Miracle-dz/UniFAR-FTUs huggingface dataset:
Sea Slug Mucus Production Is Supported by Photosynthesis of Stolen Chloroplasts

Simple Summary Kleptoplasty is the capacity of a non-photosynthetic organism to acquire and maintain structurally intact chloroplasts from its algal food source(s), thereafter termed kleptoplasts. In animals, the capacity for long-term (several weeks to months) maintenance of photosynthetic active kleptoplasts is a unique characteristic in a handful of sea slugs, mostly within the genus Elysia. In this study, we investigated the role of kleptoplast photosynthesis on mucus production by the tropical sea slug Elysia crispata. Mucus secretion is paramount for mollusks, playing a role in locomotion, feeding, reproduction and protection. Limiting photosynthesis by rearing animals under reduced light led to lower mucus production and lower carbohydrate concentrations in the secreted mucus. This study indicates that production of mucus by kleptoplast-bearing sea slugs is supported by photosynthesis, confirming the biological relevance of kleptoplasty to the fitness of these peculiar mollusks. Abstract A handful of sea slugs of the order Sacoglossa are able to steal chloroplasts—kleptoplasts—from their algal food sources and maintain them functionally for periods ranging from several weeks to a few months. In this study, we investigated the role of kleptoplast photosynthesis on mucus production by the tropical sea slug Elysia crispata. Animals reared for 5 weeks in quasi dark (5 μmol photons m−2 s−1) showed similar growth to those under regular light (60–90 μmol photons m−2 s−1), showing that kleptoplast photosynthesis was not relevant for growth when sea slugs were fed ad libitum. However, when subjected to short-term desiccation stress, animals reared under regular light produced significantly more mucus. Furthermore, the carbohydrate content of secreted mucus was significantly lower in slugs limited in the photosynthetic activity of their kleptoplasts by quasi-dark conditions. This study indicates that photosynthesis supports the synthesis of protective mucus in kleptoplast-bearing sea slugs.
Early Serum Creatinine Levels after Aneurysmal Subarachnoid Hemorrhage Predict Functional Neurological Outcome after 6 Months

Acute kidney injury (AKI) is a known predictor of unfavorable outcome in patients treated at the ICU, irrespective of the disease. However, data on the potential influence of serum creatinine (sCr) on hospital admission on the outcome in patients suffering from aneurysmal subarachnoid hemorrhage (SAH) is scarce. A total of 369 consecutive patients suffering from SAH were included in this retrospective cohort study. Patients were divided into good-grade (WFNS I–III) versus poor-grade (WFNS IV–V). Outcome was assessed according to the modified Rankin Scale (mRS) after 6 months and stratified into favorable (mRS 0–2) versus unfavorable (mRS 3–6). SAH patients with sCr levels <1.0 mg/dL achieved significantly a favorable outcome more often compared to patients with sCr levels ≥1.0 mg/dL (p = 0.003). In the multivariable analysis, higher levels of sCr (p = 0.014, OR 2.4; 95% CI 1.2–4.7), poor-grade on admission (p < 0.001, OR 9.8; 95% CI 5.6–17.2), age over 65 years (p < 0.001, OR 3.3; 95% CI 1.7–6.1), and delayed cerebral ischemia (p < 0.001, OR 7.9; 95% CI 3.7–17.1) were independently associated with an unfavorable outcome. We identified increased sCr on admission as a predictor for unfavorable functional outcome after SAH. Further studies elucidating the pathophysiology of this association are necessary.
Radio frequency response of flexible microstrip patch antennas under compressive and bending loads using multiphysics modeling approach

This research article studies the effect of compression and bending loads on resonant frequency of microstrip patch antennas using COMSOL Multiphysics software (will be called COMSOL hereafter). In this study, copper microstrip patch antenna of dimension 30 mm × 25 mm on polydimethylsiloxane (PDMS) substrate of dimension 50 mm × 50 mm is considered. The interface bonding is assumed to be ideal between the patch and substrate. Both Ansoft HFSS and COMSOL are used to model and analyze the original geometry of the microstrip patch antenna without applying physical load to make sure that the design and the impedance match is satisfactory. Then, COMSOL is used to find deformed shape of the microstrip patch antenna under different values of compression and bending loads. The deformed geometries are reanalyzed using COMSOL radio frequency (RF) simulation. The resonant frequencies at different load levels are obtained and the effect of loading and boundary conditions on the resonant frequency shift is discussed.
BugTalk: Online Prediction for the Life of Spodoptera Litura (Common Cutworm)

Due to climate change, Spodoptera litura has the potential to become an increasingly severe pest because of increased habitat suitability. To support precision agriculture, it is essential to accurately predict the life cycle of Spodoptera litura, and use the information for pest control. This paper proposes BugTalk to predict the life of Spodoptera litura (Common Cutworm). Based on the Internet of Things (IoT) technology, BugTalk is a real-time prediction version of modified Insect Life Cycle Modeling software (ILCYM), an open-source software package that implements the functions used in the four models of the four life stages of Spodoptera litura. In this paper, we significantly improve the ILCYM functions and several models of the previous studies to improve the accuracy of the prediction. We develop the first real-time prediction system that can predict the number of Spodoptera litura for the farm fields in real-time. The results are compared with the measurements during 2014-2020. We extend the temperature-based ILCYM model to accommodate humidity for the larvae emergence stage, which further improves the accuracy of the model. The Mean Arctangent Absolute Percentage Error (MAAPE) between the measurements and the BugTalk prediction is 24.593%. This result has been practically utilized in farm management.
THE EFFECTS OF BIOLOGICAL AND CHEMICAL FERTILIZER SOURCES ON THE PRODUCTION AND QUALITY OF SUNFLOWER

<p class="abstrakinggris"><span lang="EN-US">Bio-fertilizers improve nutrient availability and reduce the need to use chemical fertilizers and hold a great promise to increase crop yields. To study the effects of different nutrition systems, this research evaluated the individual and combined effects of bio-fertilizers and compared their efficiency with nitrogen (N) and phosphorus (P) chemical fertilizers on the yield of sunflower (<em>Helianthus annuus</em> L.) in the 2017–2018 crop year in Hamedan, Iran. The field experiment included a combination of two factorial experiments, consisting of (a) the application of chemical nitrogen (N<sub>0</sub>=0, N<sub>1</sub>=45, N<sub>2</sub>=90 kg ha<sup>-1</sup>) and phosphorus (P<sub>0</sub>=0, P<sub>1</sub>=40, P<sub>2</sub>=80 kg ha<sup>-1</sup>) fertilizers, and (b) the application of N-fixing bacteria <em>Azospirillum</em> and <em>Azotobacter</em> (BN<sub>0</sub>=0, BN<sub>1</sub>=0.5, BN<sub>2</sub>=1 L ha<sup>-1</sup>), and P-solubilizing bacteria <em>Pseudomonas</em> and <em>Bacillus</em> (BP<sub>0</sub>=0, BP<sub>1</sub>=0.5, BP<sub>2</sub>=1 L ha<sup>-1</sup>) bio-fertilizers. Each experiment used a 3×3 design with nine treatments and arranged in a randomized complete block design with three replicates. Results indicated that the chemical N and P fertilizers significantly increased the grain yield, thousand-grain weight, plant dry weight, protein percentage, and harvest index. Furthermore, bio-fertilizers significantly increased the grain yield and harvest index. The application of bio-fertilizers with N-fixing bacteria and P-solubilizing bacteria improved the growth and yield characteristics of sunflower. Sunflower oil yield was similar among all treatments, ranged from 2.22 to 5.7 t ha<sup>-1</sup>. Grain yield ranged from 5.12 to 5.88 t ha<sup>-1</sup>. A similar result between treatments with the chemical and bio-fertilizers suggests that bio-fertilizers are potential alternatives to chemical fertilizers. </span></p>
Synbiotics and Gut Microbiota: New Perspectives in the Treatment of Type 2 Diabetes Mellitus

The number of people with type 2 diabetes mellitus (T2DM) has increased sharply over the past decades. Apart from genetic predisposition, which may cause some of the diagnosed cases, an unhealthy diet and lifestyle are incentive triggers of this global epidemic. Consumption of probiotics and prebiotics to gain health benefits has become increasingly accepted by the public in recent years, and their critical roles in alleviating T2DM symptoms are confirmed by accumulating studies. Microbiome research reveals gut colonization by probiotics and their impacts on the host, while oral intake of prebiotics may stimulate existing metabolisms in the colon. The use of synbiotics (a combination of prebiotics and probiotics) can thus show a synergistic effect on T2DM through modulating the gastrointestinal microenvironment. This review summarizes the research progress in the treatment of T2DM from the perspective of synbiotics and gut microbiota and provides a class of synbiotics which are composed of lactulose, arabinose, and Lactobacillus plantarum, and can effectively adjust the blood glucose, blood lipid, and body weight of T2DM patients to ideal levels.
Rapid Oxford Nanopore Technologies MinION Sequencing Workflow for Campylobacter jejuni Identification in Broilers on Site—A Proof-of-Concept Study

Simple Summary Current culture methods for Campylobacter enumeration in poultry meat require at least five days. To improve this outcome, we have assembled a portable sequencing kit consisting of the Bento Lab and the MinION and developed a workflow for on-farm use that can detect and report the presence of Campylobacter in less than five hours from sampling to data. In addition, this workflow allows for the determination of microbiota profiles within those five hours. Overall, this workflow and approach can be helpful in the control of zoonotic agents, with a particular focus on poultry meat, where it takes less than 24 h from the time the animals arrive at the slaughterhouse to the time the carcasses are available on the supermarket shelves. Abstract Campylobacter is recognised as one of the most important foodborne bacteria, with a worldwide health and socioeconomic impact. This bacterium is one of the most important zoonotic players in poultry, where efficient and fast detection methods are required. Current official culture methods for Campylobacter enumeration in poultry usually include >44 h of culture and >72 h for identification, thus requiring at least five working shifts (ISO/TS 10272-2:2017). Here, we have assembled a portable sequencing kit composed of the Bento Lab and the MinION and developed a workflow for on-site farm use that is able to detect and report the presence of Campylobacter from caecal samples in less than five hours from sampling time, as well as the relationship of Campylobacter with other caecal microbes. Beyond that, our workflow may offer a cost-effective and practical method of microbiologically monitoring poultry at the farm. These results would demonstrate the possibility of carrying out rapid on-site screening to monitor the health status of the poultry farm/flock during the production chain.
A New Modulation for Leakage Current Reduction of Modular Multilevel Converter

Leakage current is an important problem in non-isolated inverters, especially implemented in a medium- and high-voltage system. First, several analyses had carried out to reduce the high-frequency component of common-mode voltage (CMV) and the leakage current of a three-phase modular multilevel converter (MMC). Then, a new modulation method is proposed to keep CMV as the half of dc voltage and suppress the leakage current effectively. The peak value of leakage current can be suppressed under 5 mA, which complies with the VDE-0126-1-1 standard. Meanwhile, the parasitic capacitor voltage can be balanced automatically without the additional capacitor voltage control. Finally, the proposed scheme is verified in MATLAB/Simulink and tested on the hardware-in-loop platform. The effectiveness of the proposed scheme is verified by the experiment results.
Cryptococcal Pneumonia: An Unusual Complication in a COVID-19 Patient

Cryptococcal superinfection is a rare but potentially fatal complication, especially if its detection and subsequent treatment are delayed. Histopathological findings of pulmonary parenchyma from a deceased patient with these complications were acquired. Quite interestingly, only a minimal inflammatory reaction could be seen in an individual with no previously known immune suppression, indicating a disturbance of the immune system. This finding was well in concordance with the described changes in cellular immunity in COVID-19. We report the case of a 60 year old male with critical coronavirus disease 2019 (COVID-19) complicated by cryptococcal pneumonia and multiorgan failure. Both X-ray and CT scans revealed lung infiltrates corresponding with COVID-19 infection early after the onset of symptoms. Despite receiving standard treatment, the patient progressed into multiple organ failure, requiring mechanical ventilation, circulatory support, and haemodialysis. Cryptococcus neoformans was detected by subsequent BAL, and specific antifungal treatment was instituted. His clinical status deteriorated despite all treatment, and he died of refractory circulatory failure after 21 days from hospital admission. Histopathological findings confirmed severe diffuse alveolar damage (DAD) caused by COVID-19 and cryptococcal pneumonia. Timely diagnosis of cryptococcal superinfection may be challenging; therefore, PCR panels detecting even uncommon pathogens should be implemented while taking care of critical COVID-19 patients.
A novel intelligent health prediction method for lithium‐ion batteries within a variable voltage range

As the Internet of Vehicles and cloud computing have rapidly developed, they have become increasingly relevant to the online prediction of the state of health (SOH) of lithium‐ion batteries (LIBs). To accurately and robustly predict the SOH of LIBs within a variable voltage range, a novel intelligent SOH prediction model for LIBs is proposed by combining a one‐dimensional convolutional (Conv1D) layer, gated recurrent unit (GRU) network and an attention‐based encoder‐decoder framework. First, based on a battery aging data set, multiple health features are extracted, and then the Conv1D layer is utilized to learn the local trends of these features. Then, the attention‐based framework is used to extract the most relevant cycle information for prediction from the encoder output of the GRU network. The experimental results show that the proposed model achieves prediction results with a root mean square error within 0.9%, which consistently outperforms existing models. An acceptable prediction can be performed within an appropriate range of available voltage data, which is verified and analyzed based on the incremental capacity curves. Furthermore, the robustness of the model is experimentally demonstrated. Even with 150 mV of voltage noise input in an incomplete process, the proposed model yields superior and robust results.
A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings

In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, we have collected real data from four multi-storied residential building. The collected data are provided as input for the acquisition layer. In the pre-processing layer, several data cleaning and preprocessing schemes were deployed to remove abnormalities from the data. In the prediction layer, we have used the deep extreme learning machine (DELM) for energy consumption prediction. Further, we have also used the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) in the prediction layer. In the DELM different numbers of hidden layers, different hidden neurons, and various types of activation functions have been used to achieve the optimal structure of DELM for energy consumption prediction. Similarly, in the ANN, we have employed a different combination of hidden neurons with different types of activation functions to get the optimal structure of ANN. To obtain the optimal structure of ANFIS, we have employed a different number and type of membership functions. In the performance evaluation layer for the comparative analysis of three prediction algorithms, we have used the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the performance of DELM is far better than ANN and ANFIS for one-week and one-month hourly energy prediction on the given data.
Multi-dimensional semi-analytical model for axial stick–slip of a rod sliding on a surface with Coulomb friction

A multi-dimensional lumped element model of a long non-rotating rod that moves on a slick surface with both dynamic and static Coulomb friction is outlined. The rod is accelerated to a constant velocity, and the free end of the rod experiences the effect of stick and slip. This article describes a new modeling approach, where the model is able to switch between different linear semi-analytical sub-models, depending on how much of the rod is moving. Fundamental understanding of the stick–slip effect is revealed, and a potential shortcoming of the model is also discussed. The model is computationally effective and may be suitable for real-time applications in, for instance, oil-well drilling.
Worst-Case Maximum Likelyhood Estimation in the Linear Model

This paper addresses the problem of maximum likelyhood parameter estimation in linear models aaected by structured deterministic uncertainty in the regression matrix and random gaussian noise. The proposed estimator maximizes the worst-case (with respect to the deterministic uncertainty) likelyhood of the measured sample. The estimate is computed solving a semideenite optimization problem (SDP). In the particular case of unstructured uncertainty, this SDP simply requires minimization of a scalar convex function of one variable.
Breast Cancer WHEN PROTEOMICS CHALLENGES BIOLOGICAL COMPLEXITY*

puberty to menopause the breast is subject to a cyclic development and constant cellular turnover rythmed by menstrual cycles. In case of pregnancy, epithelial cell differentiation leads to milk production and ultimately starvation induces a regression of epithelial cells. Hormones and growth factors play a crucial role in the menstrual cycle and pregnancy.
Fabrication of a Flexible Three-Dimensional Hybrid Directional Lead for Deep Brain Stimulation and Control of Micturition Reflex in Rats

A miniaturized three-dimensional (3D) cylindrical directional lead was fabricated through micro-electromechanical system (MEMS) technology and used to investigate micturition control via a rodent model. After verifying the feasibility of the design with a simulation tool, the directional lead was fabricated in two steps: (1) patterning a gold layer on the polydimethylsiloxane substrate and insulating it with SU-8 and (2) wrapping the film electrode around a polyimide tubing to form the 3D cylinder. The fabricated lead was characterized by measuring its bending stiffness and impedance and was tested on the micturition control of stimulated rats. The rats were randomly divided into two groups: concentric lead stimulation (Con-stim) and directional lead stimulation (Dir-stim). The lead exhibited a considerably lower bending stiffness than conventional leads. The impedances of the channels on the fabricated lead were 138, 73, 46, 87, 100, and 111 $\text{k}\Omega $ at 1 kHz. During the stimulation, the intercontraction interval and voiding volume were increased in both rat groups, while the inhibition effect remained after the stimulation in the Dir-stim group. The difference in these results means that the difference of E-field distribution induced by two different leads can affect the modulating effects. MEMS technology can overcome the limitations of conventional fabrication methods and can be used in conjunction with a wrapping technique to construct the miniaturized 3D lead with a complex patterned electrode. This novel 3D lead will allow advanced brain research in small animals, ultimately expanding deep brain modulation.
Journal of Earth Science & Climatic Change

Our objective was to test how liming influence fungal biomass, abundance and diversity of organisms belonging to mesofauna and soil respiration associated to soil during a microcosms experiment. Soil was collected in the Vosges Mountains (North-eastern France) on acidified forested catchments lying on sandstone and granite of Vosges Mountain. liming, we followed 2 by using LiCor 6400, fungal biomass by quantifying soil ergosterol content during 8 weeks. At the end of the experiment, we extracted mesofauna by using Berlese method. Soil pH, rain, temperature and rate of liming were similar to natural conditions. Liming decreased the abundance of mesofauna including collembola on sansdstone substrate, while on granite, collembola abundance decreased but total mesofauna did not show any significant differences between acid control and limed samples. The ergosterol soil content showed immediate increasing tendencies on both geological substrates and followed by decreasing tendencies on both geological substrates. The CO 2 efflux was significantly increased by liming on sandstone and granite substrates, indicating an increase of soil respiration in limed samples. However, this result is depending on the geological substrate.
Tripartite Evolutionary Game of Multiparty Collaborative Supervision of Personal Information Security in App: Empirical Evidence From China

At present, the problem of the personal information security of apps has attracted widespread attention from the government and society, and there is an urgent need for multiple subjects to participate in the regulation together. Based on this, we first considered the information feedback from users. Secondly, we constructed an evolutionary game model of three-party collaborative supervision among local governments, app distribution platforms, and users. Then the stability of the equilibrium points and their stability conditions are analyzed based on Lyapunov’s first law. Finally, MatlabR2021a software is used to analyze the influence of key parameter changes on the strategy choice of game players. The conclusions are as follows: (1) Increasing the penalty amount for local governments and app distribution platforms, increasing the reputation value premium or reputation loss from users’ feedback, and increasing synergistic benefits can promote local governments to adopt the strategies of supervision and app distribution platforms to adopt the strategies of review. (2) Reducing the cost of supervision for local governments can promote their adoption of supervisory strategies, and reducing the cost of review for app distribution platforms can promote their adoption of review strategies. (3) Reducing users’ feedback cost not only improves users’ enthusiasm to participate in supervision, but also promotes local governments and app distribution platforms to adopt positive strategies. (4) When the coefficient of synergistic benefit distribution is more even, it is more favorable for local governments and app distribution platforms to adopt positive strategies. Based on the simulation results, we propose some feasible strategies.