Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of clinical effect of titanium-prepared platelet-rich fibrin and platelet- rich fibrin in treatment of gingival recession

Objectives: The aim of the study is to evaluate the effectiveness of titanium prepared platelet rich fibrin (TPRF) for treatment of gingival recession and comparing it to platelet rich fibrin (PRF). Subjects and methods : This study was conducted on subjects having multiple gingival recession. Twenty-four patients of both sexes, aged between (23-41) and having miller class I, II were selected to participate in the present study. Subjects were randomly divided into two equal groups with coin toss method. The plaque index, gingival index, Periodontal pocket depth, Recession width, Recession depth, Clinical attachment level, Keratinized tissue width, keratinized gingival mucosa, Gingival thickness, T-TRF membrane thickness, and VAS were measured. Results : At 6 and 9 months: there was a statistically significant difference in mean RD in the two groups. T. PRF group showed a less mean RD, RW, and KTW than PRF group. At 9 months: T. PRF group showed a significant higher mean GT than PRF group. At day 3 and 7: T. PRF group showed a significant lower mean VAS than PRF group. Conclusion: In conclusion Within the limits of this study, the results demonstrated that T-PRF procedure is a safe, effective method in treating gingival recessions. In addition, this procedure can be recommended to treat localized or multiple-adjacent gingival recessions without additional surgery. However, future randomized clinical trials with a split-mouth design and larger sample size are essential for evaluating the TPRF efficiency in gingival
Convergence analysis of least squares-Epsilon-Ritz algorithm for solving a general class of pantograph equations

In this paper, we propose an approximate method for solving a general class of pantograph differential equations. The proposed method is based on a combination of least squares, epsilon and Ritz methods. The convergence properties of the method are analyzed and discussed. Finally, several numerical examples are given to illustrate the applicability and efficiency of the method.
Does health spending affect the economic growth of lower-middle-income economies? CS-ARDL approach

Spending on healthcare may lead to improved health outcomes, which can stimulate human capital growth, increase productivity, and boost the economy. Accordingly, this paper aims to assess whether health spending influences the growth of lower-middle-income countries between 2000 and 2019 in both short-and long-term. Since government and private health expenditures account for the vast majority of healthcare expenditures, the study employs two models to examine whether health expenditures affect economic growth. The first model examines private health spending’s influence on economic growth, whereas the second model analyzes general government health spending’s influence on economic growth. The study uses "cross-sectional autoregressive distributed lag (CS-ARDL)" method, along with extra diagnostic and specification tests. The findings of the two models for the overall sample reveal that health spending had no significant influence on GDP per capita due to the insignificant health spending in these countries. Moreover, the study divides the main data set into two groups based on the average current health expenditure as a percentage of GDP from 2000 to 2019. The first subgroup includes the countries with an average health expenditure lower than 5%, and the second subgroup includes the countries with an average equal to or greater than 5%, and the study conducts the CS-ARDL technique on both models for these subgroups. The findings reveal that all variables are insignificant in both models for the first subgroup. For the second subgroup, all variables are insignificant in both models except government health expenditure which is significant in the long term with economic growth, indicating that increasing health government spending may affect economic growth in the long term. Therefore, lower-middle-income countries have to prioritize public health spending and develop alternative funding sources, while also supporting growth-oriented policies and collaborating with private health sector.
Phonation gestures studied with HSDI and their glottal air-flow and EGG correlates

Current high-speed digital imaging (HSDI) allows thousands of images per second to be obtained, hence it provides significant information on the behavior of the vocal folds (VF) during phonation. Here we present our findings of glottis behavior for normal and exaggerated phonatory modes studied with HSDI, acoustics, and EGG.
Anti-seizure Medication Classification using EEG signals via Attention-based CNN

Anti-Seizure medications (also known as anticonvulsants or anti-epileptic medications) are a broad class of medicines mostly used to treat epileptic seizures. Objectively relating anticonvulsant identification with electroencephalogram (EEG) signals of the subject having a history of taking medication(s), can result in evaluating the effectiveness of the course of treatments for seizure-related illness, which is helpful in patient care. In this work, we propose a deep learning based method to discriminate the EEG recordings of the subjects who have used anti-epileptic medicines in the past from those who have not. Specifically, EEG recordings taken from the subjects are used to distinguish between the presence of the two common anticonvulsants, namely- keppra & dilantin; and the presence of no anticonvulsants at all. We extracted frequency domain features from the EEG recordings using the Fourier transform, and we then used the information as input to the proposed model. Frequency domain features are extracted since the frequency domain representation of EEG signals has some notable differences across the medication paradigms. We then suggest a model based on a convolutional neural network framework with attention to solve the three class (keppra, dilantin, and no medication) classification problem. The attention network aids in giving the features differential weights. The model's classification performance is shown to increase as a result of differential weighting, which makes it possible to focus more on the pertinent aspects needed for classification. We demonstrate that our approach yields improvements over the state-of-the-art results using the standard publicly available Temple University Hospital EEG Corpus (TUEG).
Effect of castration and vasectomy on some oxidative stress parameters and blood hormone levels in rats

ABSTRACT Vasectomy and castration are the most preferred surgical methods to control reproduction in males. While sexual functions are terminated reversibly in vasectomy, they are removed irreversibly in castration. After these processes, changes are observed in hormones and oxidative stress parameters. In this study, we investigated the effects of vasectomy and castration operations on blood follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone, nitric oxide (NO) and malondialdehyde (MDA) levels in rats. As a result of the analysis, it was determined that FSH, LH, NO, and MDA levels increased (p<0.05) and testosterone levels decreased (p<0.05) in the castration group compared to the sham and vasectomy groups. Considering the data obtained from the present study, when the two operations (vasectomy and castration) are compared in rats, which are preferred for the control of reproduction, it is thought that vasectomy is a healthier method because it is reversible, does not affect hormone levels, and does not increase oxidative stress.
A Millimeter-Wave Antenna With Filtering Radiation Response Based on Absorptive Ring Feeding Circuit

This letter presents and analyzes an absorptive filtering antenna design operating in the 5G millimeter-wave (mm-wave) band. It consists of a two-port ring feeding circuit, a magneto-electric dipole (ME-dipole), and a square metal loop. The two-port ring circuit integrates bandstop-type transmission performance. Within the stopband of this two-port ring circuit, two controllable transmission zeros are deliberately generated to prevent the input signal from transmitting from one port to the other. As a result, the antenna operates as an effective radiator within this band. However, at the upper band frequencies, the input signal transmits from one port to the other, and thus the antenna does not work efficiently. To further improve the out-of-band radiation suppression level, a square metal loop around the ME-dipole arms is used. Based on such a simple structure, a bandpass-type filtering radiation response is realized. For demonstration, a filtering antenna array for 5G mm-wave applications (n257/n258/n261 bands: 24.25–29.5 GHz) is implemented and tested as an example. The measured average in-band gain is about 11.5 dBi, and a better than 16 dB out-of-band radiation rejection level is achieved.
Direct method for Solving Nonlinear Variational Problems by Using Hermite Wavelets

Abstract: In this work, we first construct Hermite wavelets on the interval [0,1) with it’s product, Operational matrix of integration is derived, and used it for solving nonlinear Variational problems with reduced it to a system of algebric equations and aid of direct method. Finally, some examples are given to illustrate the efficiency and performance of presented method.
Sparsest representations and approximations of an underdetermined linear system

In an underdetermined linear system of equations, constrained l1 minimization methods such as the basis pursuit or the lasso are often used to recover one of the sparsest representations or approximations of the system. The null space property is a sufficient and ‘almost’ necessary condition to recover a sparsest representation with the basis pursuit. Unfortunately, this property cannot be easily checked. On the other hand, the mutual coherence is an easily checkable sufficient condition insuring the basis pursuit to recover one of the sparsest representations. Because the mutual coherence condition is too strong, it is hardly met in practice. Even if one of these conditions holds, to our knowledge, there is no theoretical result insuring that the lasso solution is one of the sparsest approximations. In this article, we study a novel constrained problem that gives, without any condition, one of the sparsest representations or approximations. To solve this problem, we provide a numerical method and we prove its convergence. Numerical experiments show that this approach gives better results than both the basis pursuit problem and the reweighted l1 minimization problem.
A Highly Configurable EMA and JITAI Mobile App Framework Utilized in a Large-Scale German Study on Breast Cancer Aftercare

A growing number of observational studies are utilizing the advances of mobile technology. In addition to diverse strategies such as digital phenotyping, ecological momentary assessments (EMA) or mobile Crowdsensing, intervention studies are becoming increasingly relevant in this context. More specifically, Just-in-Time Adaptive Interventions (JITAIs) appear to be a key component in mobile health for behavioral support, aimed at providing the proper kind of aid at the right time by dynamically adapting to a person's changing condition. Fully or partially electronically supported treatments can relieve the health system and bridge or shorten the waiting time for treatments for patients. In this work we combine our technical expertise with the theoretical foundations of JITAIs in an interdisciplinary collaboration to facilitate the appropriate use of theory in building JITAIs in a dynamic system. As a result, we present a highly configurable, generic and modular EMA and JITAI mobile framework which enabled us to generate a cross-platform breast cancer aftercare mobile app in a large-scale German study. The aim is to learn more about the validity, usefulness and feasibility of such mobile-device-assisted studies, taking into account administrative burden as well as user acceptance. We discuss the background, implementation, and whether these features could leverage similar study types in the future to overcome the static nature of existing behavioral and interventional apps.
MIMO YOLO - A Multiple Input Multiple Output Model for Automatic Cell Counting

Across basic research studies, cell counting requires significant human time and expertise. Trained experts use thin focal plane scanning to count (click) cells in stained biological tissue. This computer-assisted process (optical disector) requires a well-trained human to select a unique best z-plane of focus for counting cells of interest. Though accurate, this approach typically requires an hour per case and is prone to inter-and intra-rater errors. Our group has previously proposed deep learning (DL)-based methods to automate these counts using cell segmentation at high magnification. Here we propose a novel You Only Look Once (YOLO) model that performs cell detection on multi-channel z-plane images (disector stack). This automated Multiple Input Multiple Output (MIMO) version of the optical disector method uses an entire z-stack of microscopy images as its input, and outputs cell detections (counts) with a bounding box of each cell and class corresponding to the z-plane where the cell appears in best focus. Compared to the previous segmentation methods, the proposed method does not require time-and labor-intensive ground truth segmentation masks for training, while producing comparable accuracy to current segmentation-based automatic counts. The MIMO-YOLO method was evaluated on systematic-random samples of NeuN-stained tissue sections through the neocortex of mouse brains (n=7). Using a cross validation scheme, this method showed the ability to correctly count total neuron numbers with accuracy close to human experts and with 100% repeatability (Test-Retest).
Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review

Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs.
Xylose Isomerase Depletion Enhances Virulence of Xanthomonas citri subsp. citri in Citrus aurantifolia

Citrus canker, caused by the bacterium Xanthomonas citri (Xcc), is one of the most devastating diseases for the citrus industry. Xylose is a constituent of the cell wall of plants, and the ability of Xcc to use this carbohydrate may play a role in virulence. Xcc has two genes codifying for xylose isomerase (XI), a bifunctional enzyme that interconverts D-xylose into D-xylulose and D-glucose into D-fructose. The aim of this work was to investigate the functional role of the two putative XI ORFs, XAC1776 (xylA1) and XAC4225 (xylA2), in Xcc pathogenicity. XI-coding genes of Xcc were deleted, and the single mutants (XccΔxylA1 or XccΔxylA2) or the double mutant (XccΔxylA1ΔxylA2) remained viable. The deletion of one or both XI genes (xylA1 and/or xylA2) increased the aggressiveness of the mutants, causing disease symptoms. RT-qPCR analysis of wild strain and xylA deletion mutants grown in vivo and in vitro revealed that the highest expression level of hrpX and xylR was observed in vivo for the double mutant. The results indicate that XI depletion increases the expression of the hrp regulatory genes in Xcc. We concluded that the intracellular accumulation of xylose enhances Xcc virulence.
Predicting Lung Function Using Biomarkers in Alpha-1 Antitrypsin Deficiency

Lung disease progression in alpha-1 antitrypsin deficiency (AATD) is heterogenous and manifests in different ways. Blood biomarkers are an attractive method of monitoring diseases as they are easy to obtain and repeatable. In non-AATD COPD, blood biomarker panels have predicted disease severity, progression, and mortality. We measured a panel of seven serum biomarkers in 200 AATD patients and compared levels between those with COPD and those without. We assessed whether biomarkers were associated with baseline lung function parameters (FEV1 and TLco) or absolute change in these parameters. In total, 111 patients with a severely deficient genotype of AATD (PiZZ) and COPD were included in the analyses. Pearson’s correlation coefficient was measured for biomarker correlations and models were compared using ANOVA. CRP and CCL18 were significantly higher in the serum of AATD COPD versus AATD with no COPD. Biomarkers were not predictive of cross-sectional lung function measurements, however, CC16 was significantly associated with an absolute change in TLco (p = 0.018). An addition of biomarkers to the predictive model for TLco added significant value over covariates alone (R2 0.13 vs. 0.02, p = 0.028). Our findings suggest that CC16 is predictive of emphysema progression in AATD COPD. Proteomics data may reveal alternative candidate biomarkers and further work should include the use of longitudinal biomarker measurements.
Attached Eddy Model of Wall Turbulence

Modeling wall turbulence remains a major challenge, as a sufficient physical understanding of these flows is still lacking. In an effort to move toward a physics-based model, A.A. Townsend introduced the hypothesis that the dominant energy-containing motions in wall turbulence are due to large eddies attached to the wall. From this simple hypothesis, the attached eddy model evolved, which has proven to be highly effective in predicting velocity statistics and providing a framework for interpreting the energy-containing flow physics at high Reynolds numbers. This review summarizes the hypothesis itself and the modeling attempts made thereafter, with a focus on the validity of the model's assumptions and its limitations. Here, we review studies on this topic, which have markedly increased in recent years, highlighting refinements, extensions, and promising future directions for attached eddy modeling.
Identification of Delamination in Composite Beams using the Fractal Dimension-Based Damage Identification Algorithm

Abstract Damage detection and identification is one of the most important tasks of proper operation of technical objects and structures. It is, therefore, essential to develop efficient and sensitive methods of early damage detection. Delamination is the type of damage occurring in laminated composites that is one of the most dangerous and most difficult to detect. In this paper, the computational study was performed on the numerical data of the modal shapes of laminated composite beams with simulated delaminations in order to detect them using a fractal dimension-based approach. The obtained results allowed for improvement of detection accuracy as compared to previously applied wavelet-based approach. An additional benefit was decreasing the computational time. Basing on the obtained results it is reasonable to consider the presented approach as a promising alternative to currently applied signal processing methods used for supporting non-destructive testing of structures.
A method to shorten setting time via outer feedback adjusting invariant set based on reference governor

In a quick motion system, the shortest setting time is usually pursued, while for a system existing both input constraint and state constraints, by using traditional reference governor, maybe there are one phenomenon: some state don't achieve maximal valve due to shape of invariant set, This will lead to a longer setting time. This paper provides a method to shorten setting time of output via an outer feedback based on traditional reference governor. Outer feedback is computed by solving a convex non-constrained quadratic optimization. For linear system with outer feedback, different invariant set is computed based on reference governor. Meanwhile, this paper describes a built process of serval invariant sets and provides a most big invariant set just to fulfill the first receding horizon to get the shortest setting time.
Microfluidic generation of self-contained multicomponent microcapsules for self-healing materials

Bioinspired self-healing carriers can cost-effectively repair crack damage in functional materials without detection, analysis, or any type of human intervention. In this study, an effective structured self-healing microcarrier is prepared through a single-step microfluidic method. These microcarriers are self-contained core-shell microcapsules with the healant and the curing agent encapsulated in separate chambers of a single capsule. Because microfluidics are versatile, our fabrication method generates unique structures in which the ratio of the agents can be precisely adjusted, and thus, the reaction kinetics and the cost can be well controlled. Such carriers can be embedded into various material matrices, including bulk pellets and film materials, which maintain satisfactory mechanical properties after being damaged and repaired. These attractive features may establish an approach for microfluidic preparation of self-healing materials and may open up avenues for the development of intelligent self-healing materials for military equipment, electronic products, architectural materials, etc.Bioinspired self-healing carriers can cost-effectively repair crack damage in functional materials without detection, analysis, or any type of human intervention. In this study, an effective structured self-healing microcarrier is prepared through a single-step microfluidic method. These microcarriers are self-contained core-shell microcapsules with the healant and the curing agent encapsulated in separate chambers of a single capsule. Because microfluidics are versatile, our fabrication method generates unique structures in which the ratio of the agents can be precisely adjusted, and thus, the reaction kinetics and the cost can be well controlled. Such carriers can be embedded into various material matrices, including bulk pellets and film materials, which maintain satisfactory mechanical properties after being damaged and repaired. These attractive features may establish an approach for microfluidic preparation of self-healing materials and may open up avenues for the development of intelligent self-hea.
Ridge estimation in functional measurement error models

We propose a ridge estimator as an alternative estimator in the presence of collinearity among the elements of unobservable values in functional measurement error models Modifications of the estimator are also considered and properties derived which are analogous to those known in ordinary linear models. Our simulation results show that the modified ridge estimate will have shorter length, mean square errors and standard errors, than other estimates of the coefficients of the model.