Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Informed Breeding Strategies for Strawberry Yield and Fruit Quality Traits

Over the last two centuries, breeders have drastically modified the fruit quality of strawberries through artificial selection. However, there remains significant variation in quality across germplasm with scope for further improvements to be made. We reported extensive phenotyping of fruit quality and yield traits in a multi-parental strawberry population to allow genomic prediction and quantitative trait nucleotide (QTN) identification, thereby enabling the description of genetic architecture to inform the efficacy of implementing advanced breeding strategies. A negative relationship (r = −0.21) between total soluble sugar content and class one yield was identified, indicating a trade-off between these two essential traits. This result highlighted an established dilemma for strawberry breeders and a need to uncouple the relationship, particularly under June-bearing, protected production systems comparable to this study. A large effect of quantitative trait nucleotide was associated with perceived acidity and pH whereas multiple loci were associated with firmness. Therefore, we recommended the implementation of both marker assisted selection (MAS) and genomic prediction to capture the observed variation respectively. Furthermore, we identified a large effect locus associated with a 10% increase in the number of class one fruit and a further 10 QTN which, when combined, are associated with a 27% increase in the number of marketable strawberries. Ultimately, our results suggested that the best method to improve strawberry yield is through selecting parental lines based upon the number of marketable fruits produced per plant. Not only were strawberry number metrics less influenced by environmental fluctuations, but they had a larger additive genetic component when compared with mass traits. As such, selecting using “number” traits should lead to faster genetic gain.
The Abnormal Imaging of SARS-CoV-2: A Predictive Measure of Disease Severity

To investigate the characteristics of SARS-CoV-2 pneumonia and evaluate whether CT scans, especially at a certain CT level, could be used to predict the severity of SARS-CoV-2 pneumonia. In total 118 confirmed patients had been enrolled. All data including epidemiological, clinical characteristics, laboratory results, and images were collected and analyzed when they were administrated for the first time. All patients were divided into two groups. There were 106 severe/critical patients and 12 common ones. A total of 38 of the patients were women. The mean age was 50.5 ± 11.5 years. Overall, 80 patients had a history of exposure. The median time from onset of symptoms to administration was 8.0 days. The main symptoms included fever, cough, anorexia, fatigue, myalgia, headaches, and chills. Lymphocytes and platelets decreased and lactate dehydrogenase increased with increased diseased severity (P < 0.05). Calcium and chloride ions were decreased more significantly in severe/critical patients than in common ones (P < 0.05). The main comorbidities were diabetes, chronic cardiovascular disease, and chronic pulmonary disease, which occurred in 47 patients. In all 69 patients had respiratory failure, which is the most common SARS-CoV-2 complication, and liver dysfunction presented in 37 patients. Nine patients received mechanical ventilation therapy. One patient received continuous blood purification and extracorporeal membrane oxygenation (EMCO) treatments. The average stay was 18.1 ± 10.8 days. Four patients died. The median of the radiographic score was four in common, and five in the severe/critical illness, which was a significant difference between the two groups. The radiographic score was in negative correlation with OI (ρ = −0.467, P < 0.01). The OI in severe/critically ill cases decreased significantly as the disease progressed, which was related to the lesion area in the left lung and right lungs (ρ = 0.688, R = 0.733). OI, the lesion area in the left lung and right lungs, lymphocytes, etc. were associated with different degrees of SARS-CoV-2 pneumonia (P < 0.05). The lesion area in both lungs were possible predictive factors for severe/critical cases. Patients with SARS-CoV-2 pneumonia showed obvious clinical manifestations and laboratory result changes. Combining clinical features and the quantity of the lesion area in the fourth level of CT could effectively predict severe/critical SARS-CoV-2 cases.
The Effect of Climate and Diet on Body Lipid Composition in the Oriental Hornet (Vespa orientalis)

Fatty acids (FA) are the primary metabolic fuel for many organisms and the fundamental component of membranes of all living organisms. FAs can be saturated (SFA), monounsaturated (MUFA), or polyunsaturated (PUFA). PUFA are not synthesized by most animals and are considered as essential nutrients. We examined the effect of climate on the saturation level of polar (mostly membranal) and neutral lipids in the body of the Oriental hornet (Vespa orientalis) from two extreme climatic zones: Mediterranean high elevation; and hot arid desert. In contrast to previous reports, the environmental temperature was shown to affect the hornet colonies’ thermal environments. The hornets nonetheless maintained their colony temperature within a narrow range. Analyses of the hornets’ unsaturation levels of polar and non-polar body lipids revealed caste differences: gynes and males contained less unsaturated lipids than workers. However, there were no differences in the respective castes between the two different climate zones tested. Experimentally manipulating the diet of queenless hornet colonies to a high Omega-3 diet (salmon) or a high Omega-6 diet (crickets) had only a minor effect on the worker-born males’ lipid composition. Although salmon-fed males had a higher Omega-3 content than cricket-fed ones, the proportion of these fatty acids was still low (below 1%). Cricket-fed males had significantly higher levels of Omega-6 than salmon-fed males. Our data show that the specific lipid composition of the hornet body is highly regulated and deficient in essential PUFA, even under different climates or high Omega-3 or Omega-6 PUFA diet. PUFA, especially Omega-3, is considered to have a beneficial effect on physiological processes. Our finding that these FA, when common in the diet, are almost absent in the body raises questions about how they affect animals’ physiology.
Old Problem, New Concerns: Hypercortisolemia in the Time of COVID-19

The ongoing coronavirus disease 2019 (COVID-19) pandemic forced a change in the way we provide medical treatment. Endocrinology in the era of COVID-19 had to transform and reduce its vast potential to the absolute necessities. Medical professionals needed to update their clinical practice to provide their patients as much support and as little harm as possible in these increasingly difficult times. International expert statements were published to offer guidance regarding proper care. It was suggested to simplify the diagnostic scheme of hypercortisolemia and to modify the approach to treatment. Hypercortisolemic patients with COVID-19 and iatrogenic hypercortisolemia due to glucocorticoid use are important clinical scenarios – we aimed to provide a cohesive summary of issues to consider.
Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.
Fast Map Matching with Vertex-Monotone Fréchet Distance

We study a generalization for map matching algorithms that includes both geometric approaches such as the Fréchet distance and global weight approaches such as those typically used by Hidden Markov Models. Through this perspective, we discovered an efficient map matching algorithm with respect to the vertex-monotone Fréchet distance while using a heuristic tie-breaker inspired by global weight methods. While the classical Fréchet distance requires parameterizations to be monotone, the vertex-monotone Fréchet distance allows backtracking within edges. Our analysis and experimental evaluations show that relaxing the monotonicity constraint enables significantly faster algorithms without significantly altering the resulting map matched paths
Graphene and Graphene Oxide for Fuel Cell Technology

The proton exchange membrane fuel cell (PEMFC) converts chemical energy into electrical energy via electrochemical reaction between hydrogen and oxygen, with heat and water as byproducts. When a PEMFC is engineered with polymer electrolyte membrane, e.g., Nafion and polybenzimidazole (PBI), it helps to enhance the performance of the fuel cell under monitored environmental conditions, i.e., high proton conductivity, improved electrode kinetics, and tailoring of properties, along with low tolerance for carbon monoxide. Recently discovered “graphene” has enticed the scientific community, because of its exceptional properties. As per the literature, PEMFCs engineered with graphene can yield high power density, along with 38% enhanced current density, and 257% improved ionic conductivity. In this context, the present review gives the state-of-the-art and progress on polymer electrolyte membranes engineered using graphene and graphene oxide, as well as their synthesis routes and the influence on the performance.
Guinea’s response to syndemic hotspots

Correspondence to Dr Delphin Kolie; drdelphinkolie88@ gmail. com © Author(s) (or their employer(s)) 2021. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION Syndemic hotspots The Guinean health system has been severely overstretched this year. It has to deal with several concurrent (re)emerging infectious diseases—Ebola, Lassa fever, measles, meningitis, yellow fever, vaccinederived poliomyelitis—as well as a second wave of the COVID19 pandemic (figure 1). A case of the highly infectious Marburg viral disease was also detected in August. These multiple concurring epidemics reflect contextualised ‘syndemic hotspots’ whereby (re)emerging infectious diseases, existing socioeconomic inequities, a fragile health system, chronic malnutrition and security constraints interact and cluster in marginalised and impoverished populations. At the same time community resistance has been observed against the epidemic control measures (related to the Ebola virus disease (EVD) and COVID19, mostly) across the country. This had implications on population health, among others through injuries and deaths as a result of police intervention. 5 This securitisation of epidemics response almost inevitably deepens community mistrust in public services, aggravates social inequalities and hinders efforts to combatting ongoing epidemics in the country. This syndemic situation is not unique to Guinea and is observed in many other countries, and specifically in countries with similar socioeconomic and health system constraints such as DR Congo. The ecological reservoir for lethal emerging diseases like viral haemorrhagic fevers has expanded in recent years. The risk that multiple epidemics occur at the same time in the nearby future is high in countries like Guinea, DR Congo and others. 7 The potential for international expansion, and thereby for longstanding chronic ‘syndemic hotspots’, is considerable because of economic globalisation and the international trade nexus in which West and Central Africa play a role. 9 This reflection is not only relevant to lowincome countries. The Independent Panel for Pandemic Preparedness and Response (IPPR) has indicated that the risk of future pandemics is very real, and that structural and urgent investments in the health system capacities of countries need to happen to meet the International Health Regulations implementation requirements. However, the governance modalities for these investments and priorities need to be decided in close coordination with national and local Summary box
Rapid eye movement predominant obstructive sleep apnoea: prognostic relevance and clinical approach

Purpose of review Rapid eye movement (REM) obstructive sleep apnoea (OSA) is a stage-specific OSA, in which obstructive events occur primarily during REM sleep. This review discusses REM-OSA definitions, its cardiometabolic correlates, associated comorbidities and treatment, and addresses diagnostic ambiguities and therapeutic pitfalls. Recent findings Current evidence indicates that REM-OSA is prevalent among younger age groups and women and is independently associated with cardiometabolic complications, particularly hypertension, metabolic complications such as insulin resistance and metabolic syndrome. However, currently, there is no consensus on the accepted diagnostic criteria for REM-OSA. Available data suggest that adherence to positive airway pressure (PAP) therapy in patients with REM-OSA is suboptimal. Moreover, the currently accepted criteria for good adherence to PAP therapy of 4 h/night, 70% of the days may not be suitable for REM-OSA, as it will not cover most of the REM sleep periods. In addition, further research is needed to assess the impact of REM-OSA treatment on cardiometabolic outcomes. Summary Patients with REM-OSA are at an increased risk of cardiometabolic complications. A high index of suspicion is needed to diagnose this disorder, and close follow-up is required to enhance adherence to therapy.
Experimental Analysis of Combustion Process in SI Engine Using Ethanol and Ethanol-Gasoline Blend

Abstract. Effect of fuel composition and ignition timing on combustion parameters of spark ignition (SI) port fuel injection (PFI) engine had been studied experimentally. The engine was fuelled with an ethanol and ethanol-gasoline blend E85. The engine was operated at steady speed at 1,500 min and four load points have been used. Minimal ignition timing advance for maximal brake torque (MBT) at stoichiometric air/ fuel ratio for the tested fuels were found. The fuels were tested at their respective MBT timing and gasoline MBT timing. MBT timing was retarded by 8–11% for ethanol and 5–10% for E85 fuel, comparing to gasoline MBT timing. Indicated mean effective pressure (IMEP) was not affected by ignition timing in tested conditions. Maximal cylinder pressure was increased and flame development phase was extended, when gasoline MBT was used with fuels with high ethanol content at tested conditions.
Distinct pathological mechanisms distinguish acute coronary syndrome caused by plaque erosion from plaque rupture

Purpose of review The current systematic review aims to provide novel insights into the distinct pathological features of acute coronary syndrome (ACS) with intact fibrous cap, also known as ‘plaque erosion’. A more detailed understanding of its underlying pathomechanisms provides the basis for future personalized therapeutic approaches to ACS. Recent findings In the past years, a mounting proportion of evidence point towards the concept of plaque erosion being responsible for more than one-third of all cases of ACS and representing an autonomous ACS-causing entity driven by biomechanical forces, immunological alternations and systemic elevation of inflammatory mediators. First pilot studies proved a therapeutic paradigm shift from primary PCI at the ACS-causing culprit lesion to systemic therapies in patients with ACS caused by plaque erosion. Summary This review provides the current status of the broad basic and clinical studies focused to the topic of plaque erosion: a new ACS-causing pathophysiology with different pathological aspects providing appropriate implications for personalized therapies in ACS.
Increase in preterm stillbirths and reduction in iatrogenic preterm births for fetal compromise: a multi-centre cohort study of COVID-19 lockdown effects in Melbourne, Australia

Objectives: The COVID-19 pandemic has been associated with a worsening of perinatal outcomes in many settings due to the combined impacts of maternal COVID-19 disease, disruptions to maternity care, and overloaded health systems. In 2020, Melbourne endured a unique natural experiment where strict lockdown conditions were accompanied by very low COVID-19 case numbers and the maintenance of health service capacity. The aim of this study was to compare stillbirth and preterm birth rates in women who were exposed or unexposed to lockdown restrictions during pregnancy. Design: Retrospective multi-centre cohort study of perinatal outcomes before and during COVID-19 lockdown Setting: Birth outcomes from all 12 public maternity hospitals in metropolitan Melbourne Inclusion criteria: Singleton births without congenital anomalies from 24 weeks gestation. The lockdown-exposed cohort were those women for whom weeks 20- 40 of gestation would have occurred during the lockdown period of 23 March 2020 to 14 March 2021. The control cohort comprised all pregnancies in the corresponding periods one and two years prior to the exposed cohort. Main outcome measures. Odds of stillbirth, preterm birth (PTB), birth weight less than 3rd centile, and iatrogenic PTB for fetal compromise, adjusting for multiple covariates. Results: There were 24,017 births in the exposed and 50,017 births in the control group. There was a significantly higher risk of preterm, but not term, stillbirth in the exposed group compared with the control group (0.26% vs 0.18%, aOR 1.49, 95%CI 1.08 to 2.05, P = 0.015). There was also a significant reduction in preterm birth < 37 weeks (5.93% vs 6.23%, aOR 0.93, 95%CI 0.87 to 0.99, P=0.03), largely mediated by a reduction in iatrogenic PTB for live births (3.01% vs 3.27%, aOR 0.89, 95%CI 0.81 to 0.98, P = 0.015), including iatrogenic PTB for suspected fetal compromise (1.25% vs 1.51%, aOR 0.79, 95%CI 0.69 to 0.91, P= 0.001). There was no significant difference in the spontaneous PTB rate between the exposed and control groups (2.69% vs 2.82%, aOR 0.94, 95%CI 0.86 to 0.1.03, P=0.25). Conclusions: Lockdown restrictions in a high-income setting with low rates of COVID-19 disease were associated with a significant increase in preterm stillbirths and a significant reduction in iatrogenic PTB for suspected fetal compromise.
Augmented NETT regularization of inverse problems

We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a regularizer consisting of a penalty term that enforces regularity in the encoder domain, augmented by a penalty that penalizes the distance to the signal manifold. We present a rigorous convergence analysis including stability estimates and convergence rates. For that purpose, we prove the coercivity of the regularizer used without requiring explicit coercivity assumptions for the networks involved. We propose a possible realization together with a network architecture and a modular training strategy. Applications to sparse-view and low-dose CT show that aNETT achieves results comparable to state-of-the-art deep-learning-based reconstruction methods. Unlike learned iterative methods, aNETT does not require repeated application of the forward and adjoint models during training, which enables the use of aNETT for inverse problems with numerically expensive forward models. Furthermore, we show that aNETT trained on coarsely sampled data can leverage an increased sampling rate without the need for retraining.
An artificial neural network model for forecasting air pollution

Air pollution has caused a lot of problems to people in terms of health and economy, as well as affecting various floras and faunas. Thus, monitoring air quality levels and forecasting the occurrence of air pollution is important so that preventive measures could be taken. In this study, Artificial Neural Network (ANN) was used to forecast the air pollution index (API) in Kuala Terengganu. This study focused on the prediction of API based on 5 years of data of main pollutants’ daily concentration taken at the air quality monitoring station in Kuala Terengganu. The aim was to develop an Artificial Neural Network model that can predict the API. A Multilayer Perceptron Neural Network (MLP) engine was implemented in the system prototype and developed by using Keras, a deep learning library in Python. The model’s performance was evaluated using the Mean Squared Error (MSE) statistical method and functionality tests were done to ensure the prototype was working correctly. In order to get a good performance model, a hyperparameter tuning process was carried out and the best hyperparameters values were selected. The performance of the model in making predictions was good as the MSE value was 0.0195.
Structure, Validity and Cut-Off Scores for the APA Emerging Measure ofDSM-5 Social Anxiety Disorder Severity Scale (SAD-D)

The APA Emerging measure the Social Anxiety Disorder Severity Scale (SAD-D) is recognized as the only social anxiety scale that is based on the DSM-5 criteria. This scale also addresses the limitations of other social anxiety measures as it is dimensional, time efficient and assesses a broad range of symptoms. However, research in community samples is needed, and no research to date has investigated the use of the SAD-D in an Australian sample. As such, this study examined the factor structure and validity of the SAD-D in an Australian non-clinical sample (N=999), and provides criterion cut off scores. The results of exploratory and confirmatory factor analyses indicated that the scale assesses a unidimensional construct of social anxiety severity. Post-hoc analyses also suggested that the 10-item SAD-D scale could be shortened to a six-item scale. Both the original SAD-D-10 scale and the SAD-D-6 scale showed good internal consistency with alphas of 0.95 and 0.93, respectively. Both scales showed evidence of concurrent validity through statistically significant associations of social anxiety severity scores with general anxiety and fear of negative evaluation by others. A receiver operator characteristic curve analysis demonstrated that the SAD-D-10 and SAD-D-6 were significant predictors of fear of negative evaluation. This analysis also provided test cut-off scores that may be usefully applied in practice, and as a criterion cut-off score in research. The results of this research suggest that the SAD-D-10 scale and the new SAD-D-6 scale may have utility in both research and practice settings, as the only social anxiety measure that is based on the DSM-5 criteria and overcomes the limitations of other measures.