Miracle-dz/UniFAR-FTUs huggingface dataset:
Which Parameters of Beat-to-Beat Blood Pressure Best Predict Poor In-Hospital Outcome in Spontaneous Intracerebral Hemorrhage?

Objective: There is increasing evidence that high blood pressure (BP) levels and BP variability (BPV) over 24 h or longer are associated with poor clinical outcomes in patients with intracerebral hemorrhage (ICH). The objective of this study was to examine the association between different beat-to-beat BP parameters and in-hospital outcomes. Methods: Patients with a diagnosis of acute spontaneous ICH were recruited consecutively and prospectively between September 2018 and January 2019. Beat-to-beat recordings were measured non-invasively for 5 min within the first 72 h after the onset of symptoms. BPV was analyzed by standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variation independent of mean (VIM). Outcome was assessed at discharge using the modified Rankin Scale (mRS) score. Multivariate logistic regression analysis was used to assess the association between BP levels, BPV, and clinical outcomes. Results: A total of 66 patients were included, of whom 34 had poor outcomes (mRS score, 3–6). Patients with poor outcomes had significantly higher National Institute of Health Stroke Scale scores (4.5 vs. 9, p < 0.001), a larger ICH volume (8 vs. 14.5 mL, p = 0.004), and an increased systolic BP (SBP) -CV (3.2 vs. 4.8, p < 0.001) and diastolic BP (DBP) -CV (3.7 vs. 4.9, p = 0.015). After adjustment for major covariates, multivariate logistic regression analysis revealed that SBP-CV was independently associated with an increased risk of poor in-hospital outcomes [odds ratio (OR) 2.535; 95% confidence interval (CI), 1.211–5.305; p = 0.014]. The receiver operating characteristic area for SBP-CV in predicting poor in-hospital outcome was 0.827 (95% CI, 0.730–0.925; p < 0.001), and the best cutoff point was 3.551 (sensitivity, 82.35%; specificity, 68.75%). Conclusion: A higher beat-to-beat BPV in the first 72 h of admission was associated with unfavorable in-hospital outcomes in patients with ICH. The stabilization of BPV during the acute phase may be a therapeutic target; this could be tested in future clinical trials.
Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics

Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.
Molecular Characterization of Virus Strain Causing Yellow Mosaic Disease (YMD) in Mungbean (Vigna radiata L. Wilczek)

Mungbean, Vigna radiata (L.) Wilczek, is one of the major legume crops cultivated in South East Asia, Indian, Southern Europe and also in Southern USA. High in protein content, low flatulence and easy to digest, mungbean consumption in combination with cereals can significantly increase the quality of protein in a meal (Thirumaran and Seralathan 1988). It is popularly grown as a component in inter cropping systems because of its ability to fix nitrogen, early maturity and relatively drought tolerance. Plant virus diseases constrained the productivity of a wide range of economically important crops worldwide. Geminiviruses have ravaged large areas of crop plants mainly in tropical and subtropical countries (Banergee el al., 2018). Begomovirus, the largest genus of the Geminiviridae family containing small circular, single-strand DNA plant viruses having monopartite or bipartite (DNA-A and DNA-B) genome of approximately 2.7 kb size with ~ 220 bp common region (CR) (Kumar et al., 2017). Mungbean (Vigna radiata (L.) Wilczek) is a fast-growing warm-season legume, native to the Indian subcontinent; widely cultivated in Southeast Asia. It is an important pulse crop because of its short growth duration, low water requirement, and as a crop suitable for crop rotation and nutritional security being a rich source of protein. Biotic stress produced by viruses especially yellow mosaic virus (YMV) is the main constraint in its production causing substantial yield losses. The detection and diagnosis of viruses is most important to excogitate a management strategy. In this study coat protein gene was employed as a well characterized and widely used means for the identification as well as classification of different strains of YMV. Molecular characterization of YMV was done by using the coat protein (CP) gene of five isolates collected from Dharwad, Belgaum, Amravati, Gulbarga and Bengaluru to confirm their identity; isolates from Dharwad and Belgaum were classified as mungbean yellow mosaic India virus (MYMIV) and isolates from Amravati, Gulbarga and Bengaluru as mungbean yellow mosaic virus MYMV. Geographical confinement of species of YMV is validated; as MYMIV strain is more prevalent in northern, central and eastern regions of India whereas, MYMV is predominant in the southern regions. K e y w o r d s
Practicing Social Cohesion in the Dark: Diverse Processes and Missing Indicators in Forced Migration Contexts

Despite the large number of studies on social cohesion, research carried out in non-European, refugee and local contexts remains limited. This article provides a structured, focused comparison of social cohesion practices in new refugee hosting settings based on examples from Turkey, Lebanon, and Jordan. The analysis reveals that social cohesion vision is implemented through peacebuilding, community engagement, and resilience building practices that aim at building safe, shared and empowered spaces, respectively. In contrast to national policies with more immediate, tangible and standardized outcomes, local practices involve intercommunal, interactional, and future-oriented processes, which complicates the assessment of social cohesion through an indicator framework. Based on the findings, the article suggests (a) acknowledging context bound manifestations of social cohesion practices, (b) emphasizing social interactions at the local level as a crucial component of social cohesion processes, and (c) advancing research on the causal mechanisms of social cohesion as a long-term policy goal.
Added Value of Dynamic EMG in the Assessment of the Equinus and the Equinovarus Foot Deviation in Stroke Patients and Barriers Limiting Its Usage

Equinus (EFD) and equinovarus foot deviation (EVFD) are the most frequent lower limb deformities in stroke survivors. The equinus component can be triggered by a combination of dorsiflexor deficits, plantar flexor overactivity, muscle stiffness, and contractures. The varus component is typically due to an imbalance between invertor and evertor muscle actions. An improvement in identifying its causes leads to a more targeted treatment. These deformities are typically assessed via a thorough clinical evaluation including the assessment of range of motions, force, spasticity, pain, and observational gait analysis. Diagnostic nerve blocks are also being increasingly used. An advantage of dynamic electromyography (dEMG) is the possibility of measuring muscle activity, overactivity or lack thereof, during specific movements, e.g., activity of both ankle plantar flexors and dorsiflexors during the swing phase of gait. Moreover, fine-wire electrodes can be used to measure the activity of deep muscles, e.g., the tibialis posterior. An impediment to systematic use of dEMG in the assessment of EFD and EVFD, as a complimentary tool to the clinical evaluation, is a lack of evidence of its usefulness. Unfortunately, there are few studies found in literature. In order to fill this void, we studied three pairs of patients suffering from chronic hemiparesis consequent to a stroke, with EFD or EVFD. At the initial evaluation they all displayed the same clinical traits, very similar walking patterns, and an overlapping gait kinematics. However, the patterns of muscle activity differed considerably. dEMG data acquired during walking provided information that was not available from the sole clinical assessment. The contribution of this information to the subsequent clinical and rehabilitation process was discusses along with the barriers that limit the use of dEMG as a routine tool in neurorehabilitation.
Earth Observation Based Monitoring of Forests in Germany: A Review

Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany’s surface area. Therefore, forests shape the character of the country’s cultural landscape. Germany’s forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.
Fixed-Point Theorems for θ - ϕ -Contraction in Generalized Asymmetric Metric Spaces

In the last few decades, a lot of generalizations of the Banach contraction principle had been introduced. In this paper, we present the notion of θ -contraction and θ − ϕ -contraction in generalized asymmetric metric spaces to study the existence and uniqueness of the fixed point for them. We will also provide some illustrative examples. Our results improve many existing results.
Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome

The successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period. Conventional studies have used the spectral power or event-related potential of specific regions as the classification feature. However, as multiple brain regions work collaboratively to process memory, it could be a better option to use functional connectivity within the memory-related brain network to predict subsequent memory performance. In this study, we acquired the EEG signals while performing an associative memory task that remembers scene–word pairs. For the connectivity analysis, we estimated the cross–mutual information within the default mode network with the time–frequency spectra at the prestimulus and encoding phases. Then, we predicted the success or failure of subsequent memory outcome with the connectivity features. We found that the classifier with support vector machine achieved the highest classification accuracy of 80.83% ± 12.65% (mean ± standard deviation) using the beta (13–30 Hz) connectivity at encoding phase among the multiple frequency bands and task phases. Using the prestimulus beta connectivity, the classification accuracy of 72.45% ± 12.52% is also achieved. Among the features, the connectivity related to the dorsomedial prefrontal cortex was found to contribute to successful memory encoding. The connectivity related to the posterior cingulate cortex was found to contribute to the failure of memory encoding. The present study showed for the first time the successful prediction with high accuracy of subsequent memory outcome using single-trial functional connectivity.
Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning

Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.
Essential Elements That Contribute to the Recovery of Persons With Severe Mental Illness: A Systematic Scoping Study

Introduction: There is an increasing emphasis on recovery-oriented care in the design and delivery of mental health services. Research has demonstrated that recovery-oriented services are understood differently depending on the stakeholders involved. Variations in interpretations of recovery lead to challenges in creating systematically organized environments that deliver a consistent recovery-oriented approach to care. The existing evidence on recovery-oriented practice is scattered and difficult to apply. Through this systematic scoping study, we aim to identify and map the essential elements that contribute to recovery outcomes for persons living with severe mental illness. Methods: We used the Arksey & O'Malley framework as our guiding approach. Seven key databases (MEDLINE, PubMed, CINAHL/EBSCO, EMBASE, ProQuest, PsycINFO, and Google Scholar) were searched using index terms and keywords relating to recovery and severe mental illness. To be included, studies had to be peer-reviewed, published after 1988, had persons with severe mental illness as the focal population, and have used recovery in the context of mental health. The search was conducted in August 2018 and last updated in February 2020. Results: Out of 4,496 sources identified, sixty (n = 60) sources were included that met all of the selection criteria. Three major elements of recovery that emerged from the synthesis (n = 60) include relationships, sense of meaning, and participation. Some sources (n = 20) highlighted specific elements such as hope, resilience, self-efficacy, spirituality, social support, empowerment, race/ethnicity etc. and their association with the processes underpinning recovery. Discussion: The findings of this study enable mental health professionals to incorporate the identified key elements into strategic interventions to facilitate recovery for clients with severe mental illness, and thereby facilitate recovery-oriented practice. The review also documents important gaps in knowledge related to the elements of recovery and identifies a critical need for future studies to address this issue.
Hydrocarbon trapping and condensation on Pt(111)

A direct, reflected beam method under ultrahigh-vacuum conditions was used to measure the sticking coefficient of several small hydrocarbon molecules (methylcyclohexane, cyclohexane, toluene, benzene, and methanol) on clean and adsorbate-covered Pt(111) surfaces and on the molecular solid phases that can be formed at temperatures below that required for multilayer sublimation. We find that the sticking coefficient for all of these molecules on the clean Pt(111) surface and the molecular solid phase at 100 K is unity. The simple, classical hard-cube model provides a useful way to understand this behavior. 12 refs., 4 figs.
Are People Experiencing the ‘Pains of Imprisonment’ During the COVID-19 Lockdown?

Background By the end of March 2020, more than a fifth of the world’s population was in various degrees of “lockdown” in order to slow the spread of COVID-19. This enforced confinement led some to liken lockdown to imprisonment. We directly compared individual’s experiences of lockdown with prisoners’ experiences of imprisonment in order to determine whether psychological parallels can be drawn between these two forms of confinement. Methods Online surveys of adults in lockdown in the UK (N = 300) and California (N = 450) were conducted 4 and 5 weeks into lockdown in each region, respectively. The UK data was then compared to Souza and Dhami’s (2010) sample of 267 medium security prisoners in England, and the Californian data was compared to Dhami et al.’s (2007) sample of 307 medium security Federal prisoners in California. We measured the effects of Group (Lockdown v. Prison) on five categories of dependent variables (i.e., activity, social contact, thoughts, feelings, and rule-breaking), controlling for demographic differences between the groups. Results In both regions, people in lockdown thought significantly less often about missing their freedom, as well as missing their family and friends living elsewhere than did first-time prisoners. However, people in lockdown in both regions were also significantly less engaged in a range of daily activities than were first-time prisoners. Additionally, in both regions, people in lockdown reported feeling more hopeless than first-time prisoners. Conclusion Although Governments introducing lockdown policies do not intend to punish their citizens as courts do when sending convicted offenders to prison, such policies can have unintended adverse consequences. Psychological parallels can be drawn between the two forms of confinement.
Gridded Satellite Sounding Retrievals in Operational Weather Forecasting: Product Description and Emerging Applications

The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition Center (SPoRT) has been part of a collaborative effort within the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) Program to develop gridded satellite sounding retrievals for the operational weather forecasting community. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) retrieves vertical profiles of temperature, water vapor, trace gases, and cloud properties derived from infrared and microwave sounder measurements. A new, optimized method for deriving NUCAPS level 2 horizontally and vertically gridded products is described here. This work represents the development of approaches to better synthesize remote sensing observations that ultimately increase the availability and usability of NUCAPS observations. This approach, known as “Gridded NUCAPS”, was developed to more effectively visualize NUCAPS observations to aid in the quick identification of thermodynamic spatial gradients. Gridded NUCAPS development was based on operations-to-research feedback and is now part of the operational National Weather Service display system. In this paper, we discuss how Gridded NUCAPS was designed, how relevant atmospheric fields are derived, its operational application in pre-convective weather forecasting, and several emerging applications that expand the utility of NUCAPS for monitoring phenomena such as fire weather, the Saharan Air Layer, and stratospheric air intrusions.