and findingright architecture for given data has been a topic of great interest incomputer vision communities. One of the most important factors ofthe CNNs architecture is the depth of the networks, which plays asignificant role in avoiding over-fitting. Grid Search is widely usedfor estimating the depth, but it requires huge computation time. Motivatedby this, a method for finding an optimal architecture depth isintroduced, which is based on a hyper-parameter optimizer calledTree-Structured Parzen Estimators (TPE). In this work, we showthat the TPE is capable of estimating the CNNs architecture depthwith an accuracy of 83.33% with CIFAR-10 dataset and 60.00%with CIFAR-100 dataset while it reduces the computation time bymore 70% compared to the Grid Search

SwiFT: Swin 4D fMRI Transformer

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI.Comment: NeurIPS 202

Higher Plasma Stromal Cell-Derived Factor 1 Is Associated with Lower Risk for Sarcopenia in Older Asian Adults

Background Despite the protective effects of stromal cell-derived factor 1 (SDF-1) in stimulating muscle regeneration shown in experimental research, there is a lack of clinical studies linking circulating SDF-1 concentrations with muscle phenotypes. In order to elucidate the role of SDF-1 as a potential biomarker reflecting human muscle health, we investigated the association of plasma SDF-1 levels with sarcopenia in older adults. Methods This cross-sectional study included 97 community-dwelling participants who underwent a comprehensive geriatric assessment at a tertiary hospital in South Korea. Sarcopenia was defined by specific cutoff values applicable to the Asian population, whereas plasma SDF-1 levels were determined using an enzyme immunoassay. Results After accounting for sex, age, and body mass index, participants with sarcopenia and low muscle mass exhibited plasma SDF-1 levels that were 21.8% and 18.3% lower than those without these conditions, respectively (P=0.008 and P=0.009, respectively). Consistently, higher plasma SDF-1 levels exhibited a significant correlation with higher skeletal muscle mass index (SMI) and gait speed (both P=0.043), and the risk of sarcopenia and low muscle mass decreased by 58% and 55% per standard deviation increase in plasma SDF-1 levels, respectively (P=0.045 and P=0.030, respectively). Furthermore, participants in the highest SDF-1 tertile exhibited significantly higher SMI compared to those in the lowest tertile (P=0.012). Conclusion These findings clinically corroborate earlier experimental discoveries highlighting the muscle anabolic effects of SDF-1 and support the potential role of circulating SDF-1 as a biomarker reflecting human muscle health in older adults

Comparative analysis of pepper and tomato reveals euchromatin expansion of pepper genome caused by differential accumulation of Ty3/Gypsy-like elements

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract Background Among the Solanaceae plants, the pepper genome is three times larger than that of tomato. Although the gene repertoire and gene order of both species are well conserved, the cause of the genome-size difference is not known. To determine the causes for the expansion of pepper euchromatic regions, we compared the pepper genome to that of tomato. Results For sequence-level analysis, we generated 35.6 Mb of pepper genomic sequences from euchromatin enriched 1,245 pepper BAC clones. The comparative analysis of orthologous gene-rich regions between both species revealed insertion of transposons exclusively in the pepper sequences, maintaining the gene order and content. The most common type of the transposon found was the LTR retrotransposon. Phylogenetic comparison of the LTR retrotransposons revealed that two groups of Ty3/Gypsy-like elements (Tat and Athila) were overly accumulated in the pepper genome. The FISH analysis of the pepper Tat elements showed a random distribution in heterochromatic and euchromatic regions, whereas the tomato Tat elements showed heterochromatin-preferential accumulation. Conclusions Compared to tomato pepper euchromatin doubled its size by differential accumulation of a specific group of Ty3/Gypsy-like elements. Our results could provide an insight on the mechanism of genome evolution in the Solanaceae family

A unified framework for transparent parallelism and fault-tolerance in distributed systems

Today, many distributed systems are deployed in high-performance computing environments such as a multi-core architecture or a managed network like a data center. As the new computing architectures require more parallelism to improve performance and responsiveness, implementing distributed applications that work consistently in parallel architectures without causing any deadlock or data race issues have become a challenging task. Even more, data center applications must handle fault-tolerance as well because random or correlated crash-restart failures can happen in data centers. Many approaches to solve these issues have been proposed independently to make data center applications to be concurrent, fault-tolerant, or both. Popular applications like graph computing systems or non-relational database systems have their own mechanism to handle concurrency and failures. There are even more generic frameworks that provide both parallelism and fault tolerance in data computing frameworks, message-passing interfaces, and software transactional memory systems. However, making a data center application that works in these generic frameworks may require major restructuring or learning a new paradigm. In this dissertation, we present a solution that provides parallelism, and another solutions that provides fault-tolerance, and both in an event-driven system framework transparently. First, we present InContext, a concurrent event execution model that runs events in parallel by associating access behaviors with the shared variables. Second, we present Ken, an uncoordinated rollback recovery protocol for event-driven systems that can mask crash-restart failures and guarantee composable reliability. We also present MaceKen, integrated with Mace frameworks, that transparently provides crash-restart fault-tolerance for legacy Mace applications. Finally, we propose MultiKen, a combined framework for parallelism and fault-tolerance in event-driven systems

The Effects of Exogenous Lactate Administration on the IGF1/Akt/mTOR Pathway in Rat Skeletal Muscle

We investigated the effects of oral lactate administration on protein synthesis and degradation factors in rats over 2 h after intake. Seven-week-old male Sprague–Dawley rats were randomly divided into four groups (n = 8/group); their blood plasma levels of lactate, glucose, insulin, and insulin-like growth factor 1 (IGF1) were examined following sacrifice at 0, 30, 60, or 120 min after sodium lactate (2 g/kg) administration. We measured the mRNA expression levels of protein synthesis-related genes (IGF receptor, protein kinase B (Akt), mammalian target of rapamycin (mTOR)) or degradation-related genes (muscle RING-finger protein-1 (MuRF1), atrogin-1) and analyzed the protein expression and phosphorylation (activation) of Akt and mTOR. Post-administration, the plasma lactate concentration increased to 3.2 mmol/L after 60 min. Plasma glucose remained unchanged throughout, while insulin and IGF1 levels decreased after 30 min. The mRNA levels of IGF receptor and mTOR peaked after 60 min, and Akt expression was significantly upregulated from 30 to 120 min. However, MuRF1 and atrogin-1 expression levels were unaffected. Akt protein phosphorylation did not change significantly, whereas mTOR phosphorylation significantly increased after 30 min. Thus, lactate administration increased the mRNA and protein expression of protein-synthesis factors, suggesting that it can potentially promote skeletal muscle synthesis