Miracle-dz/UniFAR-FTUs huggingface dataset:
Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders

We develop amachine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder (CVAE) in the form of a convolutional neural network and is trained with SZ maps generated from the IllustrisTNG simulation. Our method can reproduce many of the details of galaxy clusters that analytical models usually lack, such as internal structure and aspherical distribution of gas created by mergers, while achieving the same computational feasibility, allowing us to generate mock SZmaps for over 105 clusters in 30 seconds on a laptop. We show that the model is capable of generating novel clusters (i.e. not found in the training set) and that the model accurately reproduces the effects of mass and mass accretion rate on the SZ images, such as scatter, asymmetry, and concentration, in addition to modeling merging sub-clusters. This work demonstrates the viability of machine-learning–based methods for producing the number of realistic, high-resolution maps of galaxy clusters necessary to achieve statistical constraints from future SZ surveys.
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier

Pre-trained models are widely used in fine-tuning downstream tasks with linear classifiers optimized by the cross-entropy loss, which might face robustness and stability problems. These problems can be improved by learning representations that focus on similarities in the same class and contradictions in different classes when making predictions. In this paper, we utilize the K-Nearest Neighbors Classifier in pre-trained model fine-tuning. For this KNN classifier, we introduce a supervised momentum contrastive learning framework to learn the clustered representations of the supervised downstream tasks. Extensive experiments on text classification tasks and robustness tests show that by incorporating KNNs with the traditional fine-tuning process, we can obtain significant improvements on the clean accuracy in both rich-source and few-shot settings and can improve the robustness against adversarial attacks. \footnote{all codes is available at https://github.com/LinyangLee/KNN-BERT}
An Intensity Mapping Constraint on the CO-galaxy Cross-power Spectrum at Redshift ∼3

The abundance of cold molecular gas plays a crucial role in models of galaxy evolution. While deep spectroscopic surveys of CO emission lines have been a primary tool for measuring this abundance, the difficulty of these observations has motivated alternative approaches to studying molecular gas content. One technique, line intensity mapping, seeks to constrain the average molecular gas properties of large samples of individually undetectable galaxies through the CO brightness power spectrum. Here we present constraints on the cross-power spectrum between CO intensity maps and optical galaxy catalogs. This cross-measurement allows us to check for systematic problems in CO intensity mapping data, and validate the data analysis used for the auto-power spectrum measurement of the CO Power Spectrum Survey. We place a 2σ upper limit on the band-averaged CO-galaxy cross-power of P × < 540 μK h−3 Mpc3. Our measurement favors a nonzero 〈T CO〉 at around 90% confidence and gives an upper limit on the mean molecular gas density at z ∼ 2.6 of 7.7 × 108 M ⊙ Mpc−3. We forecast the expected cross-power spectrum by applying a number of literature prescriptions for the CO luminosity–halo mass relation to a suite of mock light cones. Under the most optimistic forecasts, the cross-spectrum could be detected with only moderate extensions of the data used here, while more conservative models could be detected with a factor of 10 increase in sensitivity. Ongoing CO intensity mapping experiments will target fields allowing for extensive cross-correlation analysis and should reach the sensitivity required to detect the cross-spectrum signal.
Bayesian neural network unit priors and generalized Weibull-tail property

The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years. Hidden units are proven to follow a Gaussian process limit when the layer width tends to infinity. Recent work has suggested that finite Bayesian neural networks may outperform their infinite counterparts because they adapt their internal representations flexibly. To establish solid ground for future research on finite-width neural networks, our goal is to study the prior induced on hidden units. Our main result is an accurate description of hidden units tails which shows that unit priors become heavier-tailed going deeper, thanks to the introduced notion of generalized Weibull-tail. This finding sheds light on the behavior of hidden units of finite Bayesian neural networks.
Modelling the Data at the LHC

Measurements at hadron colliders rely on large scale quantum chromodynamics (QCD) Monte Carlo (MC) production for interpretation of the data. MC simulations allow testing Standard Model (SM) with more accurate and precise calculations to understand perturbative QCD as well as electroweak effects, and extrapolations of the irreducible backgrounds to signal phase-space regions for new physics searches or for the measurements of rare SM processes. In the MC codes, there are many pieces, approximations, and parameters and settings to compare to the data and tune. Precise experimental measurements at the LHC require similar level of precision in theoretical calculations. Cross sections measured at the LHC both by ATLAS and CMS experiments cover more than 14 orders of magnitude. So far, SM cross section predictions are found to be in very good agreement with the data. These cross sections are measured at different pp collision energies and compared to prediction up to next-to-next-to-leading order (NNLO) for many processes, and recently up to N3LO for some of them. In this note, a few measurements relevant to data modelling are discussed.
Optimal rates of convergence and error localization of Gegenbauer projections

Motivated by comparing the convergence behavior of Gegenbauer projections and best approximations, we study the optimal rate of convergence for Gegenbauer projections in the maximum norm. We show that the rate of convergence of Gegenbauer projections is the same as that of best approximations under conditions of the underlying function being either analytic on and within an ellipse and $\lambda \leqslant 0$ or differentiable and $\lambda \leqslant 1$, where $\lambda $ is the parameter in Gegenbauer projections. If the underlying function is analytic and $\lambda>0$ or differentiable and $\lambda>1$, then the rate of convergence of Gegenbauer projections is slower than that of best approximations by factors of $n^{\lambda }$ and $n^{\lambda -1}$, respectively. An exceptional case is functions with endpoint singularities, for which Gegenbauer projections and best approximations converge at the same rate for all $\lambda>-1/2$. For functions with interior or endpoint singularities, we provide a theoretical explanation for the error localization phenomenon of Gegenbauer projections and for why the accuracy of Gegenbauer projections is better than that of best approximations except in small neighborhoods of the critical points. Our analysis provides fundamentally new insight into the power of Gegenbauer approximations and related spectral methods.
Prevalence and Antimicrobial-Susceptibility Profiles of Salmonella in Smallhold Broiler Supply Chains in Central Ethiopia

Purpose Salmonellosis is a foodborne zoonoses found worldwide. The main purpose of this study was to isolate and identify Salmonella and assess their antimicrobial susceptibility profiles from smallhold broilers supply chains and slaughterhouses in Bishoftu and Modjo, central Ethiopia. Methods Four smallhold broiler farms under the auspices of Chico Meat were selected randomly. Feed, water, and water- and feed-trough samples were collected from broiler farms, while cecal contents were collected from slaughtered chicken at Chico Meat slaughterhouse. Conventional bacteriological techniques were used to isolate and identify Salmonella from the samples. Kirby–Bauer disk diffusion was employed to assess the antimicrobial susceptibility of the isolates. Results Salmonella was isolated from 131 (24.3%, 95% CI 20.74–28.15) of the 539 samples tested. Salmonella was found in 43 of the 250 samples collected from Bishoftu (22%, 95% CI 17.02%–27.65%) and 76 of the 289 samples collected from Modjo (26.29%, 95% CI 21.32%–31.77%). Salmonella was isolated from 26.46% of the cloacal samples, 21% of the cecal contents, 30.77% of the feed samples, 25% of the water samples, 22.22% of samples from feed troughs, and 20% of samples from water troughs. The highest level of resistance (80.81%) was observed against tetracycline, followed by kanamycin (71.72%), chloramphenicol and amoxicillin (67.68%), sulfamethazole–trimethoprim (61.62%), naldixic acid (63.64%), and streptomycin (59.60%), whereas most of the isolates were susceptible to gentamicin (69.70%). Resistance to more than two drugs was also observed. Conclusion Salmonella was found in high prevalence in broilers, their feed, and their environment. Moreover, a majority of the isolates were resistant to most antimicrobials used in medical and poultry practices. This has significant implications for public health and antimicrobial resistance.
The Exosomal lncRNA KLF3-AS1 From Ischemic Cardiomyocytes Mediates IGF-1 Secretion by MSCs to Rescue Myocardial Ischemia-Reperfusion Injury

The purpose of the study was to explore the mechanism by which myocardial ischemia-reperfusion (I/R) injury-induced exosomes modulate mesenchymal stem cells (MSCs) to regulate myocardial injury. In this study, we established an I/R injury model in vivo and a hypoxia-reoxygenation (H/R) model in vitro. Then, exosomes isolated from H/R-exposed H9c2 cells were characterized using transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blot analysis. CCK-8 assays and flow cytometry were performed to assess cell injury. ELISA was applied to determine the level of insulin-like growth factor 1 (IGF-1). Echocardiography was used to assess cardiac function in vivo. HE staining and TUNEL assays were conducted to analyze myocardial injury in vivo. In the present study, H/R-exposed H9c2 cells induced IGF-1 secretion from MSCs to inhibit cell myocardial injury. Moreover, exosomes derived from H/R-exposed H9c2 cells were introduced to MSCs to increase IGF-1 levels. The lncRNA KLF3-AS1 was dramatically upregulated in exosomes derived from H/R-treated H9c2 cells. Functional experiments showed that the exosomal lncRNA KLF3-AS1 promoted IGF-1 secretion from MSCs and increased H9c2 cell viability. In addition, miR-23c contains potential binding sites for both KLF3-AS1 and STAT5B, and miR-23c directly bound to the 3'-UTRs of KLF3-AS1 and STAT5B. Furthermore, the lncRNA KLF3-AS1 promoted IGF-1 secretion from MSCs and rescued myocardial cell injury in vivo and in vitro by upregulating STAT5B expression. The lncRNA KLF3-AS1 may serve as a new direction for the treatment of myocardial I/R injury.
Impact of Different Zinc Application Methods on Yield and Yield Components of Various Wheat (Triticum aestivum L.) Cultivars

To evaluate the impact of different zinc application methods on yield and yield components of various wheat cultivars, a field experiment was carried out at Student’s Farm, Department of Agronomy, University of Agriculture Faisalabad, during winter 2014-2015. The experiment was laid out in RCBD (Randomized Complete Block Design) with factorial arrangement. In this experiment, all the treatments were replicated three times, using the net plot size of 1.8 m × 5.0 m. The experiment comprised of two factors. Factor A consists of two varieties: Faisalabad-2008 and Punjab-2011. While, Factor B consists of different methods of zinc application, control, zinc application in soil before planting 23 kg·ha-1, zinc foliar application, 4% ZnSO4 solution at two stages (tillering and stem elongation stage). The data regarding different parameters were collected and analyzed from the crop using standard procedures. Regarding the impact of zinc application method maximum plant height at maturity (103.6 cm), total number of tillers (564.67 m-2), spike length (10.83 cm), number of spikelets spike-1 (19.50), number of grains spike-1 (50.36), 1000-grain weight (34.16 g), biological yield (11.93 t·ha-1), grain yield (6.00 t·ha-1) and harvest index (39.25%) were recorded in treatment where zinc was applied both in the soil before planting and by foliar application on later growth stages. Similarly, grain zinc contents (33.11 mg·kg-1), grain protein contents (10.1%) and grain carbohydrate contents (64.23%) were also observed in the treatment where zinc was applied both in the soil before planting and by foliar application on later growth stages, which is better than all other treatments. In case of wheat varieties, Faisalabad-2008 performed better than Punjab-2011, with maximum method maximum total number of tillers (460.67 m-2), spike length (9.70 cm), number of grains spike-1 (48.80), 1000-grain weight (33.81 g), biological yield (10.32 t·ha-1), grain yield (5.0 t·ha-1) and harvest index (33.93%). It is concluded that sowing of wheat cultivar Faisalabad-2008 + application of zinc in soil before planting with foliar application at later stages (tillering and stem elongation stage) of wheat could give better results in terms of yield.
Prevalence of bacteriologically confirmed pulmonary tuberculosis and associated risk factors: A community survey in Thirvallur District, south India

Background Tuberculosis (TB) prevalence surveys add to the active case detection in the community level burden of TB both national and regional levels. The aim of this study was to assess the prevalence of bacteriologically confirmed pulmonary tuberculosis (PTB) in the community. Methods Household community-based tuberculosis disease survey was conducted targeting 69054 population from 43 villages of 5 blocks in Tiruvallure district adopting cluster sampling methodology of ≥15 years old adult rural population of South India during 2015–2018. All eligible individuals with suspected symptoms of PTB were screened with chest X-ray. Two sputum specimens (one spot and the other early morning sample) were collected for M.tb smear and culture examination. Conversely demographical, smoking and alcohol drinking habits information were also collected to explore the risk factor. Stepwise logistic regression was employed to associate risk factors for PTB. Results A total of 62494 were screened among 69054 eligible population, of whom 6340 were eligible for sputum specimen collection. Sputum for M.tb smear and culture examination were collected in 93% of participants. The derived prevalence of PTB was 307/100000 population (smear-positive 130; culture positive 277). As expected that PTB has decreased substantially compared to preceding surveys and it showed that older age, male, low BMI, diabetes, earlier history of TB and alcohol users were significantly associated (p < .0001) with an increased risk of developing PTB. Conclusion Upshot of the active survey has established a reduction in the prevalence of PTB in the rural area which can be accredited to better programmatic implementation and success of the National TB Control Programme in this district. It also has highlighted the need for risk reduction interventions accelerate faster elimination of TB.
Amyloid-driven disruption of default mode network connectivity in cognitively healthy individuals

Abstract Cortical accumulation of amyloid beta is one of the first events of Alzheimer’s disease pathophysiology, and has been suggested to follow a consistent spatiotemporal ordering, starting in the posterior cingulate cortex, precuneus and medio-orbitofrontal cortex. These regions overlap with those of the default mode network, a brain network also involved in memory functions. Aberrant default mode network functional connectivity and higher network sparsity have been reported in prodromal and clinical Alzheimer’s disease. We investigated the association between amyloid burden and default mode network connectivity in the preclinical stage of Alzheimer’s disease and its association with longitudinal memory decline. We included 173 participants, in which amyloid burden was assessed both in CSF by the amyloid beta 42/40 ratio, capturing the soluble part of amyloid pathology, and in dynamic PET scans calculating the non-displaceable binding potential in early-stage regions. The default mode network was identified with resting-state functional MRI. Then, we calculated functional connectivity in the default mode network, derived from independent component analysis, and eigenvector centrality, a graph measure recursively defining important nodes on the base of their connection with other important nodes. Memory was tested at baseline, 2- and 4-year follow-up. We demonstrated that higher amyloid burden as measured by both CSF amyloid beta 42/40 ratio and non-displaceable binding potential in the posterior cingulate cortex was associated with lower functional connectivity in the default mode network. The association between amyloid burden (CSF and non-displaceable binding potential in the posterior cingulate cortex) and aberrant default mode network connectivity was confirmed at the voxel level with both functional connectivity and eigenvector centrality measures, and it was driven by voxel clusters localized in the precuneus, cingulate, angular and left middle temporal gyri. Moreover, we demonstrated that functional connectivity in the default mode network predicts longitudinal memory decline synergistically with regional amyloid burden, as measured by non-displaceable binding potential in the posterior cingulate cortex. Taken together, these results suggest that early amyloid beta deposition is associated with aberrant default mode network connectivity in cognitively healthy individuals and that default mode network connectivity markers can be used to identify subjects at risk of memory decline.
Perceived Support for Recovery and Level of Functioning Among People With Severe Mental Illness in Central and Eastern Europe: An Observational Study

Background: Many people with severe mental illness experience limitations in personal and social functioning. Care delivered in a person's community that addresses needs and preferences and focuses on clinical and personal recovery can contribute to addressing the adverse impacts of severe mental illness. In Central and Eastern Europe, mental health care systems are transitioning from institutional-based care toward community-based care. The aim of this study is to document the level of functioning and perceived support for recovery in a large population of service users with severe mental illness in Central and Eastern Europe, and to explore associations between perceived support for recovery and the degree of functional limitations. Methods: The implementation of community mental health teams was conducted in five mental health centers in five countries in Central and Eastern Europe. The present study is based on trial data at baseline among service users across the five centers. Baseline data included sociodemographic, the World Health Organization Disability Assessment Schedule (WHODAS 2.0) for functional limitations, and the Recovery Support (INSPIRE) tool for perceived staff support toward recovery. We hypothesized that service users reporting higher levels of perceived support for their recovery would indicate lower levels of functional limitation. Results: Across all centers, the greatest functional limitations were related to participation in society (43.8%), followed by daily life activities (33.3%), and in education or work (35.6%). Service users (N = 931) indicated that they were satisfied overall with the support received from their mental health care provider for their social recovery (72.5%) and that they valued their relationship with their providers (80.3%). Service users who perceived the support they received from their provider as valuable (b = −0.10, p = 0.001) and who reported to have a meaningful relationship with them (b = −0.13, p = 0.003) had a lower degree of functional limitation. Conclusion: As hypothesized, the higher the degree of perceived mental health support from providers, the lower the score in functional limitations. The introduction of the community-based care services that increase contact with service users and consider needs and which incorporate recovery-oriented principles, may improve clinical recovery and functional outcomes of service users with severe mental illness.
Evolution of Drug Delivery Systems for Recurrent Aphthous Stomatitis

Abstract Recurrent aphthous stomatitis (RAS) is a disease marked by painful oral lesions on the buccal and labial mucosa or tongue. Drug delivery systems (DDS) for RAS include topical forms that manage wound healing, cover the ulcer, and relieve the associated pain. DDS targeting the oral mucosa face a major challenge, especially the short residence times in the mouth due to the effect of “saliva wash-out”, which continually removes the drug. The objective of this review is to study the development of preparation forms and delivery systems of various types and preparations that have been used for RAS management from 1965 until February 2020. There are 20 types of DDS for RAS which were discussed in 62 articles. The preparations were classified into 4 preparation forms: liquid, semi-solid, solid, and miscellaneous. In addition, the ultimate DDS for RAS preparations is the semi-solid forms (41.94%), which include 5 types of DDS are gel, paste, patch, cream, and ointment. This preparation was developed into new preparation form (11.29%), such as adhesive alginates, dentifrice, OraDisc, membranes, bioresorbable plates, pellicles, and gelosomes. Generally, the mucosal drug delivery system is the method of choice in RAS treatment because the ulcer is commonly located in the oral mucosa. In conclusion, these preparations are designed to improve drug delivery and drug activity for the treatment of RAS ulcers. Moreover, almost all of these DDS are topical preparations that use various types of mucoadhesive polymers to increase both residence time in the oral mucosa and pain relief in RAS treatment.