Miracle-dz/UniFAR-FTUs huggingface dataset:
Activated barrier crossing : applications in physics, chemistry and biology

The passage of a system from one minimum energy state to another via a potential energy barrier provides a model for the microscopic description of a wide range of physical, chemical and biological phenomena. Examples include diffusion of atoms in solids or on surfaces, flux transitions in superconducting quantum interference devices (SQUIDS), isometrization reactions in solution, electron transfer processes and ligand binding in proteins. In general, both tunnelling and thermally activated barrier crossing may be involved in determining the rate. This book surveys key experiments chosen from physics, chemistry and biology, and describes theoretical methods appropriate for both classical and quantum barrier crossing. A major feature of the book is the attempt to integrate the experimental and theoretical work in one volume.
Analysis of the Relationship between Gut Flora Levels in Childhood Obese Population and Normal Healthy Population Based on Machine Learning

Aims To explore the study of the relationship between the level of gut flora in childhood obese people and normal healthy people based on the analysis of machine learning. Materials and Methods The stools of 54 normal weight, 53 overweight, and 59 obese children from May 2021 to May 2022 were selected. And DNA was extracted, and primers specific for the four bacteria were designed according to the specificity of the four bacteria to the 16 S rDNA gene sequences of the bacteria to be tested, and real-time fluorescence quantitative PCR reactions were performed to compare whether there was any difference in the number of the four bacteria between the three groups. Results. The results of agarose gel electrophoresis showed that the PCR amplification products of all four target bacteria showed clear bands at the corresponding positions, and no nonspecific bands appeared. When compared with the marker, the size matched with the target fragment, indicating good primer specificity. The comparison between normal body recombinant, super recombinant, and obese groups was statistically significant (P < 0.05) for rectal eubacteria, polymorphic anaplasma, bifidobacteria spp. , and lactobacilli. The median number of bifidobacteria in the three groups was significantly higher than the median number of rectal eubacteria, polymorphomycetes, and lactobacilli. The difference in comparison was statistically significant (P < 0.05). Stratified analysis of children's age revealed that normal body composition of Lactobacillus decreased with increasing age, and the difference was statistically significant (P < 0.05). Conclusion An increase in rectal eubacteria and a decrease in polymorphomycetes, bifidobacteria spp. , and lactobacilli may be associated with the development of obesity. The numbers of rectal eubacteria, polymorphic methanobacteria, bifidobacteria spp. , and lactobacilli in the intestine of normal weight and obese children were less affected by sex and age.
Virtual Reality among Individuals with Intellectual and/or Developmental Disabilities: A Systematic Literature Review

With the capability of virtual reality (VR) interventions outside the field of therapeutic recreation to produce meaningful outcomes in various domains for individuals with varying disabilities, it would be valuable to establish VR as an evidence-based therapeutic recreation practice that could greatly benefit individuals with intellectual and/or developmental disabilities (IDD). This systematic literature review explores VR evidence-based practices with individuals with IDD in various fields. Twenty-two databases were searched in October 2020, and 32 articles were reviewed. Thematic topics included: VR increases skills; VR motivates; VR to support traditional services; most effective types of VR; suitability and accessibility of VR; complimentary support for VR implementation and successful engagement; VR challenges, lessons learned, and needed improvements; and full potential of VR not yet realized. Results revealed a variety of implications and future recommendations that support future research to test VR with individuals with IDD and establish evidence-based therapeutic recreation practice.
Secular Trends of Mortality and Years of Life Lost Due to Chronic Obstructive Pulmonary Disease in Wuhan, China from 2010 to 2019: Age-Period-Cohort Analysis

Background: Chronic obstructive pulmonary disease (COPD) has been an important public health issue in China. This study aimed to analyze the temporal trends in mortality and years of life lost (YLL) from COPD, and explore the effects of age, period, and cohort in Wuhan, China from 2010 to 2019. Methods: Data were collected from the cause of death surveillance system in Wuhan. Age-standardized mortality rate (ASMR), age-standardized YLL rate (ASYR) and the estimated annual percent changes (EAPC) were calculated to evaluate the temporal trends. The age-period-cohort (APC) model was adopted to estimate the age, period, and cohort effects. Results: From 2010 to 2019, COPD accounted for 26,051.15 deaths and 394,659.58 person years YLL in Wuhan. Recently, the death burden of COPD in Wuhan has somewhat improved, especially after 2015, with declining trends in ASMR and ASYR. Additionally, the ASMR and ASYR of COPD was higher in males. And these of males showed the overall upward trends, with EAPCs of 1.06 (0.13, 2.00) and 1.21 (0.12, 2.31), respectively, while females showed downward trends since 2010. According to APC model, the age effect of COPD increased with age, and the cohort risk ratios (RRs) followed the overall downward trends. Period RRs for the Wuhan population generally tended to rise and then fall, with females showing a clear downward trend after 2015, while period RRs for males maintained an upward trend throughout the study period. Conclusions: Recently, the death burden from COPD in Wuhan has improved, especially after 2015, with improvements in ASMR, ASYR and period RRs. Sex differences still exist. COPD posed a greater threat to the elderly, especially males. Public health managers should continue to execute more targeted programs to lessen the death burden of COPD in Wuhan.
Health Wearable IoT (WIoT) Technology Devices Security and Privacy Vulnerability Analysis

In aura and era of the Internet of Things (IoT) and the fourth industrial revolution, modern wearable electronic devices and their communication networks are marching into every corner of modern society and changing every aspect of our daily life. Thus, the progress of digitalization including miniaturization of sensor and wearable technology and its growing importance of physical and psychological well-being have a tremendous impact on almost all consumer goods from wearable to nonwearable industries. Different types of signals are used in communication between the devices for wireless transmission of data. such as Radio Frequency, Infrared, and Lightwave Transmissions. Wearable devices are becoming a hot topic in many fields such as medical, fashion, education, etc. Digital dependency of WIoT devices, introduced new security challenges, and vulnerabilities. This research is focused on Fitness Wearable Technology Devices Security and Privacy Vulnerability Analysis and highlights the importance of this topic by revealing the potential security concerns. Fog Computing, Sidera and Blockchain technologies were researched as Security Techniques to enhance security and efficiency while providing access to medical and personal records.
Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy.

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium-glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin-angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P = 0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P = 0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P = 0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years. (Funded by Janssen Research and Development; CREDENCE ClinicalTrials.gov number, NCT02065791.).
Primary TSC2-/meth Cells Induce Follicular Neogenesis in an Innovative TSC Mouse Model

Cutaneous lesions are one of the hallmarks of tuberous sclerosis complex (TSC), a genetic disease in which mTOR is hyperactivated due to the lack of hamartin or tuberin. To date, novel pharmacological treatments for TSC cutaneous lesions that are benign but still have an impact on a patient’s life are needed, because neither surgery nor rapamycin administration prevents their recurrence. Here, we demonstrated that primary TSC2-/meth cells that do not express tuberin for an epigenetic event caused cutaneous lesions and follicular neogenesis when they were subcutaneously injected in nude mice. Tuberin-null cells localized in the hair bulbs and alongside mature hairs, where high phosphorylation of S6 and Erk indicated mTOR hyperactivation. Interestingly, 5-azacytidine treatment reduced hair follicles, indicating that chromatin remodeling agents might be effective on TSC lesions in which cells lack tuberin for an epigenetic event. Moreover, we demonstrated that the primary TSC2-/meth cells had metastatic capability: when subcutaneously injected, they reached the bloodstream and lymphatics and invaded the lungs, causing the enlargement of the alveolar walls. The capability of TSC2-/meth cells to survive and migrate in vivo makes our mouse model ideal to follow the progression of the disease and test potential pharmacological treatments in a time-dependent manner.
EFog-IoT: Harnessing Power Consumption in Fog-Assisted of Things

The ever-increasing use of Internet of Things (IoT) devices like smartphones, PDAs, smartwatches, etc. by the users has also drastically increased the volume of data that needs to be processed by the cloud servers. The cloud servers are very powerful and are capable of processing data at once. However, being a centralized paradigm and the existence of the physical gap from the IoT layer, it is incapable of bulk processing of data thereby resulting in latency overhead, increased power consumption, and increased service rate. This work proposes an energy-efficient method with the introduction of Fog computing as an intermediate layer between the IoT and the Cloud for computing the tremendous data generated by the IoT devices in a distributed manner in order to reduce the power consumption. Here, a Multi-level Feedback Queue is used for target node classification for minimizing the service rate, and the Fuzzy C-means++ approach is applied for clustering of available fog nodes for parallel processing. This research proposes a parallel algorithm devised for task scheduling in the fog nodes which results in power consumption while maximizing the resource utilization. Simulation results show that the proposed method performs at 98% efficiency in processing all the tasks generated by the users.
Generation of High-order Modes in Sub-THz Dielectric Waveguides by Misalignment of the Transition Structure

Dielectric waveguides are a potential platform for future integrated THz electronics but are still challenging to interface with standard measurement equipment. The commonly used tapered transition to hollow metallic waveguides involves manual alignment, leading to measurement inaccuracies. In this paper, we report the effects of misalignment between a dielectric and a metallic waveguide in the W-band. The results indicate the excitation of unwanted high-order modes in the dielectric waveguide that are expected to degrade the performance, especially at higher frequencies.
The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]

Any data modeling exercise has two main components: parameter estimation and model selection. The latter will be the topic of this lecture note. More concretely, we introduce several Monte-Carlo sampling-based rules for model selection using the maximum a posteriori (MAP) approach. Model selection problems are omnipresent in signal processing applications: examples include selecting the order of an autoregressive predictor, the length of the impulse response of a communication channel, the number of source signals impinging on an array of sensors, the order of a polynomial trend, the number of components of a nuclear magnetic resonance signal, and so on.
A Phenomenological Model-Based Controller for Position Tracking of a Pneumatic Muscle Actuator Driven Setup

Pneumatic muscle actuators (PMAs) have been attracting attention in recent years due to characteristics of natural compliance, high power output, lightweight, and low cost. Various apparatus and robots are developed by adopting PMAs and kinds of advanced controlling strategies are proposed to improve achieving accuracy. However, many of the proposed models and strategies are very complex and not suitable for the actual application. In this paper, we devise a single PMA driven setup and build up empirical quartic polynomial models of the apparatus by fitting sampled pressure-displacement data. The novel models are compact and easily computable. An improved PID algorithm by employing the phenomenological models as feed-forward is devised to improve the dynamical response of the setup. The experiments are performed to assess the proposed controller in improving the position accuracy of the apparatus.
Unsupervised Deep Sparse Features Extraction for SAR Image Segmentation

Deep learning (DL) methods usually need to collect a large amount of labeled data to extract deep features. However, due to the difficulty of obtaining numerous labeled data from synthetic aperture radar (SAR) images, unsupervised feature learning has been focused on SAR image processing. In this article, we propose a 3-D sparse model (3-DSM) to extract deep sparse features from SAR images in an unsupervised way. Concretely, 3-DSM learns the convolution kernels by minimizing the error between the features and the constructed sparse maps, without labeled samples. Thus, the discriminative features can be extracted in an unsupervised way by the learned convolution kernels and are able to capture the main structural information of SAR images. Furthermore, to the best of our knowledge, 3-DSM first specifies the sparsity of convolution kernels, with each convolution kernel exhibiting its independence from the others and the redundancy of convolution kernels being diminishing. It means that each convolution kernel extracts its unique structural features of SAR images. Consequently, in the feature extraction, 3-D sparsities have been specified, including width, height, and depth, with the acquisition of discriminative less-redundant features. The effectiveness of 3-DSM is demonstrated by the feature extraction and segmentation of the simulated and real SAR images.
SPINOPELVIC MOBILITY IN PATIENTS WITH HIP OSTEOARTHRITIS AND TOTAL HIP ARTHROPLASTY INDICATION

ABSTRACT Introduction: Reduction of spinopelvic mobility is associated with an increased dislocation of total hip arthroplasty (THA). Objective: To assess 1) spinopelvic mobility in patients with primary hip osteoarthritis and THA indication and 2) the influence of hip flexion contracture on spinopelvic mobility. Methods: Thirty adult patients with primary hip osteoarthritis and THA indication were evaluated using radiographic parameters (pelvic incidence, pelvic tilt, sacral slope, lumbar flexibility, and spinopelvic mobility). Results: Spinopelvic mobility ranged from 6.90 a 54.50° (mean 32.79 ± 11.42) and the group of patients with hip flexion contracture had higher mobility. Spinopelvic mobility was correlated with pelvic tilt as well as with lumbar flexibility. Conclusion: Around 13.4% of patients had spinopelvic mobility under 20° , indicating reduced spinopelvic mobility and risk of THA dislocation. Level of Evidence III, Retrospective Comparative Study.
A Shape Generation Method for 3D Printed Antennas With Unintuitive Geometries

Additive manufacturing used in combination with versatile shape generation methods can enable designers to realize unintuitive antenna designs with bespoke electromagnetic behaviors that would normally be extremely difficult or even impossible to manufacture using conventional techniques. In this paper, we present a new custom algorithm that produces arbitrary three-dimensional (3D) meander line antennas. This algorithm is used in conjunction with multi-objective optimization to create two quadrifilar helix antennas (QHAs) and one monopole antenna, all with unique electromagnetic performances. One QHA has a wide bandwidth, high broadside gain, and compact size, while the other has a dual-band nature with different radiation patterns in each band. Similarly, the monopole example is a dual-wideband design which targets Wi-Fi applications. These structures possess a meander radius that varies with height as well as conductor thickness that varies along the meander path which allows for improved antenna performance over conventional meander line antennas. An example design was fabricated and tested in order to validate the performance of the optimized virtual antenna model.
Analysis of the Equilibrium Distribution of Ligands in Heterogeneous Media–Approaches and Pitfalls

The equilibrium distribution of small molecules (ligands) between binding agents in heterogeneous media is an important property that determines their activity. Heterogeneous systems containing proteins and lipid membranes are particularly relevant due to their prevalence in biological systems, and their importance to ligand distribution, which, in turn, is crucial to ligand’s availability and biological activity. In this work, we review several approaches and formalisms for the analysis of the equilibrium distribution of ligands in the presence of proteins, lipid membranes, or both. Special attention is given to common pitfalls in the analysis, with the establishment of the validity limits for the distinct approaches. Due to its widespread use, special attention is given to the characterization of ligand binding through the analysis of Stern–Volmer plots of protein fluorescence quenching. Systems of increasing complexity are considered, from proteins with single to multiple binding sites, from ligands interacting with proteins only to biomembranes containing lipid bilayers and membrane proteins. A new formalism is proposed, in which ligand binding is treated as a partition process, while considering the saturation of protein binding sites. This formalism is particularly useful for the characterization of interaction with membrane proteins.
Real-Life Performance of F-18-FDG PET/CT in Patients with Cervical Lymph Node Metastasis of Unknown Primary Tumor

Background: Neoplasms in the head and neck region possess higher glycolytic activity than normal tissue, showing increased glucose metabolism. F-18-Flourodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) can identify an unknown primary tumor (CUP). Aim: The aim of this study was to assess the real-life performance of F-18-FDG-PET/CT in detecting primary sites in patients with cervical lymph node metastasis of CUP. Methods: A retrospective data analysis of 31 patients who received FDG-PET/CT between June 2009 and March 2015 in a CUP context with histologically confirmed cervical lymph node metastasis was included. Results: In 48% of the patients (15/31), PET/CT showed suspicious tracer accumulation. In 52% of the patients (16/31), there was no suspicious radiotracer uptake, which was confirmed by the lack of identification of any primary tumor in 10 cases until the end of follow-up. FDG-PET/CT had a sensitivity of 67%, specificity of 91%, PPV of 92%, and NPV of 63% in detecting the primary tumor. Additionally, PET/CT showed suspicious tracer accumulation according to further metastasis in 32% of the patients (10/31). Conclusion: FDG-PET/CT imaging is a useful technique for primary tumor detection in patients in a cervical CUP context. Furthermore, it provides information on the ulterior metastasis of the disease.