Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Fog-Aided D2D Networks with Multicast-Based Opportunistic Content Delivery

In this paper, we develop a comprehensive and tractable analytical framework based on stochastic geometry to evaluate the performance of large-scale fog-aided device-to-device (F-D2D) networks with opportunistic content multicasting. As a part of the analysis, to resolve the contentions of file requests from the cache-incapable conventional user equipments (C-UEs), two simple yet typical candidate file selection schemes for cache-enabled fog user equipments (F-UEs), namely the random file selection (RFS) scheme and the most requested file selection (MRFS) scheme, are considered. Further, to suppress the harmful interference among the concurrent transmissions of F-UEs, a multicast-based opportunistic content delivery strategy is proposed by exploring the idea of opportunistic spectrum access (OSA). Assuming decentralized probabilistic caching, we first derive the activation probability of the F-UEs. Then, by adopting an appropriate approximation, the cache-hit probability, the coverage probability, and thereby the successful content delivery probability (SCDP) of the F-D2D network are evaluated. We also develop an iterative algorithm based on the gradient projection method to obtain a suboptimal caching policy for the maximization of SCDP. Extensive simulation and numerical results are presented to verify our analysis and demonstrate the superior performance of the proposed multicast-based opportunistic content delivery strategy.
Emergent Properties in a V1-Inspired Network of Hodgkin-Huxley Neurons

This article is devoted to the analysis of a network of excitatory and inhibitory neurons of Hodgkin-Huxley (HH) type, for which the topology is inspired by that of a layer of visual cortex V1. Our model relies on recent work in this area and thus combines a stochastic drive -- which may be interpreted as an ambient drive for each neuron -- with recurrent inputs resulting from the network activity. After a review of the dynamics of a single HH equation for both the deterministic and the stochastically driven case, we proceed to an analysis of the network. This analysis reveals emergent properties of the system such as partial synchronization and synchronization (defined here as a state of the network for which all the neurons spike within a short interval of time), correlation between excitatory and inhibitory conductances, and oscillations in the Gamma-band frequency. The collective behavior enumerated herein is observed when the input-amplitude parameter $S^{EE}$ measuring excitatory-to-excitatory coupling increases to within a certain range. This article contributes to the understanding of how assemblies of inhibitory and excitatory cells interact together to produce rhythms in the network. It also aims to bring problems from neuroscience to the realm of mathematics.
Development of a functional impedimetric immunosensor for accurate detection of thyroid-stimulating hormone

Thyroid-stimulating hormone (TSH), which regulates the synthesis of thyroid gland hormones affecting the whole metabolism, is a pituitary hormone. Determination of TSH is crucial for monitoring thyroid gland-related disorders and some metabolic diseases.In this study, a nonlabeled immunosensor based on covalent immobilization of anti-TSH antibody by using the formation of self-assembled monolayers (SAM) of 4-mercaptophenylacetic acid (4-MPA) and functionalization of carboxyl ends with 1-ethyl-3-(3-dimetilaminopropil) carbodiimide (EDC)/N-Hydroxysuccinimide (NHS) was fabricated for detection of TSH. Immobilization steps including the concentration of 4-MPA, the concentration of anti-TSH antibody, and duration of anti-TSH antibody incubation were optimized by utilizing electrochemical impedance spectroscopy. Under optimal conditions, a sensitive, rapid, and accurate determination of TSH at a concentration range between 0.7 and 3.5 mIU/L was accomplished with a notable linearity and LOD value of 0.034 mIU/L, as well as reproducibility and repeatability. Moreover, for comparison, linear range experiments were also carried out by using other electrochemical methods, including linear sweep voltammetry, cyclic voltammetry, and capacitance spectroscopy. Finally, the constructed immunosensor was used for analyzing TSH levels spiked in the artificial serum samples.
Cancer caregivers unmet needs and emotional states across cancer treatment phases

Study objective To investigate the association between family cancer caregivers’ unmet daily needs and emotional states of depression, anxiety and stress across their care recipient’s treatment phases. Method A cross-sectional study design and self-report questionnaires were used. Family caregivers (N = 237) of cancer patients in ambulatory cancer clinics were recruited from May to December 2017, and completed a sociodemographic and medical questionnaire, the Depression Anxiety Stress Scale and Needs Assessment of Family Caregivers-Cancer Scale. Hierarchical linear regression was conducted to examine the influence of each predictor (sociodemographic variables, unmet personal care and role management needs, cancer treatment phase) on the Depression Anxiety Stress Scale total score, depression subscale, anxiety subscale, and the stress subscale. Results Family caregivers’ unmet daily activity needs, in particular higher unmet personal care needs, during the intermediate phase (6–9 months), were significantly associated (ps<0.05) with overall distress (b = 4.93) and stress (b = 2.26). In the chronic treatment phase (>9 months), the significant association of unmet personal care needs was with overall distress (b = 5.91), anxiety (b = 1.97) and stress (b = 2.53). After completing treatment, unmet role management needs were only significantly associated with stress (b = -1.59). Caregivers’ higher depression was also associated with greater unmet role management needs, regardless of treatment phases. Conclusions Intermediate and chronic cancer treatment phases were identified as having greatest effect on caregivers’ unmet daily activity needs and emotions. Unmet personal care needs played the major effect on overall negative emotional states in the intermediate treatment phase and stress in the chronic treatment phase. Close attention to caregivers needs in intermediate and chronic treatment phases, would be highly beneficial in alleviating negative emotional disturbances.
Speed invariant gait recognition—The enhanced mutual subspace method

This paper introduces an enhanced MSM (Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. The enhanced MSM (eMSM) methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace. Furhtermore, to enhance the discrimination capability, we rotate images over a number of angles, which enables us to extract richer gait features to then be fused by a boosting method. The eMSM methodology is evaluated on existing data sets which provide variable walking speed, i.e. CASIA-C and OU-ISIR gait databases, and it is shown to outperform state-of-the art methods. While the enhancement to MSM discussed in this paper uses combinations of 2D-PCA, rotation, boosting, other combinations of operations may also be advantageous.
Acute Effects of Low- and High-Speed Resistance Exercise on Cognitive Function in Frail Older Nursing-Home Residents: A Randomized Crossover Study

Aim The present study investigated the acute effects of low- and high-speed resistance exercise on the cognitive function of frail older women living in nursing home. Materials and Methods Ten institutionalized frail older women were recruited. Rey Auditory Verbal Learning Test and Stroop test were performed before, immediately after, 1 h after, and 24 h after the end of the experimental session. Participants randomly performed low- and high-speed resistance exercise and a control session. Exercise sessions were composed of 4 resistance exercises with 4–8 sets of 4–10 repetitions at moderate intensity. Results Results indicated that the performance of Rey Auditory Verbal Learning Test was similarly increased immediately after both low- and high-speed resistance exercises. However, only improvements elicited by low-speed resistance exercise remained significant 1 h after the end of the exercise session. No acute effects of resistance exercise were observed on Stroop performance. Conclusion Our findings indicated that both low- and high-speed resistance exercises acutely increased episodic memory in frail older women, whereas no changes on Stroop were observed.
Bioinformatic Analysis of Prognostic and Immune-Related Genes in Pancreatic Cancer

Pancreatic cancer (PC) is a malignant tumor with poor prognosis. The poor effect of surgery and chemotherapy makes the research of immunotherapy target molecules significant. Therefore, identifying the new molecular targets of PC is important for patients. In our study, we systematically analyzed molecular correlates of pancreatic cancer by bioinformatic analysis. We characterized differentially expressed analysis based on the TCGA pancreatic cancer dataset. Then, univariate Cox regression was employed to screen out overall survival- (OS-) related DEGs. Based on these genes, we established a risk signature by the multivariate Cox regression model. The ICGC cohort and GSE62452 cohort were used to validate the reliability of the risk signature. The impact of T lymphocyte-related genes from risk signature was confirmed in PC. Here, we observed the correlation between the T lymphocyte-related genes and the expression level of targeted therapy. We established a five-mRNA (LY6D, ANLN, ZNF488, MYEOV, and SCN11A) prognostic risk signature. Next, we identified ANLN and MYEOV that were associated with T lymphocyte infiltrations (P < 0.05). High ANLN and MYEOV expression levels had a poorer prognosis in decreased T lymphocyte subgroup in PC. Correlation analysis between ANLN and MYEOV and immunomodulators showed that ANLN and MYEOV may have potential value in pancreatic cancer immunotherapy.
Synthesis and molecular docking study of novel COVID-19 inhibitors

In 2020, the world tried to combat the corona virus (COVID-19) pandemic. A proven treatment method specific to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is still not found. In this study, seven new antiviral compounds were designed for COVID-19 treatment. The ability of these compounds to inhibit COVID-19’s RNA processing was calculated by the molecular docking study. It has been observed that the compounds can have high binding affinities especially against NSP12 (between -9.06 and -8.00 kcal/mol). The molecular dynamics simulation of NSP12-ZG 7 complex proved the stability of interaction. The synthesis of two most active molecules was performed by one-pot reaction and characterized by FT-IR, 1H-NMR, 13C-NMR, and mass spectroscopy. The compounds presented with their synthesis are inhibitory core structures against SARS-CoV-2 infection.
Quantification of Volatile Acetone Oligomers Using Ion-Mobility Spectrometry

Background Volatile acetone is a potential biomarker that is elevated in various disease states. Measuring acetone in exhaled breath is complicated by the fact that the molecule might be present as both monomers and dimers, but in inconsistent ratios. Ignoring the molecular form leads to incorrect measured concentrations. Our first goal was to evaluate the monomer-dimer ratio in ambient air, critically ill patients, and rats. Our second goal was to confirm the accuracy of the combined (monomer and dimer) analysis by comparison to a reference calibration system. Methods Volatile acetone intensities from exhaled air of ten intubated, critically ill patients, and ten ventilated Sprague-Dawley rats were recorded using ion-mobility spectrometry. Acetone concentrations in ambient air in an intensive care unit and in a laboratory were determined over 24 hours. The calibration reference was pure acetone vaporized by a gas generator at concentrations from 5 to 45 ppbv (parts per billion by volume). Results Acetone concentrations in ambient laboratory air were only slightly greater (5.6 ppbv; 95% CI 5.1–6.2) than in ambient air in an intensive care unit (5.1 ppbv; 95% CI 4.4–5.5; p < 0.001). Exhaled acetone concentrations were only slightly greater in rats (10.3 ppbv; 95% CI 9.7–10.9) than in critically ill patients (9.5 ppbv; 95% CI 7.9–11.1; p < 0.001). Vaporization yielded acetone monomers (1.3–5.3 mV) and dimers (1.4–621 mV). Acetone concentrations (ppbv) and corresponding acetone monomer and dimer intensities (mV) revealed a high coefficient of determination (R2 = 0.96). The calibration curve for acetone concentration (ppbv) and total acetone (monomers added to twice the dimers; mV) was described by the exponential growth 3-parameter model, with an R2 = 0.98. Conclusion The ratio of acetone monomer and dimer is inconsistent and varies in ambient air from place-to-place and across individual humans and rats. Monomers and dimers must therefore be considered when quantifying acetone. Combining the two accurately assesses total volatile acetone.
Discrimination of lactic acid bacteria Enterococcus and Lactococcus by infrared spectroscopy and multivariate techniques

Raw milk is often described as a major source of lactic acid bacteria for indigenous lactic starter. These indigenous starters contribute to the sensorial quality of cheese. Raw milk, rich in Lactoccocus lactis may therefore be very interesting for the cheese making. Currently, the most commonly used methods to differentiate lactic acid bacteria, and particularly the closely related phenotypes Lactococcus and Enterococcus, are based on DNA sequencing, but the cost and time required for these analytical methods hinder their use for rapid screening of raw material. The present study therefore proposes a simple alternative method to identify and discriminate against Lactococcus and Enterococcus, at the genus, but also at the species level, that is based on collecting near infrared spectra directly from bacterial colonies in Petri dishes. The infrared spectra of 280 strains of Lactococcus and Enterococcus cultured on solid media were collected by using a spectrometer with a wavelength range of 908 to 1684 nm and a remote probe. The best Classification And Regression Trees models for genus and species discrimination gave an excellent classification rate of 87% on an external validation set (30 strains). Loading line plots, with prominent bands at 900–960 and 1270–1390 nm, confirmed that the source of variation was due to changes in the polysaccharides.
Redox Homeostasis and Prospects for Therapeutic Targeting in Neurodegenerative Disorders

Reactive species, such as those of oxygen, nitrogen, and sulfur, are considered part of normal cellular metabolism and play significant roles that can impact several signaling processes in ways that lead to either cellular sustenance, protection, or damage. Cellular redox processes involve a balance in the production of reactive species (RS) and their removal because redox imbalance may facilitate oxidative damage. Physiologically, redox homeostasis is essential for the maintenance of many cellular processes. RS may serve as signaling molecules or cause oxidative cellular damage depending on the delicate equilibrium between RS production and their efficient removal through the use of enzymatic or nonenzymatic cellular mechanisms. Moreover, accumulating evidence suggests that redox imbalance plays a significant role in the progression of several neurodegenerative diseases. For example, studies have shown that redox imbalance in the brain mediates neurodegeneration and alters normal cytoprotective responses to stress. Therefore, this review describes redox homeostasis in neurodegenerative diseases with a focus on Alzheimer's and Parkinson's disease. A clearer understanding of the redox-regulated processes in neurodegenerative disorders may afford opportunities for newer therapeutic strategies.
Effects of step length and cadence on hip moment impulse in the frontal plane during the stance phase

Background An excessive daily cumulative hip moment in the frontal plane (determined as the product of hip moment impulse in the frontal plane during the stance phase and mean number of steps per day) is a risk factor for the progression of hip osteoarthritis. Moreover, walking speed and step length decrease, whereas cadence increases in patients with hip osteoarthritis. However, the effects of step length and cadence on hip moment impulse in the frontal plane during the stance phase are not known. Therefore, this study aimed to examine the effects of step length and cadence on hip moment impulse in the frontal plane during the stance phase. Methods We used a public dataset (kinetic and kinematic data) of over-ground walking and selected 31 participants randomly from the full dataset of 57 participants. The selected participants walked at a self-selected speed and repeated the exercise 15 times. We analyzed the data for all 15 trials for each participant. Multiple regression analysis was performed with the hip moment impulse in the frontal plane during the stance phase as the dependent variable and step length and cadence as independent variables. Results The adjusted R2 in this model was 0.71 (p < 0.001). The standardized partial regression coefficients of step length and cadence were 0.63 (t = 5.24; p < 0.001) and −0.60 (t = − 4.58; p < 0.001), respectively. Conclusions Our results suggest that low cadence, not short step length, increases the hip moment impulse in the frontal plane. Our findings help understand the gait pattern with low hip moment impulse in the frontal plane.
Efficacy of Low Molecular Heparin on Preeclampsia by Inhibiting Apoptosis of Trophoblasts via the p38MAPK Signaling Pathway

Objective To explore the efficacy of low molecular heparin on preeclampsia by inhibiting apoptosis of trophoblasts via the p38MAPK signaling pathway. Methods A preeclampsia rat model was established, and the effects of low molecular heparin on preeclampsia via the p38MAPK signaling pathway were analyzed based on intervention of the rats with different combinations of low molecular heparin and p38MAPK signaling pathway activator. Furthermore, a hypoxia/reoxygenation model of trophoblasts in vitro was established to explore the effects of low molecular heparin on trophoblasts via the p38MAPK signaling pathway. Results After treatment with low molecular heparin, pregnant rats in the heparin group showed significantly decreased blood pressure, 24 h proteinuria, and p38MAPK protein levels in placenta tissues and decreased apoptosis rate of placenta tissue cells (all P < 0.05) and showed more fetal rats and lowered weight of them (both P < 0.05) but showed no significant change in the weight of placenta (all P > 0.05). Pregnant rats treated with low molecular heparin and p38MAPK activator showed significantly higher blood pressure, 24 h proteinuria, and p38MAPK protein levels in placenta tissues and apoptosis rate of placenta tissue cells than those of pregnant rats in the heparin group (all P < 0.05) and also showed less fetal rats and lighter fetal rats than those in the heparin group (both P < 0.05) but showed no difference with them in the weight of placenta (P > 0.05). Further analysis revealed that low molecular heparin could protect the survival and migration of trophoblasts under hypoxia/reoxygenation conditions and reduce apoptosis of them (all P < 0.05). Conclusion Low molecular heparin can alleviate preeclampsia by inhibiting the p38MAPK signaling pathway and can inhibit apoptosis of trophoblasts and promote proliferation and migration of them.
Genome-Wide Expression Difference of MicroRNAs in Basal Cell Carcinoma

Distinct expression of the miRNAs has rarely been explored in basal cell carcinoma (BCC) of skin, and the regulatory role of miRNAs in BCC development remains quite opaque. Here, we collected control tissues from adjacent noncancerous skin (n = 15; control group) and tissues at tumor centers from patients with cheek BCC (n = 15; BCC group) using punch biopsies. After six small RNA sequencing- (sRNA-seq-) based miRNA expression profiles were generated for both BCC and controls, including three biological replicates, we conducted comparative analysis on the sRNA-seq dataset, discovering 181 differentially expressed miRNAs (DEMs) out of the 1,873 miRNAs in BCCs. In order to validate the sRNA-seq data, expression of 15 randomly selected DEMs was measured using the TaqMan probe-based quantitative real-time PCR. Functional analysis of predicted target genes of DEMs in BCCs shows that these miRNAs are primarily involved in various types of cancers, immune response, epithelial growth, and morphogenesis, as well as energy production and metabolism, indicating that BCC development is caused, at least in part, by changes in miRNA regulation for biological and disease processes. In particular, the “basal cell carcinoma pathways” were found to be enriched by predicted DEM targets, and regulatory relationships between DEMs and their targeted genes in this pathway were further uncovered. These results revealed the association between BCCs and abundant miRNA molecules that regulate target genes, functional modules, and signaling pathways in carcinogenesis.
Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning Models

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors, and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. Application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.