Miracle-dz/UniFAR-FTUs huggingface dataset:
Clear advantages for fall armyworm larvae from feeding on maize relative to its ancestor Balsas teosinte may not be reflected in their mother's host choice

Consistent with an increasing number of comparisons between crop plants and their wild ancestors, a previous study showed that in the field, maize [Zea mays ssp. mays L. (Poaceae)] suffers more herbi‐vory by larvae of Spodoptera frugiperda JE Smith (Lepidoptera: Noctuidae) than its ancestor Balsas teosinte (Zea mays ssp. parviglumis Iltis & Doebley). This study addressed plausible causes of the differing herbivory levels between maize and Balsas teosinte, specifically whether host plant selection by adult females is biased for either host plant depending on larval performance—i.e., the preference‐performance hypothesis—or risk of mortality by parasitoids and predators—i.e., the natural enemy‐free space hypothesis. Field observations showed that performance of S. frugiperda larvae was superior on maize compared with Balsas teosinte, a result partially explainable by the greater toughness of teosinte compared with maize tissue, but not by a difference in inhibition of protein digestion by larvae. Additional field observations showed that the mortality risk of S. frugiperda larvae is higher on Balsas teosinte, as indicated by higher parasitism (ca. four‐fold) and predation (ca. three‐fold) rates on the teosinte compared with maize. However, laboratory observations showed that S. frugiperda females did not discriminate between maize and Balsas teosinte for oviposition. Overall, the study's results were consistent with prior observations that direct and indirect defenses of maize against S. frugiperda larvae were weakened with domestication. In contrast, the results were inconsistent with predictions of the preference‐performance and natural enemy‐free space hypotheses, because selection of maize or Balsas teosinte plants by S. frugiperda females was independent of their offspring's performance and risk of mortality by natural enemies. On the one hand, this study's results partially explained differing herbivory levels between a crop and its wild ancestor, and on the other hand they suggested that host selection by S. frugiperda may be mediated by larval dispersal behavior and host choices.
Effective Discovery of Meaningful Outlier Relationships

We propose Predictable Outliers in Data-trendS (PODS), a method that, given a collection of temporal datasets, derives data-driven explanations for outliers by identifying meaningful relationships between them. First, we formalize the notion of meaningfulness, which so far has been informally framed in terms of explainability. Next, since outliers are rare and it is difficult to determine whether their relationships are meaningful, we develop a new criterion that does so by checking if these relationships could have been predicted from non-outliers, i.e., whether we could see the outlier relationships coming. Finally, searching for meaningful outlier relationships between every pair of datasets in a large data collection is computationally infeasible. To address that, we propose an indexing strategy that prunes irrelevant comparisons across datasets, making the approach scalable. We present the results of an experimental evaluation using real datasets and different baselines, which demonstrates the effectiveness, robustness, and scalability of our approach.
MC-Safe

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.
Interval-based Queries over Lossy IoT Event Streams

Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation through surveillance monitoring to business workflows. In many real-world scenarios, however, timestamps of events may be erroneously recorded, and events may be dropped from a stream due to network failures or load shedding policies. In this work, we present SimpMatch, a novel simplex-based algorithm for probabilistic evaluation of event queries using constraints over event orderings in a stream. Our approach avoids learning probability distributions for time-points or occurrence intervals. Instead, we employ the abstraction of segmented intervals and compute the probability of a sequence of such segments using the notion of order statistics. The algorithm runs in linear time to the number of lost events and shows high accuracy, yielding exact results if event generation is based on a Poisson process and providing a good approximation otherwise. We demonstrate empirically that SimpMatch enables efficient and effective reasoning over event streams, outperforming state-of-the-art methods for probabilistic evaluation of event queries by up to two orders of magnitude.
Chaos, Solitons and Fractals

In this paper, we applied support vector regression to predict the number of COVID-19 cases for the 12 most-affected countries, testing for different structures of nonlinearity using Kernel functions and analyzing the sensitivity of the models’ predictive performance to different hyperparameters settings using 3-D interpolated surfaces. In our experiment, the model that incorporates the highest degree of nonlinearity (Gaussian Kernel) had the best in-sample performance, but also yielded the worst out-of-sample predictions, a typical example of overﬁtting in a machine learning model. On the other hand, the linear Kernel function performed badly in-sample but generated the best out-of-sample forecasts. The ﬁndings of this paper provide an empirical assessment of fundamental concepts in data analysis and evidence the need for caution when applying machine learning models to support real-world decision making, notably with respect to the challenges arising from the COVID-19 pandemics.
Use of Imazosulfuron in Herbicide Programs for Drill-Seeded Rice (Oryza sativa) in the Mid-South United States

Abstract Research was conducted in 2009 and 2010 to evaluate influence of imazosulfuron rate and application timing on weed control in drill-seeded rice at Stuttgart, AR, and to evaluate imazosulfuron-containing herbicide programs in drill-seeded rice at Keiser and Stuttgart, AR. Weed species evaluated included barnyardgrass, broadleaf signalgrass, hemp sesbania, and yellow nutsedge. Imazosulfuron applied at 224 and 336 g ai ha−1 during PRE, early POST (EPOST), or preflood (PREFLD) growth periods provided similar control of all weeds. Imazosulfuron applied EPOST or PREFLD controlled hemp sesbania and yellow nutsedge ≥ 93% both years at 5 and 7 wk after planting (WAP), except in 2009 when hemp sesbania control was ≤ 79% at 7 WAP. In 2010, because of inadequate rainfall, hemp sesbania and yellow nutsedge control with PRE-applied imazosulfuron was ≤29% at 5 and 7 WAP. Imazosulfuron plus clomazone PRE followed by (fb) quinclorac plus propanil EPOST and imazosulfuron plus quinclorac EPOST fb thiobencarb plus propanil PREFLD programs controlled hemp sesbania and barnyardgrass (in at least two site-years), and yellow nutsedge and broadleaf signalgrass (in at least one site-year) greater than or equal to clomazone plus quinclorac PRE fb propanil plus halosulfuron PRELD (standard program). No rice injury was observed with any herbicide program. Rice yield with all imazosulfuron-containing herbicide programs (6,630 to 8,130 kg ha−1) was similar to the standard herbicide program (7,240 kg ha−1). Imazosulfuron in mixture with clomazone, propanil, or quinclorac can be incorporated into herbicide programs of mid-South rice production for the control of broadleaf weeds and sedges. Nomenclature: Clomazone; halosulfuron; imazosulfuron; propanil; quinclorac; thiobencarb; barnyardgrass, Echinochloa crus-galli (L.) P. Beauv. ; broadleaf signalgrass, Urochloa platyphylla (Griseb.) Nash. ; hemp sesbania, Sesbania herbacea (P. Mill.) McVaugh; yellow nutsedge, Cyperus esculentus L. ; rice, Oryza sativa L.
THE LIGHT PROMOTED GERMINATION OF THE SEEDS OF CHENOPODIUM ALBUM L. I. THE INFLUENCE OF THE INCUBATION TIME ON QUANTITY AND RATE OF THE RESPONSE TO RED LIGHT

SUMMARY At progressively later moments after the beginning of the imbibition a single red irradiation(R) has been given to the positive photoblastic seeds of Chenopodium album. It could be demonstrated that not only the final germination percentage, but also the period between R and the visible germination, are dependent on the irradiation moment.
Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort

Background: The rapidly evolving dynamics of coronavirus disease 2019 (COVID-19) and the steadily increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. Methods: We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on PCR positivity. IL-6, CRP, leukocyte and lymphocyte counts were determined in blood samples. Two radiologists evaluated the severity of imaging findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for intensive care unit treatment. Findings: Patients with a severe course of COVID-19 had significantly increased IL-6, CRP and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean +/- standard deviation sensitivity, specificity and accuracy of 0.72 +/- 0.1, 0.86 +/- 0.16 and 0.80 +/- 0.1 and a ROC-AUC of 0.79 +/- 0.1. The most important predictive parameters were affected lung volume, radiological severity score, CRP and IL-6. Summary and Conclusion: Estimation of need for intensive care treatment is possible based on the clinical and radiological parameters.
Social determinants of COVID-19 mortality at the county level

Objective: To assess the associations between COVID-19 mortality and immigrant and farm worker population at the county level. Methods: We used publicly accessible datasets to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) Percentage of Non-English speaking households, (2) percentage of individuals engaged in hired farm work, (3) percentage of uninsured individuals under the age of 65, and (3) percentage of individuals living at or below the poverty line. Results: In urban counties (n=114), only population density was significantly associated with COVID19 mortality (b = 0.21, p <0.001). In non-urban counties (n=2,629), all hypothesized social determinants were significantly associated with higher levels of mortality. Percentage of uninsured individuals was associated with lower reported COVID-19 mortality (b = -0.36, p = 0.001). Conclusions: Individuals who do not speak English, individuals engaged in farm work, and individuals living in poverty may be at heightened risk for COVID-19 mortality in non-urban counties. Mortality among the uninsured may be being systematically undercounted in county and national level surveillance.
The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study

Objective. In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas. Approach. We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas. Main results. Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task. Significance. To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.
IDentif.AI: Artificial Intelligence Pinpoints Remdesivir in Combination with Ritonavir and Lopinavir as an Optimal Regimen Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks.
The tolerance of wheat to hight temperatures during reproductive growth. 2. Grain development

A study was undertaken in the Canberra phytotron of the response of 66 cultivars of wheat to high temperature (30/25¦C) imposed throughout the period of grain development. The results from 20 Australian cultivars and their parental lines showed half of these to be clustered in the range where grain dry weight was reduced by 30-35% in response to high temperature when compared with 18/13¦C controls. The non-Australian lines were more normally distributed in their response to high temperature and included some cultivars that were less sensitive to high temperature than the best of the Australian lines. It was concluded that there is a need for a wider assessment of genetic variation if a response to high temperature is to be incorporated in a breeding programme. A more specific study on Kalyansona, one of the better cultivars under high temperature conditions and high radiation levels, failed to reveal a specific source of temperature insensitivity in the background of Kalyansona. A comparison of the response to high temperature during grain development of twoF1 hybrids with their parental lines (Banks, Kalyansona and Pinnacle) provided some evidence that traits such as high grain number per ear, large grains and low sensitivity to high temperature could be effectively combined to produce a high grain weight per ear under high temperature conditions.
Effects of COVID-19 home confinement on physical activity and eating behaviour Preliminary results of the ECLB-COVID19 international online-survey

Abstract Background Public health recommendations and governmental measures during the COVID-19 pandemic have enforced numerous restrictions on daily living including social distancing, isolation and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on health behaviours and lifestyle at home is undefined. Therefore, an international online survey was launched in April 2020 in seven languages to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from the first thousand responders on physical activity (PA) and nutrition behaviours. Methods Thirty-five research organisations from Europe, North-Africa, Western Asia and the Americas promoted the survey through their networks to the general society, in English, German, French, Arabic, Spanish, Portugese, and Slovenian languages. Questions were presented in a differential format with questions related to responses before and during confinement conditions. Results 1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%) were included into a general analysis. The COVID-19 home confinement had a negative effect on all intensities of PA (vigorous, moderate, walking and overall). Conversely, daily sitting time increased from 5 to 8 hours per day. Additionally, food consumption and meal patterns (the type of food, eating out of control, snacks between meals, number of meals) were more unhealthy during confinement with only alcohol binge drink decreasing significantly. Conclusion While isolation is a necessary measure to protect public health, our results indicate that it alters physical activity and eating behaviours in a direction that would compromise health. A more detailed analysis of survey data will allow for a segregation of these responses in different age groups, countries and other subgroups which will help develop bespoke interventions to mitigate the negative lifestyle behaviors manifest during the COVID-19 confinement.
Acute mental health responses during the COVID-19 pandemic in Australia

The acute and long-term mental health impacts of the COVID-19 pandemic are unknown. The current study examined the acute mental health responses to the COVID-19 pandemic in 5070 adult participants in Australia, using an online survey administered during the peak of the outbreak in Australia (27th March to 7th April 2020). Self-report questionnaires examined COVID-19 fears and behavioural responses to COVID-19, as well as the severity of psychological distress (depression, anxiety and stress), health anxiety, contamination fears, alcohol use, and physical activity. 78% of respondents reported that their mental health had worsened since the outbreak, one quarter (25.9%) were very or extremely worried about contracting COVID-19, and half (52.7%) were worried about family and friends contracting COVID-19. Uncertainty, loneliness and financial worries (50%) were common. Rates of elevated psychological distress were higher than expected, with 62%, 50%, and 64% of respondents reporting elevated depression, anxiety and stress levels respectively, and one in four reporting elevated health anxiety in the past week. Participants with self-reported history of a mental health diagnosis had significantly higher distress, health anxiety, and COVID-19 fears than those without a prior mental health diagnosis. Demographic (e.g., non-binary or different gender identity; Aboriginal and Torres Strait Islander status), occupational (e.g., being a carer or stay at home parent), and psychological (e.g., perceived risk of contracting COVID-19) factors were associated with distress. Results revealed that precautionary behaviours (e.g., washing hands, using hand sanitiser, avoiding social events) were common, although in contrast to previous research, higher engagement in hygiene behaviours was associated with higher stress and anxiety levels. These results highlight the serious acute impact of COVID-19 on the mental health of respondents, and the need for proactive, accessible digital mental health services to address these mental health needs, particularly for those most vulnerable, including people with prior history of mental health problems. Longitudinal research is needed to explore long-term predictors of poor mental health from the COVID-19 pandemic.
COVID-19 Dynamics: A Heterogeneous Model

The mathematical model reported here describes the dynamics of the ongoing coronavirus disease 2019 (COVID-19) epidemic, which is different in many aspects from the previous severe acute respiratory syndrome (SARS) epidemic. We developed this model when the COVID-19 epidemic was at its early phase. We reasoned that, with our model, the effects of different measures could be assessed for infection control. Unlike the homogeneous models, our model accounts for human population heterogeneity, where subpopulations (e.g., age groups) have different infection risks. The heterogeneous model estimates several characteristics of the epidemic more accurately compared to the homogeneous models. According to our analysis, the total number of infections and their peak number are lower compared to the assessment with the homogeneous models. Furthermore, the early-stage infection increase is little changed when population heterogeneity is considered, whereas the late-stage infection decrease slows. The model predicts that the anti-epidemic measures, like the ones undertaken in China and the rest of the world, decrease the basic reproductive number but do not result in the development of a sufficient collective immunity, which poses a risk of a second wave. More recent developments confirmed our conclusion that the epidemic has a high likelihood to restart after the quarantine measures are lifted.