Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing transboundary animal disease.

Despite strenuous efforts, the control of transboundary animal diseases continues to elude livestock services in certain parts of the world. Good scientific progress has been made in the development and application of technologies and tools for control, including diagnostic tests, vaccines and remote sensing methods, as well as context-adapted disease investigation methods such as participatory epidemiology. However, the management of disease control in many countries lags behind the science. Controlling transboundary animal diseases (TADs) poses severe management challenges to veterinary services, since disease emergence and spread is affected by a range of social and environmental factors that are outside of the control of the state. The veterinary service ideally provides leadership and carries out actions that complement and reinforce private sector activities and are appropriate to the epidemiological conditions. Controlling TADs requires from the government a strategy and operational plans (a national prevention plan and an emergency contingency plan) backed up by sufficient resources and an appropriate organisational structure and culture within the animal health system. Indicators of good management include: disease status that is accurately known; outbreaks occurring seldom and quickly controlled; and minimal costs to producers and government. Co-operation between producers and between the private sector and the government, as well as management of biosecurity in individual units, all contribute to good disease management. Even the best leadership within the livestock sector is limited by the national economic and institutional environment. Economic development, institutional systems and social factors such as increasing urbanisation and human mobility all impact on the spread of disease and the functioning of veterinary services. Governance of the country, manifested by indicators (World Bank, 2005) such as the rule of law, regulatory quality and control of corruption, as well as general government effectiveness, can be expected to affect the extent to which the veterinary service is able to develop effective disease control policies and implement regulations. This paper examines the management of four major transboundary diseases on three continents: Classical Swine fever (CSF) and Foot-andMouth Disease (FMD) in Latin America, Contagious Bovine Pleuropneumonia (CBPP) in Africa and Highly Pathogenic Avian Influenza (HPAI) in South East Asia. The diseases affect cattle (CBPP), pigs (CSF), poultry (HPAI), and multiple species (FMD). They are found in countries with large livestock economies that are aggressively seeking export markets as well as countries whose livestock sectors do not satisfy their own protein demands. By reviewing successful and less successful attempts at control of these diseases, the paper identifies the economic and institutional conditions that must be met within and outside the veterinary service before a sustainable TAD management programme can be established.
How ethics influence intentions to buy counterfeit drugs: perceptions of policymakers, community pharmacists and consumers in Sudan

The ethical attitudes of consumers have been widely explored in the literature as a key factor influencing the purchase of counterfeit products. However, the manner in which consumers make decisions regarding the consumption of counterfeit drugs in developing countries remains unclear. This study aims to fill this literature gap by investigating the role of perceived unethical consumer behaviour in discouraging the purchase of counterfeit drugs in a developing country.
Early Detection of Relapse by Prospective Reverse Transcriptase-Polymerase Chain Reaction Analysis of the PML/RARα Fusion Gene in Patients With Acute Promyelocytic Leukemia Enrolled in the GIMEMA-AIEOP Multicenter “AIDA” Trial

Although the majority of patients with acute promyelocytic leukemia (APL) are potentially cured by treatments combining all-trans retinoic acid (ATRA) and chemotherapy (CHT), a sizable proportion (around 30%) will relapse during follow-up. Retrospective molecular monitoring studies using reverse transcriptase-polymerase chain reaction (RT-PCR) for the specific PML/RARalpha fusion gene, have shown that a positive test usually precedes the occurrence of hematologic relapse. Prospective RT-PCR analyses were performed since 1993 at diagnosis and at preestablished time intervals during follow-up in bone marrow (BM) samples of 163 patients with PML/RARalpha+ APL enrolled in the multicenter Gruppo Italiano Malattie Ematologiche Maligne dell' Adulto (GIMEMA) trial AIDA (All-trans retinoic acid plus Idarubicin). Treatment consisted of ATRA and idarubicin for induction followed by three polychemotherapy courses as consolidation. The sensitivity level of the RT-PCR assay for PML/RARalpha, as assessed by serial dilution experiments, was 10(-4). All patients were in hematologic remission and tested PCR- at the end of consolidation. Of 21 who converted to PCR-positive thereafter, 20 underwent hematologic relapse at a median time of 3 months (range, 1 to 14) from the first PCR+ result. Seventeen of these 21 (81%) PCR+ conversions were recorded within the first 6 months postconsolidation. Of 142 who tested persistently PCR- in >/=2 tests after consolidation, 8 had hematologic relapse and 134 remained in complete remission (CR) after a median follow-up of 18 months (range, 6 to 38) postconsolidation. Using a time-dependent Cox model, the relative risk of hematologic relapse of patients who converted to PCR+ was 31.8 (confidence limits 95%, 12.9 to 78.3). Our results indicate that conversion to PCR positivity for PML/RARalpha during remission is highly predictive of subsequent hematologic relapse and highlight the prognostic value of stringent molecular monitoring during the early postconsolidation phase in APL. As a result of the present study, salvage treatment in patients enrolled in the GIMEMA trial AIDA is now anticipated at the time of molecular relapse, defined as the conversion to PCR positivity in two successive BM samplings during follow-up.
Food Insecurity in University Students Receiving Financial Aid

Purpose: To evaluate the adequacy of loans funding and the risk for food insecurity in students living away from home and receiving loans at the University of Alberta. Methods: A nutritious seven-day menu was developed. The menu was priced for skilled and unskilled shoppers at a grocery store near to campus and a discount-style grocery store further away. Results: The menu was more costly for males, unskilled shoppers and those students who shopped near to campus. The menu cost was as high as $296/month and often exceeded the $196 allocated by the Canada Student Loans Program for food each month. Conclusions: Based on the findings from this study, students reliant on financial assistance likely have insufficient money for a nutritionally adequate diet and are at risk for food insecurity. Shopping skills and budgeting, while important, would be insufficient to alleviate food insecurity for many students who require financial aid. Increased funding for food in student loans is necessary.
Prevalence of herbal therapy usage in patients with psoriasis in Turkey

Many patients with psoriasis look for treatment options other than conventional treatment to control their disease with less side effects. We evaluated the prevalence and characteristics of herbal therapy usage among patients with psoriasis in Turkey. A self-prepared questionnaire about herbal therapies was applied to the psoriatic patients attended our dermatology clinic between October 2013 and December 2013. A total of 100 patients (55 male, 45 female; 7 - 80 years of age) were included in this study. Fortynine percent of patients used at least one form of herbal therapies. Juniperus oxycedrus and Aloe vera were found to be the most frequently used plants. 36.7% of the patients get benefit from herbal therapy. Only 12.2% of patients had informed their clinicians during or after herbal therapy usage. 22.4% of the patient had continued conventional treatment during herbal therapies. Side effects related with herbal therapy were developed in 26.5% of the patients. The risk of side effects was found to be 5.23 times more in patients using phytotherapies systemically compared with ones using topically. Our results show that herbal therapy usage is common among patients with psoriasis in Turkey. Since herbal therapies have the potential of interacting with the medical treatment modalities and causing life threatening allergic reactions, clinicians should be aware of plants used in psoriasis and possible adverse reactions related with them and also should not forget asking about herbal therapy history.
Community Detection in Multiplex Networks

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.
The impact of the connectivity of the cosmic web on the physical properties of galaxies at its nodes

We investigate the impact of the number of filaments connected to the nodes of the cosmic web on the physical properties of their galaxies using the Sloan Digital Sky Survey. We compare these measurements to the cosmological hydrodynamical simulations H orizon-(no)AGN and Simba. We find that more massive galaxies are more connected, in qualitative agreement with theoretical predictions and measurements in dark-matter-only simulations. The star formation activity and morphology of observed galaxies both display some dependence on the connectivity of the cosmic web at a fixed stellar mass: Less star forming and less rotation supported galaxies also tend to have higher connectivity. These results qualitatively hold both for observed and for virtual galaxies, and can be understood given that the cosmic web is the main source of fuel for galaxy growth. The simulations show the same trends at a fixed halo mass, suggesting that the geometry of filamentary infall impacts galaxy properties beyond the depth of the local potential well. Based on simulations, it is also found that active galactic nucleus feedback is key to reversing the relationship between stellar mass and connectivity at a fixed halo mass. Technically, connectivity is a practical observational proxy for past and present accretion (minor mergers or diffuse infall).
Quantifying Suspiciousness within correlated data sets

We propose a principled Bayesian method for quantifying tension between correlated data sets with wide uninformative parameter priors. This is achieved by extending the Suspiciousness statistic, which is insensitive to priors. Our method uses global summary statistics, and as such it can be used as a diagnostic for internal consistency. We show how our approach can be combined with methods that use parameter space and data space to identify the existing internal discrepancies. As an example, we use it to test the internal consistency of the KiDS-450 data in four photometric redshift bins, and to recover controlled internal discrepancies in simulated KiDS data. We propose this as a diagnostic of internal consistency for present and future cosmological surveys, and as a tension metric for data sets that have non-negligible correlation, such as Large Synoptic Spectroscopic Survey and Euclid.
Sharper bounds for uniformly stable algorithms

Deriving generalization bounds for stable algorithms is a classical question in learning theory taking its roots in the early works by Vapnik and Chervonenkis (1974) and Rogers and Wagner (1978). In a series of recent breakthrough papers by Feldman and Vondrak (2018, 2019), it was shown that the best known high probability upper bounds for uniformly stable learning algorithms due to Bousquet and Elisseef (2002) are sub-optimal in some natural regimes. To do so, they proved two generalization bounds that significantly outperform the simple generalization bound of Bousquet and Elisseef (2002). Feldman and Vondrak also asked if it is possible to provide sharper bounds and prove corresponding high probability lower bounds. This paper is devoted to these questions: firstly, inspired by the original arguments of Feldman and Vondrak (2019), we provide a short proof of the moment bound that implies the generalization bound stronger than both recent results (Feldman and Vondrak, 2018, 2019). Secondly, we prove general lower bounds, showing that our moment bound is sharp (up to a logarithmic factor) unless some additional properties of the corresponding random variables are used. Our main probabilistic result is a general concentration inequality for weakly correlated random variables, which may be of independent interest.
Dropping forward-backward algorithms for feature selection

In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward, backward and stepwise feature selection regression remained widely used due to their simplicity and efficiency. However, they are not sufficient enough when it comes to large datasets. In this paper, we analyze the issues associated with those approaches and introduce a novel algorithm that may boost the speed up to 23.11% compared to stepwise while maintaining good performance compared to stepwise regression in terms of error sum of squares and number of selected features.
An ALMA survey of the SCUBA-2 CLS UDS field: physical properties of 707 sub-millimetre galaxies

We analyse the physical properties of a large, homogeneously selected sample of ALMA-located sub-millimetre galaxies (SMGs). This survey, AS2UDS, identified 707 SMGs across the ∼1 deg2 field, including ∼17 per cent, which are undetected at K ≳ 25.7 mag. We interpret their ultraviolet-to-radio data using magphys and determine a median redshift of z = 2.61 ± 0.08 (1σ range of z = 1.8–3.4) with just ∼6 per cent at z > 4. Our survey provides a sample of massive dusty galaxies at z ≳ 1, with median dust and stellar masses of Md = (6.8 ± 0.3) × 108 M⊙ (thus, gas masses of ∼1011 M⊙) and M* = (1.26 ± 0.05) × 1011 M⊙. We find no evolution in dust temperature at a constant far-infrared luminosity across z ∼ 1.5–4. The gas mass function of our sample increases to z ∼ 2–3 and then declines at z > 3. The space density and masses of SMGs suggest that almost all galaxies with M* ≳ 3 × 1011 M⊙ have passed through an SMG-like phase. The redshift distribution is well fit by a model combining evolution of the gas fraction in haloes with the growth of halo mass past a critical threshold of Mh ∼ 6 × 1012 M⊙, thus SMGs may represent the highly efficient collapse of gas-rich massive haloes. We show that SMGs are broadly consistent with simple homologous systems in the far-infrared, consistent with a centrally illuminated starburst. Our study provides strong support for an evolutionary link between the active, gas-rich SMG population at z > 1 and the formation of massive, bulge-dominated galaxies across the history of the Universe.
BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge

We introduce a simple yet effective method of integrating contextual embeddings with commonsense graph embeddings, dubbed BERT Infused Graphs: Matching Over Other embeDdings. First, we introduce a preprocessing method to improve the speed of querying knowledge bases. Then, we develop a method of creating knowledge embeddings from each knowledge base. We introduce a method of aligning tokens between two misaligned tokenization methods. Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings. We also show BERTs tendency to correct lower accuracy question types. Our model achieves a higher accuracy than BERT, and we score fifth on the official leaderboard of the shared task and score the highest without any additional language model pretraining.
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy

We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosyn- thesis route planning without human intervention. The single-step ret- rosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is con- structed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and aca- demic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.Available on IBM RXN for Chemistry: https://rxn.res.ibm.com.
Detection of Late-time Optical Emission from SN 1941C in NGC 4136

We report the detection of broad, high-velocity oxygen emission lines from the site of SN 1941C nearly eight decades after outburst, making it the oldest optically detected historical core-collapse supernova (CCSN) and one of the youngest CCSN remnants with a well determined age. In contrast to the strongly blueshifted emission line profiles observed for other late-time CCSNe thought to be due to internal dust extinction of far-side hemisphere of expanding ejecta, SN 1941C’s spectrum exhibits stronger redshifted than blueshifted emissions of [O i] 6300, 6364 Å, [O ii] 7319, 7330 Å, and [O iii] 4959, 5007 Å. These oxygen emissions exhibit rest-frame expansion velocities from −2200 to . No other significant broad line emissions were detected including Hα emission. We discuss possible causes for this unusual spectrum and compare SN 1941C’s optical and X-ray luminosities to other evolved CCSNe.
Smoothing Graph Signals via Random Spanning Forests

Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo estimators for graph signal smoothing are proposed. These random forests are sampled efficiently via a variant of Wilson’s algorithm –in time linear in the number of edges. The theoretical variance of the proposed estimators are analyzed, and their application to several problems are considered, such as Tikhonov denoising of graph signals or semi-supervised learning for node classification on graphs.
Stochastic resetting and applications

In this topical review we consider stochastic processes under resetting, which have attracted a lot of attention in recent years. We begin with the simple example of a diffusive particle whose position is reset randomly in time with a constant rate r, which corresponds to Poissonian resetting, to some fixed point (e.g. its initial position). This simple system already exhibits the main features of interest induced by resetting: (i) the system reaches a nontrivial nonequilibrium stationary state (ii) the mean time for the particle to reach a target is finite and has a minimum, optimal, value as a function of the resetting rate r. We then generalise to an arbitrary stochastic process (e.g. Lévy flights or fractional Brownian motion) and non-Poissonian resetting (e.g. power-law waiting time distribution for intervals between resetting events). We go on to discuss multiparticle systems as well as extended systems, such as fluctuating interfaces, under resetting. We also consider resetting with memory which implies resetting the process to some randomly selected previous time. Finally we give an overview of recent developments and applications in the field.
Parents of Premature Infants: Their Emotional World

Introduction. Prematurity and the Dynamics of Pregnancy. Prematurity and the Dynamics of Birth. Mothers of Full-term Infants. Fathers of Full-term Infants. Narrative of a Mother of a Premature Infant. The Premature Infant in the Mind of the Mother. A Couple with a Premature Infant from an Attachment Perspective. A Snapshot - A Father's Reaction to Prematurity. Narrative of a Father of a Premature Infant. An Intergenerational Family Perspective. A Migrant Couple with a Premature Infant. Two Mothers without Mothers. "That Big Glass Barrier": Exploring the Neonatal Intensive Care Unit . "Whose baby is It? ". The Experience of Early Infancy for the Premature Infant. Infants and their Families in the Neonatal Intensive Care Unit. Social Work in the Neonatal Intensive Care Environment. The Space between Life and Death. The Trauma of Prematurity: The Impact on the Parents.
Active Learning for Graph Neural Networks via Node Feature Propagation

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly.
Measurements of Low Temperature Rate Coefficients for the Reaction of CH with CH2O and Application to Dark Cloud and AGB Stellar Wind Models

Rate coefficients have been measured for the reaction of CH radicals with formaldehyde, CH2O, over the temperature range of 31–133 K using a pulsed Laval nozzle apparatus combined with pulsed laser photolysis and laser-induced fluorescence spectroscopy. The rate coefficients are very large and display a distinct decrease with decreasing temperature below 70 K, although classical collision rate theory fails to reproduce this temperature dependence. The measured rate coefficients have been parameterized and used as input for astrochemical models for both dark cloud and Asymptotic Giant Branch stellar outflow scenarios. The models predict a distinct change (up to a factor of two) in the abundance of ketene, H2CCO, which is the major expected molecular product of the CH + CH2O reaction.
Synchronisation by noise for the stochastic quantisation equation in dimensions $2$ and $3$

We prove uniform synchronisation by noise with rates for the stochastic quantisation equation in dimensions two and three. The proof relies on a combination of coming down from infinity estimates and the framework of order-preserving Markov semigroups derived in [Butkovsky, Scheutzow; 2019]. In particular, it is shown that this framework can be applied to the case of state spaces given in terms of H\"older spaces of negative exponent.