bluuebunny/crossref_metadata_2025 huggingface dataset:
Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System
Authors: Bi Mingwen , Zhang Qingchuan , Zuo Min , Xu Zelong , Jin Qingyu
Published: June 2021

The intelligent question answering system aims to provide quick and concise feedback on the questions of users. Although the performance of phrase-level and numerous attention models have been improved, the sentence components and position information are not emphasized enough. This article combines Ci-Lin and word2vec to divide all of the words in the question-answer pairs into groups according to the semantics and select one kernel word in each group. The remaining words are common words and realize the semantic mapping mechanism between kernel words and common words. With this Chinese semantic mapping mechanism, the common words in all questions and answers are replaced by the semantic kernel words to realize the normalization of the semantic representation. Meanwhile, based on the bi-directional LSTM model, this article introduces a method of the combination of semantic role labeling and positional context, dividing the sentence into multiple semantic segments according to semantic logic. The weight is given to the neighboring words in the same semantic segment and propose semantic role labeling position attention based on the bi-directional LSTM model (BLSTM-SRLP). The good performance of the BLSTM-SRLP model has been demonstrated in comparative experiments on the food safety field dataset (FS-QA).
CONCEALING-Gate: Optical Contactless Probing Resilient Design
Authors: Rahman M. Tanjidur , Dipu Nusrat Farzana , Mehta Dhwani , Tajik Shahin , Tehranipoor Mark , Asadizanjani Navid
Published: June 2021

Optical probing, though developed as silicon debugging tools from the chip backside, has shown its capability of extracting secret data, such as cryptographic keys and user identifications, from modern system-on-chip devices. Existing optical probing countermeasures are based on detecting any device modification attempt or abrupt change in operating conditions during asset extraction. These countermeasures usually require additional fabrication steps and cause area and power overheads. In this article, we propose a novel low-overhead design methodology to prevent optical probing. It leverages additional operational logic gates, termed as “CONCEALING-Gates,” inserted as neighbor gates of the logic gates connected to the nets carrying asset signals. The switching activity of the asset carrying logic is camouflaged with the switching activity of the concealing-gate. The input signal and placement in the layout of the concealing-gates must be selected in such a way that they remain equally effective in preventing different variants of optical probing, i.e., electro-optical frequency mapping and Electro-optical probing. The methodology is suitable for the existing ASIC/FPGA design flow and fabrication process, since designing new standard logic cells is not required. We have performed a comprehensive security evaluation of the concealing-gates using a security metric developed based on the parameters that are crucial for optical probing. The attack resiliency of the logic cells, protected by concealing-gates, is evaluated using an empirical study-based simulation methodology and experimental validation. Our analysis has shown that in the presence of concealing-gates, logic cells achieve high resiliency against optical contactless probing techniques.
Dynamic Regularization on Activation Sparsity for Neural Network Efficiency Improvement
Authors: Yang Qing , Mao Jiachen , Wang Zuoguan , Hai “Helen” Li
Published: June 2021

When deploying deep neural networks in embedded systems, it is crucial to decrease the model size and computational complexity for improving the execution speed and efficiency. In addition to conventional compression techniques, e.g., weight pruning and quantization, removing unimportant activations can also dramatically reduce the amount of data communication and the computation cost. Unlike weight parameters, the pattern of activations is directly related to input data and thereby changes dynamically. To regulate the dynamic activation sparsity (DAS), in this work, we propose a generic low-cost approach based on winners-take-all (WTA) dropout technique. The network enhanced by the proposed WTA dropout, namely
DASNet
, features structured activation sparsity with an improved sparsity level. Compared to the static feature map pruning methods, DASNets provide better computation cost reduction. The WTA dropout technique can be easily applied in deep neural networks without incurring additional training variables. More importantly, DASNet can be seamlessly integrated with other compression techniques, such as weight pruning and quantization, without compromising accuracy. Our experiments on various networks and datasets present significant runtime speedups with negligible accuracy losses.
Improving Barnes-Hut t-SNE Algorithm in Modern GPU Architectures with Random Forest KNN and Simulated Wide-Warp
Authors: Meyer Bruno Henrique , Pozo Aurora Trinidad Ramirez , Zola Wagner M. Nunan
Published: June 2021

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction but is limited by its scalability when applied to large datasets. Recently, BH-tSNE was proposed; this is a successful approximation that transforms a step of the original algorithm into an N-Body simulation problem that can be solved by a modified Barnes-Hut algorithm. However, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research we have developed a new GPU BH-tSNE implementation that produces the embedding of multidimensional data points into three-dimensional space. We examine scalability issues in two of the most expensive steps of GPU BH-tSNE by using efficient memory access strategies , recent acceleration techniques , and a new approach to compute the KNN graph structure used in BH-tSNE with GPU. Our design allows up to 460% faster execution when compared to the t-SNE-CUDA implementation. Although our SIMD acceleration techniques were used in a modern GPU setup, we have also verified a potential for applications in the context of multi-core processors.
Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware
Authors: Adnan Md Musabbir , Sayyaparaju Sagarvarma , Brown Samuel D. , Shawkat Mst Shamim Ara , Schuman Catherine D. , Rose Garrett S.
Published: June 2021

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.
Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning
Authors: Do Phuc , Phan Truong H. V. , Gupta Brij B.
Published: June 2021

In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.
Kernel-based Methods for Bandit Convex Optimization
Authors: Bubeck Sébastien , Eldan Ronen , Lee Yin Tat
Published: June 2021

We consider the adversarial convex bandit problem and we build the first poly(
T
)-time algorithm with poly(
n
) √
T
-regret for this problem. To do so, we introduce three new ideas in the derivative-free optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, and (iii) a new annealing schedule for exponential weights (with increasing learning rate). The basic version of our algorithm achieves Õ(
n
9.5
√
T
)-regret, and we show that a simple variant of this algorithm can be run in poly(
n
log (
T
))-time per step (for polytopes with polynomially many constraints) at the cost of an additional poly(
n
)
T
o(1)
factor in the regret. These results improve upon the Õ(
n
11
√
T
-regret and exp (poly(
T
))-time result of the first two authors and the log (
T
)

poly(
n
)

√
T
-regret and log(
T
)

poly(
n
)

-time result of Hazan and Li. Furthermore, we conjecture that another variant of the algorithm could achieve Õ(
n
1.5
√
T
)-regret, and moreover that this regret is unimprovable (the current best lower bound being Ω (
n
√
T
) and it is achieved with linear functions). For the simpler situation of zeroth order stochastic convex optimization this corresponds to the conjecture that the optimal query complexity is of order
n
3
/ ɛ
2
.
SILVerIn: Systematic Integrity Verification of Printed Circuit Board Using JTAG Infrastructure
Authors: Paul Shubhra Deb , Bhunia Swarup
Published: June 2021

A printed circuit board (PCB) provides necessary mechanical support to an electronic system and acts as a platform for connecting electronic components. Counterfeiting and in-field tampering of PCBs have become significant security concerns in the semiconductor industry as a result of increasing untrusted entities in the supply chain. These counterfeit components may result in performance degradation, profit reduction, and reputation risk for the manufacturers. While Integrated Circuit (IC) level authentication using physical unclonable functions (PUFs) has been widely investigated, countermeasures at the PCB level are scarce. These approaches either suffer from significant overhead issues, or opportunistic counterfeiters can breach them like clockwork. Besides, they cannot be extended to system-level (both chip and PCB together), and their applications are also limited to a specific purpose (i.e., either counterfeiting or tampering). In this article, we introduce
SILVerIn
, a novel systematic approach to verify the authenticity of all chips used in a PCB as well as the board for combating attacks such as counterfeiting, cloning, and in-field malicious modifications. We develop this approach by utilizing the existing boundary scan architecture (BSA) of modern ICs and PCBs. As a result, its implementation comes at a negligible (∼0.5%) hardware overhead.
SILVerIn
 is integrated into a PCB design during the manufacturing phase. We implement our technique on a custom hardware platform consisting of an FPGA and a microcontroller. We incorporate the industry-standard JTAG (Joint Test Action Group) interface to transmit test data into the BSA and perform hands-on measurement of supply current at both chip and PCB levels on 20 boards. We reconstruct these current values to digital signatures that exhibit high uniqueness, robustness, and randomness features. Our approach manifests strong reproducibility of signatures at different supply voltage levels, even with a low-resolution measurement setup.
SILVerIn
 also demonstrates a high resilience against machine learning-based modeling attacks, with an average prediction accuracy of ∼51%. Finally, we conduct intentional alteration experiments by replacing the on-board FPGA to replicate the scenario of PCB tampering, and the results indicate successful detection of in-field modifications in a PCB.
Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network
Authors: Javed Abdul Rehman , Rehman Saif Ur , Khan Mohib Ullah , Alazab Mamoun , Khan Habib Ullah
Published: June 2021

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes
BetaLogger
, an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope).
BetaLogger
efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN).
BetaLogger
is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that
BetaLogger
generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.
A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy Under a Cloud Environment
Authors: Zhou Zhou , Li Fangmin , Yang Shuiqiao
Published: June 2021

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.
How does AI challenge design practice?
Authors: Olsson Thomas , Väänänen Kaisa
Published: June 2021

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations.
--- Juho Kim and Henriette Cramer, Editors
Effects of Gas Diffusion Layer Substrates on PEFC Water Management: Part I. Operando Liquid Water Saturation and Gas Diffusion Properties
Authors: Xu Hong , Bührer Minna , Marone Federica , Schmidt Thomas J. , Büchi Felix N. , Eller Jens
Published: June 2021

Gas diffusion layers (GDLs) are commonly known as one of the critical water management components in polymer electrolyte fuel cells with significant impact on the electrochemical cell performance. Increasing levels of liquid saturation in GDLs, especially during high-current-density operation, limit gas transport from the flow field channels to the catalyst layer surfaces and hence reduce cell performance. To provide GDL material selection and modification guidelines, a thorough understanding of the underlying structural factors of GDL materials and their influence on water management is required. In this work, operando X-ray tomographic microscopy (XTM) was employed to investigate the liquid saturation behavior for three commercial GDL materials during i-E curves and current jump characterization. Liquid volume fractions, saturation profiles and cluster distributions were analyzed to understand observed discrepancies in cell performance. Furthermore, saturation-dependent relative diffusivities were derived via direct numerical simulations, and the impact of GDL substrates on cell performance is thoroughly discussed with respect to structure and thermal properties.
AT1a-dependent GABAA inhibition in the MnPO following chronic intermittent hypoxia
Authors: Farmer George E. , Little Joel T. , Marciante Alexandria B. , Cunningham J. Thomas
Published: June 2021

Chronic intermittent hypoxia (CIH) is associated with diurnal hypertension, increased sympathetic nerve activity (SNA), and increases in circulating angiotensin II (ANG II). In rats, CIH increases angiotensin type 1 (AT1a) receptor expression in the median preoptic nucleus (MnPO), and pharmacological blockade or viral knockdown of this receptor prevents CIH-dependent increases in diurnal blood pressure. The current study investigates the role of AT1a receptor in modulating the activity of MnPO neurons following 7 days of CIH. Male Sprague-Dawley rats received MnPO injections of an adeno-associated virus with an shRNA against the AT1a receptor or a scrambled control. Rats were then exposed to CIH for 8 h a day for 7 days. In vitro, loose patch recordings of spontaneous action potential activity were made from labeled MnPO neurons in response to brief focal application of ANG II or the GABAA receptor agonist muscimol. In addition, MnPO K-Cl cotransporter isoform 2 (KCC2) protein expression was assessed using Western blot. CIH impaired the duration but not the magnitude of ANG II-mediated excitation in the MnPO. Both CIH and AT1a knockdown also impaired GABAA-mediated inhibition, and CIH with AT1a knockdown produced GABAA-mediated excitation. Recordings using the ratiometric Cl− indicator ClopHensorN showed CIH was associated with Cl− efflux in MnPO neurons that was associated with decreased KCC2 phosphorylation. The combination of CIH and AT1a knockdown attenuated reduced KCC2 phosphorylation seen with CIH alone. The current study shows that CIH, through the activity of AT1a receptors, can impair GABAA-mediated inhibition in the MnPO and contribute to sustained hypertension.
Vascular effects of disrupting endothelial mTORC1 signaling in obesity
Authors: Reho John J. , Guo Deng-Fu , Beyer Andreas M. , Wegman-Points Lauren , Pierce Gary L. , Rahmouni Kamal
Published: June 2021

The mechanistic target of rapamycin complex 1 (mTORC1) signaling complex is emerging as a critical regulator of cardiovascular function with alterations in this pathway implicated in cardiovascular diseases. In this study, we used animal models and human tissues to examine the role of vascular mTORC1 signaling in the endothelial dysfunction associated with obesity. In mice, obesity induced by high-fat/high-sucrose diet feeding for ∼2 mo resulted in aortic endothelial dysfunction without appreciable changes in vascular mTORC1 signaling. On the other hand, chronic high-fat diet feeding (45% or 60% kcal: ∼9 mo) in mice resulted in endothelial dysfunction associated with elevated vascular mTORC1 signaling. Endothelial cells and visceral adipose vessels isolated from obese humans display a trend toward elevated mTORC1 signaling. Surprisingly, genetic disruption of endothelial mTORC1 signaling through constitutive or tamoxifen inducible deletion of endothelial Raptor (critical subunit of mTORC1) did not prevent or rescue the endothelial dysfunction associated with high-fat diet feeding in mice. Endothelial mTORC1 deficiency also failed to reverse the endothelial dysfunction evoked by a high-fat/high-sucrose diet in mice. Taken together, these data show increased vascular mTORC1 signaling in obesity, but this vascular mTORC1 activation appears not to be required for the development of endothelial impairment in obesity.
BK channel activation by L-type Ca2+ channels CaV1.2 and CaV1.3 during the subthreshold phase of an action potential
Authors: Plante Amber E. , Whitt Joshua P. , Meredith Andrea L.
Published: June 2021

BK K+ channels are important regulators of firing. Although Ca2+ channels are required for their activation in excitable cells, it is not well understood how BK channels activate using these Ca2+ sources during an action potential. This study demonstrates the differences in BK current activated by CaV1.2 and CaV1.3 channels in clock neurons and heterologous cells. The results define how specific ion channel partnerships can be engaged during distinct phases of the action potential.
Effect of timing delay between visual and vestibular stimuli on heading perception
Authors: Rodriguez Raul , Crane Benjamin T.
Published: June 2021

The effect of timing on visual-inertial integration on heading perception has not been previously examined. This study finds that visual headings influence inertial heading perception when timing differences are within 250 ms. This suggests visual-inertial stimuli can be integrated over a wider range than reported for visual-auditory integration and may be due to the unique nature of inertial sensation, which can only sense acceleration while the visual system senses position but encodes velocity.
Nuclear Fusion Pattern Recognition by Ensemble Learning
Authors: Farias G. , Fabregas E. , Martínez I. , Vega J. , Dormido-Canto S. , Vargas H.
Published: June 2021

Nuclear fusion is the process by which two or more atomic nuclei join together to form a single heavier nucleus. This is usually accompanied by the release of large quantities of energy. This energy could be cheaper, cleaner, and safer than other technology currently in use. Experiments in nuclear fusion generate a large number of signals that are stored in huge databases. It is impossible to do a complete analysis of this data manually, and it is essential to automate this process. That is why machine learning models have been used to this end in previous years. In the literature, several popular algorithms can be found to carry out the automatic classification of signals. Among these, ensemble methods provide a good balance between success rate and internal information about models. Specifically, AdaBoost algorithm will allow obtaining an explicit set of rules that explains the class for each input data, adding interpretability to the models. In this paper, an innovative approach to perform an online classification, that is, to identify the discharge before it actually ends, using interpretable models is presented. In order to evaluate and reveal the benefits of rule‐based models, an illustrative example has been implemented to perform an online classification of five different signals of the TJ‐II stellarator fusion device located in Madrid, Spain.
Management of Refractory Gastrointestinal Bleeding in Hereditary Hemorrhagic Telangiectasia with Bevacizumab
Authors: Masood Muaaz , Coles Michael , Sifuentes Humberto
Published: June 2021

Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disorder resulting in vascular malformations of several organs including the pulmonary, cerebral, and gastrointestinal systems. One sequela is recurrent gastrointestinal (GI) bleeding. Bevacizumab (Bev) is emerging as an effective treatment of recurrent gastrointestinal bleeding in HHT. Bev is a recombinant monoclonal antibody that inhibits vascular endothelial growth factor (VEGF), an integral part of angiogenesis.
How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?
Authors: Yang Hongtai , Li Xuan , Li Chaojing , Huo Jinghai , Liu Yugang
Published: June 2021

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.
Antimicrobial Peptides from Amphibian Innate Immune System as Potent Antidiabetic Agents: A Literature Review and Bioinformatics Analysis
Authors: Soltaninejad Hossein , Zare-Zardini Hadi , Ordooei Mahtab , Ghelmani Yaser , Ghadiri-Anari Akram , Mojahedi Sanaz , Hamidieh Amir Ali
Published: June 2021

Antimicrobial peptides, as an important member of the innate immune system, have various biological activities in addition to antimicrobial activity. There are some AMPs with antidiabetic activity, especially those isolated from amphibians. These peptides can induce insulin release via different mechanisms based on peptide type. In this review study, we collected all reported AMPs with antidiabetic activity. We also analyze the sequence and structure of these peptides for evaluation of sequence and structure effect on their antidiabetic activity. Based on this review, the biggest peptide family with antidiabetic activity is temporins with nine antidiabetic peptides. Frogs are the most abundant source of antidiabetic peptides. Bioinformatics analysis showed that an increase of positive net charge and a decrease of hydrophobicity can improve the insulinotropic effect of peptides. Peptides with higher positive net charge and Boman index showed higher activity. Based on this review article, AMPs with antidiabetic activity, especially those isolated from amphibians, can be used as novel antidiabetic drug for type 2 diabetes disease. So, amphibians are potential sources for active peptides which merit further evaluation as novel insulin secretagogues. However, strategy for the increase of stability and positive activity as well as the decrease of negative side effects must be considered.
Autophagy: A Novel Horizon for Hair Cell Protection
Authors: Liu Chang , Zheng Zhiwei , Wang Pengjun , He Shuangba , He Yingzi
Published: June 2021

As a general sensory disorder, hearing loss was a major concern worldwide. Autophagy is a common cellular reaction to stress that degrades cytoplasmic waste through the lysosome pathway. Autophagy not only plays major roles in maintaining intracellular homeostasis but is also involved in the development and pathogenesis of many diseases. In the auditory system, several studies revealed the link between autophagy and hearing protection. In this review, we aimed to establish the correlation between autophagy and hair cells (HCs) from the aspects of ototoxic drugs, aging, and acoustic trauma and discussed whether autophagy could serve as a potential measure in the protection of HCs.
BMSCs Regulate Astrocytes through TSG-6 to Protect the Blood-Brain Barrier after Subarachnoid Hemorrhage
Authors: Wan Yilv , Song Min , Xie Xun , Chen Zhen , Gao Ziyun , Wu Xiang , Huang Rui , Chen Min
Published: June 2021

Background. In patients with subarachnoid hemorrhage (SAH), the damage of the blood-brain barrier (BBB) can be life-threatening. Mesenchymal stem cells are widely used in clinical research due to their pleiotropic properties. This study is aimed at exploring the effect of BMSCs regulating astrocytes on the BBB after SAH. Methods. The SAH model was established by perforating the blood vessels. BMSCs were transfected with TSG-6 inhibitor plasmid and cocultured with astrocytes. Intravenous transplantation of BMSCs was utilized to treat SAH rats. We performed ELISA, neurological scoring, Evans blue staining, NO measurement, immunofluorescence, BBB permeability, Western blot, HE staining, Nissl staining, and immunohistochemistry to evaluate the effect of BMSCs on astrocytes and BBB. Results. SAH rats showed BBB injury, increased BBB permeability, and brain histological damage. BMSCs will secrete TSG-6 after being activated by TNF-α. Under the influence of TSG-6, the NF-κB and MAPK signaling pathways of astrocytes were inhibited. The expression of iNOS was reduced, while occludin, claudin 3, and ZO-1 expression was increased. The production of harmful substances NO and ONOO- decreased. The level of inflammatory factors decreased. The apoptosis of astrocytes was weakened. TSG-6 secreted by BMSCs can relieve inflammation caused by SAH injury. The increase in BBB permeability of SAH rats was further reduced and the risk of rebleeding was reduced. Conclusion. BMSCs can regulate the activation of astrocytes through secreting TSG-6 in vivo and in vitro to protect BBB.
Proximate and Nutritional Composition of Stored Bulb Onions as Affected by Harvest and Postharvest Treatments
Authors: Kiura Irene N. , Gichimu Bernard M. , Rotich Felix
Published: June 2021

Productivity of bulb onions (Allium cepa L.) is largely constrained by postharvest losses. There are several postharvest strategies applicable to onions, but they are mostly applied singularly and therefore their combined effects have not been well studied. This study was set out to evaluate the effects of harvesting stage, curing period, and time of topping on postharvest quality of stored red bulb onions. The study was carried out in Yatta Subcounty, Machakos County, Kenya. The experimental design was split-split plot laid out in a 3 × 3 × 2 factorial arrangement. The treatments consisted of harvesting stage (25%, 50%, and 75% top fall), curing period (none, 1 week, and 2 weeks after harvesting), and time of topping (before and after curing). All the treatments were replicated three times. After 3 months of storage, the bulbs were analyzed for proximate and nutritional composition. Bulbs that were harvested at 75% top fall and cured for one or two weeks before topping retained higher moisture content, bulb weight, crude protein, vitamin C, zinc, potassium, calcium, and iron but lower sodium content after three-month storage. These practices are therefore recommended for maintaining the nutritional quality of bulb onions after harvesting.
Analysis of Volatile Organic Compounds by HS-GC-IMS in Powdered Yak Milk Processed under Different Sterilization Conditions
Authors: Feng Duo , Wang Jing , Ji Xiao-Jiao , Min Wen-Xiang , Yan Wen-Jie
Published: June 2021

Headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) was used to detect the volatile organic compounds (VOCs) of yak milk powders (YMPs) under ultra-high-pressure sterilization (UHPS) and thermization (TH) methods. The analyses led to the identification of several characteristic of compounds, therefore, exploitation and comparison of the different flavors. A total of 46 peaks were detected, and 17 compounds were identified, including 7 aldehydes, 5 ketones, 3 acids, 1 terpene, and 1 ester. Furthermore, principal component analysis (PCA) and fingerprint similarity analysis based on Euclidean distance compared the YMPs and found that the YMPs had certain differences, which can distinguish the YMPs with different sterilization methods. In conclusion, different sterilization methods possibly affect the flavor of YMPs, and UHPS is bettedslfr than TH. Also, aldehydes were mainly be detected in UHPS groups, whereas the ketones and acids mostly appeared in TH groups. Most importantly, UHPS can retain the original flavor of yak milk to a greater extent.
Genotyping of Helicobacter pylori Virulence Genes cagA and vacA: Regional and National Study
Authors: Kishk Rania M. , Soliman Nashaat M. , Anani Maha M. , Nemr Nader , Salem Ayman , Attia Fawzy , Allithy Amal Nooredeen Ahmed , Fouad Marwa
Published: June 2021

Helicobacter pylori (H. pylori) plays a crucial role in the pathogenesis of gastritis, peptic ulcer, and gastric cancer. The presence of pathogenicity islands (PAI) genes contributes to the pathogenesis of many gastrointestinal disorders. Cytotoxin-associated gene A (cagA) and vacuolating cytotoxin gene (vacA) are the most known virulence genes in H. pylori. So, our aim was to study H. pylori virulence genes’ role in gastric disorders pathogenesis. Our study included 150 adult patients who suffered dyspeptic symptoms and were referred to the GIT endoscopy unit. Gastric biopsies were attained for rapid urease test (RUT) and histopathological examination, and multiplex PCR technique for detection of virulence genes was performed. It was found that 100 specimens were (RUT) positive, of which sixty samples (60%) were PCR positive for H. pylori ureC gene. The vacA and cagA genes were identified in 61.6% and 53% of H. pylori strains, respectively. Only 5 cases were vacA-positive and cagA-negative. The most virulent vacA s1 allele existed in 56.6% of cases. Out of the 60 H. pylori strains, 66% had at least one virulence gene and 34% did not show any virulence gene. H. pylori infection showed significant increase with age. H. pylori are prevalent amid dyspeptic patients in our region. The main genotype combinations were vacA+/cagA+ of s1m1 genotype and they were frequently associated with peptic ulcer diseases, gastritis, and gastroesophageal reflux disease.
A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic Constraints
Authors: Wang Huaijun , Du Dandan , Li Junhuai , Ji Wenchao , Yu Lei
Published: June 2021

Motion capture technology plays an important role in the production field of film and television, animation, etc. In order to reduce the cost of data acquisition and improve the reuse rate of motion capture data and the effect of movement style migration, the synthesis technology of motion capture data in human movement has become a research hotspot in this field. In this paper, kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Firstly, cycle‐consistent adversarial network (CCycleGAN) is constructed, and the motion style migration network based on convolutional self‐encoder is used as a generator to establish the cyclic consistent constraint between the generated motion and the content motion, so as to improve the action consistency between the generated motion and the content motion and eliminate the lag phenomenon of the generated motion. Then, kinematic constraints are introduced to normalize the movement generation, so as to solve the problems such as jitter and sliding step in the movement style migration results. Experimental results show that the generated motion of the cyclic consistent style transfer method with kinematic constraints is more similar to the style of style motion, which improves the effect of motion style transfer.
Tanshinone IIA Inhibits Osteosarcoma Growth through a Src Kinase-Dependent Mechanism
Authors: Hu Chao , Zhu Xiaobin , Zhang Taogen , Deng Zhouming , Xie Yuanlong , Yan Feifei , Cai Lin
Published: June 2021

Introduction. Osteosarcoma is a malignant tumor associated with high mortality rates due to the toxic side effects of current therapeutic methods. Tanshinone IIA can inhibit cell proliferation and promote apoptosis in vitro, but the exact mechanism is still unknown. The aims of this study are to explore the antiosteosarcoma effect of tanshinone IIA via Src kinase and demonstrate the mechanism of this effect. Materials and Methods. Osteosarcoma MG-63 and U2-OS cell lines were stable transfections with Src-shRNA. Then, the antiosteosarcoma effect of tanshinone IIA was tested in vitro. The protein expression levels of Src, p-Src, p-ERK1/2, and p-AKt were detected by Western blot and RT-PCR. CCK-8 assay and BrdU immunofluorescence assay were used to detect cell proliferation. Transwell assay, cell scratch assay, and flow cytometry were used to detect cell invasion, migration, and cell cycle. Tumor-bearing nude mice with osteosarcoma were constructed. The effect of tanshinone IIA was detected by tumor HE staining, tumor inhibition rate, incidence of lung metastasis, and X-ray. Results. The oncogene role of Src kinase in osteosarcoma is reflected in promoting cell proliferation, invasion, and migration and in inhibiting apoptosis. However, Src has different effects on cell proliferation, apoptosis, and cell cycle regulation among cell lines. At a cellular level, the antiosteosarcoma effect of tanshinone IIA is mediated by Src downstream of the MAPK/ERK and PI3K/AKt signaling pathways. At the animal level, tanshinone IIA played a role in resisting osteosarcoma formation by Src downstream of the MAPK/ERK and PI3K/AKt signaling pathways. Conclusion. Tanshinone IIA plays an antiosteosarcoma role in vitro and in vivo and inhibits the progression of osteosarcoma mediated by Src downstream of the MAPK/ERK and PI3K/AKt signaling pathways.
Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks
Authors: Yadav Saneh Lata , Ujjwal R. L. , Kumar Sushil , Kaiwartya Omprakash , Kumar Manoj , Kashyap Pankaj Kumar
Published: June 2021

Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource‐restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP‐hard and it has been realized that a heuristic‐based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA‐HA) to improve the network performance. This approach is a combination of traffic‐oriented and resource‐oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion‐free alternate paths. The forward ant constructs multiple congestion‐free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.
The Clinical Significance of Procalcitonin Elevation in Patients over 75 Years Old Admitted for COVID-19 Pneumonia
Authors: Ticinesi Andrea , Nouvenne Antonio , Prati Beatrice , Guida Loredana , Parise Alberto , Cerundolo Nicoletta , Bonaguri Chiara , Aloe Rosalia , Guerra Angela , Meschi Tiziana
Published: June 2021

Aim. To investigate the clinical significance of procalcitonin (PCT) elevation on hospital admission for coronavirus disease-19 (COVID-19) and its association with mortality in oldest old patients (

age
>
75
 
years

). Methods. The clinical records of 1074 patients with chest high-resolution computed-tomography (HRCT) positive for interstitial pneumonia and symptoms compatible for COVID-19, hospitalized in medical wards during the first pandemic wave in a single academic center in Northern Italy, were retrospectively analyzed. All patients had serum PCT testing performed within six hours from admission. Information on COVID-19-related symptoms, comorbidities, drugs, autonomy in daily activities, respiratory exchanges, other routine lab tests, and outcomes were collected. Clinical characteristics were compared across different admission PCT levels and ages. The association of admission PCT with mortality was tested separately in participants

aged
>
75

and ≤75 years old by stepwise multivariate Cox regression model with forward selection. Results. With increasing classes of PCT levels (<0.05, 0.05-0.49, 0.5-1.99, and ≥2 ng/ml), there was a significant trend (

P
<
0.0001

) towards older age, male gender, wider extension of lung involvement on HRCT, worse respiratory exchanges, and several other laboratory abnormalities. Each incremental PCT class was associated with increased risk of hospital death at multivariate models in subjects older than 75 (hazard ratio for

PCT
≥
2

vs. <0.05 ng/ml: 30.629, 95% confidence interval 4.176-224.645,

P
=
0.001

), but not in subjects aged 75 or younger. Conclusions. In patients admitted for COVID-19, PCT elevation was associated with several clinical, radiological, and laboratory characteristics of disease severity. However, PCT elevation was strongly associated with hospital mortality only in oldest old subjects (

age
>
75

).
[Retracted] Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network
Authors: Wu Ye , Sun Xiaowen
Published: June 2021

In the human resource system of modern enterprises, human‐post matching big data occupies an important irreplaceable position. With the deepening of the reform of state‐owned enterprises, some shortcomings of human‐post matching big data have become prominent. The purpose of this article is to solve the current state‐owned enterprises. There are a variety of problems with big data in the enterprise, and an effective method is found that can accurately evaluate the degree of human‐job matching in state‐owned enterprises and provide a scientific basis for the manager of talent and resource allocation to make more rational decisions. Through the radial basis function (RBF) neural network‐based big data model of human‐post matching evaluation of state‐owned enterprises, we scientifically and effectively evaluate the matching degree of the quality and ability of the personnel with the relevant requirements of the position and then help the company to adjust the personnel at any time changes in positions to maximize the efficiency of human resources. In this paper, considering the actual situation of the enterprise, the RBF neural network and the analytic hierarchy process (AHP) method are used comprehensively. Firstly, the AHP is used to obtain the weight of each evaluation index in the human‐post matching index system. At the same time, the artificial neural network theory is self‐adapting. Learning is helpful to solve the problem that the AHP method is too subjective. The two learn from each other’s strong points and combine their weaknesses organically to increase the convenience and effectiveness of evaluation.
Formulation and In Vitro Evaluation of Oral Capsules and Suspension from the Ethanolic Extract of Cola nitida Seeds for the Treatment of Diarrhea
Authors: Owusu Fredrick W. A. , Asare Christiana O. , Enstie Philomena , Adi-Dako Ofosua , Yeboah Genevieve Naana , Kumadoh Doris , Tetteh-Annor Amanda , Amenuke Edem M. , Karen Mordey
Published: June 2021

Management of diarrhea has evolved over the years from relatively inadequate interventions in the early years to more successful physiological approaches. The use of herbal medicinal products and supplements has grown significantly over the past three decades, with more than half of the global population depending on it for some aspect of their primary health care needs. This study is aimed at formulating solid and liquid oral dosage forms of the ethanolic extract of Cola nitida seeds for the treatment of diarrhea. The flow property of the dried ethanolic extract was determined and subsequently formulated into granules for encapsulation. The ethanolic extract was also used in formulating an oral suspension. Pharmacopeia tests such as uniformity of weight, disintegration, drug content, and dissolution were carried out on the formulated capsules. The formulated suspension was also assessed using the following parameters; viscosity, flow rate, drug content, dissolution, sedimentation rate, and sedimentation volume. The dried ethanolic extract and formulated granules exhibited good flow properties. The formulated capsules exhibited optimal in vitro release of extract (>90% after 45 minutes) and passed the uniformity of weight, disintegration, and drug content tests. The formulated suspension also passed the drug content test and had a good sedimentation rate, sedimentation volume, and flow rate. The formulated suspension also exhibited pseudoplastic flow, optimal viscosity, and a good in vitro release profile (>90% after 45 minutes). Capsules and suspension of the ethanolic extract of Cola nitida seeds have been successfully formulated and can be used as standard dosage forms for the management of diarrhea.