Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular Healthcare Cost Savings Associated with Increased Whole Grains Consumption among Adults in the United States

Little is known about the potential health economic impact of increasing the proportion of total grains consumed as whole grains to align with Dietary Guidelines for Americans (DGA) recommendations. Health economic analysis estimating difference in costs developed using (1) relative risk (RR) estimates between whole grains consumption and outcomes of cardiovascular disease (CVD) and a selected component (coronary heart disease, CHD); (2) estimates of total and whole grains consumption among US adults; and (3) annual direct and indirect medical costs associated with CVD. Using reported RR estimates and assuming a linear relationship, risk reductions per serving of whole grains were calculated and cost savings were estimated from proportional reductions by health outcome. With a 4% reduction in CVD incidence per serving and a daily increase of 2.24 oz-eq of whole grains, one-year direct medical cost savings were estimated at US$21.9 billion (B) (range, US$5.5B to US$38.4B). With this same increase in whole grains and a 5% reduction in CHD incidence per serving, one-year direct medical cost savings were estimated at US$14.0B (US$8.4B to US$22.4B). A modest increase in whole grains of 0.25 oz-eq per day was associated with one-year CVD-related savings of $2.4B (US$0.6B to US$4.3B) and CHD-related savings of US$1.6B (US$0.9B to US$2.5B). Increasing whole grains consumption among US adults to align more closely with DGA recommendations has the potential for substantial healthcare cost savings.
Domain Adaptation by Joint Distribution Invariant Projections

Domain adaptation addresses the learning problem where the training data are sampled from a source joint distribution (source domain), while the test data are sampled from a different target joint distribution (target domain). Because of this joint distribution mismatch, a discriminative classifier naively trained on the source domain often generalizes poorly to the target domain. In this article, we therefore present a Joint Distribution Invariant Projections (JDIP) approach to solve this problem. The proposed approach exploits linear projections to directly match the source and target joint distributions under the $L^{2}$ -distance. Since the traditional kernel density estimators for distribution estimation tend to be less reliable as the dimensionality increases, we propose a least square method to estimate the $L^{2}$ -distance without the need to estimate the two joint distributions, leading to a quadratic problem with analytic solution. Furthermore, we introduce a kernel version of JDIP to account for inherent nonlinearity in the data. We show that the proposed learning problems can be naturally cast as optimization problems defined on the product of Riemannian manifolds. To be comprehensive, we also establish an error bound, theoretically explaining how our method works and contributes to reducing the target domain generalization error. Extensive empirical evidence demonstrates the benefits of our approach over state-of-the-art domain adaptation methods on several visual data sets.
Return of the AI: An Analysis of Legal Research on Artificial Intelligence using Topic Modeling

AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.
Escherichia coli enterohemorrágica O157: H7 em bovinos leiteiros saudáveis no Centro-Oeste do Brasil: ocorrência e caracterização molecular

Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 represents the major Shiga toxin-producing E. coli (STEC) strain related to large outbreaks and severe diseases such as hemorrhagic colitis (HC) and the potentially lethal hemolytic uremic syndrome (HUS). The aim of this study was to report the occurrence and molecular characterization of O157:H7 isolates obtained by rectal swab from 52 healthy dairy cattle belonging to 21 farms in Mid-West of Brazil. Detection of 16SrRNA, stx1, stx2, rfbO157, fliCh7, eae, ehxA, saa, cnf1, chuA, yjaA and TSPE4.C2 genes was performed by PCR. The isolates were further characterized by serotyping. Two hundred and sixty E. coli isolates were obtained, of which 126 were characterized as STEC. Two isolates from the same cow were identified as serotype O157:H7. Both isolates presented the stx2, eae, ehxA, saa and cnf1 virulence factor genes and the chuA gene in the phylogenetic classification (virulent group D), suggesting that they were clones. The prevalence of O157:H7 was found to be 1.92% (1/52 animals), demonstrating that healthy dairy cattle from farms in the Mid-West of Brazil are an important reservoir for highly pathogenic E. coli O157:H7.
Allozymic variation in Mediterranean hakeMerluccius merluccius(Gadidae)

Abstract Four hundred and twenty individual hake from 10 sample sites in the Mediterranean Sea were analysed in order to study genetic variability and identify genetic stock structure. Twenty loci were identified, four of which were polymorphic at the 95% level: ADH*, PGI‐1*, PGI‐2* and SOD‐1*. Average observed and expected heterozygosity were 0.084 and 0.090, respectively. PG1–1* deviated from Hardy‐Weinberg expectations due to an excess of heterozygotes and F‐statistic analysis showed also a significant excess of heterozygosity at SOD‐1*. FST was not significant for each locus except for PGI‐2*, where a single sample from the Channel of Sicily (C5) showed a different pattern in allelic frequencies, probably caused by a stochastic event. The samples analyzed seemed to be homogeneous, but future analyses may be necessary using other complementary molecular markers.
Simultaneous Detection of 10 Foodborne Pathogens using Capillary Electrophoresis-Based Single Strand Conformation Polymorphism

This report outlines the development of a rapid, simple, and sensitive detection system for pathogenic bacteria using a capillary electrophoresis-based, single strand conformation polymorphism (CE-SSCP) combined with PCR. We demonstrate that this method, used with primers targeting the V4 region of the16S rRNA gene, is capable of the simultaneous detection of 10 microbes that could be associated with foodborne illness, caused by animal-derived foods: Salmonella enterica, Listeria monocytogenes, Escherichia coli O157:H7, Campylobacter jejuni, Staphylococcus aureus, Bacillus cereus, Clostridium perfringens, Yersinia enterocolitica, Vibrio parahaemolyticus, and Enterobacter sakazakii. The traditional detection techniques are time-consuming and labor-intensive, due to the necessary task of separate cultivation of each target species. As such, the CE-SSCP-PCR method, that we have developed, has the potential to diagnose pathogens rapidly, unlike the traditional technique, in order to prevent foodborne illness in a much more efficient manner.
Incipient Speciation and Additional Diversity within the Simulium Arcticum Complex of Black Flies (Diptera: Simuliidae)

Abstract I analyzed larval polytene chromosomes to determine if the hypothesized remnants of an incipient speciation event in the Simulium arcticum complex of black flies previously discovered at the Coeur d'Alene River, Idaho existed elsewhere. This population not only had Y chromosomes that defined two members of the S. arcticum complex: S. arcticum sensu stricto and S. saxosum, but also possessed combinations of sex chromosomes of the two that were in genetic equilibrium with respect to all sex chromosome types. The geographic overlap between S. saxosum to the west and S. arcticum s. s. to the east generally runs north and south of the Coeur d'Alene River. Accordingly, I made 37 additional collections in a north-south orientation during 2011–2013. Larvae from 10 of the 37 collections had sex chromosome types identical to those of the previously studied site at the Coeur d'Alene River, thus expanding the area of putative remnant populations. The St. Joe River not only had Y chromosome combinations identical to those of larvae at the Coeur d'Alene but also had a cytotype new to science based on distinct sex chromosomes (X0YIIL-79) in males. These observations: (1) increase the known geographic area of presumed remnant populations of S. arcticum s. s. , of S. saxosum and their combinational types to about 3500 km2; (2) suggest that mating trials still occur; and (3) describe the structure and frequency of inversions in two new cytotypes of the S. arcticum complex.
Functional Network Mapping Reveals State-Dependent Response to IGF1 Treatment in Rett Syndrome

Rett Syndrome (RTT) is a neurodevelopmental disorder associated with mutations in the gene MeCP2, which is involved in the development and function of cortical networks. The clinical presentation of RTT is generally severe and includes developmental regression and marked neurologic impairment. Insulin-Like growth factor 1 (IGF1) ameliorates RTT-relevant phenotypes in animal models and improves some clinical manifestations in early human trials. However, it remains unclear whether IGF1 treatment has an impact on cortical electrophysiology in line with MeCP2’s role in network formation, and whether these electrophysiological changes are related to clinical response. We performed clinical assessments and resting-state electroencephalogram (EEG) recordings in eighteen patients with classic RTT, nine of whom were treated with IGF1. Among the treated patients, we distinguished those who showed improvements after treatment (responders) from those who did not show any changes (nonresponders). Clinical assessments were carried out for all individuals with RTT at baseline and 12 months after treatment. Network measures were derived using statistical modelling techniques based on interelectrode coherence measures. We found significant interaction between treatment groups and timepoints, indicating an effect of IGF1 on network measures. We also found a significant effect of responder status and timepoint, indicating that these changes in network measures are associated with clinical response to treatment. Further, we found baseline variability in network characteristics, and a machine learning model using these measures applied to pretreatment data predicted treatment response with 100% accuracy (100% sensitivity and 100% specificity) in this small patient group. These results highlight the importance of network pathology in RTT, as well as providing preliminary evidence for the potential of network measures as tools for the characterisation of disease subtypes and as biomarkers for clinical trials.
Should Networks Supplant Tree Building?

Recent studies suggested that network methods should supplant tree building as the basis of genealogical analysis. This proposition is based upon two arguments. First is the observation that bacterial and archaeal lineages experience processes oppositional to bifurcation and hence the representation of the evolutionary process in a tree like structure is illogical. Second is the argument tree building approaches are circular—you ask for a tree and you get one, which pins a verificationist label on tree building that, if correct, should be the end of phylogenetic analysis as we currently know it. In this review, we examine these questions and suggest that rumors of the death of the bacterial tree of life are exaggerated at best.
Effects of a chemical additive on the fermentation, microbial communities, and aerobic stability of corn silage with or without air stress during storage.

We evaluated the effects of a chemical additive on the microbial communities, fermentation profile, and aerobic stability of whole-plant corn silage with or without air stress during storage. Whole-plant corn was either untreated or treated with a chemical additive containing sodium benzoate, potassium sorbate, and sodium nitrite at 2 or 3 L/t of fresh forage weight. Ten individually treated and replicated silos (7.5 L) were made for each treatment. Half of the silos remained sealed throughout a 63-d storage period, and the other half was subjected to air stress for 2 h/wk. The composition of the bacterial and fungal communities of fresh forage and silages untreated or treated with 2 L/t of fresh forage weight was analyzed by Illumina Miseq sequencing. Treated silage had greater (P < 0.05) aerobic stability than untreated, even when subjected to air stress during storage, but the numbers of yeasts culturable on selective agar were not affected. However, the additive reduced the relative abundance of the lactating-assimilating yeast Candida tropicalis (P < 0.01). In air stressed silages, untreated silage had a greater (P < 0.05) relative abundance of Pichia kudriavzevii (also a lactate assimilator) than treated silage, whereas treated silage was dominated by C. humilis, which is usually unable to assimilate lactate or assimilates it slowly. The additive improved aerobic stability by specifically preventing the dominance of yeast species that can consume lactate and initiate aerobic spoilage. To the best of our knowledge, this is the first work that identifies the specific action of this additive on shifting the microbial communities in corn silage.
Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose‐Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X‐Ray, and Point‐of‐Care Ultrasound Images for Quality

Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color‐ and scene‐rich standard training images compare quantitatively to medical images. We sought to quantitatively compare ImageNet images to point‐of‐care ultrasound (POCUS), computed tomographic (CT), magnetic resonance (MR), and chest x‐ray (CXR) images.
FUNCTIONAL CHARACTERIZATION OF PROTEIN STABILIZED EMULSIONS: EFFECT OF PROCESSING

In order to evaluate the emulsifying characteristics of a protein the effect of processing of a protein stabilized soybean oil‐in‐water emulsion on the creaming stability of the emulsion was investigated. Three protein systems were used, namely soybean protein isolate, sodium caseinate and whey protein concentrate (WPC). The effects of four different types of emulsifying equipment, an ultra‐turrax, a Sorvall omnimixer, a valve homogenizer and an ultrasonic apparatus were studied. It was found that the type of emulsifying apparatus, the emulsifying time and intensity had a marked influence on the properties of the protein stabilized emulsions. It was demonstrated for all the types of apparatus and proteins used, that by increasing emulsifying intensity and time better emulsions, when characterized by the creaming method, are obtained up to a certain limit whereupon there is nothing to be gained by an additional increase of these factors. The necessary emulsifying intensity and time to obtain a certain stability range differed considerably according to the various proteins investigated as well as environmental conditions. This intensity‐time dependence may be considered as an emulsifying characteristic of the protein. (Less)
Event Detection and Time Series Alignment to Improve Stock Market Forecasting

Buying commodities is a critical issue for multiple industries because the variations of stock prices are induced not only by multiple economic parameters but also by external events. Raw material buyers must keep track of information in numerous fields, which constitutes a major challenge considering the exponential growth of online data. To tackle this issue, we propose an event detection approach in order to assist them in their anticipation process. Indeed, a lot of contextual information is contained in text and exploiting it can allow one to improve its anticipation ability. Thus, we develop a framework of event detection and qualification, then we quantify the impact of these events on stock market to help buyers in their anticipation process. In this paper, we will first introduce our context, then explain the scope of our work and our goals. After detailing the related work, we will present our proposition, conclude and propose some future work possibilities.
Amicus cum Laude: Ethnodramatic Inquiry Regarding Sharing Mental Health in College

In this manuscript, I utilize an ethnodramatic methodology in reanalyzing two data sets about college friends disclosing and receiving mental health-related information. After describing ethnodrama and how this methodology applies to mental health–related inquiry, I detail my process of creating an ethnodrama from two extant data sets. The result is an ethnodrama called Amicus cum Laude: Becoming a Friend with Honor for Mental Illness, a one-act play about how friends discuss mental health issues with one another. After providing the ethnodrama, I offer recommendations for taking the ethnodrama from page to stage while reflecting on and critiquing the final product.
Effects of wind-driven spatial structure and environmental heterogeneity on high-altitude wetland macroinvertebrate assemblages with contrasting dispersal modes

SUMMARY 1. Dispersal is a major determinant of connectivity between communities that may modulate theimportance of environmental and spatial processes on taxonomic composition. While wind is likelyto inﬂuence transport distance, rate and direction for numerous species, its effects on communitycomposition remain poorly understood.2. Using eigenvector-based spatial analyses, we investigated the inﬂuence of wind ﬂows, estimatedby mesoscale models, on the spatial structure of benthic macroinvertebrate assemblages with con-trasting ﬂying abilities in high Andean wetlands (26–32 S). We further quantiﬁed the relative impor-tance of local and regional processes through a variation partitioning approach.3. The inﬂuence of environmental heterogeneity was prevalent and of relatively similar amplitude inboth ﬂying and non-ﬂying assemblages. Signiﬁcant spatial structure more strongly driven by windﬂows than by Euclidean schemes was detected for both assemblages. As expected, the highest levelsof spatial structure were observed for the ﬂying macroinvertebrate assemblage and they occurred ata much larger spatial scale in this group, suggesting a greater dispersal aptitude of the ﬂying taxa.4. Our results show that environmental effects are predominant in shaping the spatial structure ofmacroinvertebrate communities in high Andean wetlands, as generally found in other systems. Theyalso demonstrate the signiﬁcance of wind ﬂows in regulating high-altitude wetland macroinverte-brate communities and illustrate the importance of considering adequate spatial models and biologi-cal characteristics of species to advance our understanding of community patterns.Keywords: benthic macroinvertebrates, dispersal, eigenvector maps, wetlands, wind
Construction of a bioinsecticidal rhizosphere isolate of Pseudomonas fluorescens

Abstract The cryIVB gene from Bacillus thuringiensis morrisoni PG-14 was cloned and expressed in Escherichia coli . A gene cassette was constructed that placed the gene under the control of the tac promotor. Three Pseudomonas ‘suicide’ vectors were made by cloning chromosomal DNA fragments from the root-colonizing Pseudomonas fluorescens strain P1 into plasmid pSUP202. The kanamycin resistance gene nptII and the cryIVB gene cassette were cloned within the Pseudomonas sequences. These constructs were introduced into the root-colonizing strain Pseudomonas fluorescens P1. Southern blot hybridizations demonstrated that the nptII and cryIVB genes were integrated into the chromosome whereas vector sequences were not. Expression of the cryIVB protein by transgenic Pseudomonas cells was demonstrated by Western blot analysis. Cell cultures of the transformed P. fluorescens were found to be toxic towards larvae of the malaria mosquito Anopheles stephensi and to leatherjackets, the larvae of Tipula oleracea .
Reasoning on Company Takeovers during the COVID-19 Crisis with Knowledge Graphs

When some country takes a disproportionate hit by a large-scale turmoil—just like Italy did during the COVID-19 pandemics—the share prices of its companies plunge Suddenly, it becomes feasible to attempt foreign takeovers of national assets, including those of strategic interest To avert this risk, the Government can veto transactions by summoning the so-called “Golden Powers” Or, it can work to proactively identify structural weaknesses in the control or shareholding chains of key companies, in order to reinforce them without resorting to special powers Sometimes, vulnerabilities and attacks hide in plain sight due to how complex and intertwined the network of mutual company shareholding is In this work, we show how to leverage Knowledge Graphs (KGs) as a representation and reasoning framework to analyze both reactive and proactive measures against takeover attempts, however intricate the setting where they take place We formally characterize a set of reasoning tasks that define when and if to employ Golden Powers, plus others that aim at pinpointing companies prone to attacks These criteria are exercised on the real network of all Italian companies, built for the occasion A rich set of experiments is provided, including on several large synthetic instances, to prove the robustness of our method Copyright © 2020 for this paper by its authors