Miracle-dz/UniFAR-FTUs huggingface dataset:
Phacoemulsification with Kahook Dual Blade goniotomy in eyes with medically treated glaucoma: analysis of surgical outcomes and success predictors.

PURPOSE The purpose of this study was to investigate the postoperative outcomes and evaluate the success predictors of phacoemulsification with Kahook Dual Blade goniotomy for cataract and glaucoma management in eyes with primary open-angle glaucoma. METHODS This was a retrospective, non-comparative; inter ventional case series in which all patients with primary open-angle glaucoma who underwent phacoemulsification with Kahook Dual Blade goniotomy between June 2018 and April 2019 were enrolled. All the participants had a minimum follow-up period of 6 months. Preoperative and postoperative intraocular pressure values (at 1, 3, and 6 months), number of antiglaucoma medications, best-corrected visual acuity, surgical complications, and any subsequent related events or procedures were recorded. A logistic regression analysis was performed to investigate the association between the different variables and surgical outcomes. RESULTS A total of 47 patients (57 eyes) were included (mean age, 70.5 ± 7 years). The mean intraocular pressure was reduced from 15.5 ± 4.2 mmHg to 12.2 ± 2.4 mmHg at the last follow-up visit (p<0.001). The mean number of antiglaucoma medications decreased significantly from 1.9 ± 1.0 to 0.6 ± 1.0 during the same period (p<0.001). On the basis of the predefined criterion (intraocular pressure reduction ≥20% and/or reduction ≥1 medication), the 6-month success rate was 86%. A higher preoperative intraocular pressure value (odds ratio [OR]= 2.01; p=0.016) and greater percentage of initial (30 days) intraocular pressure reduction (OR= 1.02; p=0.033) were sig nificantly associated with surgical success. CONCLUSION Our findings suggest that phacoemulsification with Kahook Dual Blade goniotomy is an effective and safe alternative for cataract management in eyes with primary open-angle glaucoma that positively impacts intraocular pressure control and medication burden. Eyes with higher baseline intraocular pressure and a more pronounced initial response to the procedure appeared to present better outcomes at 6 months. Further studies are needed to evaluate the long-term efficacy and safety profile of the procedure.
The Growing Value of Digital Health

: Some of the healthtech of Kerala are iCliniq, QKDoc.com, e-Sanjeevani, Amrita Telemedicine Services, etc. These e-health services are rapidly expanding and growing popular now-a-days due to the covid pandemic. Especially since new covid19 care packs are being launched by the enterprises, even by the government in the form of e-Sanjeevani. Healthtech is not only about services like doctor consultations, they also include fitness training of both the mind and the body. Applications like CureFit, Workout apps, etc. have also gained popularity in society due to our fast-paced life. Mental and physical fitness is one of our biggest concerns, but going to a gym or training center is no longer an option for the majority of the population because of the lack of time in our daily lives. In such situations online apps which work to our convenience are trending. This study analyses the growing value of digital health in Ernakulam City.
Robots as an interactive-social medium in storytelling to multiple children

This paper investigates the effects of group interaction in a storytelling situation for children using two robots: a reader robot and a listener robot as a side-participant. We developed a storytelling system that consists of a reader robot, a listener robot, a display, a gaze model, a depth sensor, and a human operator who responds and provides easily understandable answers to the children’s questions. We experimentally investigated the effects of using a listener robot and either one or two children during a storytelling situation on the children’s preferences and their speech activities. Our experimental results showed that the children preferred storytelling with the listener robot. Although two children obviously produced more speech than one child, the listener robot discouraged the children’s speech regardless of whether one or two were listening.
Microsheaves from Hitchin fibers via Floer theory

Fix a non-stacky component of the moduli of stable Higgs bundles, on which the Hitchin fibration is proper. We show that any smooth Hitchin fiber determines a microsheaf on the global nilpotent cone, that distinct fibers give rise to orthogonal microsheaves, and that the endomorphisms of the microsheaf is isomorphic to the cohomology of the Hitchin fiber. These results are consequences of recent advances in Floer theory. Natural constructions on our microsheaves provide plausible candidates for Hecke eigensheaves for the geometric Langlands correspondence.
Diets Containing Reduced Protein Levels on Performance of Growing Pigs

* This study was funded by the SGRP/HTDP (Hi^i-Technology Development Project for Agriculture and Forestry) of Korean Ministry of Agriculture and Forestry. 1 Address reprint requests to In K. Han. Received July 14, 1997; Accepted October 31, 1997 ABSTRACT : Three hundred and fifty-two growing pigs were utilized in three growth trials to investigate the effects of crystalline amino acid supplementation on growing pigs' growth performance and feed consumption. In Exp. 1, diets were formulated on the basis of digestible amino acids. Utilization of rapeseed meal plus cottonseed meal (R + C) in diet supported similar average daily gain (ADG), feed/gain (F/G) and cost per gain (p > 0.05) to group of soybean meal (SBM). In Exp. 2, 14% CP diets containing equal amount of cottonseed meal and rapeseed meal were formulated with the addition of threonine and/ or tryptophan. Supplementation with threonine improved ADG and F/G (p < 0.05). Supplementation with only tryptophan made no benefits for ADG of growing pigs. In Exp. 3, diets with different threonine level were for­ mulated. Increasing dietary threonine to 0.54% improved (p v 0.05) ADG and F/G compared to 0.45% dietary threonine. Increase in dietary threonine did not improve ADG (p > 0.05) but decreased F/G (p < 0.05) because of a decrease in average daily feed intake. Pigs fed diet with dietary threonine/lysine ratio of 67.5% supported optimum growth performance. (
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed. Violation of any of these assumptions leads to significant error in the effect estimate. However, unlike cross-validation for predictive models, there is no global validator method for a causal estimate. As a result, expressing different causal assumptions formally and validating them (to the extent possible) becomes critical for any analysis. We present DoWhy, a framework that allows explicit declaration of assumptions through a causal graph and provides multiple validation tests to check a subset of these assumptions. Our experience with DoWhy highlights a number of open questions for future research: developing new ways beyond causal graphs to express assumptions, the role of causal discovery in learning relevant parts of the graph, and developing validation tests that can better detect errors, both for average and conditional treatment effects. DoWhy is available at https://github.com/microsoft/dowhy.
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning

Data-driven models created by machine learning gain in importance in all fields of design and engineering. They have high potential to assist decision-makers in creating novel artefacts with better performance and sustainability. However, limited generalization and the black-box nature of these models lead to limited explainability and reusability. To overcome this situation, we propose a component-based approach to create partial component models by machine learning (ML). This component-based approach aligns deep learning with systems engineering (SE). For the domain of energy efficient building design, we first demonstrate better generalization of the component-based method by analyzing prediction accuracy outside the training data. We observe a much higher accuracy (R2 = 0.94) compared to conventional monolithic methods (R2 = 0.71). Second, we illustrate explainability by exemplary demonstrating how sensitivity information from SE and rules from low-depth decision trees serve engineering. Third, we evaluate explainability by qualitative and quantitative methods demonstrating the matching of preliminary knowledge and data-driven derived strategies and show correctness of activations at component interfaces compared to white-box simulation results (envelope components: R2 = 0.92.0.99; zones: R2 = 0.78.0.93). The key for component-based explainability is that activations at interfaces between the components are interpretable engineering quantities. The large range of possible configurations in composing components allows the examination of novel unseen design cases with understandable data-driven models. The matching of parameter ranges of components by similar probability distribution produces reusable, well-generalizing, and trustworthy models. The approach adapts the model structure to engineering methods of systems engineering and to domain knowledge.
Music Demixing Challenge at ISMIR 2021

Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIREX or SiSEC connected state-of-the-art models and corresponding papers, which can help researchers integrate the best practices into their models. In recent years, however, it has become increasingly difﬁcult to measure real-world performance as the music separation community had to rely on a limited amount of test data and was biased towards speciﬁc genres and mixing styles. To address these issues, we designed the Music Demix-ing (MDX) Challenge on a crowd-based machine learning competition platform where the task is to separate stereo songs into four instrument stems (Vocals, Drums, Bass, Other). The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate and 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i.e., the test set is not included in the training set. In this paper, we provide the details of the datasets, baselines, evaluation metrics, evaluation results, and technical challenges for future competitions.
A Search Engine for Discovery of Biomedical Challenges and Directions

The ability to keep track of scientiﬁc challenges, advances and emerging directions is a fundamental part of research. However, researchers face a ﬂood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientiﬁc challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In studies with researchers, including those working directly on COVID-19, we outperform a popular scientiﬁc search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain, highlighting its broad utility. We make our data, model and search engine publicly available. 1
Design of Quasi-LPV Observers for Interconnected Vehicle Dynamics Estimation with Experimental Validation

This paper addresses the quasi linear parametervarying (Quasi-LPV) Luenberger Interconnected Fuzzy (QLIF) observer to estimate simultaneously both longitudinal and lateral vehicle dynamics. In a different manner from the commonplace state-of-the-art of vehicle state observers that consider single driving motion, the proposed approach considers the coupled dynamics with tire-ground interaction to estimate the most important states while reducing the complexity related to the observability and conservatism. This consideration leads to a nonlinear parameter-dependent interconnected model with unmeasured premise variables. Thereafter, the Takagi-Sugeno (TS) fuzzy form is considered to deal with the nonlinearities of the vehicle forward speed and the slip velocity at the front and rear tires. The concept of “Input to State Stability (ISS)” is exploited using Lyapunov stability arguments to guarantee the boundlessness of the estimation errors. Finally, the performances and effectiveness of the suggested approach are evaluated through hardware experiments performed with the well-known SHERPA car simulator under real-world driving situations.
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations

We consider the query recommendation problem in closed loop interactive learning settings like online information gathering and exploratory analytics. The problem can be naturally modelled using the Multi-Armed Bandits (MAB) framework with countably many arms. The standard MAB algorithms for countably many arms begin with selecting a random set of candidate arms and then applying standard MAB algorithms, e.g., UCB, on this candidate set downstream. We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms. We show that in tasks like online information gathering, where sequential query recommendations are employed, the sequences of queries are correlated and the number of potentially optimal queries can be reduced to a manageable size by selecting queries with maximum utility with respect to the currently executing query. Our experimental results using a recent real online literature discovery service log file demonstrate that the proposed arm selection strategy improves the cumulative regret substantially with respect to the state-of-the-art baseline algorithms. % and commonly used random selection strategy for a variety of contextual multi-armed bandit algorithms. Our data model and source code are available at ~\url{https://anonymous.4open. science/r/0e5ad6b7-ac02-4577-9212-c9d505d3dbdb/}.
Cumulant-based calculations of the correlation energy in a molecule

The problem of constructing a guaranteed convergent sequence of corrections to the Hartree--Fock ground state energy of a molecule without storing the many-electron wave function is considered. Several methods based on cumulants are considered and it is shown that such a sequence is obtained by Lanczos tridiagonalization, in which the elements of the tridiagonal matrix are calculated through cumulants.
Epitope-Based Immunoinformatic Approach on Heat Shock 70 kDa Protein Complex of Cryptococcus neoformans var. grubii

Introduction Cryptococcosis is a ubiquitous opportunistic fungal disease caused by Cryptococcus neoformans var. grubii. It has high global morbidity and mortality among HIV patients and non-HIV carriers with 99% and 95%, respectively. Furthermore, the increasing prevalence of undesired toxicity profile of antifungal, multidrug-resistant organisms and the scarcity of FDA-authorized vaccines were the hallmark in the present days. This study was undertaken to design a reliable epitope-based peptide vaccine through targeting highly conserved immunodominant heat shock 70 kDa protein of Cryptococcus neoformans var. grubii that covers a considerable digit of the world population through implementing a computational vaccinology approach. Materials and Methods A total of 38 sequences of Cryptococcus neoformans var. grubii's heat shock 70 kDa protein were retrieved from the NCBI protein database. Different prediction tools were used to analyze the aforementioned protein at the Immune Epitope Database (IEDB) to discriminate the most promising T-cell and B-cell epitopes. The proposed T-cell epitopes were subjected to the population coverage analysis tool to compute the global population's coverage. Finally, the T-cell projected epitopes were ranked based on their binding scores and modes using AutoDock Vina software. Results and Discussion. The epitopes (ANYVQASEK, QSEKPKNVNPVI, SEKPKNVNPVI, and EKPKNVNPVI) had shown very strong binding affinity and immunogenic properties to B-cell. (FTQLVAAYL, YVYDTRGKL) and (FFGGKVLNF, FINAQLVDV, and FDYALVQHF) exhibited a very strong binding affinity to MHC-I and MHC-II, respectively, with high population coverage for each, while FYRQGAFEL has shown promising results in terms of its binding profile to MHC-II and MHC-I alleles and good strength of binding when docked with HLA-C∗12:03. In addition, there is massive global population coverage in the three coverage modes. Accordingly, our in silico vaccine is expected to be the future epitope-based peptide vaccine against Cryptococcus neoformans var. grubii that covers a significant figure of the entire world citizens.
Contagion Impact and Safe Asset Shortages: Implications for Secular Stagnation

This paper investigates the contagiousness of safe asset shortages as an implication of the secular stagnation hypothesis. Our motivation is to quantify the degree of financial contagion of safe asset demand among developed and emerging markets. We know that the insufficient supply of safe assets in developed economies led to increasing demand for risky EM Bonds. However, lower yields might not be observed in Emerging Economies due to expected inflation. To investigate this idea, we proxied expected inflation via a new variable called the Marginal Impact of Inflation (MII). Our dataset consists of 2-year and 10-year generic government yields, and we analyzed the reaction of developed and emerging economies to the United States and the United Kingdom’s safe asset demand. Our findings confirm that demand for US and UK safe assets shows the contagious impact for Developed Economies, not for Emerging Economies. Additionally, longer yields show stronger statistical evidence than shorter yields, as suggested by the secular stagnation hypothesis. According to empirical results, expected inflation is statistically insignificant for long-term and short-term yields for emerging economies. Thus, we can conclude that the Secular Stagnation Hypothesis is the problem of Developed Economies rather than Emerging Economies in that manner.
Injuries and Use of Safety Equipment in Stand-up Paddle Boarding

Background: Stand-up paddleboarding (SUP) is a water sport that has gained in popularity. Still, very little is known about its injury profile. Purpose: To analyze the incidence, mechanisms, and risk factors for SUP-related injuries in mainly calm waters and the use of safety equipment. Study Design: Descriptive epidemiology study. Methods: The authors distributed an online survey that gathered information on characteristics, injury history over the past 12 months, use of (safety) equipment, and health issues of SUP riders in German-speaking countries. Results: A total of 438 participants completed the survey. The mean participant age was 45 years (range, 8-82 years), 48% were female athletes, and 19.6% took part in competitions. Over the past 12 months, 72 (17%) of 431 respondents experienced a SUP-related injury, resulting in an overall incidence rate (IR) of 1.95 injuries per 1000 hours of activity. Male competitive athletes using a hardboard had the highest risk of injury. The IR for competitive riders was higher (3.21 injuries per 1000 hours) than that for recreational riders (1.41 injuries per 1000 hours). Mechanisms of injury were overuse (n = 29) and contact with the board (n = 18) or ground (n = 12). Injuries affected mainly muscles/tendons and joints of the upper arm/shoulder (n = 20), followed by wrist/hand (n = 18), knee (n = 16), elbow/forearm (n = 12), and foot (n = 11). The highest risk of injury occurred when paddling in wild water (33.3%), followed by SUP surfing (29.4%), and the lowest risk occurred while paddling on a lake (16.1%). A life vest was always used by 27, depending on the situation by 201, and never by 202 participants; a leash was always used by 161, depending on the situation by 244, and never by 26 participants. Of the 272 participants who paddled in winter, 253 wore a drysuit or wetsuit, whereas 19 paddled without any special clothing. Conclusion: SUP-related injuries predominantly happened in wild water or while SUP surfing, and male competitive athletes using a hardboard had the highest risk of injury. The main mechanisms of injury were overuse and hitting the board or ground. The upper extremity was more prone to injury, followed by the knee and foot. More effort should be made to educate paddlers on the necessity and correct use of the life vest, leash, and specialized outfits.
Enhancement of Melanocortin-4 Receptor (MC4R) and Constancy of Kiss1 mRNAs Expression in the Hypothalamic Arcuate Nucleus in a Model of Polycystic Ovary Syndrome Rat

Background: Hypothalamic Melanocortin-4 Receptor (MC4R) and kiss1/kisspeptin systems play roles in reproductive processes. This study was conducted to evaluate changes in MC4R and kiss1 genes expression in the arcuate nucleus (ARC) of the hypothalamus and its relationship with polycystic ovary syndrome (PCOS) in rats. Materials and Methods: In the current experimental study, 24 female rats were randomly and equally allocated into nulliparous and primiparous groups and then were divided into two subgroups of PCOS and control. PCOS was induced by exposure to continuous light. Sex-related hormones were evaluated by radioimmunoassay or immunoradiometric assay. Expressions of MC4R and kiss1 gene in the ARC of the hypothalamus of the rats were evaluated by real-time PCR. Histomorphometric alterations of ovaries were compared between groups. Results: Number of tertiary follicles and their size and number of atretic follicles in the PCOS subgroups were more than those in the controls (P<0.05) whereas the number of secondary follicles and corpus luteum in the PCOS subgroups were lower than those in the controls (P<0.05). Antrum and total diameters of tertiary follicles in the PCOS subgroups were greater and granulosa layer diameter was lower than those in the controls (P<0.05). The MC4R mRNA expression in the PCOS subgroups was 6.5-fold in nulliparous and 3.5-fold in primiparous groups more than their controls’ pairs (P<0.05). However, parity did not affect the expression of MC4R gene (P>0.05). The kiss1 mRNA expression in the PCOS and control subgroups was not significantly different (P>0.05). Conclusion: Overexpression of MC4R gene after PCOS induction in the ARC of the hypothalamus may link to metabolic disorders of induced PCOS in the rats. However, alteration in the kiss1 mRNA expression after PCOS induction was not observed in the rats.