Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of steam sauna bath on weight loss and lipid profile

1 Assistant Professor, Bharati Vidyapeeth Deemed University Medical College & Hospital Sangli Mobile No. 9921225953, (0233) 2374502, Email: vandanashiralkar@gmail.com 2 Professor & Head, Bharati Vidyapeeth Deemed University Medical College & Hospital Sangli, Mobile No. 9422042968, Email: drjepratap@gmail.com 3 Associate Professor, Bharati Vidyapeeth Deemed University Medical College & Hospital Sangli, Dist. Sangli, State: Maharashtra, India Mobile No. 9422515529 (0233) 2227688, Email: gbelwalkar@yahoo.com Abstract Objective: The aim of the study is to assess the effects of steam sauna bath on weight loss and lipid profile. Material & Methods: Ninety physically active subjects in the age group of 30-50 years were selected for the study. Forty-five males and forty-five females were subjected to seven steam sauna baths on alternate days. The temperature was maintained at 50 0 C. The duration was 15 minutes for each sauna bath. The weight, total serum cholesterol [TC], serum triglycerides [TG], low density lipoprotein (LDL) and high density lipoprotein (HDL) were estimated before and after seven steam sauna baths. Results: Serum total cholesterol, TG , and LDL were significantly decreased and HDL was significantly increased after seven steam sauna baths. There was significant reduction in weight after seven steam sauna baths. Conclusion: Steam sauna bath helps weight reduction and improves lipid profile.
Silicon fertilization as a sustainable approach to disease management of agricultural crops

Silicon (Si) is the second most abundant element present in the lithosphere, and it constitutes one of the major inorganic nutrient elements of many plants. Although Si is a nonessential nutrient element, its beneficial role in stimulating the growth and development of many plant species has been generally recognized. Silicon is known to effectively reduce disease severity in many plant pathosystems. The key mechanisms of Si-mediated increased plant disease resistance involve improving mechanical properties of cell walls, activating multiple signaling pathways leading to the expression of defense responsive genes and producing antimicrobial compounds. This article highlights the importance and applicability of Si fertilizers in integrated disease management for crops.
BDNF/TrkB.T1 signaling is a novel mechanism for astrocyte morphological maturation

Brain derived neurotrophic factor (BDNF) is a critical growth factor involved in the maturation of neurons, including neuronal morphology and synapse refinement. Herein, we demonstrate astrocytes express high levels of BDNF’s receptor, TrkB (in the top 20 of protein-coding transcripts), with nearly exclusive expression of the truncated isoform, TrkB.T1 which peaks in expression during astrocyte morphological maturation. Using a novel culture paradigm, we show that astrocyte morphological complexity is increased in the presence of BDNF and is dependent upon BDNF/TrkB.T1 signaling. Deletion of TrkB.T1 in vivo revealed morphologically immature astrocytes with significantly reduced volume and branching, as well as dysregulated expression of perisynaptic genes associated with mature astrocyte functions, including synaptogenic genes. Indicating a role for functional astrocyte maturation via BDNF/TrkB.T1 signaling, TrkB.T1 KO astrocytes do not support normal excitatory synaptogenesis. These data suggest a significant role for BDNF/TrkB.T1 signaling in astrocyte morphological maturation, a critical process for CNS development.
Phytosanitary condition and yield of wheat (Triticum aestivum) and einkorn wheat (Triticum monococcum) after application of different types of organic compost in organic and biodynamic farming systems

Maneva, V. & Atanasova, D. (2018). Phytosanitary condition and yield of wheat (Triticum aestivum) and einkorn wheat (Triticum monococcum) after application of different types of organic compost in organic and biodynamic farming systems. Rastenievadni nauki, 55(3), 10-15 The experiment was conducted in the period 2013-2016 on the experimental fields of the Institute of Agriculture in Karnobat, Bulgaria. Wheat and einkorn wheat were grown in biodynamic and organic farming systems after application of different types of organic compost. Studies were conducted on the effect of compost types on phytosanitary condition and yield of the crops. The experiments established that the phytosanitary condition of wheat was better when it was biodynamically cultivated with applied standard biodynamic compost in the soil. No diseases and pests were reported for einkorn wheat in the three variants, whereas weeds had lower density and species composition in einkorn than in wheat and the variant with standard biodynamic compost was the most weakly infested. The highest yields of wheat and einkorn wheat were obtained at biodynamic cultivation with applied standard biodynamic compost, followed by the variant of biodynamic compost according to Maria Thun’s compost preparations.
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes. Its interface mimics the popular OpenAI Gym library and is both extensible and intuitive to use. We aim at making this library a standardized platform that will lower the bar of entry and accelerate innovation in the field. Documentation and code can be found at this https URL.
Quantum Random Access Codes for Boolean Functions

An n↦pm random access code (RAC) is an encoding of n bits into m bits such that any initial bit can be recovered with probability at least p, while in a quantum RAC (QRAC), the n bits are encoded into m qubits. Since its proposal, the idea of RACs was generalized in many different ways, e.g. allowing the use of shared entanglement (called entanglement-assisted random access code, or simply EARAC) or recovering multiple bits instead of one. In this paper we generalize the idea of RACs to recovering the value of a given Boolean function f on any subset of fixed size of the initial bits, which we call f-random access codes. We study and give protocols for f-random access codes with classical (f-RAC) and quantum (f-QRAC) encoding, together with many different resources, e.g. private or shared randomness, shared entanglement (f-EARAC) and Popescu-Rohrlich boxes (f-PRRAC). The success probability of our protocols is characterized by the noise stability of the Boolean function f. Moreover, we give an upper bound on the success probability of any f-QRAC with shared randomness that matches its success probability up to a multiplicative constant (and f-RACs by extension), meaning that quantum protocols can only achieve a limited advantage over their classical counterparts.
Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects

Many different studies have shown that a wealth of cosmological information resides on small, nonlinear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that when considering some simple scenarios. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can (1) extract the maximum available cosmological information, (2) marginalize over baryonic effects, and (3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on, affecting their extrapolation properties. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects.
Sensor Placement for Contamination Detection in Water Distribution Systems

Sensor placement for contaminant detection in water distribution systems (WDS) has become a topic of great interest aiming to secure a population's water supply. Several approaches can be found in the literature with differences ranging from the objective selected to optimize to the methods implemented to solve the optimization problem. In this work we aim to give an overview of the current work in sensor placement with focus on contaminant detection for WDS. We present some of the objectives for which the sensor placement problem is defined along with common optimization algorithms and Toolkits available to help with algorithm testing and comparison.
Gene Subset Selection for Leukemia Classification Using Microarray Data

Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. associations while known disease-lncRNA associations are required only. In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multiobjective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.
The Complexity of Counting Problems over Incomplete Databases

We study the complexity of various fundamental counting problems that arise in the context of incomplete databases, i.e., relational databases that can contain unknown values in the form of labeled nulls. Specifically, we assume that the domains of these unknown values are finite and, for a Boolean query q , we consider the following two problems: Given as input an incomplete database D , (a) return the number of completions of D that satisfy q ; or (b) return the number of valuations of the nulls of D yielding a completion that satisfies q . We obtain dichotomies between #P-hardness and polynomial-time computability for these problems when q is a self-join–free conjunctive query and study the impact on the complexity of the following two restrictions: (1) every null occurs at most once in D (what is called Codd tables ); and (2) the domain of each null is the same. Roughly speaking, we show that counting completions is much harder than counting valuations: For instance, while the latter is always in #P, we prove that the former is not in #P under some widely believed theoretical complexity assumption. Moreover, we find that both (1) and (2) can reduce the complexity of our problems. We also study the approximability of these problems and show that, while counting valuations always has a fully polynomial-time randomized approximation scheme (FPRAS), in most cases counting completions does not. Finally, we consider more expressive query languages and situate our problems with respect to known complexity classes.
VCE: Variational Convertor-Encoder for One-Shot Generalization

Variational Convertor-Encoder (VCE) converts an image to various styles; we present this novel architecture for the problem of one-shot generalization and its transfer to new tasks not seen before without additional training. We also improve the performance of variational auto-encoder (VAE) to filter those blurred points using a novel algorithm proposed by us, namely large margin VAE (LMVAE). Two samples with the same property are input to the encoder, and then a convertor is required to processes one of them from the noisy outputs of the encoder; finally, the noise represents a variety of transformation rules and is used to convert new images. The algorithm that combines and improves the condition variational auto-encoder (CVAE) and introspective VAE, we propose this new framework aim to transform graphics instead of generating them; it is used for the one-shot generative process. No sequential inference algorithmic is needed in training. Compared to recent Omniglot datasets, the results show that our model produces more realistic and diverse images.
Neuronal and Glial Expression of GluN2C and GluN2D NMDA Receptor Subunit mRNA in Different Regions of the Mouse Forebrain

Neuronal and Glial Expression of GluN2C and GluN2D NMDA Receptor Subunit mRNA in Different Regions of the Mouse Forebrain Hassan A. Alsaad, Ph.D. University of Nebraska, 2018 Supervisor: Daniel T. Monaghan, Ph.D. N-methyl-D-aspartate (NMDA) receptors are a subtype of glutamate receptors that are widely expressed in the brain, where they mediate critical functions. While the actions of the predominate GluN2 NMDAR subunits, GluN2A and GluN2B are relatively well understood, the function of GluN2C and GluN2D in the telencephalon is largely unknown. To better understand the possible role of GluN2C and GluN2D subunits, we used fluorescence in situ hybridization together with multiple cell markers to define the distribution and type of cells expressing GluN2C and GluN2D mRNA. Also, a GluN2CKO mouse was used as a negative control. GluN2C mRNA expression was only found in non-neuronal cells in the telencephalon (except for globus pallidus). In addition, a significant fraction of astrocytes expressed GluN2C mRNA. In contrast to GluN2C, GluN2D subunit mRNA colocalized with neuronal and not astrocyte markers in the telencephalon. In the thalamus, GluN2C and GluN2D mRNA showed region-specific distributions as previously reported, and both subunits mRNA were expressed in neurons. Consistent with these findings, cellular colocalization of GluN2C and GluN2D mRNA was found in the thalamus but not in the
Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM

It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals. The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach demonstrated that the cascade of two deep learning network has higher performance than the concatenation of them, achieving a weighted f1 score of 0.82. The experimental results have successfully validated that the cascade of CNN and LSTM can achieve satisfactory performance on discriminating ECG signals.
Learning to Personalize Treatments When Agents Are Strategic

There is increasing interest in using observed individual-level data to formulate personalized policy. Examples of this include heterogeneous pricing, individualized credit offers, and targeted social programs. This paper provides a general model of how personalized policy creates incentives for individuals to modify their behavior to obtain a better treatment. For a given planner objective, we show that standard estimators based on repeated risk minimization produce a suboptimal policy. We propose a dynamic experiment that estimates the optimal treatment allocation function when agents are strategic and has regret that decays at a linear rate. A key insight is that random variation in how treatment assignment depends on observed characteristics is required, and that randomized treatment assignment alone is not sufficient to identify the optimal policy. We show this experimental method outperforms alternative methods that do not learn strategic effects in simulations and in a small MTurk experiment.
Edge photocurrent in bilayer graphene due to inter-Landau-level transitions

We report the observation of the resonant excitation of edge photocurrents in bilayer graphene subjected to terahertz radiation and a magnetic field. The resonantly excited edge photocurrent is observed for both inter-band (at low carrier densities) and intra-band (at high densities) transitions between Landau levels (LL). While the intra-band LL transitions can be traced to the classical cyclotron resonance (CR) and produce strong resonant features, the inter-band-LL resonances have quantum nature and lead to the weaker features in the measured photocurrent spectra. The magnitude and polarization properties of the observed features agree with the semiclassical theory of the intra-band edge photogalvanic effect, including its Shubnikov-de-Haas oscillations at low temperatures.
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.
Galerkin force model for transient dynamics of the fluidic pinball.

We propose an aerodynamic force model associated with a Galerkin model for the unforced fluidic pinball, the two-dimensional flow around three equal cylinders with one radius distance to each other. The starting point is a Galerkin model of a bluff-body flow. The force on this body is derived as a constant-linear-quadratic function of the mode amplitudes from first principles following the pioneering work of Noca (1997, 1999) and Liang & Dong (2014). The force model is simplified for the mean-field model of the unforced fluidic pinball (Deng et al. 2020) using symmetry properties and sparse calibration. The model is successfully applied to transient dynamics in different Reynolds number regimes: the periodic vortex shedding after the Hopf-bifurcation and the asymmetric vortex shedding after the pitchfork bifurcation comprising six different Navier-Stokes solutions. We foresee many applications of the Galerkin force model for other bluff bodies and flow control.
Evolution of dissipative and nondissipative universes in holographic cosmological models with a power-law term

Density perturbations related to structure formations are expected to be different in dissipative and nondissipative universes, even if the background evolution of the two universes is the same. To clarify the difference between the two universes, first-order density perturbations are studied, using two types of holographic cosmological models. The first type is a ``$\mathrm{\ensuremath{\Lambda}}(t)$ model'' similar to a time-varying $\mathrm{\ensuremath{\Lambda}}(t)$ cosmology for the nondissipative universe. The second type is a ``BV model'' similar to a bulk viscous cosmology for the dissipative universe. To systematically examine the two different universes, a power-law term proportional to ${H}^{\ensuremath{\alpha}}$ is applied to the $\mathrm{\ensuremath{\Lambda}}(t)$ and BV (bulk-viscous-cosmology-like) models, assuming a flat Friedmann-Robertson-Walker model for the late universe. Here, $H$ is the Hubble parameter and $\ensuremath{\alpha}$ is a free parameter whose value is a real number. The $\mathrm{\ensuremath{\Lambda}}(t)\text{\ensuremath{-}}{H}^{\ensuremath{\alpha}}$ and $\mathrm{BV}\text{\ensuremath{-}}{H}^{\ensuremath{\alpha}}$ models are used to examine first-order density perturbations for matter, in which the background evolution of the two models is equivalent. In addition, thermodynamic constraints on the two models are discussed, with a focus on the maximization of entropy on the horizon of the universe, extending previous analyses [Phys. Rev. D 100, 123545 (2019); Phys. Rev. D102, 063512 (2020)]. Consequently, the $\mathrm{\ensuremath{\Lambda}}(t)\text{\ensuremath{-}}{H}^{\ensuremath{\alpha}}$ model for small $|\ensuremath{\alpha}|$ values is found to be consistent with observations and satisfies the thermodynamic constraints, compared with the $\mathrm{BV}\text{\ensuremath{-}}{H}^{\ensuremath{\alpha}}$ model. The results show that the nondissipative universe described by the $\mathrm{\ensuremath{\Lambda}}(t)\text{\ensuremath{-}}{H}^{\ensuremath{\alpha}}$ model similar to lambda cold dark matter models is likely favored.
Testing for Typicality with Respect to an Ensemble of Learned Distributions

Methods of performing anomaly detection on high-dimensional data sets are needed, since algorithms which are trained on data are only expected to perform well on data that is similar to the training data. There are theoretical results on the ability to detect if a population of data is likely to come from a known base distribution, which is known as the goodness-of-fit problem. One-sample approaches to this problem offer significant computational advantages for online testing, but require knowing a model of the base distribution. The ability to correctly reject anomalous data in this setting hinges on the accuracy of the model of the base distribution. For high dimensional data, learning an accurate-enough model of the base distribution such that anomaly detection works reliably is very challenging, as many researchers have noted in recent years. Existing methods for the one-sample goodness-of-fit problem do not account for the fact that a model of the base distribution is learned. To address that gap, we offer a theoretically motivated approach to account for the density learning procedure. In particular, we propose training an ensemble of density models, considering data to be anomalous if the data is anomalous with respect to any member of the ensemble. We provide a theoretical justification for this approach, proving first that a test on typicality is a valid approach to the goodness-of-fit problem, and then proving that for a correctly constructed ensemble of models, the intersection of typical sets of the models lies in the interior of the typical set of the base distribution. We present our method in the context of an example on synthetic data in which the effects we consider can easily be seen.