bluuebunny/crossref_metadata_2025 huggingface dataset:
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
Authors: Zhang Yongan , Banta Anton , Fu Yonggan , John Mathews M. , Post Allison , Razavi Mehdi , Cavallaro Joseph , Aazhang Behnaam , Lin Yingyan
Published: March 2022

There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable
R
eal-
T
ime and high-quality
R
econstruction of E
C
G signals from E
G
M signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.
GaitTracker: 3D Skeletal Tracking for Gait Analysis Based on Inertial Measurement Units
Authors: Xie Lei , Yang Peicheng , Wang Chuyu , Gu Tao , Duan Gaolei , Lu Xinran , Lu Sanglu
Published: March 2022

Gait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, which implies that the patients cannot receive adequate gait assessment anytime and anywhere. In this article, we propose GaitTracker, a novel system to remotely and continuously perform gait monitoring and analysis by three-dimensional (3D) skeletal tracking in a wearable approach. Specifically, this system consists of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. According to the measurements from these IMUs, we can obtain the motion signals of lower limbs during gait rehabilitation. By adaptively synchronizing coordinate systems of different IMUs and building the geometric model of lower limbs, the exact gait movements can be reconstructed, and gait parameters can be extracted without any prior knowledge. GaitTracker offers three key features: (1)
a unified 3D skeletal model
to depict the precise gait movement and parameters in 3D space, (2)
a coordinate system synchronization scheme
to perform space synchronization over all the IMU sensors, and (3)
an automatic estimation method
for the user-specific geometric parameters. In this way, GaitTracker is able to accurately perform 3D skeletal tracking of lower limbs for gait analysis, such as evaluating the gait symmetry and the gait parameters including the swing/stance time. We implemented GaitTracker and evaluated its performance in real applications. The experimental results show that, the average error for skeleton angle estimation, joint displacement estimation, and gait parameter estimation are 3∘, 2.3%, and 3%, respectively, outperforming the state of the art.
A Deep Multi-level Attentive Network for Multimodal Sentiment Analysis
Authors: Yadav Ashima , Vishwakarma Dinesh Kumar
Published: March 2022

Multimodal sentiment analysis has attracted increasing attention with broad application prospects. Most of the existing methods have focused on a single modality, which fails to handle social media data due to its multiple modalities. Moreover, in multimodal learning, most of the works have focused on simply combining the two modalities without exploring the complicated correlations between them. This resulted in dissatisfying performance for multimodal sentiment classification. Motivated by the status quo, we propose a Deep Multi-level Attentive network (DMLANet), which exploits the correlation between image and text modalities to improve multimodal learning. Specifically, we generate the bi-attentive visual map along the spatial and channel dimensions to magnify Convolutional neural network representation power. Then, we model the correlation between the image regions and semantics of the word by extracting the textual features related to the bi-attentive visual features by applying semantic attention. Finally, self-attention is employed to fetch the sentiment-rich multimodal features for the classification automatically. We conduct extensive evaluations on four real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty Images, which verify our method's superiority.
Performance and Power Prediction for Concurrent Execution on GPUs
Authors: Moolchandani Diksha , Kumar Anshul , Sarangi Smruti R.
Published: March 2022

The unprecedented growth of edge computing and 5G has led to an increased offloading of mobile applications to cloud servers or edge cloudlets.

1

The most prominent workloads comprise computer vision applications. Conventional wisdom suggests that computer vision workloads perform significantly well on SIMD/SIMT architectures such as GPUs owing to the dominance of linear algebra kernels in their composition. In this work, we debunk this popular belief by performing a lot of experiments with the concurrent execution of these workloads, which is the most popular pattern in which these workloads are executed on cloud servers. We show that the performance of these applications on GPUs does not scale well with an increase in the number of concurrent applications primarily because of contention at the shared resources and lack of efficient virtualization techniques for GPUs. Hence, there is a need to accurately predict the performance and power of such ensemble workloads on a GPU. Sadly, most of the prior work in the area of performance/power prediction is for only a single application. To the best of our knowledge, we propose the first machine learning-based predictor to predict the performance and power of an ensemble of applications on a GPU. In this article, we show that by using the execution statistics of stand-alone workloads and the fairness of execution when these workloads are executed with three representative microbenchmarks, we can get a reasonably accurate prediction. This is the first such work in the direction of performance and power prediction for concurrent applications that does not rely on the features extracted from concurrent executions or GPU profiling data. Our predictors achieve an accuracy of 91% and 96% in estimating the performance and power of executing two applications concurrently, respectively. We also demonstrate a method to extend our models to four or five concurrently running applications on modern GPUs.
Model Transformation Testing and Debugging: A Survey
Authors: Troya Javier , Segura Sergio , Burgueño Lola , Wimmer Manuel
Published: March 2022

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state-of-the-art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This article presents a survey on testing and debugging model transformations based on the analysis of 140 papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorize the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.
Verification column
Authors: Lazić Ranko
Published: March 2022

Notwithstanding the rapid recent progress in settling the very high computational complexity of the reachability problem, VASS (also known as Petri nets) remain an intriguing model of concurrency with extensive significance for verification and synthesis. In this engaging article, Tony introduces us to asymptotic analysis of VASS, showing that many relevant problems have attractive complexities, and concluding with several inviting directions for further investigations.
Algorithmic analysis of termination and counter complexity in vector addition systems with states
Authors: Kučera Antonín
Published: March 2022

We give an overview of recently established results about the effective asymptotic analysis of termination and counter complexity of VASS computations. In contrast to "classical" problems such as reachability, boundedness, liveness, coverability, etc., that are EXPSPACE-hard, the decision problems related to VASS asymptotic analysis tend to have low complexity and many important variants are even decidable in polynomial time. We also present selected concepts and techniques used to achieve these results.
Conference reports
Authors: Pérez Jorge A.
Published: March 2022

In this new installment, Daniele Gorla ("Sapienza" University of Rome) reports on LICS 2021, the 36th ACM/IEEE Symposium on Logic in Computer Science,1 for which he served as general chair.
Voltammetric Determination of Total Phenolic Compounds in Cotton Leaves of Gossypium barbadense and Gossypium hirsutum Challenged with Spodoptera cosmioides (Walker)
Authors: Magarelli Gabriella , Dutra Juliana K. S. , da Silva Rafaela G. , Tripode Bruna M. D. , Hoffmann Lúcia V. , de Castro Clarissa S. P.
Published: March 2022

A voltammetric method for phenolic compounds determination detected differences betweenGossypium hirsutumandGossypium barbadense, injured or not by caterpillars.G. barbadenseseeds collected in Amazon were multiplied and compared to a commercial Brazilian variety (G. hirsutum). Plants were conducted in a net protected house for two assays where: i) floral buds were inoculated with boll weevil eggs and ii) leaves were infested withSpodoptera cosmioides(Walker) and leaf phenolic compounds determined by a voltammetric method based on the differential pulse technique and glassy carbon electrode. The proportion of emerging boll weevils was greater inG. hirsutum(0.24) than inG. barbadense(0.14).G. hirsutumnon infested older leaves presented a higher amount of total phenolics than the younger leaves. For the infested plants, total phenolic levels were similar in both developmental stages and to the not infested younger leaves. The most intense current peak in the voltammetric profile was at the oxidation potential of 0.5 V for all samples, and more intense forG. hirsutumolder leaves. Another less intense current peak at 0.8 V, found at all profiles, was great forG. barbadenseattacked by caterpillars. Compounds coincident with these peaks may be related to insect tolerance.
Protons in Catalytic Architectures: Near (NMR) and Far (Impedance)
Authors: Pitman Catherine L. , Miller Joel B. , DeSario Paul A. , Pietron Jeremy J. , Rolison Debra R.
Published: March 2022

High performance in the realm of electrochemical energy/catalysis/sensing requires WIRING! —the timely arrival and departure of the electrons, ions, and molecules necessary for reactivity. Architectural design of mesoscale porous nanostructures integrates such multifunctionality to ensure that timely electronic, ionic, and mass transport occurs to electrified reactive interfaces. We probe the nature of proton transport in TiO2 aerogels modified with Au nanoparticles (NPs) as a function of relative humidity using impedance spectroscopy for a long-range assessment and 1H NMR for an environmental assessment of shorter-range proton diffusion and spin–spin relaxation. We synthetically site the Au NPs either between the covalently bonded TiO2 NPs that comprise the oxide aerogel network or by supporting them on the preformed, calcined oxide aerogel; we prepare two Au weight loadings (1 and 4 wt%) using both geometric arrangements of Au∣∣TiO2. A high degree of intimacy between Au nanoparticles and the networked TiO₂ nanoparticles affects the nature of proton transport of the aerogel as a function of equilibration with gas-phase water, decreasing the resistance to proton transport along the oxide network. The presence of even a modest weight loading of Au NPs increases the ionicity of the catalytic platform over that of native, Au-free TiO₂ aerogel.
Boundary Investigation of High-Temperature Co-Electrolysis Towards Direct CO2 Electrolysis
Authors: Wolf Stephanie E. , Dittrich Lucy , Nohl Markus , Duyster Tobias , Vinke Izaak C. , Eichel Rüdiger-A. , (Bert) de Haart L. G. J.
Published: March 2022

In the temperature range of high temperature co-electrolysis of both steam and carbon dioxide, the reverse water-gas shift reaction (RWGS) takes place. Prior studies were conducted with a narrow gas composition range to investigate the role of RWGS during co-electrolysis. The results for steam electrolysis, CO2 electrolysis, and co-electrolysis caused different conclusions regarding the role of electrochemical CO2 and H2O conversion compared to RWGS during co-electrolysis. This work aims to resolve the role of CO2 conversion as part of RWGS in co-electrolysis. The boundary is characterized by AC and DC measurements over a broad gas composition range from CO2 electrolysis towards co-electrolysis with nearly 50%eq H2O. Especially, the electrochemical CO2 reduction and CO2 conversion in the RWGS are compared to clarify their role during co-electrolysis. The results revealed that gas composition determined the predominant reaction (H2O or CO2 reduction). The cell performance of co-electrolysis in the boundary region up to 5%eq H2O was similar to the performance of CO2 electrolysis. Up to 30%eq H2O, the performance increases with H2O concentration. Here, both CO2 and H2O electrolysis occur. Above 30%eq H2O, steam electrolysis and the RWGS reaction both dominate the co-electrolysis process.
Principal Component Analysis as a Tool for Electrochemical Characterization of Modified Electrodes: A Case Study
Authors: Sirley Reis Teixeira Wanderson , Kássio Leme Silva Martin , Grasseschi Daniel , Senna Carlos Alberto , de Oliveira André Guimarães , Gruber Jonas , Cesarino Ivana , Salles Maiara Oliveira
Published: March 2022

Characterizing electrodes surface is a mandatory step in developing any electrochemical methodology, especially when electrode modification is performed. One or two electrochemical techniques are often used, and their results are evaluated separately, leading to the loss of valuable information. Principal Component Analysis (PCA) is presented as an appealing strategy to optimize the electrochemical characterization of the modified-electrode’s surface. The developed strategy was applied for the characterization of glassy carbon (GC) electrode modified with reduced graphene oxide (GC/RGO), RGO with gold nanoparticles (GC/RGO/AuNP), and RGO with copper nanoparticles (GC/RGO/CuNP). The electrochemical characterization techniques used were differential pulse voltammetry (DPV), linear scan voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). The PCA showed the data from the four electrodes separately, and it was possible to determine that the GC/RGO/AuNP electrode had the most efficient surface for charge transfer. By scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) experiments, the metal:RGO ratio was determined, and by normalizing the results with this ratio, GC/RGO/CuNP and GC/RGO/AuNP presented similar responses. With the proposed methodology, it was possible to evaluate simultaneously and straightforwardly all data, making this propose an innovative method for a robust electrochemical characterization based on usual electrochemical techniques.
Early secretory pathway-resident Zn transporter proteins contribute to cellular sphingolipid metabolism through activation of sphingomyelin phosphodiesterase 1
Authors: Ueda Sachiko , Manabe Yuki , Kubo Naoya , Morino Naho , Yuasa Hana , Shiotsu Miku , Tsuji Tokuji , Sugawara Tatsuya , Kambe Taiho
Published: March 2022

Sphingomyelin phosphodiesterase 1 (SMPD1) converts sphingomyelin into ceramide and phosphocholine; hence, loss of SMPD1 function causes abnormal accumulation of sphingomyelin in lysosomes, which results in the lipid-storage disorder Niemann–Pick disease (types A and B). SMPD1 activity is dependent on zinc, which is coordinated at the active site of the enzyme, and although SMPD1 has been suggested to acquire zinc at the sites where the enzyme is localized, precisely how SMPD1 acquires zinc remains to be clarified. Here, we addressed this using a gene-disruption/reexpression strategy. Our results revealed that Zn transporter 5 (ZNT5)-ZNT6 heterodimers and ZNT7 homodimers, which localize in the compartments of the early secretory pathway, play essential roles in SMPD1 activation. Both ZNT complexes contribute to cellular sphingolipid metabolism by activating SMPD1 because cells lacking the functions of the two complexes exhibited a reduced ceramide to sphingomyelin content ratio in terms of their dominant molecular species and an increase in the sphingomyelin content in terms of three minor species. Moreover, mutant cells contained multilamellar body-like structures, indicative of membrane stacking and accumulation, in the cytoplasm. These findings provide novel insights into the molecular mechanism underlying the activation of SMPD1, a key enzyme in sphingolipid metabolism.
Detection techniques for epitranscriptomic marks
Authors: Ofusa Ken , Chijimatsu Ryota , Ishii Hideshi
Published: March 2022

Similar to epigenetic DNA modification, RNA can be methylated and altered for stability and processing. RNA modifications, namely, epitranscriptomes, involve the following three functions: writing, erasing, and reading of marks. Methods for measurement and position detection are useful for the assessment of cellular function and human disease biomarkers. After pyrimidine 5-methylcytosine was reported for the first time a hundred years ago, numerous techniques have been developed for studying nucleotide modifications, including RNAs. Recent studies have focused on high-throughput and direct measurements for investigating the precise function of epitranscriptomes, including the characterization of severe acute respiratory syndrome coronavirus 2. The current study presents an overview of the development of detection techniques for epitranscriptomic marks and briefs about the recent progress in this field.
Effects of the number of sit-stand maneuver repetitions on baroreflex sensitivity and cardiovascular risk assessments
Authors: Mori Shoya , Tarumi Takashi , Kosaki Keisei , Matsui Masahiro , Yoshioka Masaki , Sugawara Jun , Kuro-O Makoto , Saito Chie , Yamagata Kunihiro , Maeda Seiji
Published: March 2022

Sit-stand maneuvers (SSMs) have increasingly been used for baroreflex sensitivity (BRS) measurement in physiological research, but it remains unknown as to how many SSMs need to be performed to measure BRS and assess its relationship with cardiovascular disease (CVD) risk. Therefore, this study aimed to determine 1) the effect of the number of SSM repetitions on BRS, and 2) the association between BRS and CVD risk factors. Data were collected from 174 individuals during 5 min of spontaneous rest and 5 min of repeated SSMs at 0.05 Hz (i.e., 15 cycles of 10-s sit and 10-s stand). During SSMs, BRS was calculated from the incremental cycles of 3, 6, 9, 12, and 15 SSMs using transfer function analysis of heart rate (HR) and systolic blood pressure (SBP). General CVD risk factors, carotid arterial stiffness, and cardiorespiratory fitness were measured. In result, HR and SBP increased during SSMs ( P < 0.05). The BRS remained at a similar level during the resting and SSM conditions, whereas the coherence function reached its peak after 3 cycles of SSMs. BRS with ≥6 cycles of SSMs was strongly correlated with age ( r = −0.721 to −0.740), carotid distensibility ( r = 0.625–0.629), and cardiorespiratory fitness ( r = 0.333–0.351) (all P < 0.001). Multiple regression analysis demonstrated that BRS with ≥6 cycles of SSMs explained >60% of the variance in CVD risk factors. Therefore, our findings suggest that repeated SSMs significantly strengthens the association between BRS and CVD risk factors. Particularly, BRS with ≥6 cycles of SSMs is strongly associated with CVD risk.
Sex differences in sympathetic neurovascular and neurohemodynamic relationships during cold pressor test
Authors: Coovadia Yasmine , Adler Tessa E. , Martin-Arrowsmith Patrick W. , Usselman Charlotte W.
Published: March 2022

Muscle sympathetic nerve activity (MSNA) affects vascular resistance differently in women and men. However, whether this sex difference persists during pronounced increases in MSNA remains unclear. Therefore, the purpose of this study was to examine sex differences in neurovascular transduction during cold pressor test (CPT)-mediated sympathoexcitation. Integrated peroneal MSNA (microneurography) was measured at rest and during a 3-min CPT in young healthy women ( n = 11) and men ( n = 10). Mean arterial pressure (MAP) was measured beat-by-beat (Finometer), and superficial femoral artery blood flow was measured using duplex ultrasound. Femoral vascular resistance (FVR) was quantified as MAP/femoral blood flow (mmHg/mL/min). Baseline MSNA was similar between women and men (14 ± 9 vs. 15 ± 9 bursts/100 heartbeat, respectively; P = 0.83), whereas MAP was lower (86 ± 7 vs. 92 ± 4 mmHg; P = 0.047), and FVR was greater in women than men (0.54 ± 0.16 vs. 0.36 ± 0.15 mmHg/mL/min; P = 0.02). CPT-induced increases in MSNA were similar between the sexes (19 ± 11 vs. 26 ± 14 bursts/100 heartbeat; P = 0.26) whereas increases in MAP (7 ± 3 vs. 10 ± 3 mmHg; P = 0.03) and FVR (3.2 ± 18.6 vs. 26.8 ± 12.8%; P < 0.01) were smaller in women than in men. Within men, CPT- induced increases in MSNA predicted increases in MAP ( R2 = 0.51, P = 0.02) and FVR ( R2 = 0.49, P = 0.02). However, MSNA did not predict MAP ( R2 = 0.11, P = 0.35) or FVR ( R2 = 0.07, P = 0.46) in women. Our findings demonstrate that men experience robust CPT-induced MAP responses that are driven by both neurovascular (MSNA-FVR) and neurohemodynamic (MSNA-MAP) coupling. These relationships were not observed in women, indicating that even during pronounced increases in sympathetic outflow, MSNA is not predictive of vascular nor blood pressure outcomes in young healthy women.
Roles of leptin in initiation of acquired growth hormone resistance and control of metabolism in rainbow trout
Authors: Gong Ningping , Lundin Jakob , Morgenroth Daniel , Sheridan Mark A. , Sandblom Erik , Björnsson Björn Thrandur
Published: March 2022

Catabolic conditions often induce concomitant changes in plasma leptin (Lep), growth hormone (GH), and insulin growth factor I (IGF-I) levels in teleost fish, but it is unclear whether these parts of the endocrine system are responding independently or functionally linked. In this study, fasted rainbow trout was used to study the effects of Lep on the GH-IGF-I system and metabolism. Fish were implanted intraperitoneally with recombinant rainbow trout Lep pellets and remained unfed. After 4 days, plasma GH levels were elevated in the Lep-treated fish in a dose-dependent manner; the expression of hepatic igf1 and plasma IGF-I levels were suppressed accordingly. In vitro Lep treatment reversed ovine GH (oGH)-stimulated expression of igf1 and igf2 in hepatocytes isolated from fasted fish, similar to the inhibitory effects of the MEK1/2 inhibitor U0126 treatment. However, Lep treatment alone had no effect on the expression of igfs or oGH-stimulated ghr2a expression in the hepatocytes. These results demonstrate an additive effect of Lep on suppression of IGF-I under catabolic conditions, indicating that Lep is likely involved in initiation of acquired GH resistance. Although the Lep-implant treatment had no effect on standard metabolic rate, it significantly suppressed gene expression of hepatic hydroxyacyl-CoA dehydrogenase, phosphoenolpyruvate carboxykinase, and glucose 6-phosphatase, which are key enzymes in lipid utilization and gluconeogenesis, in different patterns. Overall, this study indicates that the Lep increase in fasting salmonids is an important regulatory component for physiological adaptation during periods of food deprivation, involved in suppressing growth and hepatic metabolism to spare energy expenditure.
Mutation of spexin2 promotes feeding, somatic growth, adiposity, and insulin resistance in zebrafish
Authors: Zhao Tingting , Ye Zhifeng , Liu Yun , Lin Haoran , Li Shuisheng , Zhang Yong
Published: March 2022

Spexin2 ( spx2) is a newly identified gene in vertebrates, but its biological functions remain unclear. In this study, we cloned the full-length cDNA of spx2 in zebrafish. The 288-bp open reading frame encodes a protein of 95 amino acids that contains a 14 amino acids mature peptide. Spx2 is highly expressed in brain and testis. Its expression was significantly downregulated in the hypothalamus after feeding treatment and 7 days of food deprivation. Using a zebrafish spx2−/− mutant line, we observed a greater amount of food intake and changes in mRNA levels of feeding factors. We found that, SPX2 acts as a satiety factor that inhibits food intake by downregulating the expression of agouti-related neuropeptide ( agrp). Moreover, spx2 mutant fish exhibited a larger body size, excessive lipid accumulation, and insulin resistance. Taken together, our results revealed that SPX2 functions as a satiety factor involved in energy metabolic regulation in zebrafish.
The effects of TMS over the anterior intraparietal area on anticipatory fingertip force scaling and the size-weight illusion
Authors: van Polanen Vonne , Buckingham Gavin , Davare Marco
Published: March 2022

Skilled object manipulation requires forming anticipatory motor plans according to the object’s properties. Here, we demonstrate the role of anterior intraparietal sulcus (aIPS) in anticipatory grip force scaling to object size, particularly during initial lifting experience. Interestingly, this role was not maintained after continued practice and was not related to perceptual judgments measured with the size-weight illusion.
Eye-hand coordination during online reach corrections is task dependent
Authors: de Brouwer Anouk J. , Spering Miriam
Published: March 2022

Eye movements support hand movements in many situations. Here, we used variations of a double-step task to investigate temporal coupling of corrective hand and eye movements in response to target displacements. Correction latency coupling depended on the visual and cognitive processing demands of the task. The hand started correcting before the eye, especially when the task required decoding a symbolic cue. These findings highlight the flexibility and task dependency of eye-hand coordination.
Effects of M currents on the persistent activity of pyramidal neurons in mouse primary auditory cortex
Authors: Ye Huan , Liu Zhen-Xu , He Ya-Jie , Wang Xin
Published: March 2022

Persistent activity (PA) in AI-L5 pyramidal neurons plays an essential role in acoustic information processing. We used in vitro whole cell patch-clamp recordings to investigate the effects of M currents on the PA in AI-L5 pyramidal neurons. Blocking M currents with XE991 facilitated PA by increasing the plateau potential and enhancing intrinsic excitability, causing the firing patterns of AI-L5 pyramidal neurons to become more bursting. These results provide new insight into our understanding of the cellular mechanisms that govern learning-induced auditory plasticity.
Neural representation in M1 and S1 cortex of bilateral hand actions during prehension
Authors: Gardner Esther P. , Putrino David F. , Chen (Van Daele) Jessie
Published: March 2022

Humans can manipulate small objects with the right or left hand but typically select the dominant hand to handle them. We trained monkeys to grasp and manipulate objects with either hand, while recording neural activity in primary motor (M1) and somatosensory (S1) cortex. Actions of both hands activate M1 neurons, but S1 neurons respond only to the contralateral hand. Bilateral sensitivity in M1 may aid skill transfer between hands after stroke or head injury.
Wild Bitter Melon Extract Abrogates Hypoxia-Induced Cell Death via the Regulation of Ferroptosis, ER Stress, and Apoptosis in Microglial BV2 Cells
Authors: Lin Chih-Hung , Wu Jiunn-Sheng , Hsieh Po-Chun , Chiu Valeria , Lan Chou-Chin , Kuo Chan-Yen
Published: March 2022

Microglial cells are well-known phagocytic cells that are resistant to the central nervous system (CNS) and play an important role in the maintenance of CNS homeostasis. Activated microglial cells induce neuroinflammation under hypoxia and typically cause neuronal damage in CNS diseases. In this study, we propose that wild bitter melon extract (WBM) has a protective effect on hypoxia-induced cell death via regulation of ferroptosis, ER stress, and apoptosis. The results demonstrated that hypoxia caused microglial BV-2 the accumulation of lipid ROS, ferroptosis, ER stress, and apoptosis. In this study, we investigated the pharmacological effects of WBM on BV-2 cells following hypoxia-induced cell death. The results indicated that WBM reversed hypoxia-downregulated antiferroptotic molecules Gpx4 and SLC7A11, as well as upregulated the ER stress markers CHOP and Bip. Moreover, WBM alleviated hypoxia-induced apoptosis via the regulation of cleaved-caspase 3, Bax, and Bcl-2. Our results suggest that WBM may be a good candidate for preventing CNS disorders in the future.
Quantitative Fit Test of a 3D Printed Frame Fitted Over a Surgical Mask: An Alternative Option to N95 Respirator
Authors: Kongkiatkamon Suchada , Wongkornchaowalit Norachai , Kiatthanakorn Valailuck , Tonphu Somkiat , Kunanusont Chaiyos
Published: March 2022

Background. COVID-19 has spread worldwide and caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to numerous dead cases. However, with the new COVID-19 outbreaks, there is a shortage of personal protective equipment (PPE) especially N95 masks worldwide including Thailand. This issue had placed the health professional in great need of an alternative mask. Aim. This study aimed to measure the fit factor of 3D printed frames by quantitative fit testing (QNFT) to find an alternative facemask by using a mask fitter together with 2 different kinds of the American Society for Testing and Materials (ASTM) level 1 surgical mask. Materials and Methods. Two commonly used surgical masks (Sultan Com-Fit Super Sensitive Ear Loop Mask or “White Mask Group,” not water-resistant, and Sultan Blue Com-Fit Super High Filtration Ear Loop Mask or “Blue Mask Group,” water-resistant) with and without 3D printed frame covering. The fit performance was measured by a quantitative fit test (QNFT) device (PortaCount, model 8048, TSI Incorporated, Minnesota, USA) accepted by the Occupational Safety and Health Administration (OSHA). The PortaCount device, which is based on a miniature continuous flow condensation nucleus counter (CNC), assesses the respiratory fit by comparing the concentration of ambient dust particles outside the surgical mask to the concentration that has leaked into the surgical mask. The ratio of these two concentrations (Cout/Cin) is called the fit factor. A fit factor of a 3D printed frame of at least 100 is required and considered as a pass level. Results. We found that the mask fitter improves the overall performance of surgical masks significantly. The improved performance is comparable to that of N95. Conclusion. The mask fitter improves the performance of surgical masks. The authors suggested that further study on frame material, shape, and expanded sample size would be beneficial to society.
Prediction on Ground Settlement Deformation and Influence of Urban Buildings in the Construction Process of Existing Tunnel Reconstruction
Authors: Liu Minghui , Yin Aiguo , Wan Cong
Published: March 2022

Surface deformation is one of the key issues in urban tunnel construction control, regardless of the construction method. The study on ground settlement deformation and urban buildings in the construction process of existing tunnel reconstruction is not many at present; moreover, rebuilding projects for older tunnels have more complicated planting conditions and limited construction methods. In this study, the construction method and process characteristics of an existing structure are analyzed. In addition, the ground settlement deformations and building displacements during construction are monitored. The results reveal that the method of the Peck formula for predicting the ground settlement deformation and urban buildings of existing tunnel reconstruction is feasible and effective based on fitting curve by monitoring data. A finite element model is established. The results show that the simulation values of the ground deformation are consistent with the measured values. The differences between the simulated and measured values of the maximum ground surface sedimentation are 4.9% and 0.72% for single-sided excavation and bilateral excavation, respectively. The maximum building settlement is -9.6 mm in the single-sided excavation, the deformation rates remain within 0.1 mm/d, and the maximum width change of the building crack occurs during the demolition of the existing tunnel concrete in the single-sided excavation (with a value of 0.45 mm); subsequently, the building crack exhibits a decreasing trend, indicating that the crack is stable. The results can provide a reference for construction control for existing tunnels.
Monitoring and Prediction Analysis of Settlement for the Substation on Soft Clay Foundation
Authors: Chen Qingwei , Yang Zhen , Xue Binglei , Wang Zhipeng , Xie Dan
Published: March 2022

The bearing capacity of soft clay foundations is low, which easily causes excessive cumulative and uneven settlement of the substation. This will seriously threaten the safe operation of equipment in the substation. Based on a 220‐kV substation project in a coastal soft clay ground, the settlement of the substation building is monitored on the spot, and the settlement law of soft clay ground is analyzed. Furthermore, a three‐dimensional finite element model is developed using finite element software named ZSOIL.PC. A numerical model for predicting the settlement of soft clay ground in a substation is proposed using the appropriate element type, material constitutive model, and calculation parameters. The rationality of the numerical method is validated by comparison with the measured data. After improving the soft clay ground with the cement‐soil deep mixing method, there are still certain degrees of cumulative and differential settlements in the substation distribution device building, but both are within the specification requirements. The settlement of the soft soil ground in the substation increases with time, but the growth rate gradually decreases and ultimately tends to stabilize after a certain period of time, ensuring the uniformity of foundation treatment is the key to controlling the differential settlement. The research results can provide a reference for the subsequent installation and construction of electrical equipment and the design of substation engineering in soft soil ground.
Sports Training Intensity Information Fusion Method Based on Kinect Sensor
Authors: Huo Yan
Published: March 2022

In order to improve the information fusion effect of sports training intensity, this paper analyzes the information fusion process of Kinect sensor. In order to prevent the simulation platform from exceeding its working space and ensure that the sports athletes obtain a more realistic sense of motion, the adaptive washout algorithm can change the adaptive parameters online in real time according to the input athlete’s acceleration and angular velocity and the current motion state of the simulation platform. Moreover, this paper uses Kinect as the input device and combines the human node model to identify the features of training intensity information. After constructing an intelligent system, the performance of the system of this paper is verified. The research results show that the sports training intensity information fusion method based on the Kinect sensor proposed in this paper has a good effect in sports training intensity information fusion.
Fermented Carrot Pulp Regulates the Dysfunction of Murine Intestinal Microbiota
Authors: YU Chenchen , Liu Ying , Xuemei Zhang , Ma Aijin , Jianxin Tan , Yiling Tian
Published: March 2022

It was the focus of attention that probiotic control drink was packed with prebiotic nutrients and lactic acid bacteria. So, this study is aimed at revealing that the fermented carrot pulp regulation and protection function to the intestinal microecological disorders usually induced by antibiotic treatment. First, we study on lactobacillus fermentation conditions and effects on the secondary metabolism of fermented carrot juice, get its phenolic acids up, and get its flavonoids down. Then, establishment of the dysbacteriosis mouse model was used to validate the fermented carrot pulp prevention and treatment of intestinal microbiota imbalance. After the antibiotic treatment, the mice showed impotence, laziness, slow movement, weight loss, thin feces, dull hair, and anal redness, while the mice in the control group were all normal in terms of the mental state, diet, weight, and bowl movement. Along with the treatment, the abnormal conditions of the mice in the model group and natural recovery group improved in different degrees, indicating that the fermentation treatment is of help to the intestinal microbiota recovery. The fermentation-treated group of mice recovered close to normal that the diarrhea disappeared, and the weight gain, mental state, and the feces became normal. The serum antioxidant (SOD, GSH, and MDA) levels of the mice were checked. The superoxide dismutase (SOD) levels and glutathione (GSH) levels in the ordinary fermentation-treated group and probiotic fermentation-treated group were significantly increased compare to the natural recovery group. The malondialdehyde (MDA) levels showed great differences between the fermentation-treated groups and the blank group. At last, the 16sRNA analysis revealed that the microbiota richness and diversity in probiotic fermentation (J) are much higher than those in the model group (H), ordinary fermentation group (I), and blank group (G). Groups J and I are of significantly higher antioxidant level than group H; however, only the glutathione (GSH) level in group J increased dramatically but not those in the other three groups. Antibiotic treatment-induced mouse intestinal microecological disorder reduce the microbiota richness and diversity. Prebiotics fermented carrot pulp treatment can help in the recovery from the microbiota richness and diversity level prior to the antibiotic treatment, which suggests it can regulate and protect the murine intestinal microbiome.
Fast Data Access through Nearest Location-Based Replica Placement
Authors: Shoaib Umar , Arshad Muhammad Junaid , Khattak Hasan Ali , Ezat Ullah Maryam , Almogren Ahmad , Ali Sikandar
Published: March 2022

Nowadays, millions of telecommunication devices, IoT sensors, and web services, especially social media sites, are producing big data every second. Such applications with massive data-generating capabilities need to access these data quickly. Among other approaches, cloud computing provides content delivery networks, which utilize data replication for better latency to such real-time applications. Fast processing, storage, and timely analysis of these data are the challenge for most of these future Internet applications. However, cloud computing is the ultimate storage and processing paradigm to resolve such issues and to deal with big data, with the same speed in which data are being produced. Moreover, cloud computing technology has been evolved with the tag line of “Everything as A Service” now, and for powering all these services, provision of data is a compulsory and significant task. To provide end user with easy and fast access to data, the cloud maintains backup and replicates the copies in multiple data centers. The geographical locations of these data centers where data are being placed have a profound impact on data access time. To deal with the challenge of effective data replication and to reduce data access time to a minimum, we propose a genetic algorithm (GA)-based technique to suggest and store data on nearly located data centers. The proposed algorithm improves the access time, and thus, the efficiency of cloud servers by providing quality of service (QoS) to end user.
A Boosting-Based Deep Distance Metric Learning Method
Authors: Li Zilong
Published: March 2022

By leveraging neural networks, deep distance metric learning has yielded impressive results in computer vision applications. However, the existing approaches mostly focus a single deep distance metric based on pairs or triplets of samples. It is difficult for them to handle heterogeneous data and avoid overfitting. This study proposes a boosting-based learning method of multiple deep distance metrics, which generates the final distance metric through iterative training of multiple weak distance metrics. Firstly, the distance of sample pairs was mapped by a convolution neural network (CNN) and evaluated by a piecewise linear function. Secondly, the evaluation function was added as a weak learner to the boosting algorithm to generate a strong learner. Each weak learner targets the difficult samples different from the samples of previous learners. Next, an alternating optimization method was employed to train the network and loss function. Finally, the effectiveness of our method was demonstrated in contrast to state of the arts on retrieving the images from the CUB-200-2011, Cars-196, and Stanford Online Products (SOP) datasets.
COVID-19 Mortality in Children: A Referral Center Experience from Iran (Mofid Children’s Hospital, Tehran, Iran)
Authors: Armin Shahnaz , Fahimzad Seyed Alireza , Rafiei Tabatabaei Sedigheh , Mansour Ghanaiee Roxana , Marhamati Noushin , Ahmadizadeh Seyyedeh Narjes , Behzad Azita , Hashemi Seyedeh Masumeh , Sadr Saeed , Rajabnejad Maryam , Jamee Mahnaz , Karimi Abdollah
Published: March 2022

Background. The novel coronavirus disease 2019 (COVID-19) started in Wuhan, China, in December 2019. It spread widely around the world and was described as a pandemic by the World Health Organization (WHO). The knowledge regarding the mortality rate and risk factors of COVID-19 among the pediatric population is lacking. In this regard, we aimed to report the clinical and laboratory characteristics of deceased pediatric patients with SARS-CoV-2 infection. Method. This cross-sectional study was conducted in Mofid Children’s Hospital, Tehran, Iran, from February 2020 to April 2021. Recorded documents of 59 pediatric patients (under 18 years old) assumed to have COVID-19 who had died in the COVID-19 ward and COVID-19 intensive care unit (ICU) were retrospectively evaluated. All statistical analyses were performed using SPSS software (v. 26.0, Chicago, IL). A

P

value of less than 0.05 was considered statistically significant. Results. From 711 COVID-19 definite and suspected patients, 59 children died. Of these deceased pediatric patients, 34 were boys (57.62%) and 25 were girls (42.37%), with a total mean age of 5.6 years. The median length of stay in the hospital was 10 days (range 1–215). 91.52% had underlying comorbidities of which neurological diseases accounted for the largest share. 54 patients were admitted to the ICU and 83.05% of them had intubation during their hospitalization. In addition, the most common reasons for death in our study were related to respiratory and multiorgan failure. Conclusion. According to our knowledge, we are the first team to report such a thorough study in the field of COVID-19 pediatric mortality in Iran. Mortality was observed in all age groups of children, especially in those with previous comorbidities, specifically neurological disease. Abnormally elevated tests of ESR, CRP, LDH, AST, and ALT as well as the presence of proteinuria and hematuria were found in more than 50% of patients in our investigations, and ICU admission between both definite and suspected groups had significant differences, so monitoring and considering these factors may help to control and reduce the progression of the disease to death.
Analysis of 3E (Energy-Economical-Environmental) for Biogas Production from Landfill: A Case Study
Authors: Alayi Reza , Sadeghzadeh Milad , Shahbazi Mohsen , Seyednouri Seyed Reza , Issakhov Alibek
Published: March 2022

Nowadays, generating energy is discussed as a paramount option in waste management, because the share of energy from waste is increasing annually. The ever-increasing need for energy and limitations of using fossil resources, and also the increase in environmental pollution from consuming these resources have made the use of renewable energies of especial importance. In this research, we first studied renewable energies, followed by technologies related to the biogas system. Biogas systems have been referred to by landfill and discussed comprehensively in this scheme for utilization. For this purpose, the input source of this system is considered as the volume of garbage in the city of Germi as the study area. In the next step, the amount of garbage produced in this city was extracted and the Landfill software was used for methane production potential assessment from this system and the Homer software was used for economic analysis and reliability evaluation. The most important results are payback period, which is about 10 years, and the amount of electricity produced per year, which is 11658.265 MWh.
A Safe and Compliant Noncontact Interactive Approach for Wheeled Walking Aid Robot
Authors: Zhao Donghui , Wang Wei , Okonkwo Moses Chukwuka , Yang Zihao , Yang Junyou , Liu Houde
Published: March 2022

Aiming at promptly and accurately detecting falls and drag-to gaits induced by asynchronous human-robot movement speed during assisted walking, a noncontact interactive approach with generality, compliance and safety is proposed in this paper, and is applied to a wheeled walking aid robot. Firstly, the structure and the functions of the wheeled walking aid robot, including gait rehabilitation robot (GRR) and walking aid robot (WAR) are illustrated, and the characteristic futures of falls and the drag-to gait are shown by experiments. To obtain gait information, a multichannel proximity sensor array is developed, and a two-dimensional gait information detection system is established by combining four proximity sensors groups which are installed in the robot chassis. Additionally, a node-iterative fuzzy Petri net algorithm for abnormal gait recognition is proposed by generating the network trigger mechanism using the fuzzy membership function. It integrates the walking intention direction vector by taking gait deviation, frequency, and torso angle as input parameters of the system. Finally, to improve the compliance of the robot during human-robot interaction, a PID_SC controller is designed by integrating the gait speed compensation, which enables the WAR to track human gait closely. Abnormal gait recognition and assisted walking experiments are carried out respectively. Experimental results show that the proposed algorithm can accurately identify abnormal gaits of different groups of users with different walking habits, and the recognition rate of abnormal gait reaches 91.2%. Results also show that the developed method can guarantee safety in human robot interaction because of user gate follow-up accuracy and compliant movements. The noncontact interactive approach can be applied to robots with similar structure for usage in walking assistance and gait rehabilitation.