Miracle-dz/UniFAR-FTUs huggingface dataset:
Unexpected Consequences of Noise-Induced Hearing Loss: Impaired Hippocampal Neurogenesis, Memory, and Stress

Noise-induced hearing loss (NIHL), caused by direct damage to the cochlea, reduces the flow of auditory information to the central nervous system, depriving higher order structures, such as the hippocampus with vital sensory information needed to carry out complex, higher order functions. Although the hippocampus lies outside the classical auditory pathway, it nevertheless receives acoustic information that influence its activity. Here we review recent results that illustrate how NIHL and other types of cochlear hearing loss disrupt hippocampal function. The hippocampus, which continues to generate new neurons (neurogenesis) in adulthood, plays an important role in spatial navigation, memory, and emotion. The hippocampus, which contains place cells that respond when a subject enters a specific location in the environment, integrates information from multiple sensory systems, including the auditory system, to develop cognitive spatial maps to aid in navigation. Acute exposure to intense noise disrupts the place-specific firing patterns of hippocampal neurons, “spatially disorienting” the cells for days. More traumatic sound exposures that result in permanent NIHL chronically suppresses cell proliferation and neurogenesis in the hippocampus; these structural changes are associated with long-term spatial memory deficits. Hippocampal neurons, which contain numerous glucocorticoid hormone receptors, are part of a complex feedback network connected to the hypothalamic-pituitary (HPA) axis. Chronic exposure to intense intermittent noise results in prolonged stress which can cause a persistent increase in corticosterone, a rodent stress hormone known to suppress neurogenesis. In contrast, a single intense noise exposure sufficient to cause permanent hearing loss produces only a transient increase in corticosterone hormone. Although basal corticosterone levels return to normal after the noise exposure, glucocorticoid receptors (GRs) in the hippocampus remain chronically elevated. Thus, NIHL disrupts negative feedback from the hippocampus to the HPA axis which regulates the release of corticosterone. Preclinical studies suggest that the noise-induced changes in hippocampal place cells, neurogenesis, spatial memory, and glucocorticoid receptors may be ameliorated by therapeutic interventions that reduce oxidative stress and inflammation. These experimental results may provide new insights on why hearing loss is a risk factor for cognitive decline and suggest methods for preventing this decline.
TCP Transcription Factors Involved in Shoot Development of Ma Bamboo (Dendrocalamus latiflorus Munro)

Ma bamboo (Dendrocalamus latiflorus Munro) is the most widely cultivated clumping bamboo in Southern China and is valuable for both consumption and wood production. The development of bamboo shoots involving the occurrence of lateral buds is unique, and it affects both shoot yield and the resulting timber. Plant-specific TCP transcription factors are involved in plant growth and development, particularly in lateral bud outgrowth and morphogenesis. However, the comprehensive information of the TCP genes in Ma bamboo remains poorly understood. In this study, 66 TCP transcription factors were identified in Ma bamboo at the genome-wide level. Members of the same subfamily had conservative gene structures and conserved motifs. The collinear analysis demonstrated that segmental duplication occurred widely in the TCP transcription factors of Ma bamboo, which mainly led to the expansion of a gene family. Cis-acting elements related to growth and development and stress response were found in the promoter regions of DlTCPs. Expression patterns revealed that DlTCPs have tissue expression specificity, which is usually highly expressed in shoots and leaves. Subcellular localization and transcriptional self-activation experiments demonstrated that the five candidate TCP proteins were typical self-activating nuclear-localized transcription factors. Additionally, the transcriptome analysis of the bamboo shoot buds at different developmental stages helped to clarify the underlying functions of the TCP members during the growth of bamboo shoots. DlTCP12-C, significantly downregulated as the bamboo shoots developed, was selected to further verify its molecular function in Arabidopsis. The DlTCP12-C overexpressing lines exhibited a marked reduction in the number of rosettes and branches compared with the wild type in Arabidopsis, suggesting that DlTCP12-C conservatively inhibits lateral bud outgrowth and branching in plants. This study provides useful insights into the evolutionary patterns and molecular functions of the TCP transcription factors in Ma bamboo and provides a valuable reference for further research on the regulatory mechanism of bamboo shoot development and lateral bud growth.
Involvement of the SIRT1/PGC-1α Signaling Pathway in Noise-Induced Hidden Hearing Loss

Objective: To establish an animal model of noise-induced hidden hearing loss (NIHHL), evaluate the dynamic changes in cochlear ribbon synapses and cochlear hair cell morphology, and observe the involvement of the SIRT1/PGC-1α signaling pathway in NIHHL. Methods: Male guinea pigs were randomly divided into three groups: control group, noise exposure group, and resveratrol treatment group. Each group was divided into five subgroups: the control group and 1 day, 1 week, 2 weeks, and 1 month post noise exposure groups. The experimental groups received noise stimulation at 105 dB SPL for 2 h. Hearing levels were examined by auditory brainstem response (ABR). Ribbon synapses were evaluated by inner ear basilar membrane preparation and immunofluorescence. The cochlear morphology was observed using scanning electron microscopy. Western blotting analysis and immunofluorescence was performed to assess the change of SIRT1/PGC-1α signaling. Levels of superoxide dismutase (SOD), malondialdehyde (MDA), catalase (CAT), ATP and SIRT1 activity were measured using commercial testing kits. Results: In the noise exposure group, hearing threshold exhibited a temporary threshold shift (TTS), and amplitude of ABR wave I decreased irreversibly. Ribbon synapse density decreased after noise exposure, and the stereocilia were chaotic and then returned to normal. The expression and activity of SIRT1 and PGC-1α protein was lower than that in the control group. SOD, CAT and ATP were also influenced by noise exposure and were lower than those in the control group, but MDA showed no statistical differences compared with the control group. After resveratrol treatment, SIRT1 expression and activity showed a significant increase after noise exposure, compared with the noise exposure group. In parallel, the PGC-1α and antioxidant proteins were also significantly altered after noise exposure, compared with the noise exposure group. The damage to the ribbon synapses and the stereocilia were attenuated by resveratrol as well. More importantly, the auditory function, especially ABR wave I amplitudes, was also promoted in the resveratrol treatment group. Conclusion: The SIRT1/PGC-1α signaling pathway and oxidative stress are involved in the pathogenesis of NIHHL and could be potential therapeutical targets in the future.
Assessment of Critical Factors Influencing Consumers’ Acceptance of Wearable Sports Devices During COVID-19 Pandemic Conditions

Amid rising COVID-19 stringency measures, sedentary behavior has been intensified globally, leading to intense chronic diseases. Due to the potential health benefits associated with digital wearables, there is a dire need to explore the crucial determinants for consumers, which could enhance the usage of sports wearables in addressing health challenges. For this purpose, a novel conceptual framework was developed, and Partial Least Square-Structural Equation Modelling (PLS-SEM) was employed on the primary data of 463 consumers from China. The results revealed a positive association of consumer innovativeness, perceived credibility, perceived ease in using sports wearables, perceived usefulness in using sports wearables, social influence for sports wearables, health benefits, and hedonic motivation for sports wearables during COVID-19 with the adoption intention of sports wearables. The study findings offer valuable policy recommendations to minimize COVID-19 health risks by efficiently monitoring consumers’ health status.
Active control of fan noise from high-bypass ratio aeroengines: a numerical study

Abstract Under the national research project, dubbed Turbotech II, in which MTU Aero Engines, DLR Institute of Propulsion Technology and EADS Corporate Research Centre participate, active noise control (ANC) has been tested with a scale model fan of one metre diameter for a high bypass ratio aeroengine. MTU’s task in this project was to develop a computer code to predict the sound field in the intake duct of the fan-rig by the use of active control. The primary objective of the numerical study was to specify numbers of actuators (loudspeakers) and error sensors (microphones) and their positioning to control the harmonic sound power, radiated upstream to the duct intake. The computer model is based on the geometry of an annular or circular duct of rigid walls and infinite length, containing a subsonic axial uniform flow. The modal amplitudes of the primary sound field are input data. The actuators are modelled by acoustic monopoles. Two control algorithms have been used for achieving the control objective. The first consists simply in the reduction of the in-duct mean squared pressures. The second, so called modal control, is designed to cancel dominant modes selectively. Numerical results are presented using a typical configuration of wall mounted actuators and error sensors in the form of a number of rings uniformly distributed along the length of the intake duct. Guidelines have also been derived to design a favourable configuration of actuators and sensors. The findings of the numerical study are compared with the results of the ANC tests.
Development of Functional Fitness Prediction Equation in Korean Older Adults: The National Fitness Award 2015–2019

The main advantage of measuring functional fitness (FF) in older adults is that individual tests can estimate and track the rate of decline with age. This study aimed to develop a multiple linear regression model for predicting FF variables using easy-to-measure independent variables (e.g., sex, age, body mass index, and percent body fat) in Korean older adults. National Fitness Award datasets from the Republic of Korea were used in this analysis. The participants were aged ≥65 years and included 61,465 older men and 117,395 older women. The FF variables included the hand grip strength, lower body strength (30-s chair stand), lower body flexibility (chair sit-and-reach), coordination (figure of 8 walk), agility/dynamic balance (timed up-and-go), and aerobic endurance (2-min step test). An estimation multiple linear regression model was developed using a stepwise technique. In the regression model, the coefficient of determination in the hand grip strength test (adjusted R2 = 0.773, p < 0.001) was significantly high. However, the coefficient of determination in the 30-s chair stand (adjusted R2 = 0.296, p < 0.001), chair sit-and-reach (adjusted R2 = 0.435, p < 0.001), figure of 8 walk (adjusted R2 = 0.390, p < 0.001), timed up-and-go (adjusted R2 = 0.384, p < 0.001), and 2-min step tests (adjusted R2 = 0.196, p < 0.001) was significantly low to moderate. Our findings suggest that easy-to-measure independent variables can predict the hand grip strength in older adults. In future studies, explanatory power will be further improved if multiple linear regression analysis, including the physical activity level and nutritional status of older adults, is performed to predict the FF variables.
Learning From Freshman Perspectives: A Two Dimensional Approach To Increasing Student Satisfaction

Improving retention of engineering students often depends on their experiences in core mathematics and science courses during their freshman year of college. For this research, freshman students enrolled in an introductory engineering design course at a large midwestern university were asked to identify five ways in which their Calculus or Chemistry course could be redesigned. Herzberg’s motivation-hygiene theory (1966) is used as an interpretive framework to examine the student’s perspectives on how to improve these core courses within the engineering curriculum. The student’s suggestions for course redesign were classified into motivation and hygiene factors. The results show that a majority of the suggestions involved extrinsic hygiene factors such as reducing class size, and providing more comfortable chairs and larger tables. Fewer responses were received related to intrinsic motivation factors such as course content. This finding points to the need for a two-dimensional approach to increasing student satisfaction. Although Herzberg cautions that the effects of improved hygiene are of short duration, educators should not disregard their role in student satisfaction by focusing exclusively on intrinsic motivation factors.
Nearest Level Modulation for Modular Multilevel Converters in HVDC Transmission

This paper describes the circuit configuration and operation mechanism of MMC.The comparisons of some modulation methods for MMC show that NLM is the best for MMC in high-power high-voltage applications,such as high voltage direct current(HVDC)transmission.The NLM method for MMC is proposed,and the formulas to calculate the fundamental and harmonic components are given.The variations of the fundamental component and the voltage THD with the number of voltage levels and the amplitude of modulation wave are exhibited.The system performance will be deteriorated in the over modulation range,which need to be avoided.Simulation results obtained with PSCAD/EMTDC are consistent with the theoretical results.Both the calculation and simulation results show that the output voltage of NLM based MMC is a good approximation to the modulation wave and has low harmonic contents in a wide operation range.
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Background Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes. Results In this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes. Conclusion Analysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.
Accelerating Sparse Neural Networks on GPUs

Ever larger networks with parameters in the order of the hundreds of millions are required to ﬁt increasingly complex and expansive datasets. In conjunction with ubiquitous machine learning applications on mobile or embedded platforms, this makes efﬁciency a vital property of ar-tiﬁcial neural networks. Therefore we build upon work on replacing fully connected dense layers with trainable, evolving sparse layers in CSR encoding. This allows us to train networks at sparsity levels of up to 97% while considerably reducing the memory footprint as well as the number of computations thereby indicating that GPU accelerated sparse layers are a viable alternative to dense layers.
Social Group Optimization-based New Routing Approach for WMN’s

—Wireless Mesh Networks (WMNs) are hop-to-hop communication networks that are quickly deployable, dynamically self-organizing, self-configuring, self-healing, self-balancing, and self-aware. In WMNs, a node can leave or join the network at any time. Due to the mobile nature of nodes, the routes between source and destination can change frequently. Computing the shortest path under dynamic conditions for a given time constraint imposed due to node mobility can also be placed in the class of highly complex problems. However, as the network size grows, the performance of the nodes decreases. As a result, we require Soft Computing approaches to handle this problem. This article proposes a Social Group Optimization (SGO) based routing approach to wireless mesh networks. The proposed approach was implemented in MATLAB and tested on different dynamic nodes network scenarios. We compare the performance of the proposed approach with Ant Colony Optimization (ACO), Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), BAT, Biogeography-based optimization (BBO), and Firefly Algorithm based routing approaches. We observe that the proposed approach outperformed all other approaches on the more than 1000 node network scenarios.
Identifying the Neuropsychological Profile of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) occurs when the protein, beta-amyloid, deposits onto blood vessels in the brain, often resulting in cognitive impairment. This study aims to identify the neuropsychological profile of CAA by comparing CAA participants to the normal population and cognitively-similar patient populations, distinguishing between CAA syndromes, and determining whether white matter hyperintensity volume or APOE ε4 mediate cognitive impairment. Thirty-four CAA, 16 Alzheimer’s disease, 69 mild cognitive impairment, and 27 minor ischemic stroke participants underwent neuropsychological testing. Cross-sectional data revealed lower performance on tests of perceptual speed, episodic memory, and executive functioning in CAA participants when compared to the normal population (t(33) = -3.76, p = <0.001; t(32) = -2.45, p = 0.02; t(33) = -6.21, p = <0.001; respectively). Overall, CAA was most cognitively similar to vascular cognitive impairment. Larger sample sizes and longitudinal analyses in future will help to advance understanding of cognitive deficits associated with CAA.
Deep Learning-based Hybrid Model for Efficient Anomaly Detection

—It is common among security organizations to run processes system call trace data to predict its anomalous behavior, and it is still a dynamic study region. Learning-based algorithms can be employed to solve such problems since it is typical pattern recognition problem. With the advanced progress in operating systems, some datasets became outdated and irrelevant. System calls datasets such as Australian Defense Force Academy Linux Dataset (ADFA-LD) are amongst the current cohort containing labeled data of system call traces for normal and malicious processes on various applications. In this paper, we propose a hybrid deep learning-based anomaly detection system. To advance the detection accurateness and competence of anomaly detection systems, Convolution Neural Network (CNN) with Long Short Term Memory (LSTM) is employed. The raw sequence of system call trace is fed to the CNN network first, reducing the traces' dimension. This reduced trace vector is further fed to the LSTM network to learn the sequences of the system calls and produce the concluding detection outcome. Tensorflow-GPU was used to implement and train the hybrid model and evaluated on the ADFA-LD dataset. Experimental results showed that the proposed method had reduced training time with an enhanced anomaly detection rate. Therefore, this method lowers the false alarm rates.
The Effects of Physical Activity Digital Product Use and Psychological Health on Low Back Paın in Adolescents: School Based Study in Eastern Turkey

Objectives: Low back pain is an important public health problem because of it’s high prevalence. The aim of this study was to determine of prevalence low back pain and to evaluate the effect of physical activity, digital product use and mental health on low back pain. Patients and Methods: Total 1285 high school students in Bingöl province were included in this cross sectional study. The data were collected from the students by the questionnaire method. Results: The mean age of the students were 15.9±1.0 and their 52.1 % were women. The rate of students who evaluate the family atmosphere as warm was 57.0%. Of the participants, 26.7% were exercising regularly. The proportion of students experiencing physical stress in any period of their life was 30.9%. It was determined that half of the students used computers and half of the users had low back pain due to the use of computers. The frequency of low back pain was found to be 24.1%. One-fourth of the students did not attend school due to pain. It was observed that there was a positive correlation between the presence of parental back pain, exposure to physical stress, have a oppressive authoritarian family, poor economic situation, daily sitting and increased tablet usage time and back pain. L. Adolescents BP in the adolescents was often accompanied by poor psychological health. Conclusion: It should be educated about postur, use time of tablet, sitting time, their smoking habits and students should be changed their life style with low back pain should be evaluated psychologically.
Bioinformatics Tools and Methods to Analyze Single-Cell RNA Sequencing Data

:- The rapid development of next generation sequencing (NGS) tool and technologies over the past few years, valuable insights have been gained into the complex and diverse biological systems with diverse range from microbial communities to cancerous genome. The NGS base technologies based of genomics, epigenomics and transcriptomics, are preoperationally concentrated on individual cells characterization. As an example, ScRNA sequencing can sort out complex and rare cellular populations, revealing the gene regulatory inter and intra relationships, and trace the progression of individual cell lineages during cell development. Researchers have previously undiscovered molecular details in single-nuclei and single-cell sequencing experiments. Since recent years, analytical approaches and sequencing methods have increased rapidly. In this review, our prime focus will be on challenges related to single cell isolation (SCI) and library preparation, analysis of ScRNA seq data by computational pipelines. These sequencing technologies will be greatly facilitating molecular biological improvement and bioinformatics due to the availability of bioinformatics tools.