Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Risk Factors Affecting Progression in Primary Open-Angle Glaucoma and Exfoliation Glaucoma in a Turkish Population

Objective: To evaluate the effects of risk factors on progression in primary open-angle glaucoma (POAG) and exfoliation glaucoma (XFG). Materials and methods: The study included 139 patients with POAG and XFG followed up at Hacettepe University Faculty of Medicine, Department of Ophthalmology, Glaucoma Unit. A number of factors were evaluated through a cross-sectional design for all the patients, including age, sex, hypertension, diabetes, thyroid disease, cardiovascular disease, migraine, alcohol, smoking, family history, affected side, lens status, central corneal thickness (CCT), number of medications, body mass index (BMI), cup-to-disc (C/D) ratio, intraocular pressure (IOP), computerized automated visual field mean deviation (MD), and prior surgery. Results: The patients were divided into two groups, POAG and XFG, and further divided into the following two subgroups: progressive and non-progressive. Of the patients, 75 (53.9%) had POAG, and 64 (46.1%) had XFG. In the patients followed up, annual MD change was 0.96 ± 1.5 dB/year, baseline MD was −5.06 ± 5.61 dB, and IOP was 15.94 ± 1.93 mmHg. Potential risk factors for progression in the whole group were evaluated, but no significant difference was found between the groups with and without progression for all factors. Subgroup analysis revealed that in the POAG group, BMI was significantly higher in the non-progressive disease subgroup (p=0.01); furthermore, in the XFG group, IOP was significantly higher in the progressive disease subgroup (p=0.02). According to multiple logistic regression analysis, a 1-unit decrease in BMI in the POAG group increased the risk by 1.3 times (p=0.01), and smoking in the XFG group resulted in a 6-fold reduction in the risk of progression (p=0.04). Conclusion: Although mean IOP was higher in XFG group, the present study found BMI in POAG and smoking in XFG as independent factors that reduced progression in our series.
No difference in treatment outcome between patients with nodal versus extranodal diffuse large B-cell lymphoma

Background and Aim: Diffuse large B-cell lymphoma (DLBCL) has been classified using various parameters, including the site of origin. Studies have reported conflicting outcomes when DLBLC patients were stratified according to the site of origin. This study aimed to investigate the response rate and survival outcomes in nodal versus extranodal DLBCL and compare the results to a region-matched study covering the 1988 – 2005 period. Methods: A single-center retrospective cohort study was conducted on all patients diagnosed with DLBCL and treated in a tertiary care hospital in Pakistan during 2014 – 2019. We calculated the mean and median for continuous variables and frequency and percentages for all categorical variables. Progression-free survival (PFS) and overall survival (OS) were calculated using Kaplan–Meier survival curves. A Cox proportional hazards model was used to determine the hazard ratio (HR) for OS. Results: Of the 118 patients, 49 patients (41.5%) had nodal disease and 69 patients (58.5%) were diagnosed with extranodal DLBCL. The majority of patients in the nodal and extranodal cohorts presented with Stages III and IV disease (73.4% and 62.3%, respectively). A complete response to (immuno) chemotherapy was achieved in 71.4% of nodal DLBCL patients and 65.2% of extranodal DLBCL patients. The 5-year PFS and median PFS in the entire cohort were 0.8% and 17 m, respectively. The PFS and median PFS in the nodal and extranodal DLBCL cohort were 0% and 1.4%, respectively, and 15 m and 19 m, respectively. The 5-year OS and median OS in the entire cohort were 16.1% and 19 m, respectively. The OS and median OS in the nodal and extranodal DLBCL cohort were 8.2% and 21.7%, respectively, and 19 m and 21 m, respectively. Multivariable linear regression revealed that the ABC phenotype (nodal, HR = 1.37, 95% CI = 1.37 – 3.20; extranodal, HR = 1.65, 95% CI = 1.46 – 3.17; GBC as reference) and double and triple hit DLBCL (nodal, HR = 1.29, 95% CI = 1.19 – 2.81; extranodal, HR = 1.87, 95% CI = 1.28 – 2.43; and non-expressors as reference) are independent negative predictors of OS. Conclusions: DLBCL incidence in the Karachi region has remained comparable but patient composition in the extranodal DLBCL cohort has shifted to predominantly advanced stage. Nodal and extranodal DLBCL were associated with similar PFS and OS profiles and first- and second-line treatment responses. Cell of origin and antigen expression status was independent negative predictors of OS, disfavoring the ABC phenotype and lesions with c-MYC and BCL2 and/or BCL6 overexpression. Relevance for Patients: DLBCL is an aggressive type of non-Hodgkin’s lymphoma, however; patients respond well to standard systemic chemotherapy. Extranodal type of DLBCL patients tend to have more residual disease after first-line systemic chemotherapy, but physicians should keep in mind that the subsequent line treatment mitigates its negative impact on survival.
EFFECTIVENESS OF LICENSING REGULATIONS ON GROWTH OF DEPOSIT TAKING SAVINGS AND CREDT CO-OPERATIVES IN MT. KENYA REGION

SACCOs for quite a long period of time have been seen as panacea to alleviate poverty in the society through financial inclusion. Licensing regulation was established to prudently control and regulate SACCOs’ joining the Deposit Taking business. The aim of the current study was to establish the effectiveness of licensing regulation on growth of Deposit Taking SACCOs in Mt. Kenya region. Descriptive research design and inferential statistics were used in the study. The study targeted 54 Chief Executive Officers/Managers in Mt. Kenya region as respondents. Census study was undertaken and questionnaire was employed to collect primary data. Secondary data was obtained from SACCO Society Regulatory Authority annual supervision reports. Quantitative data analysis was done for numerical data obtained from the field. This was carried out using descriptive statistics by use of statistical Package of Social Sciences (SPSS) Version 25. Tables were used to present the results. Regressions were used to test the research hypothesis for turnover against licensing regulations to determine the association among the study variable. The study concluded that there was negative and statistically significant correlation between licensing regulation and growth of SACCOs in Mt. Kenya Region. The study recommends that SACCOs need to adhere to all the licensing regulations as spelt out in the SACCO society’s Act. Further the study recommends review of the licensing regulations by the government through SASRA to save witnessed decline of SACCO and bring more entities onboard as SACCOs for sustainable financial inclusion. Keywords: Licensing Regulation, Deposit Taking Savings and Credit Cooperative Societies, Growth.
Wpływ infodemii towarzyszącej COVID-19 na weryfikowanie informacji przez dziennikarzy i korzystanie z serwisów fact-checkingowych

Pandemia COVID-19 i towarzysząca jej infodemia zmieniły sposób pracy dziennikarzy, także w zakresie weryfikowania informacji. Celem niniejszego tekstu jest ukazanie zmian, jakie zaszły w tym obszarze, czemu służy analiza wyników przeprowadzonego badania sondażowego. Metody badań: badanie zrealizowano techniką CAWI wśród dziennikarzy znajdujących się w bazach Polskiej Agencji Prasowej, w maju 2020 roku. Otrzymano 316 wypełnionych kwestionariuszy. Sprawdzono, czy dziennikarze deklarowali weryfikowanie uzyskiwanych informacji (w tym informacji przekazywanych im bezpośrednio przez firmy) oraz czy korzystali z serwisów fact-checkingowych. Wyniki i wnioski: dokładniejsza weryfikacja informacji pochodzących z różnych źródeł rzeczywiście miała miejsce, jednak stosunek dziennikarzy do fact-checkingu nie zmienił się. Oryginalność/wartość poznawcza: wartość poznawcza pracy wynika przede wszystkim z oryginalnych badań, które zostały w niej zaprezentowane.
Evaluation of Genotoxicity and Toxicity of Annona muricata L. Seeds and In Silico Studies

Cancer is a multifactorial organic dysfunction for which great efforts are being devoted in searching for new treatments and therapeutic adjuvants. Annona muricata is a fruit that has promising activity against several types of cancer, as it contains acetogenins, the metabolite group associated with this action. Thus, the objective of this study was to evaluate, in experimental models, the toxic behavior of an extract and fraction rich in acetogenins from A. muricata seeds and study the acetogenin, Annonacin, in silico. Phytochemical characterization was made by thin layer chromatography, spectroscopy in the infrared region and nuclear magnetic resonance. Toxicity was evaluated by tests of Allium cepa and Artemia salina, and in silico studies using the SwissDock servers DockThor, PharmMapper, ADMETLab, PreADME, Osiris and ProTox. The extract and fraction showed genotoxic activity against meristematic cells of A. cepa, reducing the mitotic index; however, the extract produced great deleterious effects on the system, even causing cell necrosis. In A. Saline, the extract was more toxic than the fraction, but both samples were considered toxic. Annonacin was effectively linked to complex I, and presented different activities regarding toxicity. Thus, the results of this study are promising, highlighting the anticancer potential of acetogenins.
An Automatic Interpretation Approach for Urban Remote Sensing Image Based on Multiple Features Integration

A new automatic approach based on multiple features integration is proposed to interpret urban remote sensing images more effectively and comprehensively.The approach builds a hierarchical objects network to organize image structure and gets precise processing units.Then the probabilistic learning integrating multiple features including colour,texture,shape and position are performed to train a best classifier,and label all of the objects according to their classification values.The approach also applies spatial smoothing which incorporates contextual information to eliminate the adverse effects caused by background disturbance,occlusion and so on.After vectorization procedure,final results are given. Experiments demonstrate that proposed approach achieve high exactness and robustness in interpreting manifold urban remote sensing images.
The Role of Machine Learning in Identifying Students At-Risk and Minimizing Failure

Education is very important for students’ future success. The performance of students can be supported by the extra assignments and projects given by the instructors for students with low performance. However, a major problem is that students at-risk cannot be identified early. This situation is being investigated by various researchers using Machine Learning techniques. Machine learning is used in a variety of areas and has also begun to be used to identify students at-risk early and to provide support by instructors. This research paper discusses the performance results found using Machine learning algorithms to identify at-risk students and minimize student failure. The main purpose of this project is to create a hybrid model using the ensemble stacking method and to predict at-risk students using this model. We used machine learning algorithms such as Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, AdaBoost Classifier and Logistic Regression in this project. The performance of each machine learning algorithm presented in the project was measured with various metrics. Thus, the hybrid model by combining algorithms that give the best prediction results is presented in this study. The data set containing the demographic and academic information of the students was used to train and test the model. In addition, a web application developed for the effective use of the hybrid model and for obtaining prediction results is presented in the report. In the proposed method, it has been realized that stratified k-fold cross validation and hyperparameter optimization techniques increased the performance of the models. The hybrid ensemble model was tested with a combination of two different datasets to understand the importance of the data features. In first combination, the accuracy of the hybrid model was obtained as 94.8% by using both demographic and academic data. In the second combination, when only academic data was used, the accuracy of the hybrid model increased to 98.4%. This study focuses on predicting the performance of at-risk students early. Thus, teachers will be able to provide extra assistance to students with low performance.
A transfer learning-based feedback training motivates the performance of SMR-BCI

Objective. Feedback training is a practical approach to brain–computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMRs). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users’ self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance. Approach. Electroencephalography (EEG) signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursor-bar (CB) feedback (control condition), for three sessions on separate days. Main results. The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects’ motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. About 41.7% of the subjects were ‘learners’ including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback. Significance. The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.
Turbulence in the Atmosphere: Contents

Preface Part I. A Grammar of Turbulence: 1. Introduction 2. Getting to know turbulence 3. Equations for averaged variables 4. Turbulent fluxes 5. Conservation equations for covariances 6. Large-eddy dynamics, the energy cascade, and large-eddy simulation 7. Kolmogrov scaling, its extensions, and two-dimensional turbulence Part II. Turbulence in the Atmospheric Boundary Layer: 8. The equations of atmospheric turbulence 9. The atmospheric boundary layer 10. The atmospheric surface layer 11. The convective boundary layer 12. The stable boundary layer Part III. Statistical Representation of Turbulence: 13. Probability densities and distributions 14. Isotropic tensors 15. Covariances, autocorrelations, and spectra 16. Statistics in turbulence analysis Index.
Doppler Ultrasound Selection and Follow-Up of the Internal Mammary Artery as Coronary Graft

The impact of coronary artery disease (CAD) on all-cause mortality and overall disabilities is well-established. Percutaneous and/or surgical coronary revascularization procedures dramatically reduced the occurrence of adverse cardiovascular events in patients suffering from atherosclerosis. Specifically, guidelines from the European Society of Cardiology on the management of myocardial revascularization promoted coronary artery by-pass graft (CABG) intervention in patients with specific alterations in the coronary tree due to the higher beneficial effects of this procedure as compared to the percutaneous one. The left internal mammary artery (LIMA) is one of the best-performing vessels in CABG procedures due to its location and its own structural characteristics. Nevertheless, the non-invasive assessment of its patency is challenging. Doppler ultrasonography (DU) might perform as a reliable technique for the non-invasive evaluation of the patency of LIMA. Data from the literature revealed that DU may detect severe (>70%) stenosis of the LIMA graft. In this case, pulsed-wave Doppler might show peak diastolic velocity/peak systolic velocity < 0.5 and diastolic fraction < 50%. A stress test might also be adopted for the evaluation of patency of LIMA through DU. The aim of this narrative review is to evaluate the impact of DU on the evaluation of the patency of LIMA graft in patients who undergo follow-up after CABG intervention.
The Role of Pension Funds on Capital Market Growth in the New EU Member States

Podstawowym celem artykułu jest analiza wpływu wywieranego przez fundusze emerytalne na rozwój rynku kapitałowego jedenastu nowych krajów członkowski UE z obszaru Europy Środkowo-Wschodniej. Wykorzystane dane mają charakter roczny i pochodzą z lat 2010–2020. Jako wskaźniki wzrostu rynku kapitałowego wykorzystujemy dwie zmienne, którymi są: kapitalizacja rynku giełdowego i kapitalizacja rynku obligacji prywatnych. Zależność między zmiennymi oszacowano za pomocą estymatorów DOLS (ang. dynamic ordinary least squares) oraz FMOLS (ang. fully modified ordinary least squares). Wyniki analizy wskazują, że fundusze emerytalne wywierają pozytywny wpływ na wzrost rynku giełdowego oraz rynku obligacji prywatnych. Wyniki badania są istotne z punktu widzenia zarządzania i strategii inwestycyjnych stosowanych przez fundusze emerytalne. W najbliższej przyszłości, gdy fundusze emerytalne zgromadzą znaczne aktywa, powinny zmienić swoje strategie inwestycyjne w kierunku portfeli bardziej nastawionych na ryzyko.
Over-the-Air Federated Multi-Task Learning via Model Sparsification, Random Compression, and Turbo Compressed Sensing

To achieve communication-efficient federated multi-task learning (FMTL), we propose an over-the-air FMTL (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). To overcome the inter-task interference inherent in the non-orthogonal transmission among tasks, we design a novel transmission method called model sparsification and random compression (MSRC) as well as a reception method called modified turbo compressed sensing (M-Turbo-CS). More specifically, at each edge device, the local model updates of all tasks are first sparsified and randomly compressed with different random compression matrices for different tasks, before being superimposed and sent over the uplink channel. Then the ES constructes the model aggregations of all the tasks from the channel observation data through a modified version of the turbo compressed sensing (Turbo-CS) algorithm called M-Turbo-CS. We analyze the performance of the proposed OA-FMTL framework with MSRC and M-Turbo-CS. Based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by adjusting the power allocation among the tasks at the edge devices. Numerical simulations show that our proposed OA-FMTL efficiently suppresses the inter-task interference to achieve a learning performance comparable to the inter-task interference free bound at a significantly reduced communication overhead. It is also shown that the proposed inter-task power allocation optimization algorithm further reduces the overall communication overhead by appropriately adjusting the power allocation among the tasks.
Low-Power Dynamic Circuit Design With Steep-Switching Hybrid Phase Transition FETs (Hyper-FETs)

Dynamic logic circuits suffer from leakage current during their evaluation period causing output voltage degradation. Although the keeper transistor is added to protect the output node from leakage, it comes with a considerable area and power penalty. In this article, we propose a novel design topology of a low-power dynamic logic circuit based on steep-switching hybrid phase transition FETs (Hyper-FETs). A Hyper-FET utilizes a phase transition material that exhibits selective insulator-to-metal transition enabling it to overcome the fundamental Boltzmann’s limit. The characteristic feature of the Hyper-FET also includes a higher ON–OFF ratio than the conventional counterpart. We utilize this unique advantage to alleviate the leakage issue in the dynamic circuits. We use a compact model of the phase transition material calibrated with experimental data of a single crystal VO2. For the baseline transistor, we use the predictive technology model of 14-nm FinFET. We comprehensively analyze the effects of different material parameters to explore the scope of further performance improvement. From our analysis, we deduce a range of insulating and metallic state resistance of the phase transition material (PTM) for optimum performance. We also examine the effect of process variation by performing the Monte Carlo variation analysis. Our analysis indicates that the leakage problem of the dynamic circuits is substantially improved as the phase transition material provides an additional high resistance with the pull-down network. We also observe that our design can outperform the conventional dynamic logic circuit even under random process variation.
On the Kronecker Product of Sn Characters

Abstract Let χ ρ , χ λ , χ μ be irreducible S n characters and assume χ ρ appears in the Kronecker product χ λ ⊗χ μ with maximal first part ρ 1 . Then ρ 1 = |λ∩μ| = ∑min(λ i , μ i ). A similar result holds for the maximal first column. We also give a recursive formula for χ λ ⊗χ μ . As an application, we show that if n = λ 1 + μ 1 − ρ 1 , then 〈χ λ ⊗χ μ χ ρ 〉 s n = 〈χ (λ2, λ3, .) ⊗χ (μ2, μ3, .) , χ (ρ2, ρ3, .) 〉 s n − ρ 1 where ⊗ denotes the outer tensor product. These results are applied to study the character ∑χ λ ⊗χ λ where λ runs through the partitions with no more then k parts. This character is closely related to the polynomial identities of the algebra of k × k matrices.
Dark Matter Searches with Top Quarks

Collider signatures with top quarks provide sensitive probes of dark matter (DM) production at the Large Hadron Collider (LHC). In this article, we review the results of DM searches in final states with top quarks conducted by the ATLAS and CMS Collaborations at the LHC, including the most recent results on the full LHC Run 2 dataset. We highlight the complementarity of DM searches in final states with top quarks with searches in other final states in the framework of various simplified models of DM. A reinterpretation of a DM search with top quarks in the context of an effective field theory description of scalar dark energy is also discussed. Finally, we give an outlook on the potential of DM searches with top quarks in LHC Run 3, at the high-luminosity LHC, and possible future colliders. In this context, we highlight new benchmark models that could be probed by existing and future searches as well as those that predict still-uncovered signatures of anomalous top-quark production and decays at the LHC.