Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1,700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Writing a Novella to Introduce Tourism Objects in Palembang

This research was aimed at knowing how to write a novella entitled “Allied” to promote several tourism destinations in Palembang. In research, the researchers used research and development (R&D) method. The researchers conducted two steps of R&D method: (1) preliminary study, which involved literature study, field study, and product drafting, and (2) model development, which were limited testing and wider testing. The researchers implemented these steps by developing the product. The data were collected from observation and interview in preliminary study step. In model development, the draft of novella regarding the language, structure of sentence, and the content of the story was revised by several experts. The novella was written in English by using Final Draft as the software of writing a story. The story was wrapped in action genre by bringing some mystery in tourism destinations in Palembang that would entertain and educate the readers at the same time. Additionally, in writing a novella entitled “Allied” there were three steps that were conducted with R&D method by Sukmadinata (2015) which the steps were linked to the sixth steps of writing a story by Grenville (2001): Getting Ideas, Choosing, Outlining, Drafting, Revising, and Editing.

Pengembangan media pembelajaran berbantuan komputer berbasis RME untuk siswa SMP kelas VII pada materi perbandingan

This research and development is motivated by student’s difficulties in learning proportion material especially in determining the direct and inverse proportion material. The students are difficult to distinguish problem about direct and inverse proportion material. Moreover, at this time there has been no development of computer assisted learning media based RME on direct and inverse proportion material. This development aims to produce a computer assisted learning media on direct and inverse proportion material which are valid, practical, and effective. The result of research and development is “GO!MATH” courseware in the form of executable file in CD that can be use independently by students both in classroom or at home. This research and development is done by adapting Thiagarajan development model which only up to the third stage, because the stage develope due only for limited testing stage. The limited testing in this research involved nine student of SMP Brawijaya Smart School Malang which has heterogeneous ability. The results show that “GO!MATH” are valid, practical, and effective.

Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values

Background Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR. Methods We calculated age specific (0–69, 70–79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time. Results The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic. Conclusions We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

Methodology and applicability of the human contact burn injury model: A systematic review

The contact burn injury model is an experimental contact thermode-based physiological pain model primarily applied in research of drug efficacy in humans. The employment of the contact burn injury model across studies has been inconsistent regarding essential methodological variables, challenging the validity of the model. This systematic review analyzes methodologies, outcomes, and research applications of the contact burn injury model. Based on these results, we propose an improved contact burn injury testing paradigm. A literature search was conducted (15-JUL-2020) using PubMed, EMBASE, Web of Science, and Google Scholar. Sixty-four studies were included. The contact burn injury model induced consistent levels of primary and secondary hyperalgesia. However, the analyses revealed variations in the methodology of the contact burn injury heating paradigm and the post-burn application of test stimuli. The contact burn injury model had limited testing sensitivity in demonstrating analgesic efficacy. There was a weak correlation between experimental and clinical pain intensity variables. The data analysis was limited by the methodological heterogenicity of the different studies and a high risk of bias across the studies. In conclusion, although the contact burn injury model provides robust hyperalgesia, it has limited efficacy in testing analgesic drug response. Recommendations for future use of the model are being provided, but further research is needed to improve the sensitivity of the contact burn injury method. The protocol for this review has been published in PROSPERO (ID: CRD42019133734).

Effective downy mildew management in basil using resistant varieties, environment modifications, and fungicides

Considerable progress has been made in managing Peronospora belbahrii, an oomycete pathogen that causes basil downy mildew, since 2007, when it was first detected in the United States (U.S.). Conventional fungicides have been registered and shown effective against P. belbahrii in replicated experiments in recent years. Unfortunately, because of their specific modes of action and P. belbahrii biology, some are at risk for resistance development which has been documented outside the U.S. Sweet basil varieties have been developed and commercialized, with most exhibiting good to high levels of resistance to basil downy mildew. Knowledge about conditions favorable for infection and disease development has resulted in the identification of cultural practices for managing basil downy mildew in the greenhouse. Practices being implemented include fans to move leaves, thus preventing water deposition and decreasing relative humidity, lighting at night to mitigate sporulation, and temperature modification to suppress disease development. While downy mildew can be more effectively managed today, growers still experience losses, particularly when conditions are highly favorable for disease development. None of the organic fungicides or programs tested have provided adequate control for susceptible varieties; and limited testing has been done on resistant varieties to date. This review aims to summarize effective basil downy mildew management tools, in particular downy mildew-resistant varieties, environment modifications, and fungicide applications.

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.

Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

With the ongoing outbreak of the COVID-19 global pandemic, the research community still struggles to develop early and reliable prediction and detection mechanisms for this infectious disease. The commonly used RT-PCR test is not readily available in areas with limited testing facilities, and it lags in performance and timeliness. This paper proposes a deep transfer learning-based approach to predict and detect COVID-19 from digital chest radiographs. In this study, three pre-trained convolutional neural network-based models (VGG16, ResNet18, and DenseNet121) have been fine tuned to detect COVID-19 infected patients from chest X-rays (CXRs). The most efficient model is further used to identify the affected regions using an unsupervised gradient-based localization technique. The proposed system uses a classification approach (normal vs. COVID-19 vs. pneumonia vs. lung opacity) using three supervised classification algorithms followed by gradient-based localization. The training, validation and testing of the system are performed using 21165 CXR images (10192 normal, 1345 pneumonia, 3616 COVID-19, and 6012 lung opacity). Simulation and evaluation results are presented using standard performance metrics, viz, accuracy, sensitivity, and specificity.

Validitas Perangkat Pembelajaran Fisika Dengan Model Post Organizer Berbantuan Video

The purpose of this study is to determine the validity of the physics learning devices with post organizer models assisted by videos. The research design used research and development (R & D) design with 4D model (define, design, develop, & disseminate). Learning tools developed are sylabus, lesson plan, learner worksheets, understanding of concept, and learning video. This paper only focused on the process of testing the validation of the contents and constructs of the learning tool. This study is restricted to limited testing by taking the results of validation from the validator and questionnaire of response from students. Validation results using CVR and CVI indicate learning tools in the very feasible categories and reliable. The results of limited trial show that students’ response of 99.94% and 98.31% with very good categories. So it can be concluded that tools with post organizer models assisted by learning videos are feasible and can be used in physics learning.