Miracle-dz/UniFAR-FTUs huggingface dataset:
Long‐Term Selenium‐Yeast Supplementation Does Not Affect Bone Turnover Markers: A Randomized Placebo‐Controlled Trial

Higher selenium status has been associated with lower bone turnover markers (BTM) in epidemiological studies. However, the long‐term impact of selenium supplementation on BTMs has not been studied. We investigated the effects of selenium supplementation on BTMs including osteocalcin (OC), procollagen type I N‐terminal propeptide (PINP), collagen type I cross‐linked C‐telopeptide (CTX), and bone alkaline phosphatase (BALP) in the short (6 months) and long term (5 years). A total of 481 Danish men and women (60–74 years) were randomized to receive placebo‐yeast versus 100, 200, or 300 μg selenium as selenium‐enriched yeast daily for 5 years. Plasma selenium concentration was measured using inductively coupled plasma mass spectrometry, and BTMs were measured in nonfasted samples at baseline, 6 months, and 5 years. Data were analyzed by ANCOVA to investigate the shape of the dose‐response relationships. Covariates included age, body mass index, baseline selenium status, baseline BTM, smoking, alcohol, supplement use, and medication. Plasma selenium concentration (mean 86.5 μg/d at baseline) increased significantly with increasing selenium supplementation to 152.6, 209.1, and 253.7 μg/L after 6 months and remained elevated at 5 years (158.4, 222.4, and 275.9 μg/L for 100, 200, and 300 μg supplemental selenium/d, respectively (p < 0.001)). There was no change in plasma selenium concentration in the placebo‐treated group. There was no significant effect of selenium supplementation on OC (6 months p = 0.37; 5 years p = 0.63), PINP (6 months p = 0.37; 5 years p = 0.79), CTX (6 months p = 0.91; 5 years p = 0.58) or BALP (6 months p = 0.17; 5 years p = 0.53). The relatively replete baseline selenium status in the study participants may explain this lack of effect. Testing in more deficient populations may provide further insights into the impact of selenium supplementation on bone health. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Stopping syphilis transmission in Arctic communities through rapid diagnostic testing: The STAR study protocol

Background Intense transmission of syphilis has emerged in some Canadian Arctic communities despite screening and prevention efforts. The remoteness of most communities and limited diagnostic infrastructure yield long delays (≥14 days) between screening and treatment of cases. These hamper syphilis control efforts and may contribute to sustained transmission. Syphilis rapid diagnostic tests (RDTs) have been developed to make screening more accessible and to inform clinical decision-making within the same clinical encounter. These RDTs have been successfully deployed in several countries, but not yet in Canada. Methods and design We describe the methodology of the “Stopping Syphilis Transmission in Arctic Communities Through Rapid Diagnostic Testing” (STAR) study, wherein the clinical and epidemiological impact of deploying a dual syphilis RDT in the context of ongoing transmission in Nunavut and Nunavik will be evaluated. In this prospective multisite field evaluation, sexually active individuals aged ≥14 years at risk for syphilis will be offered screening by an RDT at the point-of-care by non-laboratory trained registered nurses. Whole blood and serum specimens will be concurrently collected, when feasible, for rapid testing with an RDT containing both treponemal and non-treponemal components (Chembio DPP® Syphilis Screen & Confirm) and compared to laboratory-based reference testing according to a reverse sequence algorithm. The diagnostic accuracy of the RDT, using both whole blood and centrifuged serum specimens, will be validated under real-world conditions in remote Northern settings, outside of specialized laboratories. Additionally, screening-to-treatment time, case detection rates, and the number of infectious contacts averted by using the RDT relative to reference testing will be estimated. The impact of both diagnostic approaches on syphilis transmission dynamics will also be modeled. Discussion This study will provide much needed evidence for strengthening rapid responses to emerging syphilis outbreaks in remote Arctic regions, by supplementing traditional diagnostic strategies with an RDT to rapidly triage patients likely in need of treatment. These results will also inform the development and tailoring of future diagnostic strategies and public health responses to emerging outbreaks in the North.
EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder

Objective: Major depressive disorder (MDD) is a persistent psychiatric condition, and the leading cause of disability, affecting up to 5% of the population worldwide. Antidepressant medications (ADMs) are often the first-line treatment for MDD, but it may take the clinician months of “trial and error” to find an effective ADM for a particular patient. Therefore, identification of predictive biomarkers that can be used to accurately determine the effectiveness of a specific treatment for an individual patient is extremely valuable. Method: Using resting EEG data, we develop a machine learning algorithm (MLA) that searches for connectivity patterns within an individual's EEG signal that are predictive of the probability of responding to the antidepressant Sertraline or Placebo. The MLA has two steps: 1) Directed phase lag index (DPLI), a measure of phase synchronization between brain regions, that is not sensitive to volume conduction is applied to resting-state EEG data, 2) the resulting DPLI matrix is searched for a pattern set of features that can be used to successfully predict the response to Sertraline or Placebo. Results: Our MLA predicted response to Sertraline (N = 105) or Placebo (N = 119) with more than 80% accuracy. Conclusion: Our findings suggest that feature patterns selected from a DPLI matrix may be predictive of response to Sertraline and to Placebo. Significance: The proposed MLA may provide an inexpensive, non-invasive, and readily available tool that clinicians may use to enhance treatment effectiveness, accelerate time to recovery, reduce personal suffering, and decrease treatment costs.
Use of common blood parameters for the differential diagnosis of childhood infections

Background Routine laboratory investigations are not rapidly available to assist clinicians in the diagnosis of pediatric acute infections. Our objective was to evaluate some common blood parameters and use them for the differential diagnosis of childhood infections. Methods This retrospective study was conducted between October 2019 and September 2020 at Guangzhou Women and Children’s Medical Center, China. We performed blood tests in patients infected with DNA viruses (n = 402), RNA viruses (n = 602), gram-positive organisms (G+; n = 421), gram-negative organisms (G−; n = 613), or Mycoplasma pneumoniae (n = 387), as well as in children without infection (n = 277). The diagnostic utility of blood parameters to diagnose various infections was evaluated by logistic regression analysis. Results The most common G+ organism, G− organism, and virus were Streptococcus pneumoniae (39.7%), Salmonella typhimurium (18.9%), and influenza A virus (40.2%), respectively. The value of logit (P) = 0.003 × C-reactive protein (CRP) − 0.011 × hemoglobin (HGB) + 0.001 × platelets (PLT) was significantly different between the control, RNA virus, DNA virus, M. pneumoniae, G− organism, and G+ organism groups (2.46 [95% CI, 2.41–2.52], 2.60 [2.58–2.62], 2.70 [2.67–2.72], 2.78 [2.76–2.81], 2.88 [2.85–2.91], and 2.97 [2.93–3.00], respectively; p = 0.00 for all). The logistic regression‐based model showed significantly greater accuracy than the best single discriminatory marker for each group (logit [Pinfection] vs. CRP, 0.90 vs. 0.84, respectively; logit [PRNA] vs. lymphocytes, 0.83 vs. 0.77, respectively; p = 0.00). The area under curve values were 0.72 (0.70–0.74) for HGB and 0.81 (0.79–0.82) for logit (Pvirus/bacteria) to diagnose bacterial infections, whereas they were 0.72 (0.68–0.74) for eosinophils and 0.80 (0.78–0.82) for logit (Pvirus/bacteria) to diagnose viral infections. Logit (Pvirus/bacteria) < −0.45 discriminated bacterial from viral infection with 78.9% specificity and 70.7% sensitivity. Conclusions The combination of CRP, HGB, PLT, eosinophil, monocyte, and lymphocyte counts can distinguish between the infectious pathogens in children.
MRI Generated From CT for Acute Ischemic Stroke Combining Radiomics and Generative Adversarial Networks

Compared to computed tomography (CT), magnetic resonance imaging (MRI) is more sensitive to acute ischemic stroke lesion. However, MRI is time-consuming, expensive, and susceptible to interference from metal implants. Generating MRI images from CT images can address the limitations of MRI. The key problem in the process is obtaining lesion information from CT. In this study, we propose a cross-modal image generation algorithm from CT to MRI for acute ischemic stroke by combining radiomics with generative adversarial networks. First, the lesion candidate region was obtained using radiomics, the radiomic features of the region were extracted, and the feature with the largest information gain was selected and visualized as a feature map. Then, the concatenation of the extracted feature map and the CT image was input in the generator. We added a residual module after the downsampling of the generator, following the general shape of U-Net, which can deepen the network without causing degradation problems. In addition, we introduced the lesion feature similarity loss function to focus the model on the similarity of the lesion. Through the subjective judgment of two experienced radiologists and using evaluation metrics, the results showed that the generated MRI images were very similar to the real MRI images. Moreover, the locations of the lesions were correct, and the shapes of lesions were similar to those of the real lesions, which can help doctors with timely diagnosis and treatment.
Antihyperlipidemic Activity of Glycoconjugated Phthalimides in Mice Submitted to a Model of Dyslipidemia and Insulin Resistance

Alterations in lipid and lipoprotein metabolism are factors that trigger several negative metabolic complications. Hyperlipidemia is the starting point for the development of comorbidities of the cardiovascular system, such as atherosclerosis. The search for compounds that reduce high levels of total cholesterol and triglycerides has been widely reported in several publications in the literature. Phthalimide derivatives have been extensively researched with various biological actions. In this study we evaluated the antihyperlipidemic ability of three phthalimide derivatives (FGT‐2, FGT‐3 and FGT‐4) on a model of obesity and insulin resistance in mice. The animals were submitted to a hyperlipid diet for 60 days. On the thirtieth day they were treated with phthalimides (20 mg/kg). The positive control group was treated with Simvastatin (20 mg/kg) and the negative control received only the carboxymethylcellulose vehicle. Biochemical and histological analyzes of all groups were analyzed. The animals treated with phthalimidic derivatives had a reduction in total cholesterol, low density and very low density lipoproteins (LDL‐c and VLDL‐c), triglycerides and fasting glycemia when compared to the negative control group. The treated animals also showed good results when analyzing the atherogenic indexes Castelli i and II and the ratio Triglycerides/HDL‐c. In the oral glucose tolerance test and in the insulin tolerance test, animals treated with phthalimides were more sensitive to the action of the hormone regulating carbohydrate uptake. In the evaluation of the transaminases (AST/ALT), the animals of the group treated with phthalimides presented a lower elevation than the other groups of the experiment, the same observed with the uric acid evaluation. Histological analyzes were performed on liver, kidney, heart and pancreas samples. The groups treated with the compounds FGT‐2 and FGT3 presented discrete alterations in the liver and kidney. FGT‐4 did not present histological alterations for both tissues and the three phthalimide derivatives did not cause alterations in the other organs. These results suggest that the phthalimides tested can act as antihyperlipidemic agents and have a pleiotropic action, by acting also reducing glycemia in insulin resistance model mimicking diabetes mellitus type 2. These compounds may appear as a new approach in the treatment of obesity and complications, which are multifaceted.
Combatting ageism through virtual embodiment? Using explicit and implicit measures

Abstract Objectives: Ageism is defined as stereotypes, prejudice, and discrimination towards people because of their age. Although ageism can be directed towards people of any age group, most research has focused on ageism towards older people. Ageism towards older people is known to have a significant impact on their health and wellbeing and to even result in higher healthcare costs. The present study evaluated the use of virtual embodiment (VE) to reduce self- and other-directed ageism. Design, setting, and participants: We randomized 80 individuals between the ages of 18 and 35 years to one of two conditions: VE as an older or a younger avatar. Results: No differences were found on explicit measures of ageism. Once multiple comparisons were accounted for, a nonsignificant reduction in implicit age bias following exposure to the older avatar (Cohen’s d = .75, p = .02) also was found. Conclusions: Past research has established the effectiveness of VE in relation to implicit measures. However, once both explicit and implicit measures are included and multiple comparisons are accounted for, neither explicit nor implicit measures of ageism show a significant effect. Given the multidimensional nature of ageism, further research is needed to establish the effectiveness of VE once multiple measures of ageism are considered.
Molecular insight of phytocompounds from Indian spices and its hyaluronic acid conjugates to block SARS-CoV-2 viral entry

Abstract Human corona viral infection leads to acute breathing disease and death if not diagnosed and treated properly in time. The disease can be treated with the help of simple natural compounds, which we use in day-to-day life. These natural compounds act against several diseases but their drug targeting mechanism needs to be improved for more efficient and promising applications. In the present study five compounds (gingerol, thymol, thymohydroquinone, cyclocurcumin, hydrazinocurcumin) from three Indian medicinal plants (ginger, black cumin, turmeric) and its hyaluronic acid (HA) conjugates were docked against initially deposited spike structural proteins (PDB ID 6WPT) and its mutant variant D-614G (PDB ID 6XS6). Docking study result reveals that all the HA conjugates showed the most effective inhibitor of S-protein of initially deposited and D-614G variant forms of SARS-CoV-2. The compounds like Gingerol, Thymol, Thymohydroquinone, Cyclocurcumin, Hydrazinocurcumin, Hydroxychloroquinone, and hyaluronic acid conjugates inhibit the viral protein of both wild-type and mutated S-protein of SARS-CoV-2. The molecular docking studies of phytocompounds with initial deposited and variant spike protein targets show superior binding affinity than with the commercial repurposed viral entry inhibitor hydroxychloroquine. Further, the docking result was modeled using MD simulation study shows excellent simulation trajectories between spike proteins and HA conjugates spices constituents than its free form. DFT analysis was carried out to affirm the reason behind the highest binding affinity of HA conjugates over its free form towards SARS-CoV-2 spike protein targets. Further HA conjugates synthesis and its evaluation against SARS-CoV-2 in vitro studies are needed to prove our novel idea for an anti-viral drug. Communicated by Ramaswamy H. Sarma
Association of Amyloid Reduction After Donanemab Treatment With Tau Pathology and Clinical Outcomes

This post hoc analysis of the randomized clinical trial, Study of LY3002813 in Participants With Early Symptomatic Alzheimer’s Disease (TRAILBLAZER-ALZ), investigates if amyloid reduction from donanemab treatment is associated with slowing of tau pathology and clinical decline in individuals with early symptomatic Alzheimer disease.
Enhanced Photovoltaic Properties of Gradient Doping Solar Cells

An optimum design of a-Si:H(n)/a-Si:H(i)/c-Si(p) heterojunction solar cell is realized with 24.27% conversion efficiency by gradient doping of the a-Si:H(n) layer. The photovoltaic properties are simulated by the AFORSHET software. Besides the additional electric field caused by the gradient doping, the enhanced and widen spectral response also improves the solar cell performance compared with the uniform-doping mode. The simulation shows that the gradient doping is efficient to improve the photovoltaic performance of the solar cells. The study is valuable for the solar cell design with excellent performances.
Nearly all $k$-SAT functions are unate

We prove that $1-o(1)$ fraction of all $k$-SAT functions on $n$ Boolean variables are unate (i.e., monotone after first negating some variables), for any fixed positive integer $k$ and as $n \to \infty$. This resolves a conjecture by Bollob\'as, Brightwell, and Leader from 2003.
New fictitious play procedure for solving Blotto games

Abstract : In this thesis, a new fictitious play (FP) procedure is presented to solve two-person zero-sum (TPZS) Blotto games. The FP solution procedure solves TPZS games by assuming that the two players take turns selecting optimal responses to the opponent's strategy observed so far. It is known that FP converges to an optimal solution, and it may be the only realistic approach to solve large games. The algorithm uses dynamic programming (DP) to solve FP subproblems. Efficiency is obtained by limiting the growth of the DP state space. Blotto games are frequently used to solve simple missile defense problems. While it may be unlikely that the models presented in this paper can be used directly to solve realistic offense and defense problems, it is hoped that they will provide insight into the basic structure of optimal and near-optimal solutions to these important, large games, and provide a foundation for solution of more realistic, and more complex, problems.
Dynamic zoom-in detection of exfoliated two-dimensional crystals using deep reinforcement learning

Owing to their tunability and versatility, the two-dimensional materials are an excellent platform to conduct a variety of experiments. However, laborious device fabrication procedures remain as a major experimental challenge. One bottleneck is searching small target crystals from a large number of exfoliated crystals that greatly vary in shapes and sizes. We present a method, based on a combination of deep reinforcement learning and object detection, to accurately and efficiently discover target crystals from a high resolution image containing many microflakes. The proposed method dynamically zooms in to the region of interest and inspects it with a fine detector. Our method can be customized for searching various types of crystals with a modest computation power. We show that our method outperformed a simple baseline in detection tasks. Finally, we analyze the efficiency of the deep reinforcement learning agent in searching crystals. Codes are available at \url{https://github.com/stephandkim/detect_crystals}.
Active fault‐tolerant control of sampled‐data nonlinear distributed parameter systems

This work presents an integrated fault detection and fault‐tolerant control architecture for spatially distributed systems described by highly dissipative systems of nonlinear partial differential equations with actuator faults and sampled measurements. The architecture consists of a family of nonlinear feedback controllers, observer‐based fault detection filters that account for the discrete measurement sampling, and a switching law that reconfigures the control actuators following fault detection. An approximate finite‐dimensional model that captures the dominant dynamics of the infinite‐dimensional system is embedded in the control system to provide the controller and fault detection filter with estimates of the measured output between sampling instances. The model state is then updated using the actual measurements whenever they become available from the sensors. By analyzing the behavior of the estimation error between sampling times and exploiting the stability properties of the compensated model, a sufficient condition for the stability of the sampled‐data nonlinear closed‐loop system is derived in terms of the sampling rate, the model accuracy, the controller design parameters, and the spatial placement of the control actuators. This characterization is used as the basis for deriving appropriate rules for fault detection and actuator reconfiguration. Singular perturbation techniques are used to analyze the implementation of the developed architecture on the infinite‐dimensional system. The results are demonstrated through an application to the problem of stabilizing the zero solution of the Kuramoto–Sivashinsky equation. Copyright © 2011 John Wiley & Sons, Ltd.
If Influence Functions are the Answer, Then What is the Question?

Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-out retraining for linear models, recent works have shown this alignment is often poor in neural networks. In this work, we investigate the specific factors that cause this discrepancy by decomposing it into five separate terms. We study the contributions of each term on a variety of architectures and datasets and how they vary with factors such as network width and training time. While practical influence function estimates may be a poor match to leave-one-out retraining for nonlinear networks, we show they are often a good approximation to a different object we term the proximal Bregman response function (PBRF). Since the PBRF can still be used to answer many of the questions motivating influence functions, such as identifying influential or mislabeled examples, our results suggest that current algorithms for influence function estimation give more informative results than previous error analyses would suggest.
Rethink Decision Tree Traversal

We will show how to evaluate binary decision tree traversal in the language of matrix computation motivated by QuickScorer in [12]. Our main contribution is a novel matrix representation of hierarchical structure of decision tree. And we propose some equivalent algorithms of binary decision tree traversal based on the rigorous theoretical analysis. The core idea is to ﬁnd the relation between the input and exit leaf node. Here we not only understand decisions without the recursive traverse but also dive into the partitioning nature of tree-based methods.