Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Characterization of Salmonella Minnesota Clonal Lineages Associated with Poultry Production in Brazil

Simple Summary Salmonella is a leading cause of foodborne illnesses and a global public health concern. Salmonella enterica serotype Minnesota has been increasingly isolated from Brazilian poultry farms. The present study investigated the phylogenetic relationships, evolution and genetic characteristics of S. Minnesota isolates from Brazilian poultry farms. The results demonstrated two main S. Minnesota lineages in the poultry production chain from Brazil, both presenting genes for antibiotic resistance and virulence. The present study also provides insights into the temporal evolution, population structure, and genetic characteristics of the two S. Minnesota lineages disseminated in Brazilian poultry farms. Abstract Salmonella serotype Minnesota has been increasingly detected in Brazilian poultry farms and food products (chicken meat, eggs) in recent years. In addition, S. Minnesota isolates from poultry are generally resistant to several antibiotics and persistent in farm environments. The present study aimed to assess phylogenomic diversity of S. Minnesota isolates from the poultry production chain in Brazil. In total, 107 worldwide S. Minnesota whole genomes (including 12 from Brazil) were analyzed using a comparative approach. Phylogenetic analysis demonstrated two clades more related to poultry production in Brazil: S. Minnesota poultry lineages I and II (SM-PLI and SM-PLII). Phylodynamic analysis demonstrated that SM-PLI had a common ancestor in 1915, while SM–PLII originated circa 1971. SM-PLII encompassed a higher number of isolates and presented a recent increase in effective population size (mainly from 2009 to 2012). Plasmids IncA/C2 and ColRNA, antimicrobial resistance genes (aph(3′)-Ia, blaCMY-2, qnrB19, sul2, and tet(A)) and mainly a virulence genetic cluster (including the yersiniabactin operon) were detected in isolates from SM-PLI and/or SM-PLII. This study demonstrates the dissemination of two distinct S. Minnesota lineages with high resistance to antibiotics and important virulence genetic clusters in Brazilian poultry farms.
The Internet of Things: Impact and Implications for Health Care Delivery

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.
Effect of Vitamin D Supplementation, Omega-3 Fatty Acid Supplementation, or a Strength-Training Exercise Program on Clinical Outcomes in Older Adults: The DO-HEALTH Randomized Clinical Trial.

Importance The benefits of vitamin D, omega-3 fatty acids, and exercise in disease prevention remain unclear. Objective To test whether vitamin D, omega-3s, and a strength-training exercise program, alone or in combination, improved 6 health outcomes among older adults. Design, Setting, and Participants Double-blind, placebo-controlled, 2 × 2 × 2 factorial randomized clinical trial among 2157 adults aged 70 years or older who had no major health events in the 5 years prior to enrollment and had sufficient mobility and good cognitive status. Patients were recruited between December 2012 and November 2014, and final follow-up was in November 2017. Interventions Participants were randomized to 3 years of intervention in 1 of the following 8 groups: 2000 IU/d of vitamin D3, 1 g/d of omega-3s, and a strength-training exercise program (n = 264); vitamin D3 and omega-3s (n = 265); vitamin D3 and exercise (n = 275); vitamin D3 alone (n = 272); omega-3s and exercise (n = 275); omega-3s alone (n = 269); exercise alone (n = 267); or placebo (n = 270). Main Outcomes and Measures The 6 primary outcomes were change in systolic and diastolic blood pressure (BP), Short Physical Performance Battery (SPPB), Montreal Cognitive Assessment (MoCA), and incidence rates (IRs) of nonvertebral fractures and infections over 3 years. Based on multiple comparisons of 6 primary end points, 99% confidence intervals are presented and P < . 01 was required for statistical significance. Results Among 2157 randomized participants (mean age, 74.9 years; 61.7% women), 1900 (88%) completed the study. Median follow-up was 2.99 years. Overall, there were no statistically significant benefits of any intervention individually or in combination for the 6 end points at 3 years. For instance, the differences in mean change in systolic BP with vitamin D vs no vitamin D and with omega-3s vs no omega-3s were both -0.8 (99% CI, -2.1 to 0.5) mm Hg, with P < . 13 and P < . 11, respectively; the difference in mean change in diastolic BP with omega-3s vs no omega-3s was -0.5 (99% CI, -1.2 to 0.2) mm Hg; P = . 06); and the difference in mean change in IR of infections with omega-3s vs no omega-3s was -0.13 (99% CI, -0.23 to -0.03), with an IR ratio of 0.89 (99% CI, 0.78-1.01; P = .02). No effects were found on the outcomes of SPPB, MoCA, and incidence of nonvertebral fractures). A total of 25 deaths were reported, with similar numbers in all treatment groups. Conclusions and Relevance Among adults without major comorbidities aged 70 years or older, treatment with vitamin D3, omega-3s, or a strength-training exercise program did not result in statistically significant differences in improvement in systolic or diastolic blood pressure, nonvertebral fractures, physical performance, infection rates, or cognitive function. These findings do not support the effectiveness of these 3 interventions for these clinical outcomes. Trial Registration ClinicalTrials.gov Identifier: NCT01745263.
Nucleic Acid Sensors and Antiviral Immunity

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Conversational Learning

Although machine learning has been highly successful in recent years, this success has been based on algorithms that exhibit only one of the multiple learning paradigms used by humans: learning statistically from many examples. Here we consider a second learning paradigm widely exhibited by humans, but rarely by computers: learning from instruction involving natural language conversations and demonstrations. We argue that this second paradigm – conversational machine learning – is ripe for rapid research progress, and that it holds the potential to make it possible for every user of a computer or mobile device to become a programmer. We deﬁne the problem of conversational learning, survey relevant literature, and provide as a case study the Learning from Instruction Agent (LIA) project. Finally we lay out a set of future research directions involving grounded conversational instruction that appear to be key to progress in this area.
Use of Two-Stage or Double Sampling in Final Status Decommissioning Surveys

When might it be desirable to allow a licensee to sample a survey unit a second time to determine compliance? In the Statistical literature this is called either two-stage sampling or double sampling. Resampling is something else altogether. The terms “double sampling” and “two-stage sampling” seem to appear interchangeably in the literature. More recently, the later seems to have gained favor, so we will use two-stage sampling in this paper when referring to survey designs specifically intended to be conducted in two stages. We will use the term double sampling to refer to the case when the survey design is a one stage design, but allowance is made for a second set of samples to be taken if the retrospective power of the test using the first set of samples does not meet the design objectives. Such allowance, if given, should be specifically mentioned in preparing the DQOs and in advance of any sampling and analysis. During the DQO process, double sampling could be considered as an option in setting the Type I error rates. The reasoning behind this is discussed in the next section.
Deep Learning-based Network Attack Detection Using Convolutional and Recurrent Neural Networks

With the advancement of networks, attacks on networks have also increased significantly. The importance of maintaining sensitive information being stored and sent through the Internet has led to the urgent need to safeguard the networks. It is imperative to detect any abnormal behavior. Several methods have been employed either to prevent or at least to detect various attacks. In this research, deep learning-based approaches such as auto-encoders, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are used in order to create an efficient Intrusion Detection System (IDS). The proposed approaches are aimed at distinguishing the normal behavior on the network from an anomaly-based one. This research focuses on the evaluation and comparison of different deep neural networks. Based on the experimental results, RNN outperforms all other approaches with an accuracy of 0.99 for training and 0.94 for the testing phase.
Learning generic semantic roles

Generic event detection is one of the main steps that natural language analysis still has to take to come to a stage in which computers really start to understand what a text is about. One approach to this task analyzes individual clauses into patterns of semantic roles that represent fundamental functional categories that reflect how humans conceptualize reality. We have designed a method for learning these domainindependent semantic roles from an annotated corpus by combining notions of frame theory and of systemic-functional grammar and simplifying the process to a pattern classification process. We have conducted a number of experiments which indicate that a regular mapping exists between superficial lexical, morphological and positional features in the surface structure of a text and its functional-semantic deep structure and that we can therefore semantically classify phrases using only superficial language analysis.
AUTOMATIC IMAGE GRADING FOR DIABETIC RETINOPATHY(DR) AND DIABETIC MACULAR EDEMA(DME)

Diabetes mellitus is one of leading cause of vision loss globally. Diabates increase the risk of a range of eye diseases, but the main cause of blindness associated with diabates is diabetic retinopathy(DR). Diabetic Macular Edema(DME) is a complication associated with DR of which retinal thickening or accumulation of ﬂuid can occur at any stage. Early detection for DR and DME is important to save the vision of diabetes patients. In this paper, we propose an ensemble pi-pline which combines convolutional neural network and feature blending for automatically predicting the DR and DME severity scores. It outperforms the usual end-to-end convolutional neural network method.
TURSIOPS TRUNCATUS POPULATION AT LAMPEDUSA ISLAND ( ITALY ) : PRELIMINARY RESULTS

INTRODUCTION. The most comprehensive studies of free-ranging dolphins have focused on accessible coastal populations; several, have examined the social system, ranges, behavior, and other aspects of the life history of bottlenose dolphin (Tursiops truncatus) (Würsig, 1984; Scott et al., 1990; Wells, 1991; Bearzi et al., 1997). Some specific surveys on this species have been undertaken in the Mediterranean Sea; however, the dolphins’ occurrence along Lampedusa Island coasts (Pelagie’s Archipelago, Sicily) had been still uninvestigated. In the last few years, the presence of an inshore population of bottlenose dolphin along the coasts of Lampedusa Island was first noted and reported by local fishermen. Due to their coastal nature, bottlenose dolphins have frequent contact with human activities, and many interactions with fishery operations have been described in this area (Mazzola et al., 1996). Tursiops truncatus commonly interact with fisheries around the world (reviews in Shane et al., 1986; Fertl and Leatherwood, 1998). However, a limited number of these studies has elaborated on the nature of the association, and few attempts to quantify the effects of this relationship on feeding ecology, sociality, inter-specific interactions, or movements were reported. The primary goal of this study was to collect systematic data on the habitat-use patterns of bottlenose dolphins along Lampedusa Island in order to provide a better understanding of their local ecology and behaviour. In addition, the purpose of these observations was also to examine animal association and/or feeding during trawling fishing activities and related behaviours.
Critical factors of digital transformation success: A literature review

Over the last two decades, the digital era and the fourth digital revolution have led to a rapid increase in the influence that digital technologies have on all aspects of life. Rapid development and diffusion of technological innovations altered the business environment, additionally accelerating the dynamics of market changes through permeating globalisation and ever stronger competition. In such digital ecosystem, product life cycles became extremely short, while consumers became more demanding and their behaviour more volatile. Compelled by such business reality, organisations are forced to continuously adapt and digitally transform their business. The aim of this paper is to identify and systemise critical success factors of attaining purposeful digital transformation by means of a literature review. The paper details the results of the conducted theoretical research, which may serve organisations as guidelines on their challenging paths to digital transformation and assist them in avoiding potential traps and mitigating risks associated with inadequate application of digital technology.
Accurate Extraction of Gate Capacitances in Leaky MOS Systems using Modified 3-element circuit Model Combining the Multi-Frequency Capacitance-Voltage Method

A multi-frequency capacitance-voltage (C-V) method with a modified three-element circuit model is reported for accurate extraction of the gate capacitance in leaky Metal-Oxide-Semiconductor (MOS) systems with a high shunt leakage current through the oxide and parasitic series resistance. Both the voltage-dependent nonlinearity of the gate leakage and the frequency-dependent dispersion with leaky path and series resistance are considered with an empirical resistance model (RP(VG,f)=RPo(VG)·(fo/f)) in the multi-frequency C-V characterization. This technique was applied to a hot-carrier stressed leaky n-channel MOS system with tox=2.7 nm and A=400×400 μm resulting in frequency-independent gate capacitance for the MOS system.
IAA/GA3 quantitative ratio of some sunflower genotypes representing CMS-Rf system

Quantitative ratio of IAA/GA3 (indole-3-acetic acid/gibberellic acid) was studied during the growth and development of different sunflower genotypes that represent a CMS-Rf system. It has been shown that IAA/GA3 ratio is variable and depends on ontogenesis stages, organs and genotype. Thus, IAA/GA3 ratio had maximal values for male sterile line and minimal ratio for restorer line RW637Rf. The highest IAA/GA3 ratio was registered in cotyledon leaves and subsequently decreased during ontogenesis whereby the hormonal ratio in reproductive stages was higher in inflorescences than in leaves. Gibberellic acid exogenously applied increased the hormonal ratio in the male-fertile line. The specificity of IAA/GA3 balance in male sterility-fertility phenotype expression and in GA-induced pollen sterility is discussed.
Applying 3D Convolutional Neural Networks to Human Psychophysics

Despite the radical simplicity of convolutional neural networks some researchers have found direct correlates between network layer properties and actual neuron responses. In model systems such as the macaque [1] there are clear analogies between the early visual cortex layers (V1-V4) and the properties of a trained convolutional network. We plan to explore this interesting dynamic in the context of motion. Our goal is to modernize an older model of the visual stream dedicated to motion [2]. The original model explicitly coded the features selected for at each layer, based on the known anatomical properties of macaque V1 and MT. In contrast, we plan to build a generic convolutional neural net architecture, which will be trained to discriminate examples of motion. Because our architecture is more generic it will fail to precisely model the known anatomy of V1. This leaves open the possibility that during training the network will ‘learn’ similar features, such as simple and complex cell receptive fields. Our goal in building this model is to develop a model system within which we can test other interesting questions, such as whether a convolutional architecture will develop similar behavioral asymmetries to the actual human (and monkey) visual systems.
The Impact of Brand Equity on Advertising Effectiveness ( Samsung and Snowa brand names as a case study)

Companies spendinga considerable amount of moneyon advertisement tointroduce their products to consumers in order to attract their attention and persuade them to prefer their brand.But can these expenses lead to a favorable result? It is assumed that brand reputation affect advertising effectiveness positively.The purpose of this studyis to establish a theoretical and empirical basis that shows the impact of brand equity on advertising effectiveness.The citizen of Isfahan city who have watched the target advertisements were the population of the study.Random sampling method was used for collecting data. In order to analysis the relationships between brand equity dimensions(perceived quality, brand loyalty, brand awareness and brand association)and advertising effectiveness, structural equation modeling (SME) is used. The results indicate that brand equity has a positive effect on advertising effectiveness.
Dosimetric study of interstitial brachytherapy for gynecological malignancies

Annual incidence of Carcinoma Cervix is around 570,000 worldwide. Carcinoma Cervix is fourth leading cause for cancer death in women worldwide. Global annual incidence of Carcinoma Vagina is around 18,000. In cervical cancer patients clinical outcome has improved with intracavitary Brachytherapy. Most common site treated with Brachytherapy is Carcinoma Cervix as per the survey done by American Brachytherapy Society (ABS). In management of Carcinoma Cervix Brachytherapy plays a crucial role. Intracavitary Brachytherapy (ICBT) is the most commonly used technique. In cases where ICBT is not technically feasible or it may result in suboptimal dose distribution, Interstitial Brachytherapy (ISBT) is recommended. The most common technique for ISBT is perineal template based implant procedure.