Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular epidemiology of bovine tuberculosis in Northern Ghana identifies several uncharacterized bovine spoligotypes and suggests possible zoonotic transmission

Objective We conducted an abattoir-based cross-sectional study in the five administrative regions of Northern Ghana to determine the distribution of bovine tuberculosis (BTB) among slaughtered carcasses and identify the possibility of zoonotic transmission. Methods Direct smear microscopy was done on 438 tuberculosis-like lesions from selected cattle organs and cultured on Lowenstein-Jensen media. Acid-fast bacilli (AFB) isolates were confirmed as members of the Mycobacterium tuberculosis complex (MTBC) by PCR amplification of IS6110 and rpoß. Characterization and assignment into MTBC lineage and sub-lineage were done by spoligotyping, with the aid of the SITVIT2, miruvntrplus and mbovis.org databases. Spoligotype data was compared to that of clinical M. bovis isolates from the same regions to identify similarities. Results A total of 319/438 (72.8%) lesion homogenates were smear positive out of which, 84.6% (270/319) had microscopic grade of at least 1+ for AFB. Two hundred and sixty-five samples (265/438; 60.5%) were culture positive, of which 212 (80.0%) were MTBC. Approximately 16.7% (34/203) of the isolates with correctly defined spoligotypes were negative for IS6110 PCR but were confirmed by rpoß. Spoligotyping characterized 203 isolates as M. bovis (198, 97.5%), M. caprae (3, 1.5%), M. tuberculosis (Mtbss) lineage (L) 4 Cameroon sub-lineage, (1, 0.5%), and M. africanum (Maf) L6 (1, 0.5%). A total of 53 unique spoligotype patterns were identified across the five administrative regions (33 and 28 were identified as orphan respectively by the SITVIT2 and mbovis.org databases), with the most dominant spoligotype being SIT1037/ SB0944 (77/203, 37.93%). Analysis of the bovine and human M. bovis isolates showed 75% (3/4) human M. bovis isolates sharing the same spoligotype pattern with the bovine isolates. Conclusion Our study identified that approximately 29% of M. bovis strains causing BTB in Northern Ghana are caused by uncharacterized spoligotypes. Our findings suggest possible zoonotic transmission and highlight the need for BTB disease control in Northern Ghana.
Clinical Severity of β-Thalassemia Pediatric Patients in Myanmar

Abstract β-Thalassemia (β-thal) is highly prevalent in Myanmar, but limited data are available on the molecular basis and the clinical manifestations in Myanmar patients. In this study, we investigated the clinical features and β-globin gene abnormalities in 15 homozygous β-thal and 60 Hb E (HBB: c.79G>A)/β-thal pediatric patients who attended Yangon Children Hospital, the biggest thalassemia day care unit center in Myanmar. Eight different β0-thal mutations were identified, with four accounting for 88.9% of alleles studied (excluding the Hb E variant). A genotype-phenotype correlation was found; all homozygous β0-thalassemias had severe clinical courses, whereas the highly variable disease severity was demonstrated among Hb E/β0-thal patients. Interactions of IVS-I-1 (G>T) (HBB: c0.92+1G>T) β0-thal with Hb E are associated with milder clinical symptoms. The number of mildly affected Hb E/β-thal patients was lower than expected, suggesting that there may be a considerable number of patients in the population who have either not been admitted to hospital or diagnosed with carrying the disease. Although the clinical severity in the Myanmar β-thal patients seems to be similar to that in other populations, the levels of hemoglobin (Hb) appears to be very low. These findings indicate the need for the improvement of patient management and the development of prevention and control programs for β-thal in Myanmar.
Assessing the Growth of Remote Working and its Consequences for Effort, Well‐Being and Work‐Life Balance

This paper critically assesses the assumption that more and more work is being detached from place and that this is a ‘win-win’ for both employers and employees. Based on an analysis of official labour market data, it finds that only one-third of the increase in remote working can be explained by compositional factors such as movement to the knowledge economy, the growth in flexible employment and organisational responses to the changing demographic make-up of the employed labour force. This suggests that the detachment of work from place is a growing trend. The paper also shows that while remote working is associated with higher organisational commitment, job satisfaction and job-related well-being, these benefits come at the cost of work intensification and a greater inability to switch off.
THE FINANCIAL COSTS OF THE PORTUGUESE COLONIAL WAR, 1961-1974: ANALYSIS AND APPLIED STUDY

ABSTRACT This article presents estimates of the financial costs incurred by Portugal with the Colonial War (1961-1974). The results obtained show that, on average, the extraordinary expenses most directly related with the war represented 22% of Portuguese state expenditure, equivalent to 3.1% of GDP. They also show that the total costs incurred by Portugal with the Colonial War amounted to between 21.8 billion euros and 29.8 billion euros, at today's prices and in the present-day currency. By estimating a dynamic model, it was also possible to identify a positive relationship between the extraordinary military expenditure on the defence and security of the overseas provinces—which included the budgetary costs of war—and Portuguese economic growth. The conclusions reached represent a valuable contribution to contemporary economic history.
Intracranial Hemorrhage Detection on CT Scan Images using Transfer Learning Approach of Convolutional Neural Network

Intracranial hemorrhage is an acute bleeding within the skull when a brain’s blood vessel is ruptured which eventually leads to disability or even death. It is extremely important to diagnose intracranial hemorrhage with high accuracy to minimize the severity of brain damage and other complications. Deep Learning is widely used in interpreting medical images and has shown promising advancements in diagnosing brain hemorrhage. This paper proposes a deep learning method called Convolutional Neural Network (CNN) on neuroimaging with transfer learning techniques to assist in the diagnosis of intracranial hemorrhage on CT scans. We have used six pre-trained CNN models (EfficientNetB6, DenseNet121, ResNet50, InceptionResNetV2, InceptionV3, VGG16) and also present a traditional CNN model of 11-layer architecture for the detection and binary classification of intracranial brain hemorrhage on CT scans. The paper depicts a comparative analysis on the performance between the proposed traditional and pre-trained models of CNN in terms of accuracy, precision, recall, F1 score, and AUC curve. EfficientNetB6 yields an accuracy of 95.99%, which is higher than any of the experimental results of the CNN models used in this dataset. Lastly, we deploy a simple web application to demonstrate real-world application.
A Comparison of Multidimensional Scaling Methods for Perceptual Mapping

Multidimensional scaling has been applied to a wide range of marketing problems, in particular to perceptual mapping based on dissimilarity judgments. The introduction of methods based on the maximum likelihood principle is one of the most important developments. In this article, the authors compare the three available Maximum Likelihood Multidimensional Scaling (MLMDS) methods, namely, MULTISCALE, MAXSCAL, and PROSCAL, and the traditional multidimensional scaling (MDS) method KYST in a Monte Carlo study with 243 synthetic data sets. The MLMDS methods outperform KYST with respect to recovering the perceptual maps. MAXSCAL recovers the true distances between brands somewhat better than MULTISCALE, which is somewhat better than PROSCAL. With regard to distance recovery, the MLMDS methods are quite robust to violations of distributional assumptions. The decision criteria for selecting the number of dimensions are less robust to distributional violations. The results support the use of Consistent Akaike Information Criterion for the selection of the number of dimensions. The authors recommend that dissimilarity judgments be collected on interval scales or on ordinal scales with a substantial number of scale values. The authors discuss implications of the results for the design and analysis of perceptual mapping studies.
NeuroScaler: neural video enhancement at scale

High-definition live streaming has experienced tremendous growth. However, the video quality of live video is often limited by the streamer's uplink bandwidth. Recently, neural-enhanced live streaming has shown great promise in enhancing the video quality by running neural super-resolution at the ingest server. Despite its benefit, it is too expensive to be deployed at scale. To overcome the limitation, we present NeuroScaler, a framework that delivers efficient and scalable neural enhancement for live streams. First, to accelerate end-to-end neural enhancement, we propose novel algorithms that significantly reduce the overhead of video super-resolution, encoding, and GPU context switching. Second, to maximize the overall quality gain, we devise a resource scheduler that considers the unique characteristics of the neural-enhancing workload. Our evaluation on a public cloud shows NeuroScaler reduces the overall cost by 22.3× and 3.0--11.1× compared to the latest per-frame and selective neural-enhancing systems, respectively.
DeepQueueNet: towards scalable and generalized network performance estimation with packet-level visibility

Network simulators are an essential tool for network operators, and can assist important tasks such as capacity planning, topology design, and parameter tuning. Popular simulators are all based on discrete event simulation, and their performance does not scale with the size of modern networks. Recently, deep-learning-based techniques are introduced to solve the scalability problem, but, as we show with experiments, they have poor visibility in their simulation results, and cannot generalize to diverse scenarios. In this work, we combine scalable and generalized continuous simulation techniques with discrete event simulation to achieve high scalability, while providing packet-level visibility. We start from a solid queueing-theoretic modeling of modern networks, and carefully identify the mathematically-intractable or computationally-expensive parts, only which are then modeled using deep neural networks (DNN). Dubbed DeepQueueNet, our approach combines prior knowledge of networks, and supports arbitrary topology and device traffic management mechanisms (given sufficient training data). Our extensive experiments show that DeepQueueNet achieves near-linear speedup in the number of GPUs, and its estimation accuracy for average and 99th percentile round-trip time outperforms existing end-to-end DNN-based performance estimators in all scenarios.
Continua with microstructure: second-gradient theory

ABSTRACT Second-gradient theories represent a frequently used subset of theories of continua with microstructure. This paper presents an extended overview of second-gradient theories, starting from a simple one-dimensional example, proceeding with a thorough description of gradient elasticity and additionally briefly describing some other theories of this kind. A series of characteristic examples is presented to demonstrate the main aspects and applications of second-gradient theories. Finally, the complications in the finite-element implementation of second-gradient theories are presented, along with a review of the finite elements that have been developed for this purpose.
SDN in the stratosphere: loon's aerospace mesh network

The Loon project provided 4G LTE connectivity to under-served regions in emergency response and commercial mobile contexts using base stations carried by high-altitude balloons. To backhaul data, Loon orchestrated a moving mesh network of point-to-point radio links that interconnected balloons with each other and to ground infrastructure. This paper presents insights from 3 years of operational experience with Loon's mesh network above 3 continents. The challenging environment, comparable to many emerging non-terrestrial networks (NTNs), highlighted the design continuum between predictive optimization and reactive recovery. By forecasting the physical environment as a part of network planning, our novel Temporospatial SDN (TS-SDN) successfully moved from reactive to predictive recovery in many cases. We present insights on the following NTN concerns: connecting meshes of moving nodes using long distance, directional point-to-point links; employing a hybrid network control plane to balance performance and reliability; and understanding the behavior of a complex system spanning physical and logical domains in an inaccessible environment. The paper validates TS-SDN as a compelling architecture for orchestrating networks of moving platforms and steerable beams, and provides insights for those building similar networks in the future.
Versatile Control of Fluid-directed Solid Objects Using Multi-task Reinforcement Learning

We propose a learning-based controller for high-dimensional dynamic systems with coupled fluid and solid objects. The dynamic behaviors of such systems can vary across different simulators and the control tasks subject to changing requirements from users. Our controller features high versatility and can adapt to changing dynamic behaviors and multiple tasks without re-training, which is achieved by combining two training strategies. We use meta-reinforcement learning to inform the controller of changing simulation parameters. We further design a novel task representation, which allows the controller to adapt to continually changing tasks via hindsight experience replay. We highlight the robustness and generality of our controller on a row of dynamic-rich tasks, including scooping up solid balls from a water pool, in-air ball acrobatics using fluid spouts, and zero-shot transferring to unseen simulators and constitutive models. In all the scenarios, our controller consistently outperforms the plain multi-task reinforcement-learning baseline.
Thermal Field Theory: Collective excitations in a plasma

Preface 1. Introduction 2. Quantum statistical mechanics 3. The scalar field at finite temperature 4. Simple applications of perturbation 5. Dirac and gauge fields at finite temperature 6. Collective excitations in a plasma 7. Hard thermal loops and resummation 8. Dynamical screening 9. Neutrino emission from stars 10. Infrared problems at finite temperature Appendices References.
Determination of Anti-phage Antibodies in Calf Sera Following Application of Escherichia Coli and Mannheimia Haemolytica-specific Bacteriophages

Abstract Introduction The widespread occurrence of drug-resistant bacteria has increased interest in alternatives to antibiotics for combatting bacterial infections, among which bacteriophages play an important role. The ability of phage proteins to induce an anti-phage immune response can significantly limit the effectiveness of treatment, which was the basis for the study described in this article. The aim of the study was to assess the effects of bacteriophages on the induction of an anti-phage humoral response in calves. Material and Methods The study was conducted using phage components of experimental preparations and sera from calves treated and not treated with phages. Levels of G, M and A immunoglobulins were analysed by ELISA. The assay plates were coated with whole Escherichia coli and Mannheimia haemolytica phages and selected phage proteins obtained in sodium dodecyl sulphate-polyacrylamide gel electrophoresis and two-dimensional electrophoresis. Neutralisation of phages by immunoglobulins was assessed by determining phage titres using double-layer plates. Results The results confirmed an increased anti-phage response affecting all immunoglobulin classes in the calf sera. The highest significant (P ≤ 0.05) level of antibodies was observed for IgG in the sera of calves receiving phages. The phage neutralisation test showed a significant differences (P ≤ 0.05) in the reduction of phage titres in comparison to untreated calves. Conclusion Despite the induction of an anti-phage response, no significant negative effect on the antibacterial activity of phages was observed in vitro.
Estimation dynamique non-linéaire de canaux de transmission pour récepteurs satellites mobiles

Cette these porte sur l'etude des techniques d'estimation Bayesienne non-lineaire, et leur applications aux problemes de synchronisation pour des systemes de communication par satellite, ainsi qu'au calcul des bornes Bayesiennes pour le probleme de synchronisation surechantillonne. D'abord, on presente le filtrage de Kalman et les methodes particulaires, et l'on propose une nouvelle vue d'ensemble des methodes deterministes. Ensuite, on etablie la modelisation pour le probleme de la synchronisation fractionnee dans des systemes satellite, et l'on calcule la borne de Cramer-Rao Bayesienne pour le probleme d'estimation de phase, et la borne de Cramer-Rao hybride pour le probleme d'estimation conjointe de phase et d'offset de frequence. Dans un deuxieme temps, on applique les methodes de filtrage (Kalman, particulaires et deterministes) aux problemes d'estimation de phase, d'estimation conjointe de phase et d'offset de frequence, d'estimation de delai et d'estimation de phase avec des bruits non-Gaussiens. Les methodes proposees ont montre de bonnes performances pour nos problemes de synchronisation. On presente aussi dans cette these, trois etudes liees aux travaux principaux. Le premier concerne l'estimation conjointe des gains complexes et du delai dans un canal de Rayleigh a variations lentes pour des signaux CPM. Le deuxieme presente l'utilisation des methodes deterministes pour la localisation avec un reseau de capteurs. Et finalement, le troisieme presente le couplage GNSS/INS ultra precis et une solution deterministes a cette problematique.
Academic buoyancy of university students and its relationship with academic average in light of some demographic variables students and its relationship with academic average in light of some variables

Academic buoyancy ref lects students’ ability to encounter ordinary challenges they face in the academic context, and it positively contributes to their behaviour. This study aims to measure the level of academic buoyancy among university students, identify its relationship with their academic average and determine the differences in their academic buoyancy levels in terms of gender and academic discipline. The descriptive – analytical research design was utilised. The academic buoyancy scale was applied to 243 university students (male and female). Data were statistically analysed using correlation coefficient, arithmetic mean and two-way analysis of variance. The results demonstrated that students have a high level of academic buoyancy; there was a significant correlation between academic buoyancy and academic average; and there were no significant differences in the academic buoyancy level with regard to gender (male and female) and academic discipline (theoretical and practical). The research recommended increasing students’ aca demic buoyancy level to help them encounter academic pressures and challenges and to solve the problems they face in their academic lives effectively.
HOUSEHOLD CATASTROPHIC HEALTH EXPENDITURES AND ITS DETERMINANTS IN PAKISTAN

Pakistan being a lower-middle income country, is always being able to allocate less than or around 2% of GDP to health due to which out-of-Pocket payments have a very large share in Pakistan’s total health financing. Hence, when this OOP health expenditures exceeds a defined threshold of Household’s non-food expenditure consumption expenditure then the Household face financial catastrophe. This research will shed light on the features that can make households in Pakistan more vulnerable to catastrophic health expenses and will fill the gap by analyzing the determinants of Catastrophic health expenditures of Pakistan and discussing about the Incidence and Intensity of these Catastrophic health expenditures. we have used survey data of Household Integrated Economic Survey (HIES) of Pakistan for the year 2015-2016 1 for 24,238 households. It contains household’s information including education, income, consumption expenditure and health expenditures. As anticipated, some determining factors significantly increase the risk of facing catastrophic health expenditures. policies. The present study used Pakistan health and non-food expenditures from HIES 2015-2016 dataset, Wagstaff and Doorslaer (2003) methodology is used, and three different thresholds to estimate the incidence, intensity and determining factors of catastrophic health expenditures of Pakistan.
Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity

A common failure mode of neural networks trained to classify abnormalities in medical images is their reliance on spurious features, which are features that are associated with the class label but are non-generalizable. In this work, we examine if supervising models with increased spatial specificity (i.e., information about the location of the abnormality) impacts model reliance on spurious features. We first propose a data model of spurious features and theoretically analyze the impact of increasing spatial specificity. We find that two properties of the data are impacted when we increase spatial specificity: the variance of the positively-labeled input pixels decreases and the mutual information between abnormal and spurious pixels decreases, both of which contribute to improved model robustness to spurious features. However, supervising models with greater spatial specificity incurs higher annotation costs, since training data must be labeled for the location of the abnormality, leading to a trade-off between annotation costs and model robustness to spurious features. We investigate this trade-off by varying the coarseness of the spatial specificity supplied and sweeping the quantity of training samples that have information about the abnormality location. Further, we assess if semi-supervised and contrastive learning methods improve the cost-robustness trade-off. We empirically examine the impact of supervising models with increased spatial specificity on two medical image datasets known to have spurious features: pneumothorax classification on chest x-rays and melanoma classification from dermoscopic images. We find that while models supervised with binary labels have near-random robust performance (robust AUROC of 0 . 46), increasing spatial specificity to bounding box detection and image segmentation achieves a robust AUROC of 0 . 72 and 0 . 82, respectively, on the pneumothorax classification task. We also observe this trend for the melanoma task, where segmentation models achieve a robust AUROC of 0 . 73, compared to worse than random performance for models trained with binary labels. Moreover, by leveraging semi-supervised and contrastive methods, models achieve a 5 point gain in robust AUROC when we have access to very few training samples.
Distinct spatial and temporal roles for Th1, Th2, and Th17 cells in asthma

Immune response in the asthmatic respiratory tract is mainly driven by CD4+ T helper (Th) cells, represented by Th1, Th2, and Th17 cells, especially Th2 cells. Asthma is a heterogeneous and progressive disease, reflected by distinct phenotypes orchestrated by τh2 or non-Th2 (Th1 and Th17) immune responses at different stages of the disease course. Heterogeneous cytokine expression within the same Th effector state in response to changing conditions in vivo and interlineage relationship among CD4+ T cells shape the complex immune networks of the inflammatory airway, making it difficult to find one panacea for all asthmatics. Here, we review the role of three T helper subsets in the pathogenesis of asthma from different stages, highlighting timing is everything in the immune system. We also discuss the dynamic topography of Th subsets and pathogenetic memory Th cells in asthma.