ScissionLite: accelerating distributed deep learning with lightweight data compression for IIoT

Funding: This work was supported in part by the Electronics and Telecommunications Research Institute through the Korean government under Grant 23zs1300 (Research on High Performance Computing Technology to overcome limitations of AI processing) and in part by the Korea Institute for Advancement of Technology (KIAT) through the Korea Government (MOTIE) under Grant P0017011 (HRD Program for Industrial Innovation). Paper no. TII-23-4829.Industrial Internet of Things (IIoT) applications can greatly benefit from leveraging edge computing. For instance, applications relying on deep neural network (DNN) models can be sliced and distributed across IIoT devices and the network edge to reduce inference latency. However, low network performance between IIoT devices and the edge often becomes a bottleneck. In this study, we propose ScissionLite, a holistic framework designed to accelerate distributed DNN inference using lightweight data compression. Our compression method features a novel lightweight down/upsampling network tailored for performance-limited IIoT devices, which is inserted at the slicing point of a DNN model to reduce outbound network traffic without causing a significant drop in accuracy. In addition, we have developed a benchmarking tool to accurately identify the optimal slicing point of the DNN for the best inference latency. ScissionLite improves inference latency by up to 15.7× with minimal accuracy degradation.Peer reviewe

Modifications of T-Scores by Quantitative Ultrasonography for the Diagnosis of Osteoporosis in Koreans

To identify a proper T-score threshold for the diagnosis of osteoporosis in Koreans using quantitative ultrasonography (QUS), normative data from 240 females and 238 males (ages 20-29 yr) were newly generated. Then, the osteoporosis prevalence estimate for men and women over 50 yr of age was analyzed using previous World Health Organization (WHO) methods and heel QUS. T-scores were calculated from the normative data. There were definite negative correlations between age and all of the QUS parameters, such as speed of sound (SOS), broadband ultrasound attenuation (BUA), and estimated heel bone mineral density (BMD) (p<0.0001). After applying the recently determined prevalence of incident vertebral fracture in Koreans over 50 yr of age (11.6% and 9.1%, female vs male, respectively) to the diagnosis of osteoporosis by T-scores from heel BMD as measured by QUS, it was revealed that applicable T-scores for women and men were -2.25 and -1.85, respectively. These data suggest that simply using a T-score of -2.5, the classical WHO threshold for osteoporosis, underestimates the true prevalence when using peripheral QUS. Further prospective study of the power of QUS in predicting the absolute risk of fracture is needed

Cutoff Values of Surrogate Measures of Insulin Resistance for Metabolic Syndrome in Korean Non-diabetic Adults

We investigated the cutoff values of surrogate of insulin resistance for diagnosing metabolic syndrome in Korean adults. The data from 976 non-diabetic individuals (484 men and 492 women) aged 30-79 yr were analyzed. We determined the odds ratios for the prevalence of metabolic syndrome according to the quartiles of fasting insulin, homeostasis model for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) as independent variables, while adjusting for age, sex, and body mass index. The cutoff values of fasting insulin, HOMA-IR, and QUICKI were estimated by the areas under the receiver-operating characteristic (ROC) curves. The cutoff points for defining insulin resistance are a fasting insulin level of 12.94 µU/mL, HOMA-IR=3.04 as the 75th percentile value, and QUICKI=0.32 as the 25th percentile value. Compared with the lowest quartile, the adjusted odds ratios for the prevalence of metabolic syndrome in the highest quartiles of fasting insulin, HOMA-IR, and QUICKI were 1.95 (1.26-3.01), 2.27 (1.45-3.56), and 2.27 (1.45-3.56), respectively. The respective cutoff values for fasting serum insulin, HOMA-IR, and QUICKI by ROC analysis were 10.57 µU/mL (sensitivity 58.5%, specificity 66.8%), 2.34 (sensitivity 62.8%, specificity 65.7%), and 0.33 (sensitivity 61.2%, specificity 66.8%). Fasting insulin, HOMA-IR, and QUICKI can be used as surrogate measures of insulin resistance in Korean non-diabetic adults

Rice Bran Fermented with Kimchi-Derived Lactic Acid Bacteria Prevents Metabolic Complications in Mice on a High-Fat and -Cholesterol Diet

This aim of this study was to investigate the potential beneficial effects of rice bran powder, fermented by Weissella koreensis DB1 isolated from kimchi, to protect against obesity and dyslipidemia induced by a high-fat and high-cholesterol diet, in a mouse model. Male mice were fed a modified AIN-93M diet containing high fat/high-cholesterol (HFCD), or same diet supplemented with non-fermented rice bran powder (HFCD-RB) or fermented rice bran powder (HFCD-FRB) for 10 weeks. In the HFCD-FRB group, body weight, liver and white fat pads weights, triglyceride (TG), total cholesterol (TC), non-high-density lipopreotein cholesterol (non-HDL-C), insulin, glucose and leptine levels in serum, TG levels and the ratio of fat droplets in the liver, TG levels and fat cell size in adipose tissue were decreased, and (high-density lipopreotein cholesterol) HDL-C and adiponectin levels in serum were increased, compared with the HFCD group. The HFCD-FRB group had significantly lower CCAAT-enhancer-binding potein α (C/EBPα), sterol regulatory element-binding transcription protein-1c (SREBP-1c), fatty acid synthase (FAS), and acetyl CoA carboxylase (ACC) gene expression when compared to the HFCD group. The anti-obesity and hypolipidemic effects were marginally greater in the HFCD-FRB group than in the HFCD-RB group. These results suggest that fermented rice bran powder by Weissella koreensis DB1 may have potential beneficial effects on the obesity-related abnormalities and the dysfunction of lipid metabolism

Laxative and antioxidant effects of ramie (Boehmeria nivea L.) leaf extract in experimental constipated rats

Abstract Ramie leaf (Boehmeria nivea L.) is rich in cellulose, polyphenol compounds, vitamin C, and minerals. The leaves of this plant, which are used for medicinal purposes, have long been reported to have anti‐inflammatory, antioxidant, anticolitis, and antidiabetic effects. We investigated the protective effects of ramie leaf ethanol extract (RLE) against loperamide‐induced constipation and oxidative stress in rats. Male Sprague‐Dawley rats were administered 200 or 400 mg/kg body weight of RLE (RLEL and RLEH groups) by gavage, while normal (NOR) and control (CON) rats received saline. Loperamide (4.0 mg/kg, twice per day) was injected subcutaneously to induce constipation in RLEL, RLEH, and CON groups. Total fecal number, wet weight, and water content decreased, while the total number of loperamide‐induced fecal pellets in the distal colon increased with administration of RLE in a dose‐dependent manner. Gastrointestinal transit time was more greatly reduced in RLE‐treated groups than in the CON group. Serum total cholesterol (TC) level, as well as alanine aminotransferase (ALT) and alkaline phosphatase (ALP) activity, was significantly lower in both RLEL and RLEH groups compared with the CON group. Intestinal mucosa malondialdehyde (MDA) and hydrogen peroxide (H2O2) production decreased significantly in a dose‐dependent manner in the RLE‐treated groups. Loperamide decreased the antioxidant enzyme activity, including that of superoxide dismutase (SOD) and glutathione peroxidase (GSH‐Px), while RLE administration increased the antioxidant activity. These results suggest that RLE exerts potent laxative and antioxidant effects in model rats with loperamide‐induced constipation

Novel Mutation in Related to Brachydactyly Type E2 Initially Confused with Unclassical Pseudopseudohypoparathyroidism

BackgroundAutosomal-dominant brachydactyly type E is a congenital abnormality characterized by small hands and feet, which is a consequence of shortened metacarpals and metatarsals. We recently encountered a young gentleman exhibiting shortening of 4th and 5th fingers and toes. Initially, we suspected him having pseudopseudohypoparathyroidism (PPHP) because of normal biochemical parameters, including electrolyte, Ca, P, and parathyroid hormone (PTH) levels; however, his mother and maternal grandmother had the same conditions in their hands and feet. Furthermore, his mother showed normal biochemical parameters. To the best of our knowledge, PPHP is inherited via a mutated paternal allele, owing to the paternal imprinting of GNAS (guanine nucleotide binding protein, alpha stimulating) in the renal proximal tubule. Therefore, we decided to further analyze the genetic background in this family.MethodsWhole exome sequencing was performed using genomic DNA from the affected mother, son, and the unaffected father as a negative control.ResultsWe selected the intersection between 45,490 variants from the mother and 45,646 variants from the son and excluded 27,512 overlapping variants identified from the father. By excluding homogenous and compound heterozygous variants and removing all previously reported variants, 147 variants were identified to be shared by the mother and son. Variants that had least proximities among species were excluded and finally 23 variants remained.ConclusionAmong them, we identified a defect in parathyroid hormone like hormone (PTHLH), encoding the PTH-related protein, to be disease-causative. Herein, we report a family affected with brachydactyly type E2 caused by a novel PTHLH mutation, which was confused with PPHP with unclassical genetic penetrance

Learning-Enabled Network-Control Co-Design for Energy-Efficient Industrial Internet of Things

In the Industrial Internet of Things (IIoT), energy efficiency is critical for effective management of physical systems. To achieve stable control of IIoT with minimal energy consumption, it is essential to co-design the controller and the wireless network. In this paper, we present a novel reinforcement learning (RL) approach called the Learning-enabled Self-triggered Wireless Networked-Control System (LS-WNCS). LS-WNCS learns complex interdependence between control and network systems, generating near-optimal control commands and sampling periods simultaneously to minimize energy consumption and maximize control performance. Compared with conventional RL algorithms, LS-WNCS reduces network energy consumption by up to 66% while maintaining a high level of control performance. IEEEFALS