Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of different proportions of dried cafeteria leftover inclusion in a concentrate mix on performance of growing pigs

An experiment was conducted to evaluate the effect of inclusion of dried Cafeteria leftover (DCLO) at various levels in concentrate mix (CM) on feed intake, digestibility, average daily gain (ADG), feed conversion ratio (FCR), carcass characteristics, and economic benefits of weaned pigs at Haramaya university piggery. Twenty Yorkshire pigs with initial live weight of 19.89±0.297kg (mean±SE) were randomly assigned to four dietary treatments in a completely randomized block design each with five replicates. The experimental rations were sole CM (mixture of maize grain, wheat short, wheat bran, noug seed cake, soybean mill, vitamin premix and salt), replacement of the concentrate with DCLO at different proportions as (33%CM and 67% DCLO; 67% CM and 33% DCLO) and feeding with only sole DCLO. The mean feed intake ranged 1.6-1.82kg/day and ADG was 0.4-0.64kg/day. The least feed intake was for sole DCLO (P<0.05) and the highest for 67% CM:33%DCLO. The FCR (w/w) was lowest (P<0.05) for pigs fed 33% DCLO diet, and pigs fed 67% DCLO:33%CM achieved highest FCR. Crude protein digestibility did not differ between treatments, but DM, EE and CF digestibility increased with increasing level of DCLO in the diets. The mean carcass weight (43.7kg-57.02kg) was highest in 67% CM but lowest in sole DCLO. Back fat thickness (2.4cm-3.2cm (SEM=±0.03)) was lowest in sole CM but highest in sole DCLO. The highest and lowest (P<0.05) rib eye area (23.9 cm2-31.9cm2 (SEM=±0.81)) were recorded for sole CM and sole DCLO, respectively. Cost of feed per kg weight gain was declined significantly (P<0.01) with increasing level of DCLO. Despite the lower production cost of sole DCLO groups, the pigs showed lower performance and this must have contributed to lowest net return of the groups. Thus, it is concluded that DCLO can replace the conventional concentrate mix up to 67% without adverse effect on pig performance. Keywords : Carcass Digestibility Live Weight Gain Nutritive Value Profitability
Fine-scale resolution and analysis of runs of homozygosity in domestic dogs

Inbreeding and consanguinity leave distinct genomic traces, most notably long genomic tracts that are identical by descent and completely homozygous. These runs of homozygosity (ROH) can contribute to inbreeding depression if they contain deleterious variants that are fully or partially recessive. Several lines of evidence have been used to show that long (> 5 megabase (Mb)) ROH are disproportionately likely to harbor deleterious variation, but the extent to which long versus short tracts contribute to autozygosity at loci known to be deleterious and recessive has not been studied. In domestic dogs, nearly 200 mutations are known to cause recessive diseases, most of which can be efficiently assayed using SNP arrays. By examining genome-wide data from over 200,000 markers, including 150 recessive disease variants, we built high-resolution ROH density maps for nearly 2,500 dogs, recording ROH down to 500 kilobases. We observed over 500 homozygous deleterious recessive genotypes in the panel, 90% of which overlapped with ROH inferred by GERMLINE. Although most of these genotypes were contained in ROH over 5 Mb in length, 14% were contained in short (0.5 - 2.5 Mb) tracts, a significant enrichment compared to the genetic background, suggesting that even short tracts are useful for computing inbreeding metrics like the coefficient of inbreeding estimated from ROH (FROH). In our dataset, FROH differed significantly both within and among dog breeds. All breeds harbored some regions of reduced genetic diversity due to drift or selective sweeps, but the degree of inbreeding and the proportion of inbreeding caused by short versus long tracts differed between breeds, reflecting their different population histories. Although only available for a few species, large genome-wide datasets including recessive disease variants hold particular promise not only for disentangling the genetic architecture of inbreeding depression, but also evaluating and improving upon current approaches for detecting ROH.
Neural reverse engineering of stripped binaries using augmented control flow graphs

We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with neural models. The main idea is to use static analysis to obtain augmented representations of call sites; encode the structure of these call sites using the control-flow graph (CFG) and finally, generate a target name while attending to these call sites. We use our representation to drive graph-based, LSTM-based and Transformer-based architectures. Our evaluation shows that our models produce predictions that are difficult and time consuming for humans, while improving on existing methods by 28% and by 100% over state-of-the-art neural textual models that do not use any static analysis. Code and data for this evaluation are available at https://github.com/tech-srl/Nero.
SQL and Data Inference Injection and Enhancing Website Security

Web applications are utilized on an extensive scale worldwide, which handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection is a procedure that abuses a security defenselessness occurring in the database layer of an application. In this paper we will research the website page database security help of optimization and encryption method. At long last, the attack will be detected dependent on the user query with the assistance of query tree Mechanism. For enhancing the security level of attack detection and prevention. Fully Homomorphic Encryption (FHE) encryption is proposed.
Cheminformatics tools for analyzing and designing optimized small molecule libraries

Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.
Modelling Icing on Power Lines at the Ålvikfjellet Test Span (Norway) Using High-Resolution Climate Model Data

Icing on a test span located at Ålvikfjellet in the Norwegian mountains has been modelled using data from highresolution (2.5 km) climate model simulations covering the years 2005-2016. The modelling follows the ISO 12494 standard on atmospheric icing of structures and uses 10-meter wind speed, cloud liquid water content at the lowest model level, surface pressure and 2-meter temperature as input from the climate model. Calculated ice loads show a low sensitivity to variations in wind speed and pressure, a moderate sensitivity for cloud liquid water content and a high sensitivity for temperature. The comparison with three winters of observations at the test span shows that the icing model is able to reproduce icing at daily and longer time scales. Inclusion of a melting assumption in the icing model shows good results. Using the climate model data, maps of icing estimates are provided for the whole Norwegian mainland at a resolution of 2.5 km covering the years 2005-2016. Keywords— Icing modelling, Icing on power lines, Norway, Highresolution climate model, Model evaluation
Interactive Factual Knowledge Learning in Dialogues

Knowledge bases (KBs) used in applications need to be continuously expanded in 1 order to serve the users well. This process is known as knowledge base completion 2 (KBC). A piece of knowledge or a fact is often represented as a triple ( s, r, t ) , 3 meaning the entity s and the entity t have the relation r or are linked by r . KBC 4 builds a model to infer missing facts from existing ones in a given KB. Existing 5 KBC research typically makes the closed-world assumption that to infer a new 6 fact ( s, r, t ) , it assumes that s , r and t are already in the KB, but are not linked. 7 Clearly, this assumption is a serious limitation. In this paper, we eliminate this 8 assumption and allow s , r and/or t to be unknown to the KB, which we call open-9 world knowledge base completion (OKBC). We focus on solving OKBC via user 10 interactions, which enables the proposed system to potentially serve as an engine 11 for learning new knowledge during dialogues. Experimental results will show the 12 effectiveness of the proposed approach. 13
IDENTIFICAÇÃO DA ATIVIDADE ANTIMICROBIANA NO VENENO DA SERPENTE Bothrops moojeni EM BACTÉRIAS GRAM NEGATIVAS.

As infeccoes causadas por microrganismos multirresistentes estao associadas com bacterias adquiridas no meio ambiente hospitalar. A pressao seletiva dos antimicrobianos e um importante fator de selecao e disseminacao dos genes de resistencia no meio ambiente. O uso de moleculas produzidas por diferentes seres vivos tem demonstrado atividade contra microrganismos. OBJETIVOS: Avaliar a atividade do veneno bruto da Bothrps moojeni sobre bacterias gram-negativas produtoras e nao produtoras de metalo- -lactamase e de -lactamases de espectro ampliado, identificar o(s) componente(s) do veneno com atividade contra bacterias gram-negativas produtoras e nao produtoras de metalo- -lactamase e -lactamases de espectro ampliado e identificar a atividade antibacteriana do veneno bruto da serpente B. moojeni em bacterias gram negativas fermentadoras e nao fermentadoras de glicose produtoras de diferentes enzimas degradadoras de -lactâmicos. METODOLOGIA: O veneno utilizado foi extraido das serpentes B. moojeni, mantidas no serpentario do Centro de Estudos e Pesquisas Biologicas (CEPB) da Pontificia Universidade Catolica de Goias. Os microrganismos utilizados neste estudo foram obtidos da “American Type Culture Collection” e do Banco de Microrganismos do Laboratorio de Microbiologia Clinica e do Mestrado em Ciencias Ambientais e Saude da Pontificia Universidade Catolica de Goias, sendo Acinetobacter baumannii (blaIMP2), Pseudomonas aeruginosa (ATCC 27853), Pseudomonas aeruginosa blaIMP1, blaVIM1, blaVIM2, blaSPM1 e produtora de carbapenemase banco n° 278) Escherichia. coli (ATCC 25922 e ATCC 35218), Klebisiella pneumoniae (ATCC 700603). O ensaio foi realizado com o veneno bruto aplicado sobre disco de difusao em diferentes diluicoes. A partir das amostras obtidas, foi realizada a dosagem de proteinas utilizando o metodo descrito por Bradford. RESULTADOS: O teste microbiologico mostrou que o veneno bruto possui atividade antimicrobiana, tanto para bacterias produtoras como para as nao produtoras de metalo- -lactamase e -lactamases de espectro ampliado e que esta atividade se deve possivelmente pela acao da L-aminoacido oxidase. CONCLUSOES: Diferentes concetracoes diluicoes do veneno bruto da serpente B. moojeni inibiram o crescimento de bacterias gram-negativas produtoras e nao produtoras de metalo- -lactamases e -lactamases de espectro ampliado e que o tamanho do alo de inibicao do crescimento bacteriano diminuiu na medida em que diminui a concentracao do veneno bruto e consequentemente a quantidade de proteinas, demonstrando que o veneno bruto foi dose dependente.
Predicting Author Gender Using Machine Learning Algorithms: Looking Beyond the Binary

This paper explores the relationship between digital humanities studies that utilize computer algorithms to identify author gender and feminist and queer literary theory. I argue that utilizing computer algorithms to sort literature into the categories “authored by a male” or “authored by a female” is too reductive in its treatment of gender as binary. However, I suggest computer algorithms could be utilized to explore the performative aspects of author gender and to ask larger questions about algorithmic criticism, the author as a subject, and the relationship between morphological and cultural properties of texts. Resume Cet article explore la relation entre les etudes des humanites numeriques, qui se servent d’algorithmes informatises afin d’identifier le genre d’un auteur, et la theorie feministe et homosexuelle. Je soutiens que l’usage d’algorithmes informatises pour categoriser la litterature comme « ecrit par un homme » ou « ecrit par une femme » est trop reducteur par rapport a son traitement binaire de genre. Cependant, je suggere que les algorithmes informatises peuvent etre employes dans le but d’explorer les aspects performatifs du genre de l’auteur, ainsi que dans le but de soulever de plus grandes questions sur la critique d’algorithme, sur l’auteur en tant que sujet et sur le lien entre les caracteristiques de textes morphologiques et culturels. Mots-cles: algorithmes d’apprentissage automatique; theorie feministe; theorie queer; genre d’auteur; expression de genre; critique algorithmique
Lethal effects of erythritol on the mosquito Aedes aegypti Linnaeus (Diptera: Culicidae)

The yellow fever mosquito Aedes aegypti Linnaeus is a major vector of several viruses that cause disease in humans and other animals. Conventional insecticides have been used for vector control, but can be problematic because Ae. aegypti and other mosquitoes can evolve resistance to them and they can negatively impact non‐target insect species. Therefore, there is a strong demand for novel methods in Ae. aegpyti vector control strategies. Our study investigated whether the sugar alcohol, erythritol, has potential as a novel insecticide for mosquito control. Erythritol is lethal to several dipteran species yet has been deemed safe for consumption by humans and other mammals. We tested the impact of erythritol on Ae. aegypti by comparing the longevity of larvae reared in erythritol‐treated water and adult females maintained on erythritol. We also tested whether sucrose supplementation impacts the effects of erythritol on adult longevity. For larvae, erythritol exposure at concentrations as low as 0.1 m is lethal, with LC50 at 48 hr of 0.11 m for early stage (3‐day old) larvae and 0.42 m for late stage (5‐day old) larvae. For adult females, maintenance on erythritol is lethal at concentrations as low as 0.5 m, with LC50 at 72 hr of 1.60 m. Further, a mixture of 1 m sucrose/1 m erythritol solution is more lethal to adult females than 1 m erythritol solution alone, suggesting that erythritol in combination with sucrose could comprise a toxic sugar bait. These results provide promising evidence for erythritol as a novel larvicide and adulticide for Ae. aegypti.
CHALLENGES AND OPPORTUNITIES OF FLUID SIMULATION GRAPH BASED APPROACHS

-Fluid simulation is one of the old topics but still have wide opportunities to be researched, as fluid simulations have applications in various fields especially for experimentation, multimedia, movie, games and virtual reality. Fluid simulation become more popular when incompressible Navier-Stokes equations proposed. Many fluid simulation approaches based on incompressible Navier-Stokes equations emerge till today. This paper presents the results of a survey of several scientific articles that discuss the synthesis approach for fluid simulation, especially graph based fluid simulation. Generally, fluid simulation approach consists of physical based and synthesis based. Physical based has limitations in terms of computational cost and complexity. Researchers have proposed at least three approaches to overcome this, ie modifications to a physical-based approach, parallelization of a physical based approach
Probing Protein Allostery as a Residue-specific Concept via Residue Perturbation Maps

Allosteric regulation is a well-established phenomenon classically defined as conformational or dynamical change of a small number of allosteric residues of the protein upon allosteric effector binding at a distance. Here, we developed a novel approach to delineate allosteric effects in proteins. In this approach, we applied robust machine learning methods, including Deep Neural Network and Random Forest, on extensive molecular dynamics (MD) simulations to distinguish otherwise similar allosteric states of proteins. Using PDZ3 domain of PDS-95 as a model protein, we demonstrated that the allosteric effects could be represented as residue-specific properties through two-dimensional property-residue maps, which we refer as “residue perturbation maps”. These maps were constructed through two machine learning methods and could accurately describe how different properties of various residues are affected upon allosteric perturbation on protein. Based on the “residue perturbation maps”, we propose allostery as a residue-specific concept, suggesting all residues could be considered as allosteric residues because each residue “senses” the allosteric events through perturbation of its one or multiple attributes in a quantitatively unique way. The “residue perturbation maps” could be used to fingerprint a protein based on the unique patterns of residue perturbations upon binding events, providing a novel way to systematically describe the protein allosteric effects of each residue upon perturbation. Author Summary Allostery is protein regulation at distance. A perturbation at one site of the protein could distantly affect another site. The residues involved in these sites are considered as allosteric residues. The allostery concept has been widely used to understand protein mechanisms and to design allosteric drugs. It is long believed only a small number of residues are allosteric residues. Here, we argue that all residues in a protein are allosteric residues. Upon the perturbation of the allosteric events, the different properties of each residue are affected at the distinct extend. We used hybrid models including molecular dynamics simulations and machine learning components to reveal that not only many properties of residues are affected upon ligand binding, but also each residue is affected through perturbation of its various properties, which makes the residue distinguishable from other residues. According to our findings in a model protein, we defined a “residue perturbation map” as a two-dimensional map that fingerprint a protein based on the extent of perturbation in different properties of all its residues in a quantitative fashion. This “residue perturbation map” provides a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.
Preliminary Study to Find Occurrence and Dynamics of Toxic Cyanobacteria in Nuwara wewa and Nachchaduwa wewa, Two Man-made Reservoirs in Sri Lanka

Bloom of cyanobacteria in water bodies causes devastating problems to the ecosystem and humans when they depend on these waters for drinking purpose. Microcystin and nodularin, produced by some cyanobacteria, are toxic for human and many other aquatic organisms. Nuwara wewa and Nachchaduwa wewa are two important reservoirs supplying water for drinking purpose in addition to agricultural use. Therefore, preliminary study was conducted to evaluate these reservoirs for the presence of toxic cyanobacteria and their dynamics is of importance to take proper measures to control cyanobacterial growth and to ensure supply of quality water for human drinking purpose. Water samples were collected for 12 months from both reservoirs and physic-chemicals parameters of water were also recorded. Correlations between the physico-chemical parameters of water and the cyanobacterial density were established. Toxic cyanobacteria were isolated and identified using conventional and molecular methods. 16S rRNA gene and mcy-A gene were targeted for molecular identification using PCR. Cyanobacterial density had strong positive correlations with some physico-chemical parameters such as pH, temperature and dissolved oxygen in both reservoirs. These parameters can be used as good indicators to evaluate the condition these reservoirs with respect to cyanobacterial bloom. Some cyanobacterial isolates recovered from both reservoirs harboured mcy-A gene, showing the potential of microcystin production.
FMRI processing with AFNI: Some comments and corrections on “Exploring the Impact of Analysis Software on Task fMRI Results”

A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use “default” settings of each software’s pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each package’s defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a “one-size-fits-all” pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNI’s processing pipeline design tool at the individual level, afni_proc. py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.
Unpaired Point Cloud Completion on Real Scans using Adversarial Training

As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i.e., plausibly filling in regions there were missed in the raw scans. These methods, however, largely rely on supervision in the form of paired training data, i.e., partial scans with corresponding desired completed scans. While these methods have been successfully demonstrated on synthetic data, the approaches cannot be directly used on real scans in absence of suitable paired training data. We develop a first approach that works directly on input point clouds, does not require paired training data, and hence can directly be applied to real scans for scan completion. We evaluate the approach qualitatively on several real-world datasets (ScanNet, Matterport, KITTI), quantitatively on 3D-EPN shape completion benchmark dataset, and demonstrate realistic completions under varying levels of incompleteness.