Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of vitamin E on non-proliferative diabetic retinopathy in type 2 diabetes mellitus: Are they sustainable with 12 months of therapy

Introduction: Prolonged uncontrolled hyperglycaemia has shown to cause oxidative stress, inflammation, thrombosis and upregulation of angiogenesis in diabetics, which all contributes to diabetic retinopathy development and progression. Vitamin E is found to have anti-inflammatory, anti-oxidative, anti-thrombogenic and anti-angiogenesis which could play an important role in early treatment of diabetic retinopathy. This study aims to investigate the effect of Tocotrienol-rich vitamin E (Tocovid) on the progression of retinal microhaemorrhages and diabetic macular oedema in patients with diabetic retinopathy. Method: This is a multi-centred, randomized, double-blinded, placebo-controlled trial which involved 55 eligible participants. The participants in the treatment group (n = 22) received Tocovid 200 mg twice daily while those in the placebo group (n = 23) would receive placebo twice daily. Both groups will be on the treatment for a total duration of 12 months. Both retinal signs will be assessed at baseline, 2 months, 6 months and 12 months of treatment to determine the progression of diabetic retinopathy. Serum vascular endothelial growth factor which reflects on the angiogenesis process in the eye was analysed as well at similar time points as the retinal findings. Results: After 12 months of treatment, the placebo group had a significant increase of 23.42% in retinal microhaemorrhages (p < 0.05), but the Tocovid group had no significant changes. Moreover, the Tocovid group showed a significant decrease of 48.38% in area of diabetic macular oedema over the 12 months period (p < 0.05), but the placebo group had no significant changes. Meanwhile, there was no significant difference in serum vascular endothelial growth factor level when comparing between both groups. Conclusion: These findings could indicate that Tocovid has an important role in preventing early diabetic retinopathy progression.
Understanding the relationship between image quality and convolutional neural network performance

Substantial research has explored methods to optimize convolutional neural networks (CNNs) for tasks such as image classification and object detection, but research into the image quality drivers of computer vision performance has been limited. Additionally, there are indications that image degradations such as blur and noise affect human visual interpretation and machine interpretation differently. The General Image Quality Equation (GIQE) predicts overhead image quality for human analysis using the the National Image Interpretability Rating Scale (NIIRS), but no such model exists to predict image quality for interpretation by CNNs. Here, we assess the relationship between image quality variables and convolutional neural network (CNN) performance using an information-theoretic framework. Specifically, we examine the impacts of resolution, blur, and noise on CNN performance for models trained with in-distribution and out-of-distribution distortions. We explore the relationships between CNN performance and image information content as measured by standard Shannon entropy and a similar metric based on image gradients. Using two datasets, we observe that while generalization remains a significant challenge for CNNs faced with out-of-distribution image distortions, CNN performance against low visual quality images remains strong with appropriate training, indicating the potential to expand the design trade space for sensors providing data to computer vision systems
Which is the best choice for Helicobacter pylori eradication? Dual therapy or quadruple therapy?

Introduction Standard triple therapy used to be the first-line treatment for Helicobacter pylori (Hp) infection, but today it is a treatment regimen with a low eradication rate due to increasing resistance to the antibiotics included in the triple therapy. Aim: To compare the eradication rates of dual treatment regimens and quadruple treatment regimens. Material and methods Patients over 18 years of age, who were indicated to undergo upper gastrointestinal system (GIS) endoscopy for any reason, had their upper gastrointestinal endoscopy performed, were detected to have Hp as a result of the histopathological evaluation of the biopsy material, and in whom eradication control was also performed by histopathological evaluation, were included in the study. These patients were divided into 4 groups, each containing 50 people. Results Considering the eradication rates of Hp-positive patients according to different treatment options, 78% (n = 39) of the patients in Group 1 were eradicated after the treatment while 66% (n = 33) of the patients in Group 3, 58% of the patients in Group 2 (n = 29), and 58% (n = 29) of the patients in Group 4 had Hp negative results after treatment. When high-dose dual treatments were compared, the highest eradication rate was obtained with rabeprazole, but no statistically significant difference was detected (p = 0.11). Conclusions This is the first study to include dual therapies consisting of 3 different PPIs at the same time. The low eradication rates obtained in our study suggest that the determination of individualized treatment strategies in which CYP2C19 polymorphism is detected may result in higher eradication rates.
DTTNet: depth transverse transformer network for monocular depth estimation

Depth estimation is an essential component in understanding the 3D geometry of a scene. In comparison to traditional depth estimation methods such as structure from motion and stereo vision matching, determining depth relation using a single camera is challenging. The recent advancements in convolutional neural networks have accelerated the research in monocular depth estimation. However, most technologies infer depth maps using lower resolution images due to network capacity and complexity issues. Another challenge in depth estimation is ambiguous and sparse depth maps. These issues are caused due to labeling errors, hardware faults, or occlusions. This paper presents a novel end-to-end trainable convolutional neural network architecture – depth transverse transformer network (DTTNet). The proposed network is designed and optimized to perform monocular depth estimation. This network aims at exploring the multi-resolution representation to perform pixel-wise depth estimation more accurately. In order to improve the accuracy of depth estimation, different kinds of ad hoc networks are proposed subsequently. Extensive computer simulations on NYU Depth V2 and SUN RGB-D dataset demonstrate the effectiveness of the proposed DTTNet against state-of-the-art methods. DTTNet can potentially optimize depth perception in intelligent systems such as automated driving and video surveillance applications, computational photography, and augmented reality. The source code is available at https://github.com/shreyaskamathkm/DTTNet
Study of Decay Characteristics of Hexagonal and Square Supersonic Jet

Abstract Experiments were carried on nozzles with different exit geometry to study their impact on supersonic core length. Circular, hexagonal, and square exit geometries were considered for the study. Numerical simulations and schlieren image study were performed. The supersonic core decay was found to be of different length for different exit geometries, though the throat to exit area ratio was kept constant. The impact of nozzle exit geometry is to enhance the mixing of primary flow with ambient air, without requiring tab, wire or secondary method to increase the mixing characteristics. The non-circular mixing is faster comparative to circular geometry, which leads to reduction in supersonic core length. The results depict that shorter the hydraulic diameter, the jet mixing is faster. To avoid the losses in divergent section, the cross section of throat was maintained at same geometry as the exit geometry. Investigation shows that the supersonic core region is dependent on the hydraulic diameter and the diagonal. In addition, it has been observed that number of shock cells remain the same irrespective of exit geometry shape for the given nozzle pressure ratio.
Fronthaul-Constrained Uplink Cloud Radio-Access Networks: Capacity Analysis and Algorithm Design

Fronthaul-Constrained Uplink Cloud Radio-Access Networks: Capacity Analysis and Algorithm Design Yuhan Zhou Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2016 This thesis considers the uplink of a cloud radio access network (C-RAN), in which base-stations (BSs) are connected to a cloud-computing based central processor (CP) via noiseless fronthaul links with finite capacities. The compress-and-forward strategy is employed, where the BSs compress the received signals and send the quantized bits to the CP. Then, the CP performs either joint decoding of both the quantization and message codewords simultaneously, or generalized successive decoding of quantization and message codewords in an arbitrary order. Under this setup, this thesis establishes several information theoretic results and proposes a number of practical algorithm designs. From a theoretical perspective, this thesis first proves that under joint decoding and Gaussian input, Gaussian quantization maximizes the achievable rate region. Second, it is shown that generalized successive decoding achieves the identical rate region as joint decoding under a sum fronthaul capacity constraint. Third, a particular successive decoding scheme, in which quantization codewords are decoded first followed by message codewords, referred to as the virtual multiple-access channel (VMAC) scheme, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. Furthermore, it is shown that under a sum fronthaul constraint, Wyner-Ziv coding, quantized at the background noise level, can achieve the sum-capacity to within a constant gap. A similar constant-gap result is shown for single-user compression under a diagonally dominant channel condition. From an optimization perspective, this thesis investigates the optimization of beamforming design and fronthaul compression for the VMAC schemes. First, under a sum fronthaul constraint, this thesis proposes a novel alternating convex optimization algorithm to maximize the weighted sum-rate for single-antenna uplink C-RAN. It is shown that setting the quantization noise levels to be proportional to the background noise levels is near optimal when the signal-to-quantization-noise-ratio is high. Second, under individual fronthaul constraints, this thesis develops a weighted minimum mean-square-error successive convex approximation algorithm to jointly optimize beamforming and fronthaul compression for multi-antenna uplink C-RAN. The performances of the proposed algorithms are verified under practical multicell and heterogeneous networks through numerical evaluation.
So close so different: what makes the difference?

The introduction of alien fish species in wetland ecosystems could have a great impact on freshwater communities and ecological processes. Despite fish introduction has been noticed as one of the principal cause of freshwater extinctions, ecosystem processes alteration, and change in aquatic community assemblage, very few data about impact on freshwater reptiles are available. As study model we used two neighbour sub-populations of the endangered Sicilian pond turtle, Emys trinacris, inhabiting two small, close each other and very similar lakes, except for the presence of allocthonous fish, Cyprinus carpio and Gambusia hoolbroki in one of the two. The multi-year study allowed highlighting significant differences in abundance, growth and reproductive output between the two freshwater turtle sub-populations, suggesting their influence on phenotypic plasticity of the studied population. These results are discussed in the light of previous evidence about the impact of these alien species on abundance and assemblage of the invertebrate community with an evident impact on niche width, diet composition and therefore energy intake by Emys trinacris. These data may provide important information to address management strategies and conservation actions of small wetland areas inhabited by pond turtles, pointing out a threats never highlighted up to now.
Aggressive corticotroph tumors and carcinomas

Pituitary tumors are generally benign, although in rare cases aggressive pituitary tumors (APTs) and carcinomas present important diagnostic and therapeutic challenges and are associated with a high mortality rate. Almost half of these APTs and carcinomas are corticotroph tumors, suggesting a specific prognosis. Clinical, pathological and molecular prognostic markers are limited and do not allow early management of these tumors. Temozolomide remains the first‐line treatment once a diagnosis of aggressive pituitary tumor or carcinoma has been made. Novel alternative treatments exist, including immune checkpoint inhibitors, which can be used in the case of temozolomide treatment failure. The aim of this review is to present the clinical, pathological and molecular characteristics of aggressive corticotroph tumors and carcinomas, and to describe the results obtained with currently available treatments.
A Multi-Class Channel Access Scheme for Cognitive Edge Computing-Based Internet of Things Networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.
DNA Methylation of Imprinted Genes KCNQ1, KCNQ1OT1, and PHLDA2 in Peripheral Blood Is Associated with the Risk of Breast Cancer

Simple Summary DNA methylation alterations of imprinted genes lead to loss of imprinting, and studies have explored the mechanism of the loss of imprinting in breast cancer development. However, the association between the methylation of imprinted genes in peripheral blood and the risk of breast cancer is largely unknown. Our study is the first to identify the CpG sites of imprinted genes associated with the risk of breast cancer by utilizing HumanMethylation450 data from public datasets. We discovered and validated that peripheral blood DNA methylation of KCNQ1, KCNQ1OT1, and PHLDA2 at chromosome 11p15.4-15.5 is associated with breast cancer susceptibility. Individuals with KCNQ1OT1-region hypermethylation (>0.474) had a lower risk of breast cancer. Additionally, the methylation level of KCNQ1OT1 was also unaffected by leukocyte heterogeneity. In summary, the KCNQ1OT1 region could be a potential biomarker for breast cancer risk assessment. Abstract Methylation alterations of imprinted genes lead to loss of imprinting (LOI). Although studies have explored the mechanism of LOI in breast cancer (BC) development, the association between imprinted gene methylation in peripheral blood and BC risk is largely unknown. We utilized HumanMethylation450 data from TCGA and GEO (n = 1461) to identify the CpG sites of imprinted genes associated with BC risk. Furthermore, we conducted an independent case-control study (n = 1048) to validate DNA methylation of these CpG sites in peripheral blood and BC susceptibility. cg26709929, cg08446215, cg25306939, and cg16057921, which are located at KCNQ1, KCNQ1OT1, and PHLDA2, were discovered to be associated with BC risk. Subsequently, the association between cg26709929, cg26057921, and cg25306939 methylation and BC risk was validated in our inhouse dataset. All 22 CpG sites in the KCNQ1OT1 region were associated with BC risk. Individuals with a hypermethylated KCNQ1OT1 region (>0.474) had a lower BC risk (OR: 0.553, 95% CI: 0.397−0.769). Additionally, the methylation of the KCNQ1OT1 region was not significantly different among B cells, monocytes, and T cells, which was also observed at CpG sites in PHLDA2. In summary, the methylation of KCNQ1, KCNQ1OT1, and PHLDA2 was associated with BC risk, and KCNQ1OT1 methylation could be a potential biomarker for BC risk assessment.
Classifier-Free Diffusion Guidance

Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.
High-efficiency, high-density MHz magnetic components for low profile converters

A highly efficient (99.5%) transformer and resonant inductor with very high power density (1500 W/in/sup 3/) and low profile (height 40) of small gaps to reduce the effect of fringing fields. The high-frequency loss measurements for such highly efficient inductors requires a special ring down measurement set-up which is also described. >
Factors Contributing to Physical Therapists’ Job and Career Satisfaction in the United States: Results From a National Survey

Supplemental Digital Content is Available in the Text. Introduction. Mentoring and professional development opportunities influence job and career satisfaction. Review of the Literature. Job and career satisfaction are multifactorial. The purpose of this study was to compare job and career satisfaction between physical therapists (PTs) who were residency trained and PTs who were neither residency nor fellowship trained and between PTs who were fellowship trained and PTs who were neither residency nor fellowship trained. Subjects. Licensed PTs in the United States. Methods. An online survey was distributed asking questions regarding job satisfaction and career satisfaction. Respondents were categorized as completing a residency, fellowship, both, or neither. Responses were respectively summed, then compared between groups: residency versus nonresidency/nonfellowship and fellowship versus nonresidency/nonfellowship. Frequency analyses and Mann–Whitney U tests were performed. Results. A total of 3,080 survey responses were analyzed. Residency-trained PTs had an overall higher job satisfaction score (P = .03) and higher career satisfaction score (P = .05) when compared with non–residency-trained or non–fellowship-trained PTs. Fellowship-trained PTs reported higher job satisfaction (P < .001) and career satisfaction (P = .001) compared with non–residency-trained or non–fellowship-trained PTs. Residency-trained PTs rated the importance of their work being interesting and learning/improving in their work of greater importance to their job satisfaction compared with non–residency-trained or non–fellowship-trained PTs (P < .05). Whereas, fellowship-trained PTs rated factors such as their jobs being positively challenging, fulfilling, and interesting, having sufficient independence, and were learning/improving in their work of greater importance than non–residency-trained and non–fellowship-trained PTs (P ≤ .004). Discussion and Conclusion. Results from the study support previous evidence that PTs overall have high job/career satisfaction and residency/fellowship training may further enhance job and career satisfaction. Findings provide valuable insight and are relevant to all PTs as well as stakeholders involved in professional and postprofessional physical therapy education and those making hiring decisions and potential employment opportunities.
Effect of serotonin and thyroid stimulating hormone in criminal behavior of Iraqi males

Aim: Investigate the role of serotonin and thyroid stimulating hormone and their relationship with criminal behavior in Iraqi males prisoners. Method: A questionnaire was taken including the age, marital status, duration of imprisonment, residence, previous delinquency, educational level, and type of crime. Blood samples were taken from all of subjects involved in this study which was 80 (40 prisoners and 40 healthy control). All prisoners and control were from the same ethnic group (Arabic). Measurement of serotonin and thyroid stimulating hormone concentrations was performed for criminals and control groups. seR ults: The results showed that the 21-30 years age group of criminals prisoners were more frequent 17(42.5%) than control 3(7.5%) followed by age group 31-40 years which was 13(32.5%) compared to control 24(60%), whereas the percentage was 10 (25 %) in 41-50 years age group compared to control group 13(32.5%). According to educational level, most of the prisoners had no degree or at primary school 15(37.5%) followed by those with secondary school 10(25%) compared to the control group who had primary school 16(40%) was more frequent followed by university degree 11(27.5%) then secondary school 8(20%) and diploma degree 5(12.5%).
Public transportation network scan for rapid surveillance

As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible.