Miracle-dz/UniFAR-FTUs huggingface dataset:
Protection of Cochlear Ribbon Synapses and Prevention of Hidden Hearing Loss

In the auditory system, ribbon synapses are vesicle-associated structures located between inner hair cells (IHCs) and spiral ganglion neurons that are implicated in the modulation of trafficking and fusion of synaptic vesicles at the presynaptic terminals. Synapse loss may result in hearing loss and difficulties with understanding speech in a noisy environment. This phenomenon happens without permanent hearing loss; that is, the cochlear synaptopathy is “hidden. ” Recent studies have reported that synapse loss might be critical in the pathogenesis of hidden hearing loss. A better understanding of the molecular mechanisms of the formation, structure, regeneration, and protection of ribbon synapses will assist in the design of potential therapeutic strategies. In this review, we describe and summarize the following aspects of ribbon synapses: (1) functional and structural features, (2) potential mechanisms of damage, (3) therapeutic research on protecting the synapses, and (4) the role of synaptic regeneration in auditory neuropathy and the current options for synapse rehabilitation.
DART: aDaptive Accept RejecT for non-linear top-K subset identification

We consider the bandit problem of selecting $K$ out of $N$ arms at each time step. The reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among $\binom{N}{K}$ options, making the action space large. To simplify the problem, existing works on combinatorial bandits {typically} assume feedback as a linear function of individual rewards. In this paper, we prove the lower bound for top-$K$ subset selection with bandit feedback with possibly correlated rewards. We present a novel algorithm for the combinatorial setting without using individual arm feedback or requiring linearity of the reward function. Additionally, our algorithm works on correlated rewards of individual arms. Our algorithm, aDaptive Accept RejecT (DART), sequentially finds good arms and eliminates bad arms based on confidence bounds. DART is computationally efficient and uses storage linear in $N$. Further, DART achieves a regret bound of $\tilde{\mathcal{O}}(K\sqrt{KNT})$ for a time horizon $T$, which matches the lower bound in bandit feedback up to a factor of $\sqrt{\log{2NT}}$. When applied to the problem of cross-selling optimization and maximizing the mean of individual rewards, the performance of the proposed algorithm surpasses that of state-of-the-art algorithms. We also show that DART significantly outperforms existing methods for both linear and non-linear joint reward environments.
Discovery of O stars in the tidal Magellanic Bridge

The Magellanic Bridge, stretching between the Small and the Large Magellanic Cloud (SMC and LMC), is the nearest tidally stripped intergalactic environment. The Bridge has a significantly low average metallicity of Z ≲ 0.1 Z⊙. Here we report the first discovery of O-type stars in the Magellanic Bridge. Three massive O stars were identified thanks to the archival spectra obtained by the ESO’s Very Large Telescope FLAMES instrument. We analyze the spectra of each star using the Potsdam Wolf-Rayet (PoWR) non-local thermodynamic equilibrium model atmosphere code, which provides the physical parameters, ionizing photon fluxes, and surface abundances. The ages of the newly discovered O stars suggest that star formation in the Bridge is ongoing. Furthermore, the discovery of O stars in the Bridge implies that tidally stripped galactic tails containing low-density but highly dynamical gas are capable of producing massive O stars. The multi-epoch spectra indicate that all three O stars are binaries. Despite their spatial proximity to one another, these O stars are chemically distinct. One of them is a fast-rotating giant with nearly LMC-like abundances. The other two are main-sequence stars that rotate extremely slowly and are strongly metal depleted. We discover the most nitrogen-poor O star known to date. Taking into account the previous analyses of B stars in the Bridge, we interpret the various metal abundances as the signature of a chemically inhomogeneous interstellar medium (ISM), suggesting that the Bridge gas might have accreted during multiple episodes of tidal interaction between the Clouds. Attributing the lowest derived metal content to the primordial gas, the time of the initial formation of the Bridge may date back several billion years. Using the Gaia and Galex color-magnitude diagrams, we roughly estimate the total number of O stars in the Bridge and their total ionizing radiation. Comparing this with the energetics of the diffuse ISM, we find that the contribution of the hot stars to the ionizing radiation field in the Bridge is less than 10% and conclude that the main sources of ionizing photons are leaks from the LMC and SMC. We estimate a lower limit for the fraction of ionizing radiation that escapes from these two dwarf galaxies.
Adherence to Antiretroviral Treatment for Prevention of Mother-to-Child Transmission of HIV in Eastern Ethiopia: A Cross-Sectional Study

Introduction Even though PMTCT (prevention of mother-to-child transmission) is the most effective option to prevent vertical transmission of HIV, achieving optimal adherence has been a universal challenge to PMTCT, including Ethiopia. Objective To assess levels of adherence and associated factors among pregnant and lactating mothers on PMTCT at public health facilities in Harar town, Harari region, Eastern Ethiopia. Methods A facility-based cross-sectional study was conducted from April 01 to April 30, 2019, at the selected public health facilities of Harar town, Harari regional state, Eastern Ethiopia. Adherence was measured using a multi-method tool that includes pill counting and self-reporting method. Binary logistic regression analysis was used to determine factors associated with PMTCT adherence. The results were adjusted for possible confounding variables and P-value <0.05 or CI not including 1 was considered statistically significant. Results Of the 190 included patients, 83.2% of them had good adherence to PMTCT. Forgetfulness (79%) and being busy (71%) were among the main reasons for non-adherence to PMTCT. In multivariate logistic regression, participants who have received counseling on side effects were 3.4 times more likely to have good adherence [AOR=3.4, 95% CI: 2.1–9.2]; those with previously known HIV status were 2.1 times more likely to have good adherence to PMTCT [AOR=2.1, 95% CI: 1.6–7.1] and women who were knowledgeable on PMTCT were 5.2 times more likely to have good adherence [AOR=5.2, 95% CI: 1.6–6.28]. Conclusion In this study, the overall level of adherence to PMTCT care and support was 83.2%. Counseling on side effects, knowledge about PMTCT, and pregnancy status during initiation of ART were the factors that determine adherence of pregnant and lactating women towards PMTCT service. Thus, it is recommended to raise awareness about PMTCT among all reproductive-age women by stressing the benefit of the early initiation of ART and by providing information on the possible side effect of the drugs.
Accelerating Distributed SGD for Linear Least-Squares Problem

This paper considers the multi-agent distributed linear least-squares problem. The system comprises multiple agents, each agent with a locally observed set of data points, and a common server with whom the agents can interact. The agents' goal is to compute a linear model that best fits the collective data points observed by all the agents. In the server-based distributed settings, the server cannot access the data points held by the agents. The recently proposed Iteratively Pre-conditioned Gradient-descent (IPG) method has been shown to converge faster than other existing distributed algorithms that solve this problem. In the IPG algorithm, the server and the agents perform numerous iterative computations. Each of these iterations relies on the entire batch of data points observed by the agents for updating the current estimate of the solution. Here, we extend the idea of iterative pre-conditioning to the stochastic settings, where the server updates the estimate and the iterative pre-conditioning matrix based on a single randomly selected data point at every iteration. We show that our proposed Iteratively Pre-conditioned Stochastic Gradient-descent (IPSG) method converges linearly in expectation to a proximity of the solution. Importantly, we empirically show that the proposed IPSG method's convergence rate compares favorably to prominent stochastic algorithms for solving the linear least-squares problem in server-based networks.
Hybrid localized graph kernel for machine learning energy‐related properties of molecules and solids

Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical and physical properties of a broad range of systems. With the aim of improving the accuracy of predictions, a large number of representations for molecules and solids for machine learning applications has been developed. In this work we propose a novel descriptor based on the notion of molecular graph. While graphs are largely employed in classification problems in cheminformatics or bioinformatics, they are not often used in regression problem, especially of energy‐related properties. Our method is based on a local decomposition of atomic environments and on the hybridization of two kernel functions: a graph kernel contribution that describes the chemical pattern and a Coulomb label contribution that encodes finer details of the local geometry. The accuracy of this new kernel method in energy predictions of molecular and condensed phase systems is demonstrated by considering the popular QM7 and BA10 datasets. These examples show that the hybrid localized graph kernel outperforms traditional approaches such as, for example, the smooth overlap of atomic positions and the Coulomb matrices.
Propagation of Annonaceous plants

Abstract This review aims to present advances in studies on the propagation of the Annonaceae species, which includes species of economic importance such as: soursop, custard apple, atemoya and cherimoya. In sexual propagation, advances are mainly related to a better understanding of the stages of seed development, dormancy mechanisms, and germination. In asexual propagation, compatibility studies between grafts and rootstocks are presented, focusing on the expression of genes involved in tissue formation. The cutting method is also discussed, which is another option for the propagation for this group of plants considered difficult to root, approaching endogenous and exogenous factors related to the subject, as well as management strategies that affect the success of this technique.
Case study on the identification and classification of small-scale flow patterns in flaring active region

Context. We propose a novel methodology to identity flows in the solar atmosphere and classify their velocities as either supersonic, subsonic, or sonic. Aims. The proposed methodology consists of three parts. First, an algorithm is applied to the Solar Dynamics Observatory (SDO) image data to locate and track flows, resulting in the trajectory of each flow over time. Thereafter, the differential emission measure inversion method is applied to six Atmospheric Imaging Assembly (AIA) channels along the trajectory of each flow in order to estimate its background temperature and sound speed. Finally, we classify each flow as supersonic, subsonic, or sonic by performing simultaneous hypothesis tests on whether the velocity bounds of the flow are larger, smaller, or equal to the background sound speed. Methods. The proposed methodology was applied to the SDO image data from the 171 Å spectral line for the date 6 March 2012 from 12:22:00 to 12:35:00 and again for the date 9 March 2012 from 03:00:00 to 03:24:00. Eighteen plasma flows were detected, 11 of which were classified as supersonic, 3 as subsonic, and 3 as sonic at a 70% level of significance. Out of all these cases, 2 flows cannot be strictly ascribed to one of the respective categories as they change from the subsonic state to supersonic and vice versa. We labeled them as a subclass of transonic flows. Results. The proposed methodology provides an automatic and scalable solution to identify small-scale flows and to classify their velocities as either supersonic, subsonic, or sonic. It can be used to characterize the physical properties of the solar atmosphere. Conclusions. We identified and classified small-scale flow patterns in flaring loops. The results show that the flows can be classified into four classes: sub-, super-, trans-sonic, and sonic. The flows occur in the complex structure of the active region magnetic loops. The detected flows from AIA images can be analyzed in combination with the other high-resolution observational data, such as Hi-C 2.1 data, and be used for the development of theories describing the physical conditions responsible for the formation of flow patterns.
Upregulated Long Noncoding RNA UCA1 Enhances Warburg Effect via miR-203/HK2 Axis in Esophagal Cancer

Reprogrammed glucose metabolism of enhanced aerobic glycolysis, also known as Warburg effect, which exerts a significant contributor to cancer progression, is regarded as a hallmark of cancer. The roles of long noncoding RNAs (lncRNA) in regulating cancer via metabolic reprogramming are mostly unknown, including esophagal cancer (EC). Here, we showed that how the lncRNA urothelial carcinoma associated 1 (UCA1) exerts pro-oncogene in regulating EC glucose metabolism. Firstly, we found that upregulated UCA1 expression enhances the malignant phenotypes of EC, including poor outcome, larger tumor size, positive lymphatic invasion, and advanced pathological stages. UCA1 silencing could suppress EC cell proliferation and metastasis. Following, bioinformatics analyses revealed that UCA1 regulated the HK2 expression through functioning as a competing endogenous RNA (ceRNA). Mechanistically, UCA1 overexpression could elevate the activation of HK2 oncogenes via inhibition of miR-203 activity, as evidenced by the positive correlation of UCA1 with HK2 and inverse correlation with miR-203 expression. Luciferase activity assay further verified the targeting relationship between UCA1, miR-203, and HK2. Upregulated UCA1 in EC cells significantly suppressed the degradation of HK2 by miR-203. Further research showed that upregulated UCA1 effectively increased the rate of glucose uptake, lactate output, and ECAR value, all of which can be attenuate by HK2 interference and 2-DG, whereas knockdown of UCA1 had the opposite effect. In sum, our findings suggest that the UCA1/miR-203/HK2 axis contributes to EC development by reprogramming tumor glucose metabolism, providing new insight into the management of EC patients.
Your: Your Unified Reader

The advancement in signal processing and GPU based systems has enabled new transient detectors at various telescopes to perform much more sensitive searches than their predecessors. Typically the data output from the telescopes is in one of the two commonly used formats: psrfits and Sigproc filterbank. Software developed for transient searches often only works with one of these two formats, limiting their general applicability. Therefore, researchers have to write custom scripts to read/write the data in their format of choice before they can begin any data analysis relevant for their research. \textsc{Your} (Your Unified Reader) is a python-based library that unifies the data processing across multiple commonly used formats. \textsc{Your} implements a user-friendly interface to read and write in the data format of choice. It also generates unified metadata corresponding to the input data file for a quick understanding of observation parameters and provides utilities to perform common data analysis operations. \textsc{Your} also provides several state-of-the-art radio frequency interference mitigation (RFI) algorithms, which can now be used during any stage of data processing (reading, writing, etc.) to filter out artificial signals.
Hypergraph partitioning using tensor eigenvalue decomposition

Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach for the partitioning of k-uniform hypergraphs. Most of the existing methods work by reducing the hypergraph to a graph followed by applying standard graph partitioning algorithms. The reduction step restricts the algorithms to capturing only some weighted pairwise interactions and hence loses essential information about the original hypergraph. We overcome this issue by utilizing tensor-based representation of hypergraphs, which enables us to capture actual super-dyadic interactions. We extend the notion of minimum ratio-cut and normalized-cut from graphs to hypergraphs and show that the relaxed optimization problem can be solved using eigenvalue decomposition of the Laplacian tensor. This novel formulation also enables us to remove a hyperedge completely by using the “hyperedge score” metric proposed by us, unlike the existing reduction approaches. We propose a hypergraph partitioning algorithm inspired from spectral graph theory and also derive a tighter upper bound on the minimum positive eigenvalue of even-order hypergraph Laplacian tensor in terms of its conductance, which is utilized in the partitioning algorithm to approximate the normalized cut. The efficacy of the proposed method is demonstrated numerically on synthetic hypergraphs generated by stochastic block model. We also show improvement for the min-cut solution on 2-uniform hypergraphs (graphs) over the standard spectral partitioning algorithm.
Knowledge, Attitude, and Practice Towards Biosimilars and Interchangeable Products: A Prescriptive Insight by the Pharmacists

Background Pharmacists being the drug experts need to be well aware of the applied handling of biosimilar medicines (BSMs). They are an integral educator, trailblazer, and advocate of biosimilar integration across all clinical settings. Therefore, the current study was conducted to assess the pharmacists’ knowledge, attitude, and practices of integrating BSMs into clinical practice. Methods The cross-sectional study was conducted from August 2019 to November 2019. The community pharmacies, clinical and academic settings in Karachi were approached for gathering the responses of pharmacists towards BSMs and interchangeable products using a 30-item survey form. Pearson correlation and independent sample t-test were used to identify the relationship among independent variables and the responses, considering p values <0.05 as statistically significant. Results Overall, there were 305 survey forms used with a response rate of 87.14%. More than 80% of the respondents have good knowledge about the definition, characteristics, safety and efficacy, compatibility, cost issues, and utilization of BSMs. Around half of the respondents (48.9%, [95% CI 46.6–51.2]) were confident in using BSMs in clinical practice. However, they were concerned about the BSM’s safety profile (45.2%, [95% CI 42.1–48.3]), quality (30.2%, [95% CI 28.3–32.1]), and efficacy issues (32.3%, [95% CI 31.2–37.5]). Conclusion The findings revealed that pharmacists were well informed about the BSMs. However, some of the responses to the attitude demonstrated a lack of understanding of the application of that knowledge. The respondents persuaded that advanced patterns of diseases, product marketing stipulations, and need for better patient care drives higher demand for developing BSMs and were enthusiastic about gaining more insight to integrate BSMs into routine clinical practice.
On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

In this paper, we address a subtopic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep neural networks, where a band-limited signal is provided as input to the network, with the goal of reconstructing a full-bandwidth output. Our main contribution centers on the impact of the choice of low-pass filter when training and subsequently testing the network. For two different state-of-the-art deep architectures, ResNet and U-Net, we demonstrate that when the training and testing filters are matched, improvements in signal-to-noise ratio (SNR) of up to 7 dB can be obtained. However, when these filters differ, the improvement falls considerably and under some training conditions results in a lower SNR than the band-limited input. To circumvent this apparent overfitting to filter shape, we propose a data augmentation strategy which utilizes multiple low-pass filters during training and leads to improved generalization to unseen filtering conditions at test time.
Enhance Gender and Identity Preservation in Face Aging Simulation for Infants and Toddlers

Realistic age-progressed photos provide invaluable biometric information in a wide range of applications. In recent years, deep learning-based approaches have made remarkable progress in modeling the aging process of the human face. Nevertheless, it remains a challenging task to generate accurate age-progressed faces from infant or toddler photos. In particular, the lack of visually detectable gender characteristics and the drastic appearance changes in early life contribute to the difficulty of the task. We propose a new deep learning method inspired by the successful Conditional Adversarial Autoencoder (CAAE, 2017) model. In our approach, we extend the CAAE architecture to 1) incorporate gender information, and 2) augment the model's overall architecture with an identity-preserving component based on facial features. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of aging on 1,156 male and 1,207 female infant and toddler face photos. Compared to the CAAE approach, our new model demonstrates noticeable visual improvements. Quantitatively, our model exhibits an overall gain of 77.0% (male) and 13.8% (female) in gender fidelity measured by a gender classifier for the simulated photos across the age spectrum. Our model also demonstrates a 22.4% gain in identity preservation measured by a facial recognition neural network.
The heat equation with order-respecting absorption and particle systems with topological interaction

A PDE formulation is proposed, referred to as a heat equation with order-respecting absorption, aimed at characterizing hydrodynamic limits of a class of particle systems on the line with topological interaction that have so far been described by free boundary problems. It consists of the heat equation with measure-valued injection and absorption terms, where the absorption measure respects the usual order on $\R$ in the sense that, for all $r\in\R$, it charges $(-\iy,r)$ only at times when the solution vanishes on $(r,\iy)$. The formulation is used to obtain new hydrodynamic limit results for two models. One is a variant of the main model studied by Carinci, De Masi, Giardina and Presutti \cite{CDGPbook} where Brownian particles undergo injection according to a general injection measure, and removal that is restricted to the rightmost particle of the configuration. This partially addresses a conjecture of \cite{CDGPbook}. Next a Brownian particle system is considered where the $Q$-quantile member of the population is removed until extinction, where $Q$ is a given $[0,1]$-valued continuous function of time. Here, unlike in earlier work on the subject, the removal mechanism acts on particles that are `at the boundary' but are not rightmost or leftmost. Finally, further potential uses of order-respecting absorption are mentioned.