Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Types of Pulmonary Disability in Patients with Gastroesophageal Reϑlux Disease (GERD)

GERD is found to be one of the risk factors for various pulmonary disorders, like Obstructive Airway Disease, Obstructive Sleep Apnea, Interstitial Lung Diseases either directly or as a confounding factor. Due to higher Prevalence of Obstructive Airway Disease in GERD patients, early diagnosis and Appropriate management can control disease progression and outcome of both conditions. GERD is found to be one of the risk factors for many pulmonary diseases, especially Obstructive Airway Disease, either directly or as a confounding factor and Data on the severity of OAD in symptomatically and endoscopically proven GERD patients are limited in the Indian population, This learning was undertaken to find out the pervasiveness of pulmonary diseases in patients with proven Gastro Esophageal Reflux Diseases (GERD) and to find out the correlation of Symptoms of GERD and Endoscopic finding findings of GERD with the severity of Obstructive Airway Disease using Spirometric Parameters. Asthma is a heterogeneous disease, typically categorized by persistent airway infection. It is described with the aid of the history of breathing symptoms together with wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, collectively with mutable expiratory airflow restriction. This study focuses on studying the occurrence and types of pulmonary disability in patients with GERD in a tertiary care hospital and on correlating the Symptoms and Endoscopic findings of GERD with the severity of Obstructive Airway Disease as assessed by Spirometric Parameters.
Genetics of leaf rust resistance in three Western Canada spring wheats

The wheat cultivars Laura, Biggar, and Genesis have recently been licensed and released for production in western Canada. These cultivars all have effective resistance to the wheat leaf rust fungus, Puccinia recondita f. sp. tritici. The objective of this study was to identify the leaf rust resistance genes in these cultivars. The three cultivars were crossed with the leaf rust-susceptible cultivar Thatcher and also with near-isogenic lines of Thatcher with known leaf rust resistance genes. Segregation of F 3 lines in an artificially inoculated rust nursery indicated that Laura has the adult plant gene Lr34. The seedling resistance genes Lr1 and Lr10 were also identified in Laura. Segregation of F 3 lines indicated that the adult plant gene Lr13 was in Genesis and Biggar [. ]
MISIM: A Novel Code Similarity System.

Code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. We argue that code similarity is now a first-order problem that must be solved. To begin to address this, we present machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to three state-of-the-art code similarity systems: (i)code2vec, (ii)Neural Code Comprehension, and (iii)Aroma. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy than all three systems.
Labour market flexibility and unemployment duration: evidence from the UK

Using a combination of UKHLS and LFS data and a discrete time model, we test the hypothesis that unstable jobs with variable hours or pay enhance the job-finding chances of the unemployed in the UK. We find no evidence that the share of unstable jobs in the unemployed person’s local labour market impacts on the probability to move into employment. This result holds both for men and women and for groups with low employability such as the low educated and the long-term unemployed. It is robust to alternative ways of defining unstable jobs and to the inclusion of unobserved heterogeneity. Overall, findings cast doubt on the importance of labour market flexibility for employment creation.
Meta Congestion Control Semester

Congestion control algorithm is one of the pearls in network research field. Congestion control algorithm is used to leverage network resources, at the same time, to prevent overloading network. The demand of high-utility network keeps growing, which requires the internet to be low-delay, high-throughput and low-loss. Consequently, a lot of novel algorithms are developed in decades. However, the complexity and diversity of network condition makes it hard to have one single-best congestion control algorithm for all scenarios. Therefore, meta congestion control is emerged as a new idea which allows switching between different algorithms according to network condition in order to obtain better utility. The intuition is that, instead of using a fixed congestion control algorithm, the optimal algorithm is selected for current network condition and usage scenario. This thesis is aimed to explore whether it is beneficial to implement such a meta congestion control and how much the potential gain it can have. It analyzes data collected from a platform consisting of real world nodes to address these two goals.
In situ micro-FTIR spectroscopic investigations of synthetic ammonium phengite under pressure and temperature

Abstract. Phengite is known to be an important mineral in the transport of alkalis and water to upper mantle depths. Since ammonium ( NH 4 + ) can substitute for K+ in K-bearing minerals, phengite is thus a potential host to transport nitrogen into the mantle. However, the temperature and pressure conditions at which devolatilisation of NH4 -bearing phengite occurs are not well constrained. In this study, NH4 -phengite ( NH4 )( Mg0.5Al1.5 ) (Al0.5Si3.5)O10(OH)2 was synthesised in piston-cylinder experiments at 700 ∘ C and 4.0 GPa. Its devolatilisation behaviour was studied by means of in situ micro-FTIR (Fourier transform infrared) spectroscopy under low and high temperatures from −180 up to 600 ∘ C at ambient pressure using a Linkam cooling–heating stage and pressures up to 42 GPa at ambient temperature in diamond anvil cell (DAC) experiments. In addition to these short-term in situ experiments, we performed quenched experiments where the samples were annealed for 24 h at certain temperatures and analysed at room conditions by micro-FTIR spectroscopy. Our results can be summarised as follows: (1) an order–disorder process of the NH 4 + molecule takes place with temperature variation at ambient pressure; (2) NH 4 + is still retained in the phengite structure up to 600 ∘ C, and the expansion of the NH 4 + molecule with heating is reversible for short-term experiments; (3) kinetic effects partly control the destabilisation of NH 4 + in phengite; (4) ammonium loss occurs at temperatures near dehydration; (5) NH 4 + in phengite is apparently distorted above 8.6 GPa at ambient temperature; and (6) the local symmetry of the NH 4 + molecule is lowered/descended/reduced by increasing pressure ( P ) or decreasing temperature ( T ), and the type and mechanism of this lowered symmetry is different in both cases. The current study confirms the wide stability range of phengite and its volatiles and thus has important implications for the recycling of nitrogen and hydrogen into the deep Earth. Moreover, it is considered as a first step in the crystallographic determination of the orientation of the NH 4 + molecule in the phengite structure.
Of Cancer Risk Prediction Models

We review development, validation, and adaptation of Risk anticipation models to analytic and citizenry practices. We focus on issues in anniversary of these accomplish and the gaps in the acreage beyond the continuum of Risk anticipation archetypal development (many models published); validation (few validated); and accomplishing (even beneath implemented in analytic settings, abundant accomplishing on web sites). Design of models for end users and analytical issues in implementing and evaluating models are addressed with examples from contiguous experience. KeywordsRisk prediction, Prevention, Cancer, Model Implementation.
Evaluation of Diurnal Temperature Models and Thermal Summation in New Zealand

Five methods of degree-day (DD) estimation using diurnal temperature models were evaluated. Seasonal data for a 5-yr period from nine locations throughout New Zealand were used to compare estimated and actual DD summations over time. All models tested showed variable efficiency in the estimation of accumulated DD among localities and seasons. Results suggest that actual hourly temperatures should be used for DD calculations in climates that have a highly variable weather pattern with low average temperatures relative to the threshold used.
On Non-Darcian Water Flow in Peat

(1) The movement of water through mires is a controlling ecological factor, since the transport of solutes by advection can dominate nutrient fluxes, determine the growth rates or survival of plants, and profoundly influence peat redox status and decomposition rates. Modelling and measuring subsurface water flows in mires requires a sound understanding of flow processes through peat. (2) The existing literature suggests that such flow violates Darcy's Law in humified peats, and casts doubt on the applicability of existing models for flow through porous media when applied to peatlands. (3) It is shown here that a large component of the reported 'non-Darcian' behaviour of peat can be explained by the elastic proporties of peat compression and the effective stress principle. We argue that Darcy's Law remains an appropriate tool for use in wetland hydrologic modelling.
Study on the Pycindiospore Germination of AppleTree Valsa Canker Pathogen

Because the pycnidiospore of apple tree valsa canker do not germinate in water,the effects of different temperature,relative humidity,pH,and light on pycnidiospore germination were studied by spreading pycnidiospore suspension on the surface of small piece PDA medium in this paper.The results showed that the pycnidiospore could germinate in 15~28℃,while the optimal is 25~28℃. Saturated relative humidity was needed during the curse of pycnidiospore germination.The pH for pycnidiospore germination was 2.14~7.10 while the optimum was 5.10~6.10. The nutrition condition had an obvious effect on pycnidiospore germination.Though the pycnidiospore had a higher germination rate in the continuous darkness than coutinuous light and alternate light,different light treatments had no significant different effect on pycnidiospore germination.
Identification of Exocarpium Citri Grandis by inter-simple sequence repeat markers

Objective To identify Exocarpium Citri Grandis by inter-simple sequence repeat markers.Methods Primers suitable for routine analysis were screened from 20 inter-simple sequence repeat primers,then,they were used in PCR and sepharose electrophoresis of 16 samples of Exocarpium Citri Grandis,Citrus reticulata and Citrus grandis(Linn.)Osbeck.Results Two of the 20 primers amplified polymophic bands and suitable for the identification of Exocarpium Citri Grandis.Conclusion Inter-simple sequence repeat markers provide a quick,reliable molecular marker for identification of Exocarpium Citri Grandis.
The role of ponds as feeding habitat for an umbrella species: best management practices for the black stork Ciconia nigra in Spain

Abstract To establish recommendations for wetland management that promote wildlife diversity in Mediterranean habitats we examined the factors that determine feeding habitat selection by the black stork Ciconia nigra in ponds. The black stork is considered an umbrella species because it is threatened, requires large foraging ranges in priority areas, is selective in its choice of diet and nesting sites, and inhabits a characteristic biological community with endemic and threatened taxa. Eighty-five ponds were monitored in central and western Spain to detect the stork feeding. At the same time, pond variables that could affect black stork feeding preferences were periodically evaluated. Generalized linear mixed models were used to analyse principal components obtained from groups of factors related to structural, location and ecological conditions. The black stork selects ponds distant from roads, with a large surface area, high water level, shallow shores, low turbidity, few traces of wild ungulates on the shores, a high diversity of fish and amphibian species, and a vegetated perimeter, in flat and open areas. Potential factors affecting feeding behaviour are discussed. We suggest measures for pond construction and management that could favour this species in particular and biodiversity in general in the Mediterranean environment.
On the Stability of Marine Bacteriophages

The author investigated the stability of some marine bacteriophages by exposing them to various conditions, changing pH and salinity of suspended media, heating at 50°C for 30min, and adding chloroform. The five marine bacteriophages examined showed different degrees of inactivation by these treatments. It is not concluded that sensitivity of the phages to heat, pH-change and chloroform constitute a fundamental marine property. However the requirement for inorganic salts, such as NaC1 and Mg-or Ca-salt, for retention of viability is considered to be a property of marine bacteriophages. In many cases, when stocks are prepared in sea water broth, the lysates of marine bacteriophages will normally survive for several months at 5°C.
Radioprotective Effect of Lifukang, a Chinese Medicinal Plants Prescription

We have examined in vitro and in vivo radioprotective effect of a Chinese medicinal plants prescription, Lifukang. Micronucleus assay was employed to evaluate in vitro radioprotective effect of Lifukang. In the presence of Lifukang, the frequencies of miconuclei were greatly reduced from 7.2 to 2.9, 1.6 and 1.6% at the concentrations of Lifukang from 0 to 2, 10 and 50㎍/ml, respectively. For in vivo assay, we monitered the incidences of apoptotic cells in mouse small intestine crypts and endogeneous spleen colonies. When Lifukang was administered to mice P.O. or I.P. at doses of 1㎎/ml in drinking water for 7 days or 0.3㎎/mouse 24 hrs prior to irradiations, respectively, the average numbers of apoptotic cells were reduced to 3.1 or 2.3, respectively, as compared to 4.4 acquired from untreated control experiments. In addition, in spleen colony assay, Lifukang increased the number of hematopoietic spleen colonies. When samples were administered after irradiation, better results were obtained. The numbers of spleen colonies were increased from 14 colonies to 18.3 or 19.6 colonies when Lifukang was given through P.O. (1㎎/ml in drinking water for 11 days) or I.P. (0.3㎎/mouse) after irradiation, respectively.
Tribal and subtribal relationship of Epidendroideae Lindl. (Orchidaceae) with emphasis on Epidendreae Humb., Bonpl. & Kunth based on matK gene

The Epidendroideae (Orchidaceae) is one of the orchids largest subfamilies with substantially larger number of genera and species than all other subfamilies altogether.Taxa incorporated in the subfamily Epidendroideae are characterized by the largest variety and diversity of forms. Once in a while, the classification inside the Epidendroideae subfamily undergoes modifications. It is because different scientists base their classification system on different features. This paper shows is to show relationship between genera within the subfamily Epidendroideae (with the particular consideration of the tribes Dendrobieae and Epidendreae) on the basis of the analysis of the nucleotide sequence matK gene.
A practical guide towards agile test-driven development for scientific software projects

Software testing has received much attention over the last years and has reached such critical importance that agile software development practices put software testing at its core. Agile software development is successfully applied in large-scale industrial software developments but due to its granular responsibilities with roles assigned to various members of the development team, these practices may not be applicable to scientific code development, especially in an academic environment, where it is not uncommon that the codebase is developed, maintained and used by a single person. Even for collaborative scientific software development, financed through external grants, the end-users are typically still part of the development team. This is in contrast to how software is developed in many industries, where the development team and end-users are two separate entities. There are, however, many good code development practices that can be adopted for scientific software projects. Specifically, the intention of this article is to take the centrepiece of agile software development and tailor it to scientific and academic, single-user code development. In this study, a c++ starter project is developed and made available, based on the meson build system, which provides native support for software testing. It is used to show how a simple linear algebra application, found in many scientific and academic applications, can be developed and how simple unit, integration and system tests can be created that are managed through the meson build system. In this way, we are able to minimise software defects and reduce the risk to interpret incorrect data generated by erroneous software that may result in the wrong conclusions to be drawn. Each layer of testing presents one additional layer of protection and we will explore how these may be incorporated with minimum overhead to produce bug-free software.
Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences

Time series sequence prediction and modelling has proven to be a challenging endeavor in real world datasets. Two key issues are the multi-dimensionality of data and the interaction of independent dimensions forming a latent output signal, as well as the representation of multi-dimensional temporal data inside of a predictive model. This paper proposes a multi-branch deep neural network approach to tackling the aforementioned problems by modelling a latent state vector representation of data windows through the use of a recurrent autoencoder branch and subsequently feeding the trained latent vector representation into a predictor branch of the model. This model is henceforth referred to as Multivariate Temporal Autoencoder (MvTAe). The framework in this paper utilizes a synthetic multivariate temporal dataset which contains dimensions that combine to create a hidden output target.
Modelling Shoot:Root Relations: the Only Way Forward?

The basic transport-resistance (TR) model of shoot:root carbon:nitrogen allocation is described. This approach assumes that the two processes of substrate transport and chemical conversion determine allocation. It is suggested thatallallocation models, whether built for the purposes of theoretical investigation or practical application, should start with this irreducible framework. Here it is assumed that the processes operate according to: (a) for substrate sources, dependence on shoot and root sizes with possible product inhibition; (b) for transport, movement down a substrate concentration gradient; and (c) for substrate sinks or utilization, linear bisubstrate kinetics. Some dynamic properties of the model are explored. Failure of this approach to allocation flags the need for additional mechanisms to control the processes. Details of the failure will indicate the modifications needed, which may involve hormones or reflect teleonomy (apparently goal-seeking behaviour), and which are added to the irreducible framework. However, these additions should not replace the irreducible framework of transport and chemical conversions, because they do not in reality. Modifications to the basic model to represent possibilities such as ontogenesis with the transition from exponential growth towards a steady state or with the scaling of within-plant transport resistances with plant size, the influence of hormones, and active transport, are described.
Near-Optimal Comparison Based Clustering

The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. However, far less is known when this information is not readily available and, instead, one only observes ordinal comparisons such as "object i is more similar to j than to k. " In this paper, we tackle this problem using a two-step procedure: we estimate a pairwise similarity matrix from the comparisons before using a clustering method based on semi-definite programming (SDP). We theoretically show that our approach can exactly recover a planted clustering using a near-optimal number of passive comparisons. We empirically validate our theoretical findings and demonstrate the good behaviour of our method on real data.
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions

Deep Neural Network (DNN) has gained unprecedented performance due to its automated feature extraction capability. This high order performance leads to significant incorporation of DNN models in different Internet of Things (IoT) applications in the past decade. However, the colossal requirement of computation, energy, and storage of DNN models make their deployment prohibitive on resource constraint IoT devices. Therefore, several compression techniques were proposed in recent years for reducing the storage and computation requirements of the DNN model. These techniques on DNN compression have utilized a different perspective for compressing DNN with minimal accuracy compromise. It encourages us to make a comprehensive overview of the DNN compression techniques. In this paper, we present a comprehensive review of existing literature on compressing DNN model that reduces both storage and computation requirements. We divide the existing approaches into five broad categories, i.e., network pruning, sparse representation, bits precision, knowledge distillation, and miscellaneous, based upon the mechanism incorporated for compressing the DNN model. The paper also discussed the challenges associated with each category of DNN compression techniques. Finally, we provide a quick summary of existing work under each category with the future direction in DNN compression.
Regularized Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions. Regularized IRL applies strongly convex regularizers to the learner's policy in order to avoid the expert's behavior being rationalized by arbitrary constant rewards, also known as degenerate solutions. We propose tractable solutions, and practical methods to obtain them, for regularized IRL. Current methods are restricted to the maximum-entropy IRL framework, limiting them to Shannon-entropy regularizers, as well as proposing the solutions that are intractable in practice. We present theoretical backing for our proposed IRL method's applicability for both discrete and continuous controls, empirically validating our performance on a variety of tasks.