Miracle-dz/UniFAR-FTUs huggingface dataset:
Subcortical volumetric alterations as potential predictors of methylphenidate treatment response in youth with attention-deficit/hyperactivity disorder

Background: Patients with attention-deficit/hyperactivity disorder (ADHD) show structural alterations in the subcortical and dopaminergic regions of the brain. Methylphenidate is a first-line treatment for ADHD, and it is known to affect the subcortical and dopaminergic systems. The degree of pretreatment structural alterations in patients with ADHD may be an important factor in predicting methylphenidate treatment outcomes. The present study examined whether pretreatment volumetric alterations in the subcortical and dopaminergic regions predicted treatment response in youth with ADHD. Methods: This study included 67 youth with ADHD and 25 healthy controls. Youth with ADHD received 8 weeks of methylphenidate treatment. They completed baseline (pretreatment) T1-weighted structural MRI scans and underwent clinical assessments before and after methylphenidate treatment. The healthy controls also completed baseline structural MRI scans. We assessed volumetric alterations using relative volumes (volume of each region of interest/intracranial volume). Results: Among 67 youth with ADHD, 44 were treatment responders and 23 were nonresponders based on post-treatment scores on the Clinical Global Impression Scale–Improvement. Nonresponders had larger volumes in the bilateral amygdala and right thalamus than responders. Nonresponders also had larger volumes in amygdalar subregions (i.e., the bilateral lateral nucleus and right basal nucleus) and hippocampal subregions (i.e., the right hippocampal head and right molecular layer) relative to responders. Limitations: We did not collect post-treatment structural T1-weighted images, so volumetric changes related to methylphenidate treatment in youth with ADHD were undetermined. Conclusion: These findings suggest that pretreatment volumetric alterations in subcortical regions may serve as biomarkers for predicting methylphenidate treatment response in youth with ADHD.
Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study

Background People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open. Objective We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies. Methods We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants—which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind. Results Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant–health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data. Conclusions The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups.
Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.
Environmental cue difference and training duration modulate spatial learning and cue preference in detour task

In this study, we investigated how different environmental cue and the proficiency of body motion influenced detour learning behaviour and cue preference in cue conflict situations. Domestic chicks were trained to detour around an obstacle and follow a fixed route to rejoin with their partners. When the environmental cue was red versus blue vertical stripes, the chicks learned the detour task quicker, and as the number of training trials after route acquisition increased, they switched their preference from the environmental cue to a body‐motion cue in the cue conflict test. On the other hand, when the environmental cue was vertical versus horizontal blue stripes, the chicks learned the detour task slower and showed a dependence on the body‐motion cue regardless of the number of training trials performed after route acquisition. When the environmental cue was removed, most chicks could still successfully detour according to the specific route on which they had been trained. Furthermore, a significant difference in detour latency was found between chicks using the environmental cue and chicks using the body‐motion cue, suggesting separate neuronal circuits responsible for processing the two types of information. Our results demonstrated that young domestic chicks could use both environmental cue and body‐motion cues to memorize the route during the detour learning task; however, the detour route preference could be dynamically modulated by difference of the environmental cue and the number of training trials they received.
EVALUATION OF TOP MANAGERS AND WORKERS’ VIEWS WHO WORK IN THE CENTRAL ORGANIZATION OF MINISTRY OF NATIONAL EDUCATION ON RESTRUCTURE

Aim. The aim of the study is to examine the views of top managers and workers that work in the central organization of Ministry of National Education on restructure (MoNE). Method. The qualitative method was used in this study. The views of top managers in MoNE on restructure were collected via semi-structured interview form. Moreover, focus group interview was held with a group of nine participants (two chefs, a teacher who works in the headquarter of MoNE, two educationalists, a branch manager, an inspector, a head manager and two MoNE educational assistant experts). Findings. In this qualitative research, content analysis was conducted and the themes after the analysis were very close to the organizational change management model of Tushman and O’Reilly. The themes of the analysis were human, culture, structure, task, strategical choices and managerial leadership; and the restructure of MoNE was evaluated via these themes. Results. When the findings related to these themes were examined, it was seen that participation in the decision making process of organizational changing was poor, the effects of change on workers were ignored, the model after the change processes was not well built, uneasiness was the dominant factor in the changing period, there was no environment of trust, organizational memory was destructed and there was no effective monitoring and evaluation mechanism. It could be said that the positive sides of the changing process were that the bulky body of MoNE decreased, the positions and criteria of MoNE educational assistant experts were organised via legal regulations, and overlap of works was more ordinate.
Risk of tuberculosis in patients with rheumatoid arthritis treated with biological and targeted drugs: meta-analysis of randomized clinical trials

Abstract Background: Concerns exist regarding the potential development of tuberculosis in patients with rheumatoid arthritis (RA) treated with biological and targeted drugs. We assessed systematically whether biological therapy increased the risk of tuberculosis in patients with RA by meta-analysis of randomized controlled trials (RCTs). Methods: A systematic literature search was conducted in PubMed, Embase, the Cochrane Library, and China Biology Medicine disc for RCTs evaluating biological therapy in patients with RA from inception through August 2021. Traditional meta-analysis and network meta-analysis were performed to compare the risk of tuberculosis for each biologics class in patients with RA. Peto odds ratio (Peto OR) and its 95% confidence interval (CI) were calculated as the primary effect measure. Results: In total, 39 studies with 20,354 patients were included in this meta-analysis, and 82 patients developed tuberculosis. The risk of tuberculosis was increased in patients treated with biologics compared with non-biologics (Peto OR: 3.86, 95% CI: 2.36–6.32, P < 0.001). Also, tumor necrosis factor-α (TNF-α) inhibitors had a higher probability of developing tuberculosis than placebo (Peto OR: 3.98, 95% CI: 2.30–6.88, P < 0.001). However, network meta-analysis demonstrated that there was no significant difference in the risk of tuberculosis for each biologics class in patients with RA. Noticeably, tuberculosis was significantly more common in patients treated with a high dose compared with patients receiving a low dose of tofacitinib (Peto OR: 7.39, 95% CI: 2.00–27.31, P = 0.003). Conclusion: This meta-analysis demonstrates the evidence of an elevated risk of tuberculosis in patients with RA treated with TNF-α inhibitors, and a dose-dependent elevated risk of tuberculosis in patients treated with tofacitinib.
Developing a Framework for the 3D Visualization of underground petroleum pipelines

A common source of damage to subsurface petroleum pipelines is their accidental cutting by excavation workers, oblivious of the precise underground location of the pipelines. Such blind cuttings have had catastrophic consequences in the past. Lives and properties are often lost in the wake of explosions that accompany blind cuttings of underground petroleum pipelines. Because of their capability to render accurate, three dimensional views of these pipelines thereby drastically reducing the possibility of accidental cuts, three dimensional (3D) maps are increasingly becoming popular. However, one major drawback is the exorbitant cost of most of the Geographical Information Systems (GIS) packages that support the 3D modeling and visualization of subsurface pipelines. Furthermore, the advanced languages used in building many of these packages make it difficult for non-GIS experts, field workers, and other professionals to relate with them. Since people from diverse disciplines (without strong GIS background) need to visualize and analyze these subsurface pipelines on a regular basis, it is pertinent to develop a system capable of performing basic 3D visualization functions, in addition to being user-friendly and highly affordable. This paper, which proposes a framework to achieve this purpose, is part of an ongoing research on the subject matter.
Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization

The article presents algorithms for parallel and vectorized full-word addition of big unsigned integers with carry propagation. Because of the propagation, software parallelization and vectorization of non-polynomial addition of big integers have long been considered impractical due to data dependencies between digits of the operands. The presented algorithms are based upon parallel and vectorized detection of carry origins within elements of vector operands, masking bits which correspond to those elements and subsequent scalar addition of the resulting integers. The acquired bits can consequently be taken into account to adjust the sum using the proposed generalization of the Kogge-Stone method. Essentially, the article formalizes and experimentally verifies parallel and vectorized implementation of carry-lookahead adders applied at arbitrary granularity of data. This approach is noticeably beneficial for manycore, CUDA and vectorized implementation using AVX-512 with masked instructions. Experiments show that the parallel and vectorized implementations of the proposed algorithms can be multiple times faster compared to a sequential ripple-carry adder or adders based on redundant number systems such as one used in the GNU Multiple Precision library.
Exploring the Quality of Life Related to Health and Vision in a Group of Patients with Diabetic Retinopathy

(1) Background: Visual impairment of people with diabetic retinopathy (DR) and its high impact on different dimensions of their lives can cause a significant deterioration in the quality of life. The aim of this study was to examine the association and relationship between quality of life related to vision and the relevant clinical and sociodemographic variables in a group of patients with DR in Spain. (2) Methods: A descriptive cross-sectional study was conducted in all patients with DR over 18 years under follow-up in the Retina Service of the University Hospital of Burgos (HUBU), recruited during the months of January and February 2020. The main study variable was quality of life related to health and vision, obtained using the National Eye Institute Visual Function Questionnaire 25 (NEI-VFQ-25). (3) Results: In total 87 participants made up the sample, and significant differences were found in the NEI-VFQ-25 according to gender, type of diabetes, episodes of decompensated diabetes and high blood pressure (HBP) (p < 0.05). Best-corrected visual acuity (BCVA) was also correlated with the NEI-VFQ-25 (p < 0.05). (4) Conclusions: These data could facilitate the design of action protocols focused on the well-being of the patient, in addition to considering the clinical characteristics. Further studies are needed to help understand the causal relationship between variables and that includes a wider variety of factors.
Novel Disulfiram Derivatives as ALDH1a1-Selective Inhibitors

Aldehyde dehydrogenase-1a1 (ALDH1a1), the enzyme responsible for the oxidation of retinal into retinoic acid, represents a key therapeutic target for the treatment of debilitating disorders such as cancer, obesity, and inflammation. Drugs that can inhibit ALDH1a1 include disulfiram, an FDA-approved drug to treat chronic alcoholism. Disulfiram, by carbamylation of the catalytic cysteines, irreversibly inhibits ALDH1a1 and ALDH2. The latter is the isozyme responsible for important physiological processes such as the second stage of alcohol metabolism. Given the fact that ALDH1a1 has a larger substrate tunnel than that in ALDH2, replacing disulfiram ethyl groups with larger motifs will yield selective ALDH1a1 inhibitors. We report herein the synthesis of new inhibitors of ALDH1a1 where (hetero)aromatic rings were introduced into the structure of disulfiram. Most of the developed compounds retained the anti-ALDH1a1 activity of disulfiram; however, they were completely devoid of inhibitory activity against ALDH2.
RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.
Computer aided process planning for multi-axis CNC machining using feature free polygonal CAD models

This dissertation provides new methods for the general area of Computer Aided Process Planning, often referred to as CAPP. It specifically focuses on 3 challenging problems in the area of multi-axis CNC machining process using feature free polygonal CAD models. The first research problem involves a new method for the rapid machining of MultiSurface Parts. These types of parts typically have different requirements for each surface, for example, surface finish, accuracy, or functionality. The CAPP algorithms developed for this problem ensure the complete rapid machining of multi surface parts by providing better setup orientations to machine each surface. The second research problem is related to a new method for discrete multi-axis CNC machining of part models using feature free polygonal CAD models. This problem specifically considers a generic 3-axis CNC machining process for which CAPP algorithms are developed. These algorithms allow the rapid machining of a wide variety of parts with higher geometric accuracy by enabling access to visible surfaces through the choice of appropriate machine tool configurations (i.e. number of axes). The third research problem addresses challenges with geometric singularities that can occur when 2D slice models are used in process planning. The conversion from CAD to slice model results in the loss of model surface information, the consequence of which could be suboptimal or incorrect process planning. The algorithms developed here facilitate transfer of complete surface geometry information from CAD to slice models. The work of this dissertation will aid in developing the next generation of CAPP tools and result in lower cost and more accurately machined components.
Journeys: understanding access, affordability and disruptions to cancer care in India

Background Much of the global cancer burden is in low- and middle-income countries (LMICs). Along with the high incidence of cancer, most LMICs have unevenly distributed health care resources. This study is a qualitative exploration of the journey of patients accessing cancer care in India and their caregivers. Methods The study followed a cross-sectional qualitative design. Participants were recruited by stratified purposive sampling, and all common cancers in India as reported by the GLOBOCAN database were included in the study. Consenting patients and their caregivers were interviewed using in-depth interview techniques. The data was analysed using principles of qualitative content analysis. Results Cancer patients (n = 100) and their caregivers (n = 48) were interviewed for the study. The six themes that emerged were related to a) the journey of patients to access care, b) the psychological journey of patients, c) stigma of cancer patients, d) decision-making and adherence to treatment, e) economic costs of cancer care and its impact and f) modifiers to accessing cancer care. Conclusions Planning and policymaking in the future of cancer care delivery need to consider the views expressed by the cancer patients and their caregivers as regards to access, adherence and disruptions to cancer care in India. Future policies will hopefully address some of the difficulties faced by patients.
The Homogenization of Carbon Management Practices: How Organizations Response to Isomorphic Pressures to Reduce GHG Emissions

Purpose: Many companies are facing an increasing pressure exerted by governments, shareholders and other stakeholders to reduce their CO2 emissions in order to mitigate climate change. The importance of managing CO2 emissions by introducing adequate practices has increased for the affected companies. The present paper discusses the results of studies pertaining to the homogenization of carbon management practices. Design/Methodology/Approach: With 122 responses from 3 European countries, we applied structural equation modeling to test our hypotheses. Findings: Our findings revealed that the source of the homogenization of carbon management practices can be traced back to the mimetic and normative pressure. On the other hand, the impact of coercive pressure was found to be insignificant. The homogenization is also influenced by the success of the model organization and the similarity of carbon strategy. The results were outlined in relation to inter and intra CM practices. Originality/Value: This study makes the following contributions. Firstly, it analyses threefold isomorphic mechanisms in carbon management practices. Secondly, it examines which practices are imitated, and whether the features and success of the model organization are significant in the imitation process. Third of all, it assesses the significance of CM practices’ imitation for the adjustment of organizations to the expectations of stakeholders.
Planar Combiner Design for High Frequency Applications

Abstract – The complete design using a novel technique for a planar high-power combiner is introduced for high-frequency (HF) applications for the first time. 3-way planar combiner is simulated, designed and built on a ceramic-based material that allowed a surface-mount (SMT) application using the method introduced. Design chart for a thermally reliable operation under high-power conditions is given. The simulated and measured results are compared and found to be very close. The design technique for planar combiners introduced here can be used to obtain power level in the several-kilowatt range in HF communication for industrial and military applications.