Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Distinguish Patients with pSS among Individuals with Dryness without Invasive Diagnostic Studies

In the course of pSS, inflammatory cell infiltration consists mainly of lymphocytes infiltrating exocrine glands, which leads to their impaired function. The characteristic feature is generalized dryness. The aim of this study was to attempt to answer the question whether it is possible to distinguish between patients with pSS and individuals with dryness caused by other pathologies without applying invasive studies. The study included 68 patients with pSS and 43 healthy controls with dryness. FS ≥ 1 was observed in 90% of patients with pSS (with or without dryness), and only in 23% of the control group (only with xerostomia). In the pSS group, anaemia (p = 0.0085), lymphocytopenia (p = 0.0006), elevated ERS (p = 0.001), higher RF titer, and ANA antibodies were noted. Configuration of anti-SSA + SSB + Ro52 antibodies was characteristic for the pSS group. Considering the clinical symptoms, statistically significant differences were noted between pSS patients and the control group in frequency (p = 0.02) and severity (p = 0.042) of fatigue, lymphadenopathy, major salivary gland involvement, and photosensitivity to UV light. In conclusion, invasive methods are pivotal in pSS diagnosis in this salivary gland biopsy. Chronic fatigue syndrome is more common in pSS patients and can be subjective distinguishing factor in the group of people with dryness.
Minimal residual disease in chronic lymphocytic leukemia: A consensus paper that presents the clinical impact of the presently available laboratory approaches

Abstract Chronic lymphocytic leukemia (CLL) is a malignancy defined by the accumulation of mature lymphocytes in the lymphoid tissues, bone marrow, and blood. Therapy for CLL is guided according to the Rai and Binet staging systems. Nevertheless, state-of-the-art protocols in disease monitoring, diagnostics, and prognostics for CLL are based on the assessment of minimal residual disease (MRD). MRD is internationally considered to be the level of disease that can be detected by sensitive techniques and represents incomplete treatment and a probability of disease relapse. MRD detection has been continuously improved by the quick development of both flow cytometry and molecular biology technology, as well as by next-generation sequencing. Considering that MRD detection is moving more and more from research to clinical practice, where it can be an independent prognostic marker, in this paper, we present the methodologies by which MRD is evaluated, from translational research to clinical practice.
Effect of Gamma Radiation on Growth and Mycotoxin Production of Alternaria

The ubiquitous genus Alternaria contains many species that are able to invade cereals, oleaginous plants and other crops. Alternaria alternata is considered one of the most important species and can produce several mycotoxins under favourable conditions of temperature and humidity, including the economically important toxins: alternariol (AOH) and alternariol monomethyl ether (AME). The aim of this study was to evaluate the radio-sensitivity of Alternaria alternata spores through different gamma radiation doses. A. alternata growth and the production of AOH and AME were then analyzed. After fungal irradiation with 2 kGy, 5 kGy and 7 kGy, the spores were suspended with sterile distilled water followed by inoculation on wheat grains. The count of the colony-forming units per gram (CFU/g) was performed using Dichloran Rose Bengal Chloramphenicol (DRBC) and Dichloran Chloramphenicol Agar Malt Extract (DCMA); AOH and AME were analyzed using Liquid Chromatography coupled with Mass Spectrometer (LC-MS). Results showed that fungal growth and toxin production increased with the increase of radiation dosage. The implications of these findings in relation to the resistance of A. alternata spores to gamma irradiation are discussed.
Uncovering Unknown System Behaviors in Uncertain Networks with Model and Search-Based Testing

Modern software systems rely on information networks for communication. Such information networks are inherently unpredictable and unreliable. Consequently, software systems behave in an unstipulated manner in uncertain network conditions. Discovering unknown behaviors of these software systems in uncertain network conditions is essential to ensure their correct behaviors. Such discovery requires the development of systematic and automated methods. We propose an online and iterative model-based testing approach to evolve test models with search algorithms. Our ultimate aim is to discover unknown expected behaviors that can only be observed in uncertain network conditions. Also, we have implemented an adaptive search-based test case generation strategy to generate test cases that are executed on the system under test. We evaluated our approach with an open source video conference application—Jitsi with three search algorithms in comparison with random search. Results show that our approach is efficient in discovering unknown system behaviors. In particular, (1+1) Evolutionary Algorithm outperformed the other algorithms.
Association of Fast‐Food and Full‐Service Restaurant Densities With Mortality From Cardiovascular Disease and Stroke, and the Prevalence of Diabetes Mellitus

Background We explored whether higher densities of fast‐food restaurants (FFRs) and full‐service restaurants are associated with mortality from cardiovascular disease (CVD) and stroke and the prevalence of type 2 diabetes mellitus (T2D) across the mainland United States. Methods and Results In this cross‐sectional study county‐level data for CVD and stroke mortality, and prevalence of T2D, were combined with per capita densities of FFRs and full‐service restaurants and analyzed using regression. Mortality and diabetes mellitus prevalence were corrected for poverty, ethnicity, education, physical inactivity, and smoking. After adjustment, FFR density was positively associated with CVD (β=1.104, R 2=2.3%), stroke (β=0.841, R 2=1.4%), and T2D (β=0.578, R 2=0.6%) and full‐service restaurant density was positively associated with CVD mortality (β=0.19, R 2=0.1%) and negatively related to T2D prevalence (β=−0.25, R 2=0.3%). In a multiple regression analysis (FFRs and full‐service restaurants together in same model), only the densities of FFRs were significant (and positive). If we assume these relationships are causal, an impact analysis suggested that opening 10 new FFRs in a county would lead to 1 extra death from CVD every 42 years and 1 extra death from stroke every 55 years. Repeated nationally across all counties, that would be an extra 748 CVD deaths and 567 stroke deaths (and 390 new cases of T2D) over the next 10 years. Conclusions These results suggest that an increased density of FFRs is associated with increased risk of death from CVD and stroke and increased T2D prevalence, but the maximal impact (assuming the correlations reflect causality) of each individual FFR is small. Clinical Trial Registration URL: http://www.clinicaltrials. gov. Unique identifier: NCT03243253.
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network

With the development of recent innovative applications (e.g., augment reality, self-driving, and various cognitive applications), more and more computation-intensive and data-intensive tasks are delay-sensitive. Mobile edge computing in ultra-dense network is expected as an effective solution for meeting the low latency demand. However, the distributed computing resource in edge cloud and energy dynamics in the battery of mobile device makes it challenging to offload tasks for users. In this paper, leveraging the idea of software defined network, we investigate the task offloading problem in ultra-dense network aiming to minimize the delay while saving the battery life of user’s equipment. Specifically, we formulate the task offloading problem as a mixed integer non-linear program which is NP-hard. In order to solve it, we transform this optimization problem into two sub-problems, i.e., task placement sub-problem and resource allocation sub-problem. Based on the solution of the two sub-problems, we propose an efficient offloading scheme. Simulation results prove that the proposed scheme can reduce 20% of the task duration with 30% energy saving, compared with random and uniform task offloading schemes.
Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network

Mammography is the common modality used for screening and early detection of breast cancer. The emergence of machine learning, particularly deep learning methods, aims to assist radiologists to reach higher sensitivity and speciﬁcity. Yet, typical supervised machine learning methods demand the radiological images to have ﬁndings annotated within the image. This is a tedious task, which is often out of reach due to the high cost and unavailability of expert radiologists. We describe a computer-aided detection and diagnosis system for weakly supervised learning, where the mammogram (MG) images are tagged only on a global level, without local annotations. Our work addresses the problem of MG classiﬁcation and detection of abnormal ﬁndings through a novel deep learning framework built on the multiple instance learning (MIL) paradigm. Our proposed method processes the MG image utilizing the full resolution, with a deep MIL convolutional neural network. This approach allows us to classify the whole MG according to a severity score and localize the source of abnormality in full resolution, while trained on a weakly labeled data set. The key hallmark of our approach is automatic discovery of the discriminating patches in the mammograms using MIL. We validate the proposed method on two mammogram data sets, a large multi-center MG cohort and the publicly available INbreast, in two different scenarios. We present promising results in classiﬁcation and detection, comparable to a recent supervised method that was trained on fully annotated data set. As the volume and complexity of data in healthcare continues to increase, such an approach may have a profound impact on patient care in many applications.
Prevalence of Tick-borne Pathogens in Co-grazed Dairy Bovines Differs by Region and Host-type in Tamil Nadu, India

In India, disease of cattle caused by tick-borne pathogens (TBP) constrains the rearing of high yielding Bos taurus dairy breeds that often develop clinical, fatal disease. Bos indicus and bufflao show resistance to TBP infection, and this has led to a policy of rearing crossbred indicus-taurus cattle. On dairy farms, co-grazing more resistant animals alongside crossbreeds could contribute positively (by removing potentially infective ticks) or negatively (by acting as a reservoir for infection). We investigated epidemiological factors that contribute to the prevalence of five tick-borne pathogens with links to milk-yield in co-grazed host types in dairy farms of two regions of Tamil Nadu, India; a region of high dairy production importance. A high prevalence of T. annulata and Anaplasma spp. was detected, but with lower prevalence in the Cauvery Delta than in the Northwestern zone. A strict host-type association with prevalence was observed: buffaloes had lower prevalence of TBP than cattle; and native breeds had a lower prevalence of Anaplasma spp. than crossbreeds. The results indicate that while susceptibility to becoming a carrier animal for TBP depends on exposure to ticks; aspects of resistance are determined by host type independent of tick exposure. There was no clear evidence that co-grazing, with more resistant host types, will provide a positive contribution (protective effect) to crossbreeds with greater milk productivity.
Growing Season Stem Water Status Assessment of Qinghai Spruce through the Sap Flow and Stem Radial Variations in the Qilian Mountains of China

Global climate change is likely to change precipitation patterns with consequences for tree water use and growth in semi-arid areas. However, little is known about the effects of variability in precipitation on growth- and water-related physiological processes of native trees in dry areas of northwestern China. In this study, sap flow and stem radial variability in four Qinghai spruce trees (Picea crassifolia) were monitored in the Qilian Mountains, China. Tree water deficit (ΔW) and basal area increment (BAI) were calculated using stem radial variation; water-use efficiency (WUE) was then estimated as the ratio of BAI and sap flow (Jt). The results showed that sap flow density (Js) increased logarithmically with increasing ΔW when ΔW < 50 μm, and then gradually stabilized. Multiple factor generalized additive models (GAM) showed that Js was closely related to all measured environmental variables except for daily mean temperature and relative air humidity. ΔW was related to the minimum daily temperature and soil water content. WUE exhibited higher values in early July. Low WUE was observed under conditions of prolonged dry weather, but it quickly increased during rainy days. WUE decreased after precipitation events due to high transpiration. We concluded that, in these semi-arid areas, precipitation is the most important controlling factor in tree growth and transpiration.
Multi-ethnic SULT1A1 copy number profiling with multiplex ligation-dependent probe amplification.

AIM To develop a SULT1A1 multiplex ligation-dependent probe amplification assay and to investigate multi-ethnic copy number variant frequencies. METHODS A novel multiplex ligation-dependent probe amplification assay was developed and tested on 472 African-American, Asian, Caucasian, Hispanic and Ashkenazi Jewish individuals. RESULTS The frequencies of atypical total copy number (i.e., greater or less than two) were 38.7% for Hispanics, 38.9% for Ashkenazi Jewish, 43.2% for Caucasians, 53.6% for Asians and 64.1% for African-Americans. Heterozygous SULT1A1 deletion carriers (slow sulfators) were most common among Caucasians (8.4%), whereas African-Americans had the highest frequencies of three or more copies (rapid sulfators; 60.9%). CONCLUSION Different ethnic and racial populations have varying degrees of SULT1A1-mediated sulfation activity, which warrants further research and that may have utility for drug response prediction among SULT1A1-metabolized medications.
Reinforced Continual Learning

Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.
Direct Control of Stem Cell Behavior Using Biomaterials and Genetic Factors

Stem cells have recently emerged as an important candidate for cell therapy. However, some major limitations still exist such as a small quantity of cell supply, senescence, and insufficient differentiation efficiency. Therefore, there is an unmet need to control stem cell behavior for better clinical performance. Since native microenvironment factors including stem cell niche, genetic factors, and growth factors direct stem cell fate cooperatively, user-specified in vitro settings are required to understand the regulatory roles and effects of each factor, thereby applying the factors for improved cell therapy. Among others, various types of biomaterials and transfection method have been employed as key tools for development of the in vitro settings. This review focuses on the current strategies to improve stemness maintenance, direct differentiation, and reprogramming using biomaterials and genetic factors without any aids from additional biochemicals and growth factors.
Quorum sensing in Enterococcus faecium, Enterococcus faecalis and Bacillus cereus strains isolated from ricotta processing

portuguesO fenomeno quorum sensing corresponde a um processo de comunicacao intra e interespecies microbianas e e mediado por sinais quimicos extracelulares, denominados moleculas sinalizadoras ou auto indutoras (AI). A molecula AI2 esta envolvida na comunicacao interespecies, denominada sistema “universal” de comunicacao. Este estudo teve como objetivo avaliar a capacidade de Enterococcus faecium, Enterococcus faecalis e Bacillus cereus isolados do processamento de ricota em produzir moleculas sinalizadoras de Quorum sensing (AI-2). Os isolados foram avaliados quanto a presenca do gene luxS utilizando a reacao em cadeia da polimerase (PCR). As moleculas sinalizadoras (AI-2) foram medidas em unidades relativas de luz (RLU) atraves de um luminometro. Um total de 74% dos isolados de E. faecium, 91% de E. faecalis e 95% de B. cereus foram positivos para o gene luxS. Alem disso, todos os isolados apresentaram capacidade de induzir o fenomeno de bioluminescencia em Vibrio harveyi BB170, indicando a presenca de auto indutores AI-2. Palavras-chave: luxS; AI-2; Vibrio harveyi; bioluminescencia; Enterococcus spp. ; Bacillus cereus EnglishThe quorum sensing phenomenon is a process of intra- and inter-species microbial communication involving the production and detection of extracellular signaling molecules. The autoinducer AI-2 has been proposed to serve as a ‘universal signal’ for interspecies communication. This study aimed to evaluate the capability of Enterococcus faecium, Enterococcus faecalis, and Bacillus cereus strains isolated from ricotta processing to produce quorum sensing signalling molecules (AI-2). The strains were evaluated for the presence of the luxS gene using the polymerase chain reaction. AI-2 quorum sensing signalling molecules were measured in relative light units (RLUs) using a luminometer. A total of 74% of E. faecium, 91% of E. faecalis, and 95% of B. cereus isolates were positive for luxS gene. In addition, the induced bioluminescence in Vibrio harveyi BB170 was observed in all strains, indicating the presence of the AI-2 autoinducer. Key words: luxS; AI-2; Vibrio harveyi; bioluminescence; Enterococcus spp. ; Bacillus cereus
Automatic human age estimation system using fusion of local and global features

Human beings can easily estimate the person's age from a facial image but this ability is not promising in machines. To address this problem, we propose a novel scheme of age estimation using fusion of local and global features. Local feature, Gabor filter is applied after detecting four parts (Forehead, left cheek, right cheek and mouth) around face and eye regions. Global feature, Histogram of Oriented Gradients (HOG) is applied on cropped face. Experimental results show that combination of local and global features outperforms many previous approaches.
Cardiopulmonary exercise testing reveals subclinical abnormalities in chronic kidney disease

Background Reductions in exercise capacity associated with exercise intolerance augment cardiovascular disease risk and predict mortality in chronic kidney disease. This study utilized cardiopulmonary exercise testing to (a) investigate mechanisms of exercise intolerance; (b) unmask subclinical abnormalities that may precede cardiovascular disease in chronic kidney disease. Design The design of this study was cross-sectional. Methods Cardiopulmonary exercise testing was carried out in 31 Stage 3–4 chronic kidney disease patients (60 ± 11 years; estimated glomerular filtration rate 43 ± 13 ml/min/1.73 m2) and 21 matched healthy individuals (healthy controls; 56 ± 5 years; estimated glomerular filtration rate>90 ml/min/1.73 m2) on a cycle ergometer with workload increased by 15 W every minute until volitional fatigue. Breath-by-breath respiratory gas analysis was performed with an automated gas analyzer and averaged over 10 s intervals. Results Peak oxygen uptake was reduced in chronic kidney disease compared to healthy controls (17.43 ± 1.03 vs 28 ± 2.05 ml/kg/min; p < 0.01), as was oxygen uptake at the ventilatory threshold (9.44 ± 0.53 vs15.55 ± 1.34 ml/kg/min; p < 0.01). A steeper minute ventilation rate/carbon dioxide production slope (32 ± 0.8 vs 28 ± 1; p < 0.01) and a lower expired carbon dioxide pressure in chronic kidney disease (27 ± 0.6 vs 31 ± 0.9 vs 0.9; p < 0.01) indicated ventilation perfusion mismatching in these patients. The ventilatory cost of oxygen uptake was higher in chronic kidney disease (37 ± 0.8 vs 33 ± 1; p < 0.01). Maximum heart rate (134 ± 5 vs 159 ± 3 bpm) and one-minute heart rate recovery (15 ± 1 vs 20 ± 2 bpm) were reduced in chronic kidney disease (p < 0.01). Conclusion This study suggests that both central and peripheral limitations likely contribute to reduced exercise capacity in non-dialysis chronic kidney disease. Additionally, cardiopulmonary exercise testing revealed subclinical cardiopulmonary abnormalities in these patients in the absence of overt cardiovascular disease. Cardiopulmonary exercise testing could potentially be a tool for unmasking cardiopulmonary abnormalities preceding cardiovascular disease in chronic kidney disease.
Mitigating Stealthy False Data Injection Attacks Against State Estimation in Smart Grid

With the enhanced capabilities of the SCADA system, the power system can monitor its operating states in real-time. On the other hand, it also makes the power system more vulnerable to various kinds of attacks. One attack that has serious consequences is the False Data Injection (FDI) attack against the state estimation process. Although some techniques have been proposed to select meters to protect, none of them considers the cost of protecting meters, and thus will not perform well when only a limited number of meters can be protected due to budget limitation. In this paper, we consider a new problem: Given a limited budget, how to select the most critical meters to protect so that the probability of attackers launching successful stealthy FDI attack is minimized? We first formalize this problem which is NP-complete, and then propose heuristic based solutions. The idea is to rank and select meters based on a metric called vulnerability index, which considers two factors: how likely the meter will be targeted by the attacker to launch FDI attacks and how much damage will be caused by compromising the meter in case of a successful stealthy FDI attack. Evaluation results show that our algorithm can significantly reduce the probability of successful attacks, as well as the potential damage caused by FDI attacks.