Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Diarreaghenic Escherichia coli Strains Isolated from Healthy Donors, including a Triple Hybrid Strain

Escherichia coli is a well-recognized inhabitant of the animal and human gut. Its presence represents an essential component of the microbiome. There are six pathogenic variants of E. coli associated with diarrheal processes, known as pathotypes. These harbor genetic determinants that allow them to be classified as such. In this work, we report the presence of diarrheagenic pathotypes of E. coli strains isolated from healthy donors. Ninety E. coli strains were analyzed, of which forty-six (51%) harbored virulence markers specifics for diarrheagenic pathotypes, including four hybrids (one of them with genetic determinants of three DEC pathotypes). We also identified phylogenetic groups with a higher prevalence of B2 (45.6%) and A (17.8%). In addition, resistance to sulfonamides (100%), and aminoglycosides (100%) was found in 100% of the strains, with a lower prevalence of resistance to cefotaxime (13.3%), ceftriaxone (12.2%), fosfomycin (10%), and meropenem (0%). All analyzed strains were classified as multidrug resistant. Virulence genes were also investigated, which led us to propose three new virotypes. Among the virulence traits observed, the ability to form biofilms stands out, which was superior to that of the E. coli and Staphylococcus aureus strains used as positive controls.
Maximizing the spread of conservation initiatives in social networks

Conservation programs and policies can preserve biodiversity and boost ecosystem services, but only when widely adopted. While thousands of conservation initiatives exist globally, most fail to spread beyond a few initial adopters. Here, we use network science to (1) determine the topology and structure of two networks of conservation actors (one regional, one national), (2) identify influential individuals in those networks, and (3) test whether the adoption of a conservation initiative by influential individuals could increase the spread of that initiative across the network. We find that initial adoption by influential individuals results in sharp improvements in the total number of adopters of a conservation initiative network‐wide, particularly when a linear threshold diffusion model is used. Under an independent cascade diffusion model, the benefits of targeting influencers are smaller but still substantial. These benefits occurred in both networks despite very different network structures: the regional network resembles a random network comprised mostly of state agencies and local entities, while the national network has a scale‐free structure with highly influential hubs of federal agency and NGO entities. Given that many conservation programs fail to reach critical mass, our findings highlight the importance of strategically targeting influential individuals to boost the spread of conservation initiatives.
High-Load and Low-Load Resistance Exercise in Patients with Coronary Artery Disease: Feasibility and Safety of a Randomized Controlled Clinical Trial

Resistance exercise (RE) remains underused in cardiac rehabilitation; therefore, there is insufficient evidence on safety, feasibility, and hemodynamic adaptations to high-load (HL) and low-load (LL) RE in patients with coronary artery disease (CAD). This study aimed to compare the safety, feasibility of HL-RE and LL-RE when combined with aerobic exercise (AE), and hemodynamic adaptations to HL and LL resistance exercise following the intervention. Seventy-nine patients with CAD were randomized either to HL-RE (70–80% of one-repetition maximum [1-RM]) and AE, LL-RE (35–40% of 1-RM) and AE or solely AE (50–80% of maximal power output) as a standard care, and 59 patients completed this study. We assessed safety and feasibility of HL-RE and LL-RE and we measured 1-RM on leg extension machine and hemodynamic response during HL- and LL-RE at baseline and post-training. The training intervention was safe, well tolerated, and completed without any adverse events. Adherence to RE protocols was excellent (100%). LL-RE was better tolerated than HL-RE, especially from the third to the final mesocycle of this study (Borgs’ 0–10 scale difference: 1–2 points; p = 0.001–0.048). Improvement in 1-RM was greater following HL-RE (+31%, p < 0.001) and LL-RE (+23%, p < 0.001) compared with AE. Participation in HL-RE and LL-RE resulted in a decreased rating of perceived exertion during post-training HL- and LL-RE, but in the absence of post-training hemodynamic adaptations. The implementation of HL-RE or LL-RE combined with AE was safe, well tolerated and can be applied in the early phase of cardiac rehabilitation for patients with stable CAD.
KDN-Based Fault-Tolerant Scheduling for VNFs in Data Centers

Network Functions Virtualization (NFV) aims to decouple network functionality from dedicated physical devices, thereby allowing Virtual Machines (VMs) to host services such as firewalls and load balancers. Hosting these services on VMs will provide new business opportunities for data centers. Currently, the available physical resources are limited, thus making efficient resource scheduling the most challenging issue towards the successful adoption of NFV. Therefore, optimizing resource allocation is crucial. This paper proposes an integrated, intelligent framework for VNF (Virtual Network Function) scheduling. In particular, the proposed solution is based on Knowledge Defined Networking (KDN) with Long Short-Term Memory (LSTM) to operate and optimize VNF scheduling. For this reason, our solution considers VNF affinity rules in a data center with LSTM- based multi-step ahead time forecast. Experimental analysis through benchmark data shows that the proposed fault-tolerant aware scheduling framework based on LSTM and affinity techniques achieves promising results.
A Novel Approach in Filter Design for Grid-Connected Inverters Used in Renewable Energy Systems

Renewable energy resources are utilized in distribution networks based on an active front end technology as a bidirectional power flow energy conversion system. Low and high frequency harmonics generated by switching pattern of the power electronics converter should be reduced by an appropriate output filter. According to international regulations, harmonics injected to the grid by pulsewidth modulated (PWM) power converters must be handled to maintain power quality indices within standard limits. LCL filters with passive damping resistors are the most reliable and renowned devices to fulfil the standards. The main objectives in the design of an efficient LCL filter are to reduce the cost and weight of the filter, as well as to increase the robustness and stability of the power electronics converter. In this paper, a comprehensive and mathematical approach is proposed to calculate the maximum current ripple of a PWM-based inverter, which helps to precisely calculate the size of the inverter-side inductors in the LCL filter. The mathematical approach to solve the problem of optimal damping is presented, which can be implemented analytically in different configurations of the LCL filters. The proposed method also works to solve an optimal damping problem for any type of filter.
Explicit Complex Solutions to the Fresnel Coefficients

– Global navigation satellite system reﬂectometry is a microwave remote sensing technique which can be used to derive information about the composition or properties of ground surfaces. The received power of the GPS signals reﬂected by the ground is proportional to the magnitude of the reﬂection Fresnel coefﬁcients. In particular, it depends on the incidence angle h and on the ground’s permittivity e . The knowledge of e is important for determining various conditions and characteristics of the surface (for example, soil moisture, salinity, freeze-thaw transitions). The value of e can be found from the Fresnel reﬂection coefﬁcients, for a given incidence angle h . For dispersive media, e is a complex quantity; we present explicit formulas which express both < e ð Þ and = e ð Þ as a function of the incident angle h and the magnitude of the linearly polarized Fresnel reﬂection coefﬁcients.
Perception of proposed preliminary food-based dietary guidelines for Lake Victoria region of Kenya: findings from a qualitative study among adult community members

Background: Diets in the Lake Victoria region of Kenya have been described as monotonous, consisting chiefly of starchy staple foods, accompanied by green vegetables and oil. To promote healthy eating in the region, 11 food-based dietary guidelines (FBDGs) were proposed. Objective: This study was designed to assess perception of the proposed FBDGs amongst consumers in Lake Victoria region. Design and setting: A qualitative, descriptive cross-sectional design was used to collect data from 36 focus-group discussions (FGD) among adult males (18) and females (18) in Kisumu and Homa Bay Counties. Subjects: A total of 207 adult males (26–74 years) and 211 females (18–71 years) participated in this study. Data collection and analysis: Proposed FBDGs were printed on posters and presented to FGD participants. Proceedings were audio-taped, transcribed, translated and analysed based on themes. Results: The proposed FBDGs were perceived as promoting the consumption of a balanced diet. As a concept, a balanced diet was misrepresented as composed of only three nutrients: carbohydrates, proteins and vitamins. There was no mention of fats/oils and minerals as other significant nutrient constituents of a balanced diet. Other concepts, which included ‘three regular meals’, ‘snacks’, ‘food variety’, ‘healthy and physically active lifestyle’, were polysemous. Conclusion: The findings provide insight to guide the adaptation of the national FBDGs. The findings also provide a basis for nutrition advocacy programmes and a rationale for the revision of nutrition education materials, including the school curriculum, to align content with current evidence-based information.
DbAPE: Denoising-based APE System for Improving English-Myanmar NMT

Automatic post-editing (APE) research aims to investigate methods for correcting systematic errors in machine translation (MT) results. Recent work has shown successful practices of APE for improving MT output quality; however, their effectiveness strongly relies on the availability of large-scale human-created APE triplets. The high production cost of human post-edited data has led to the absence of APE triplets for most language pairs, including English-Myanmar, which has become a limiting factor for the applicability of the APE task. This work investigates how to conduct the APE task on the English-Myanmar MT where human-edited APE triplets are unavailable. We build an APE system using only the monolingual and parallel MT corpora. The system takes the source sentence (src) and the MT output (mt) as inputs and produces the post-edited mt as output by operating the three processes together, including word alignment extraction, enriching mt using the extracted word alignment information, and denoising the enriched-version of mt. We conduct extensive experiments by applying our APE system as a post-processor to the raw output of the existing English-Myanmar MT systems and show that it significantly improves the quality of baseline translation results in terms of TER and BLEU scores. In addition, we perform word alignment experiments with four types of alignment methods and demonstrate that the proposed multilingual word aligner can achieve robust performance over previous state-of-the-art models.
High Dietary Organic Iron Supplementation Decreases Growth Performance and Induces Oxidative Stress in Broilers

Simple Summary Iron (Fe) is an essential trace element nutrient that plays a vital role in metabolic processes, such as oxygen transportation and electron transfer during respiration. However, exposure to high levels of dietary Fe can have negative effects on growth performance and antioxidant function in broilers. Organic Fe has a considerably higher bioavailability than inorganic Fe. To evaluate the safety of Fe chelates with lysine and glutamic acid (Fe–LG) in broilers, we investigated the effects of high concentrations of dietary Fe–LG on serum biochemical parameters, antioxidant status, and duodenal Fe transporter mRNA expression in broilers. A supplementation of 800 mg/kg Fe induced kidney function injury and liver oxidative stress in broilers, and decreased duodenal Fe transporter mRNA expression. Hence, this study demonstrated that high levels of Fe–LG negatively affect broiler health, and proposed a maximum supplemental dose of Fe–LG in broiler diet as a nutritional feed additive. Abstract Although Iron (Fe) is an essential nutrient that plays a vital role in respiratory processes, excessive Fe in the diet can affect the health of broilers. We investigated the effects of diet supplemented with high levels of iron chelates with lysine and glutamic acid (Fe–LG) on the growth performance, serum biochemical parameters, antioxidant status, and duodenal mRNA expression of Fe transporters in broilers. A total of 800 1-day-old male Arbor Acres broilers were assigned to 5 groups, with 8 replicates each. Broilers were fed a corn–soybean meal basal diet or basal diets supplemented with 40, 80, 400, or 800 mg Fe/kg as Fe–LG for 6 weeks. The body weight (BW) was increased in the 80 mg Fe/kg treatment group, but decreased in the 800 mg Fe/kg treatment group on day 21. During days 1–21, compared with the control group, the supplementation of the 80 mg Fe/kg increased the average daily gain (ADG) and average daily feed intake (ADFI); however, the supplementation of the 800 mg Fe/kg group decreased the ADG and increased the FCR in broilers (p < 0.05). The heart, liver, spleen, and kidney indices were reduced in the 800 mg Fe/kg treatment group (p < 0.05). The supplementation of the 800 mg Fe/kg group increased the serum aspartate aminotransferase activity and the levels of creatinine and urea nitrogen on day 42 (p < 0.05). The broilers had considerably low liver total superoxide dismutase activity and total antioxidant capacity in the 800 mg Fe/kg treatment group (p < 0.05). Serum and liver Fe concentrations were elevated in the 400 and 800 mg Fe/kg treatment groups, but were not affected in the 40 and 80 mg Fe/kg treatment groups. The duodenal Fe transporters divalent metal transporter 1 (DMT1) and ferroportin 1 (FPN1) were downregulated in the Fe–LG treatment groups (p < 0.05). We conclude that a high dietary supplement of 800 mg Fe/kg in broilers leads to detrimental health effects, causing kidney function injury and liver oxidative stress.
Coordinated Investment Planning of Distributed Multi-Type Stochastic Generation and Battery Storage in Active Distribution Networks

This paper presents a scenario-based stochastic active distribution network planning (ADNP) model considering the multi-type distributed generation and battery energy storage (BES). The proposed solution aims to identify the optimal mix, siting, and sizing of wind turbine (WT), photovoltaic (PV), and BES units to maximize the net present value of distribution network operator (DNO) while fully exploiting the BES arbitrage benefit. First, a heuristic moment matching based uncertainty matrix comprising of representative scenarios is generated to effectively capture the stochastic characteristics and correlation among historical WT generation, PV generation, and load demand. Then, the uncertainty matrix is incorporated to formulate the stochastic ADNP problem. The proposed solution minimizes the costs and maximizes the revenues of the DNO. The effectiveness and scalability of the proposed model are evaluated through case studies in the 53-bus and IEEE 123-bus distribution systems. Finally, the performance of the proposed model is compared against the deterministic planning model, and a sensitivity analysis is performed to assess the impact of various planning factors on the proposed solution.
A Heart Rate Based Algorithm to Estimate Core Temperature Responses in Elite Athletes Exercising in the Heat

Purpose Non-invasive non-obtrusive continuous and real-time monitoring of core temperature (Tc) may enhance pacing strategies, the efficacy of heat mitigation measures, and early identification of athletes at risk for heat-related disorders. The Estimated Core Temperature (ECTemp™) algorithm uses sequential heart rate (HR) values to predict Tc. We examined the validity of ECTemp™ among elite athletes exercising in the heat. Methods 101 elite athletes performed an exercise test in simulated hot and humid environmental conditions (ambient temperature: 31.6 ± 1.0°C, relative humidity: 74 ± 5%). Tc was continuously measured using a validated ingestible telemetric temperature capsule system. In addition, HR was continuously measured and used to compute the estimated core temperature (Tc−est) using the ECTemp™ algorithm. Results Athletes exercised for 44 ± 10 min and n = 5,025 readouts of Tc (range: 35.8–40.4°C), HR (range: 45–207 bpm), and Tc−est (range: 36.7–39.9°C) were collected. Tc−est demonstrated a small yet significant bias of 0.15 ± 0.29°C (p < 0.001) compared to Tc, with a limit of agreement of ±0.45°C and a root mean square error of 0.35 ± 0.18°C. Utilizing the ECTemp™ algorithm as a diagnostic test resulted in a fair to excellent sensitivity (73–96%) and specificity (72–93%) for Tc−est thresholds between 37.75 and 38.75°C, but a low to very-low sensitivity (50–0%) for Tc−est thresholds >39.0°C, due to a high prevalence of false-negative observations. Conclusion ECTemp™ provides a valuable and representative indication of thermal strain in the low- to mid-range of Tc values observed during exercise in the heat. It may, therefore, be a useful non-invasive and non-obtrusive tool to inform athletes and coaches about the estimated core temperature during controlled hyperthermia heat acclimation protocols. However, the ECTemp™ algorithm, in its current form, should not solely be used to identify athletes at risk for heat-related disorders due to low sensitivity and high false-negative rate in the upper end of the Tc spectrum.
Unsupervised Domain Adaptation Based on Progressive Transfer for Ship Detection: From Optical to SAR Images

In recent years, synthetic aperture radar (SAR) ship detection methods based on convolutional neural networks (CNNs) have attracted wide attention in remote sensing fields. However, these methods require a large number of labeled SAR images to train the network, where labeling for SAR images is more expensive and time-consuming than that for optical images. To address the problem of lacking labeled SAR images, in this article, we proposed an unsupervised domain adaptation (UDA) framework based on progressive transfer for SAR ship detection by transferring knowledge from the optical domain to the SAR domain. Due to the prominent difference between the optical and SAR images, our approach progressively transfers knowledge from three levels: pixel level, feature level, and prediction level. At the pixel level, considering the difference in imaging mechanism, we propose a special data augmentation method for ship targets and build the generator with skip connection based on generative adversarial networks (GANs) to generate transition domain, which can reduce the appearance discrepancy between the optical and SAR images. At the feature level, the detector is trained to learn the domain-invariant features with adversarial alignment. At the prediction level, we further use the predicted pseudo-labels obtained by the feature-aligned detector to learn more discriminative features of the SAR images directly and propose the robust self-training (RST) method to reduce the influence of noisy pseudo-labels on detector training. Specially, RST is formulated as a loss minimization problem for object detection. The experimental results based on the domain adaptation from optical dataset to SAR dataset demonstrate that our approach achieves superior SAR ship detection performance with unlabeled SAR images.
Thermal self-effect of wave beams in a plasma with a nonlocal nonlinearity

The steady-state self-effect of wave beams is analyzed in a weakly ionized plasma in which nonlinear effects due to heating and ionization are governing. The thermal self-effect of the beam is analyzed numerically on a computer and in the aberrationless approximation. The self-focusing of axisymmetric beams is analyzed, and its quantitative characteristics are determined. The conditions for the instability of a broad beam (or plane wave) with respect to breakup into ''narrower'' beams are determined for the case of a nonlocal nonlinearity. The influence on the instability of an oppositely directed (reflected) wave is determined. The influence of additional ionization on the propagation of a wave beam is studied.
Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks

Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.