Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Inoculation for Productivity Improvements and Potential Biological Control in Sugar Beet Crops

Used mainly for sucrose production, sugar beet is one of the most important crops in Castilla y León (Spain). Several studies have demonstrated the benefits of microorganisms in different crop management programs, among which Plant Growth Promoting Rhizobacteria (PGPR). This research aims to assess the beneficial effects of two PGPRs strains (Pseudomonas fluorescens Pf0-1 and Pseudomonas chlororaphis CECT 462) on sugar beet (Beta vulgaris) production. Three treatments: a PGPRs co-inoculation assay of untreated seeds without any chemical treatment (TB), a conventional treatment with commercial seeds and fungicide application (TT); and a control with seeds without protective coating, bacterial inoculation and chemical treatment (ST). The efficacy of PGPRs inoculation on sugar beet production was determined measuring periodically the photosynthetic status of plants, and the final yield and quality of tubers. Aerial and root plant biomass, maximum beet perimeter, polarization, and sugar values of the sugar beet plants inoculated with PGPRs showed higher values and significant differences to sugar beet subjected to other treatments. We could see that PGPRs inoculation (TB treatment) produced significant differences in the quantum yield of PSII (ΦPSII). TB showed the highest value for ΦPSII and the NPQ (non-photochemical quenching), the lowest value, even though the PSII (maximum quantum yield of photosystem II) was very similar in all treatments. The two assayed PGPR strains triggered a significant increase in sugar beet production yield and quality. PGPRs inoculation techniques could be used in different crops and they could be applied as biofertilizers, improving the agricultural production.
Re-examining the Role of Coping Strategies in the Associations Between Infertility-Related Stress Dimensions and State-Anxiety: Implications for Clinical Interventions With Infertile Couples

Research has shown a direct relationship between infertility-related stress and anxiety in infertile patients. The present study goes into this relationship in depth, testing the moderating role of coping strategies (Seeking Social Support, Avoidant, Positive Attitude, Problem-Solving, Turning to Religion) in the associations between specific infertility-related stress dimensions (Social Concern, Need for Parenthood, Rejection of Childfree Lifestyle, Couple’s Relationship Concern) and State-Anxiety among male and female partners of infertile couples. Gender differences were also explored. Both members of 254 infertile couples completed a questionnaire consisting of Socio-demographics, Fertility Problem Inventory–Short Form (FPI-SF), Coping Orientation to Problem Experienced–New Italian Version (COPE-NIV), and State-Trait Anxiety Inventory-Y (STAI-Y). The results revealed that Social Concern and Couple’s Relationship Concern, in both partners, and Need for Parenthood, in female partners, had positive correlations with State-Anxiety. Seeking Social Support and Avoidant coping were related to increasing levels of State-Anxiety in both partners, whereas Positive Attitude coping strategies were related to lower levels of State-Anxiety in female partners. Problem-Solving and Avoidant coping played moderating roles between specific infertility-related stress dimensions and State-Anxiety in unexpected directions. Problem-Solving exacerbated the negative effects of Social Concern, whereas Avoidant coping buffered the negative effects of several infertility-related stress dimensions in both partners. Interventions to improve stress management and psychological health in infertile couples should consider that the adequacy of coping strategies is inherently situation specific. It therefore follows that patient-centered clinical interventions should consider the potential inadequacy of promoting Problem-Solving strategies, and that even Avoidance can be an efficient strategy for dealing with specific infertility-related stress dimensions.
Microvascular Fluid Exchange: Implications of the Revised Starling Model for Resuscitation of Dengue Shock Syndrome

Dengue is the most common mosquito-borne viral infection in the world. The most feared complication is a poorly understood vasculopathy that occurs in only a small minority of symptomatic individuals, especially children and young adults, but can result in potentially fatal dengue shock syndrome (DSS). Based mainly on expert opinion, WHO management guidelines for DSS recommend prompt infusion of a crystalloid fluid bolus followed by a tapering crystalloid fluid regimen, supplemented if necessary by boluses of synthetic colloid solutions. However, following publication of a number of major trials undertaken in other, primarily adult, critical care scenarios, use of both synthetic colloid solutions and of fluid boluses for volume expansion have become controversial. Synthetic colloids tend to be used for severe DSS cases in order to boost intravascular oncotic pressure, based on the classic Starling hypothesis in which opposing hydrostatic and oncotic forces determine fluid flow across the microvascular barrier. However, the revised Starling model emphasizes the critical contribution of the endothelial glycocalyx layer (EGL), indicating that it is the effective oncotic pressure gradient across the EGL not endothelial cells per se that opposes filtration. Based on several novel concepts that are integral to the revised Starling model, we review the clinical features of DSS and discuss a number of implications that are relevant for fluid management. We also highlight the need for context-specific clinical trials that address crucially important questions around the management of DSS.
Impacts of Climatic Variation on the Growth of Black Spruce Across the Forest-Tundra Ecotone: Positive Effects of Warm Growing Seasons and Heat Waves Are Offset by Late Spring Frosts

Climate strongly limits the physiological processes of trees near their range limits, leading to increased growth sensitivity. Northeastern North America is experiencing considerable warming, so the growth of trees near the northern treeline represents a key indicator of forest responses to climate change. However, tree-ring series and corresponding climatic data are scarce across the forest-tundra ecotone when compared to southern boreal regions, resulting in fewer studies on growth-climate relationships focused on this ecotone. Using daily climatic data, we identified trends in growing season heat accumulation and the intensity of acute climatic events over the last several decades in the southern and the northern parts of the forest-tundra ecotone in northeastern North America, and investigated their influence on black spruce radial growth. We found that black spruce trees responded positively to the increase in growing season temperatures and heat wave intensity, suggesting that growth is currently limited by suboptimal temperatures. While tree growth in the southern region generally benefited from warm spring temperatures, vulnerability to late spring frosts reduced tree growth in the northern region and increased probability of abrupt growth decline. In this region, late spring frosts offset approximately half of the additional growth that would otherwise occur over the course of a warm growing season. This vulnerability of northern trees may result from local adaptations to short growing seasons, which initiate biological activities at colder temperatures in the spring. Overall, our results highlight the need to explicitly incorporate acute climatic events into modeling efforts in order to refine our understanding of the impact of climate change on forest dynamics.
SARS CoV-2 Organotropism Associated Pathogenic Relationship of Gut-Brain Axis and Illness

COVID-19 has resulted in a pandemic after its first appearance in a pneumonia patient in China in early December 2019. As per WHO, this global outbreak of novel COVID-19 has resulted in 28,329,790 laboratory-confirmed cases and 911,877 deaths which have been reported from 210 countries as on 12th Sep 2020. The major symptoms at the beginning of COVID-19 are fever (98%), tussis (76%), sore throat (17%), rhinorrhea (2%), chest pain (2%), and myalgia or fatigue (44%). Furthermore, acute respiratory distress syndrome (61.1%), cardiac dysrhythmia (44.4%), shock (30.6%), hemoptysis (5%), stroke (5%), acute cardiac injury (12%), acute kidney injury (36.6%), dermatological symptoms with maculopapular exanthema (36.1%), and death can occur in severe cases. Even though human coronavirus (CoV) is mainly responsible for the infections of the respiratory tract, some studies have shown CoV (in case of Severe Acute Respiratory Syndrome, SARS and Middle East Respiratory Syndrome, MERS) to possess potential to spread to extra-pulmonary organs including the nervous system as well as gastrointestinal tract (GIT). Patients infected with COVID-19 have also shown symptoms associated with neurological and enteric infection like disorders related to smell/taste, loss of appetite, nausea, emesis, diarrhea, and pain in the abdomen. In the present review, we attempt to evaluate the understanding of basic mechanisms involved in clinical manifestations of COVID-19, mainly focusing on interaction of COVID-19 with gut-brain axis. This review combines both biological characteristics of the virus and its clinical manifestations in order to comprehend an insight into the fundamental potential mechanisms of COVID-19 virus infection, and thus endorse in the advancement of prophylactic and treatment strategies.
Thymic Squamous Cell Carcinoma: A Population-Based Surveillance, Epidemiology, and End Result Analysis

Objectives Thymic squamous cell carcinoma (TSCC) is a rare neoplasm that has been sparsely cited in the literature. The aim of this study was to determine disease characteristics and prognostic factors of patients in a Surveillance, Epidemiology, and End Results (SEER) analysis. Methods Cases from 1990–2016 were retrieved from the SEER database and demographics, treatments, and survival outcomes were analyzed. Results The TSCC accounted for 72.4% of the thymic carcinomas and 7.2% of thymic tumors. The 276 patients (165 men) selected for analysis had a median age of 65 (24–85) years, and 201 patients were diagnosed with Masaoka-Koga stage III/IV. The median survival of TSCC was 59 months with a 49.0% 5-year OS rate, a better prognosis than lymphoepithelioma-like carcinoma (32.1%) and undifferentiated carcinoma (33.3%). Multivariate analysis revealed the Masaoka-Koga stage (p = 0.003) and surgical types (complete resection, incomplete resection, and none; p < 0.001) were determinants of survival. Complete resection had the best prognosis with a 72.7% 5-year OS rate. Chemotherapy was an independent protective factor (HR = 0.555, 95% CI 0.347–0.886; p = 0.014) though poor survival was showed in univariate analysis. And the survival benefit of chemotherapy was validated in PSM analysis (3-year OS rate was 77.7% with chemotherapy vs. 52.8% without chemotherapy; p = 0.014). Conclusions TSCC was frequently diagnosed in older patients with advanced Masaoka-Koga stage and had more favorable survival than other subtypes of thymic carcinomas. Complete resection is the preferred treatment. Masaoka-Koga stage and chemotherapy had a strong association with prognosis.
Structure properties of dextran. 2. Dilute solution

Dilute solution properties of 13 dextrans with different molar masses in water and 0.5 M NaOH were investigated. Seven of these samples were products of Sigma prepared by bacteria from Leuconostoc mesenteroides, one was obtained by fractionation of the dextran with the highest molar mass, and five samples were obtained by degradation via controlled acid hydrolysis. Static and dynamic light scattering, viscometry and analysis of the reducing end group were applied. The molar mass dependencies of the radius of gyration Rg, the hydrodynamic radius Rh, the second virial coefficient A2 and the intrinsic viscosity [η] for the nonfractionated samples were determined. The results are compared with data from the literature. Size exclusion chromatography in on-line combination with multiple-angle laser light scattering and viscosity detection revealed different calibration curves Mi vs Ve for the studied samples. The molar mass dependencies of the radii and intrinsic viscosities could be measured. The data of the r.
Cultivation-Independent Analysis of the Bacterial Community Associated With the Calcareous Sponge Clathrina clathrus and Isolation of Poriferisphaera corsica Gen. Nov., Sp. Nov., Belonging to the Barely Studied Class Phycisphaerae in the Phylum Planctomycetes

Marine ecosystems serve as global carbon sinks and nutrient source or breeding ground for aquatic animals. Sponges are ancient parts of these important ecosystems and can be found in caves, the deep-sea, clear waters, or more turbid environments. Here, we studied the bacterial community composition of the calcareous sponge Clathrina clathrus sampled close to the island Corsica in the Mediterranean Sea with an emphasis on planctomycetes. We show that the phylum Planctomycetes accounts for 9% of the C. clathrus-associated bacterial community, a 5-fold enrichment compared to the surrounding seawater. Indeed, the use of C. clathrus as a yet untapped source of novel planctomycetal strains led to the isolation of strain KS4T. The strain represents a novel genus and species within the class Phycisphaerae in the phylum Planctomycetes and displays interesting cell biological features, such as formation of outer membrane vesicles and an unexpected mode of cell division.
Low Contrast Visual Acuity Might Help to Detect Previous Optic Neuritis

Optic neuritis (ON) has been considered to be an important factor in the diagnosis of multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), making ON detection increasingly critical for early diagnosis. Furthermore, subclinical ONs presenting no distinct decrease in visual acuity can be missed. Low contrast visual acuity (LC-VA) is known to be able to capture visual loss not seen in conventional high-contrast visual acuity (HC-VA) in MS. Therefore, to increase the sensitivity of ON detection, we investigated the advantage of LC-VA over conventional HC-VA. One hundred and eight patients with demyelinating disease (35 MS, 73 NMOSD) with ON at least 3 months prior and 35 controls underwent neuro-ophthalmic evaluation, including best-corrected conventional high contrast visual acuity (HC-VA) and 2.5% and 1.25% low contrast visual acuity (LC-VA). Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) of various visual functions were used to determine the most relevant visual function test for the detection of optic nerve involvement. Additionally, the optimal cutoff point was obtained from the Youden index (J-index) as the points with the best sensitivity-specificity balance. When distinguishing ON from non-ON, the area under the ROC curve (AUC) was highest for the 2.5% LC-VA (0.835, P < 0.001; sensitivity 71.5%, specificity 88.6%), while it was 0.710 (P < 0.001) for the HC-VA and 0.770 (P < 0.001) for the 1.25% LC-VA. In discriminating between controls and ON, the AUC was also highest for the 2.5% LC-VA 0.754 (P < 0.001; sensitivity 71.5%, specificity 78.5%), while it was 0.719 (P < 0.001) for HC-VA and 0.688 (P < 0.001) for 1.25% LC-VA. In eyes with a history of ON (n = 137), the HC-VA and 2.5% LC-VA were abnormal in 64.2 and 71.5%, respectively (P < 0.001), with their combination detecting abnormalities in approximately 85.4% (P < 0.001). The 2.5% LC-VA was superior to HC-VA in detecting ON when distinguishing ON from non-ON or control. The 2.5% LC-VA might be a useful, feasible, and rapid method to detect ON. Furthermore, combining 2.5% LC-VA with conventional HC-VA would be better for detecting optic nerve involvements.
Laryngeal fractures in professional and semiprofessional ice hockey players

Injuries in professional ice hockey players are common, however significant laryngeal trauma is rare. Here, we present a case series of professional and semiprofessional ice hockey players to demonstrate the mechanism and nature of laryngeal injuries they sustain during play, and to recommend best practices for treatment, prevention, and return to the ice.
TensorFlow Enabled Deep Learning Model Optimization for enhanced Realtime Person Detection using Raspberry Pi operating at the Edge

In this paper Quantization eﬀects are assessed for a real time Edge based person detection use case that is based on the use of a Rasp-berry Pi. TensorFlow architectures are presented that enable the use of real-time person detection on the Raspberry Pi. The model quantization is performed, performance of quantized models is analyzed, and worst-case performance is established for a number of deep learning object detection models that are capable of being deployed on the Pi for real-time applications. The study shows that the inference time for a suitably optimized TensorFlow enabled solution architecture is signiﬁcantly lower than for an unquantized model with only slight cost implications in terms of accuracy when benchmarked against a desktop implementation. An industrial standard ﬂoor limit value of greater than 70% is achieved on the quantized models considered with a reduced detection time of less than 3ms. The Deep Neural Network model is trained using the INRIA Person Detection benchmark Dataset.
Incorporating Explainable Artificial Intelligence (XAI) to aid the Understanding of Machine Learning in the Healthcare Domain

In the healthcare domain, Artiﬁcial Intelligence (AI) based systems are being increasingly adopted with applications ranging from surgical robots to automated medical diagnostics. While a Machine Learning (ML) engineer might be interested in the parameters related to the performance and accuracy of these AI-based systems, it is postulated that a medical practitioner would be more concerned with the applicability, and utility of these systems in the medical setting. However, medical practitioners are unlikely to have the prerequisite skills to enable reasonable interpretation of an AI-based system. This is a concern for two reasons. Firstly, it inhibits the adoption of systems capable of automating routine analysis work and prevents the associated productivity gains. Secondly, and perhaps more importantly, it reduces the scope of expertise available to assist in the validation, iteration, and improvement of AI-based systems in providing healthcare solutions. Explainable Artiﬁ-cial Intelligence (XAI) is a domain focused on techniques and approaches that facilitate the understanding and interpretation of the operation of ML models. Research interest in the domain of XAI is becoming more widespread due to the increasing adoption of AI-based solutions and the associated regulatory requirements [1]. Providing an understanding of ML models is typically approached from a Computer Science (CS) perspective [2] with a limited research emphasis being placed on supporting alternate domains [3]. In this paper, a simple, yet powerful solution for increasing the explainability of AI-based solutions to individuals from non-CS domains (such as medical practitioners), is presented. The proposed solution enables the explainability of ML models and the underlying workﬂows to be readily integrated into a standard ML workﬂow. Central to this solution are feature importance techniques that measure the impact of individual features on the outcomes of AI-based systems.
COVID-19 in children treated with immunosuppressive medication for kidney diseases

Background Children are recognised as at lower risk of severe COVID-19 compared with adults, but the impact of immunosuppression is yet to be determined. This study aims to describe the clinical course of COVID-19 in children with kidney disease taking immunosuppressive medication and to assess disease severity. Methods Cross-sectional study hosted by the European Rare Kidney Disease Reference Network and supported by the European, Asian and International paediatric nephrology societies. Anonymised data were submitted online for any child (age <20 years) with COVID-19 taking immunosuppressive medication for a kidney condition. Study recruited for 16 weeks from 15 March 2020 to 05 July 2020. The primary outcome was severity of COVID-19. Results 113 children were reported in this study from 30 different countries. Median age: 13 years (49% male). Main underlying reasons for immunosuppressive therapy: kidney transplant (47%), nephrotic syndrome (27%), systemic lupus erythematosus (10%). Immunosuppressive medications used include: glucocorticoids (76%), mycophenolate mofetil (MMF) (54%), tacrolimus/ciclosporine A (58%), rituximab/ofatumumab (11%). 78% required no respiratory support during COVID-19 illness, 5% required bi-level positive airway pressure or ventilation. Four children died; all deaths reported were from low-income countries with associated comorbidities. There was no significant difference in severity of COVID-19 based on gender, dialysis status, underlying kidney condition, and type or number of immunosuppressive medications. Conclusions This global study shows most children with a kidney disease taking immunosuppressive medication have mild disease with SARS-CoV-2 infection. We therefore suggest that children on immunosuppressive therapy should not be more strictly isolated than children who are not on immunosuppressive therapy.
On the Usage of Deep Learning for Modelling Energy Consumption in Simulation Models

With the increasing availability of data, the desire to interpret that data and use it for behavioral predictions arises. Traditionally, simulation has used data about the real system for input data analysis or within data-driven model generation. Automatically extracting behavioral descriptions from the data and representing it in a simulation model is a challenge for these approaches. Machine learning on the other hand has proven successful in extracting knowledge from large data sets and transforming it into more useful representations. Combining simulation approaches with methods from machine learning seems, therefore, promising. Representing some aspects of a real system by a traditional simulation model and others by a model generated from machine learning, a hybrid system model (HSM) is generated. This paper discusses such HSMs and suggests a specific HSM incorporating a deep learning method for predicting the power consumption of machining jobs.
Grade Prediction Method for University Course Selection Based on Decision Tree

Educational Data Mining (EDM) refers to the use of suitable data mining methods and tools to discover knowledge from educational big data to help improve the quality of education, such as determining the priority learning needs of different student groups, increasing graduation rates, effectively evaluating the performance of the institution, and maximizing the use of campus resources and optimizing the update of subject courses. In this paper, we propose a grade prediction method to help university students choose courses reasonably. First of all, using big data analysis techniques, we can obtain the relationship between the performance of all students and course selection in recent years, and discover the influence of different course selection orders. Then, based on the current academic performance of a single student, the method can predict the impact of different elective courses for that student. Since students need to improve their future grade scores, predicting their performance will remind students to understand their performance and provide opportunities for their future performance. This method provides decision-making support for students' choices and enables them to choose courses reasonably to improve grades. This method also provides a foundation for university curriculum programs and reduces the number of dropouts. The data set for this study comes from the Student Information System of the Sokoine University of Agricultural (SUASIS), which includes data on student performance during academic activities.