A comparison of five epidemiological models for transmission of SARS-CoV-2 in India.

BACKGROUND: Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). METHODS: Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. RESULTS: For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. CONCLUSIONS: In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

Association between particulate matter air pollution and risk of depression and suicide: systematic review and meta-analysis.

BACKGROUND: Some recent studies examined the effect of ambient particulate matter (PM) pollution on depression and suicide. However, the results have been inconclusive.AimsTo determine the overall relationship between PM exposure and depression/suicide in the general population. METHOD: We conducted a systematic review and meta-analysis of case-crossover and cohort studies to assess the association between PM2.5 (particles with an aerodynamic diameter of 2.5 µm or less) or PM10 (particles with an aerodynamic diameter between 2.5 and 10 µm) exposure and depression/suicide. RESULTS: A total of 14 articles (7 for depression and 7 for suicide) with data from 684 859 participants were included in the meta-analysis. With a 10 µg/m3 increase in PM2.5 we found a 19% (odds ratio [95% CI] 1.19 [1.07, 1.33]) increased risk of depression and a marginally increased risk of suicide (odds ratio [95% CI] 1.05 [0.99, 1.11]) in the general population. We did not observe any significant associations between increasing exposure to PM10 and depression/suicide. Sensitivity and subgroup analyses were used to determine the robustness of results. The strongest estimated effect of depression associated with PM2.5 appeared in a long-term lag pattern (odds ratio [95% CI] 1.25 [1.07, 1.45], P < 0.01) and cumulative lag pattern (odds ratio [95% CI] 1.26 [1.07, 1.48], P < 0.01). CONCLUSIONS: The meta-analysis suggested that an increase in ambient PM2.5 concentration was strongly associated with increased depression risk in the general population, and the association appeared stronger at long-term lag and cumulative lag patterns, suggesting a potential cumulative exposure effect over time.Declaration of interestNone.

Research on Emotion Recognition of EEG Signal Based on Convolutional Neural Networks and High-Order Cross-Analysis.

Emotion recognition means the automatic identification of a human's emotional state by obtaining his/her physiological or nonphysiological signals. The EEG-based method is an effective mechanism, which is commonly used for the recognition of emotions in real environments. In this paper, the convolutional neural network is used to classify the EEG signal into three and four emotional states under the DEAP dataset, which is defined as a Database for Emotion Analysis using physiological signals. For this purpose, a high-order cross-feature sample is extracted to recognize the emotional state with a single channel. A seven-layer convolutional neural network is used to classify the 32-channel EEG signal, and the average accuracy of four and three emotional states is 65% and 58.62%. The single-channel high-order cross-sample is classified with convolutional neural networks, and the average accuracy of four emotional states is 43.5%. Among all the channels related to emotion recognition, the F4 channel gets the best classification accuracy of 44.25%, and the average accuracy of the even number channel is higher than the odd number channel. The proposed method provides a basis for the real-time application of EEG-based emotion recognition.

COVID-19 PREDICTION IN SOUTH AFRICA: ESTIMATING THE UNASCERTAINED CASES- THE HIDDEN PART OF THE EPIDEMIOLOGICAL ICEBERG.

Understanding the impact of non-pharmaceutical interventions as well as accounting for the unascertained cases remain critical challenges for epidemiological models for understanding the transmission dynamics of COVID-19 spread. In this paper, we propose a new epidemiological model (eSEIRD) that extends the widely used epidemiological models such as extended Susceptible-Infected-Removed model (eSIR) and SAPHIRE (initially developed and used for analyzing data from Wuhan). We fit these models to the daily ascertained infected (and removed) cases from March 15, 2020 to Dec 31, 2020 in South Africa that reported the largest number of confirmed COVID-19 cases and deaths from the WHO African region. Using the eSEIRD model, the COVID-19 transmission dynamics in South Africa was characterized by the estimated basic reproduction number (R 0) starting at 3.22 (95%CrI: [3.19, 3.23]) then dropping below 2 following a mandatory lockdown implementation and subsequently increasing to 3.27 (95%CrI: [3.27, 3.27]) by the end of 2020. The initial decrease of effective reproduction number followed by an increase suggest the effectiveness of early interventions and the combined effect of relaxing strict interventions and emergence of a new coronavirus variant in South Africa. The low estimated ascertainment rate was found to vary from 1.65% to 9.17% across models and time periods. The overall infection fatality ratio (IFR) was estimated as 0.06% (95%CrI: [0.04%, 0.22%]) accounting for unascertained cases and deaths while the reported case fatality ratio was 2.88% (95% CrI: [2.45%, 6.01%]). The models predict that from December 31, 2020, to April 1, 2021, the predicted cumulative number of infected would reach roughly 70% of total population in South Africa. Besides providing insights on the COVID-19 dynamics in South Africa, we develop powerful forecasting tools that enable estimation of ascertainment rates and IFR while quantifying the effect of intervention measures on COVID-19 spread.

Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning.

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.

Prefrontal fNIRS-based clinical data analysis of brain functions in individuals abusing different types of drugs.

BACKGROUND: The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain. METHODS: Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics. RESULTS: Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres. CONCLUSIONS: The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

TaWRKY13-A Serves as a Mediator of Jasmonic Acid-Related Leaf Senescence by Modulating Jasmonic Acid Biosynthesis.

Leaf senescence is crucial for crop yield and quality. Transcriptional regulation is a key step for integrating various senescence-related signals into the nucleus. However, few regulators of senescence implicating transcriptional events have been functionally characterized in wheat. Based on our RNA-seq data, we identified a WRKY transcription factor, TaWRKY13-A, that predominately expresses at senescent stages. By using the virus-induced gene silencing (VIGS) method, we manifested impaired transcription of TaWRKY13-A leading to a delayed leaf senescence phenotype in wheat. Moreover, the overexpression (OE) of TaWRKY13-A accelerated the onset of leaf senescence under both natural growth condition and darkness in Brachypodium distachyon and Arabidopsis thaliana. Furthermore, by physiological and molecular investigations, we verified that TaWRKY13-A participates in the regulation of leaf senescence via jasmonic acid (JA) pathway. The expression of JA biosynthetic genes, including AtLOX6, was altered in TaWRKY13-A-overexpressing Arabidopsis. We also demonstrated that TaWRKY13-A can interact with the promoter of AtLOX6 and TaLOX6 by using the electrophoretic mobility shift assay (EMSA) and luciferase reporter system. Consistently, we detected a higher JA level in TaWRKY13-A-overexpressing lines than that in Col-0. Moreover, our data suggested that TaWRKY13-A is partially functional conserved with AtWRKY53 in age-dependent leaf senescence. Collectively, this study manifests TaWRKY13-A as a positive regulator of JA-related leaf senescence, which could be a new clue for molecular breeding in wheat.

Association between particulate matter air pollution and risk of depression and suicide: a systematic review and meta-analysis.

An increasing number of studies examined the potential effects of ambient particulate matter (PM: PM2.5 and PM10-PMs with diameters not greater than 2.5 and 10 µm, respectively) pollution on the risk of depression and suicide; however, the results have been inconclusive. This study aimed to determine the overall relationship between PM exposure and depression/suicide based on current evidence. We conducted a systematic review and meta-analysis of current available studies. Thirty articles (20 for depression and 10 for suicide) with data from 1,447,313 participants were included in the meta-analysis. For a 10 µg/m3 increase in short-term exposure to PM2.5, we found a 2% (p < 0.001) increased the risk of depression and a 2% (p = 0.001) increased risk of suicide. A 10 µg/m3 increase in long-term exposure to PM2.5 was associated with a more apparent increase of 18% (p = 0.005) in depression risk. In addition, a 10 µg/m3 increase in short-term exposure to PM10 was associated with a 2% (p = 0.003) increase in depression risk and a 1% (p = 0.002) increase in suicide risk. Subgroup analyses showed that associations between PM and depression were more apparent in people over 65 years and from developed regions. Besides, the study design and study quality might also have an impact on their associations. The meta-analysis found that an increase in ambient PM concentration was strongly associated with an increased risk of depression and suicide, and the associations for depression appeared stronger for smaller particles (PM2.5) and at a long-term time pattern.

Association Between Ambient Air Pollution and Daily Hospital Admissions for Depression in 75 Chinese Cities.

OBJECTIVE: Although the association between ambient air pollution and risk of depression has been investigated in several epidemiological studies, the evidence is still lacking for hospital admissions for depression, which indicates a more severe form of depressive episode. The authors used national morbidity data to investigate the association between short-term exposure to ambient air pollution and daily hospital admissions for depression. METHODS: Using data from the Chinese national medical insurance databases for urban populations, the authors conducted a two-stage time-series analysis to investigate the associations of short-term exposure to major ambient air pollutants-fine particles (PM2.5), inhalable particles (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO)-and daily hospital admission risk for depression in 75 Chinese cities during the period 2013-2017. RESULTS: The authors identified 111,620 hospital admissions for depression in 75 cities. In the single-pollutant models, the effect estimates of all included air pollutants, with the exception of O3, were significant at several lags within 7 days. For example, 10 µg/m3 increases in PM2.5, PM10, and NO2 at lag01 were associated with increases of 0.52% (95% CI=0.03, 1.01), 0.41% (95% CI=0.05, 0.78), and 1.78% (95% CI=0.73, 2.83), respectively, in daily hospital admissions for depression. Subgroup, sensitivity, and two-pollutant model analyses highlighted the robustness of the effect estimates for NO2. CONCLUSIONS: The study results suggest that short-term exposure to ambient air pollution is associated with an increased risk of daily hospital admission for depression in the general urban population in China, which may have important implications for improving mental wellness among the public.

The Novel Omega-6 Fatty Acid Docosapentaenoic Acid Positively Modulates Brain Innate Immune Response for Resolving Neuroinflammation at Early and Late Stages of Humanized APOE-Based Alzheimer's Disease Models.

Neuroinflammation plays a crucial role in the development and progression of Alzheimer's disease (AD), in which activated microglia are found to be associated with neurodegeneration. However, there is limited evidence showing how neuroinflammation and activated microglia are directly linked to neurodegeneration in vivo. Besides, there are currently no effective anti-inflammatory drugs for AD. In this study, we report on an effective anti-inflammatory lipid, linoleic acid (LA) metabolite docosapentaenoic acid (DPAn-6) treatment of aged humanized EFAD mice with advanced AD pathology. We also report the associations of neuroinflammatory and/or activated microglial markers with neurodegeneration in vivo. First, we found that dietary LA reduced proinflammatory cytokines of IL1-ß, IL-6, as well as mRNA expression of COX2 toward resolving neuroinflammation with an increase of IL-10 in adult AD models E3FAD and E4FAD mice. Brain fatty acid assays showed a five to six-fold increase in DPAn-6 by dietary LA, especially more in E4FAD mice, when compared to standard diet. Thus, we tested DPAn-6 in aged E4FAD mice. After DPAn-6 was administered to the E4FAD mice by oral gavage for three weeks, we found that DPAn-6 reduced microgliosis and mRNA expressions of inflammatory, microglial, and caspase markers. Further, DPAn-6 increased mRNA expressions of ADCYAP1, VGF, and neuronal pentraxin 2 in parallel, all of which were inversely correlated with inflammatory and microglial markers. Finally, both LA and DPAn-6 directly reduced mRNA expression of COX2 in amyloid-beta42 oligomer-challenged BV2 microglial cells. Together, these data indicated that DPAn-6 modulated neuroinflammatory responses toward resolution and improvement of neurodegeneration in the late stages of AD models.

A Framework on Optimization Strategy for EEG Motor Imagery Recognition.

At present, in the process of encephalogram motor imagery decoding, facing the background of big data analysis, it has the necessity to design an effective system which is subject-independent. Pre-training is common to carry out before each experiment, which affects the practicability of the EEG system. In order to solve this problem, the most feasible method is to design a unified framework for deep learning optimization, which could capture the spatial and spectral dependence of original motor imagery EEG signals according to the features extracted by CNN and the temporal dependence extracted by RNN-LSTM. The framework is superimposed from both end-to-end and time-frequency domains so as to retain and learn interpretable motor imagery features. In addition, artificial EEG signals can be automatically generated by training the generated adversarial network, which can generate the feature distribution similar to the original EEG signals, increase the capacity of EEG samples, and ultimately improve the classification performance and robustness of EEG motor imagery recognition. This deep learning framework can improve the classification accuracy of motor imagery for different subjects. In addition, the network can learn from the original data with the least amount of preprocessing, thus eliminating the time-consuming data preparation process.

Ambient particulate air pollution and circulating C-reactive protein level: A systematic review and meta-analysis.

BACKGROUND: Ambient particulate air pollution is a major threat to the cardiovascular health of people. Inflammation is an important component of the pathophysiological process that links air pollution and cardiovascular disease (CVD). A classical marker of inflammation-C-reactive protein (CRP), has been recognized as an independent predictor of CVD risk. Exposure to ambient particulate matter (PM) may cause systemic inflammatory response but its association with CRP has been inconsistently reported. OBJECTIVES: To estimate the potential effects of short-term and long-term exposures to ambient particulate air pollution on circulating CRP level based on previous epidemiological studies. METHODS: A systematic literature search of PubMed, Web of Science, Embase, and Scopus databases for publications up to January 2018 was conducted for studies reporting the association between ambient PM (PM2.5 or PM10, or both) and circulating CRP level. We performed a meta-analysis for the associations reported in individual studies using a random-effect model and evaluated the effect modification by major potential modifiers. RESULTS: This meta-analysis comprised data from 40 observational studies conducted on 244,681 participants. These included 32 (27 PM2.5 studies and 13 PM10 studies) and 11 (9 PM2.5 studies and 5 PM10 studies) studies that investigated the associations of CRP with short-term and long-term exposure to particulate air pollution, respectively. A 10 µg/m3 increase in short-term exposure to PM2.5 and PM10 was associated with increases of 0.83 % (95% CI: 0.30%, 1.37%) and 0.39% (95% CI: -0.04%, 0.82%) in CRP level, respectively, and a 10 µg/m3 increase in long-term exposure to PM2.5 and PM10 was associated with much higher increases of 18.01% (95% CI: 5.96%, 30.06%) and 5.61% (95% CI: 0.79%, 10.44%) in CRP level, respectively. The long-term exposure to particulate air pollution was more strongly associated with CRP level than short-term exposure and PM2.5 had a greater effect on CRP level than PM10. CONCLUSION: Exposure to ambient particulate air pollution is associated with elevated circulating CRP level suggesting an activated systemic inflammatory state upon exposure, which may explain the association between particulate air pollution and CVD risk.

Development of a quantitative PCR assay for residual mouse DNA and comparison of four sample purification methods for DNA isolation.

Reliable and sensitive assays are required to determine whether a pharmaceutical product meets current regulatory guidelines for residual host cell DNA. In this study, the sensitivity of the qPCR assay was significantly improved by targeting the repetitive elements of mouse genome. This improved method allowed for sensitive and accurate quantitation of mouse genomic DNA in the range of 1 to 10(6)pg/mL. In addition, four sample purification methods for DNA isolation (Wako DNA extractor kit, MasterPure™ DNA purification kit, PrepSEQ™ residual DNA sample preparation kit, and phenol-chloroform extraction method with addition of glycogen), each representing a different strategy for DNA isolation from proteinaceous solutions, were evaluated by isolating DNA from a mouse monoclonal IgG antibody. Among these methods, Wako DNA extractor kit and MasterPure™ DNA purification kit demonstrated superior DNA recovery, repeatability, and sensitivity, with quantitation limits of 1pg/mL. To further evaluate these two DNA isolation methods, six replicates of an unspiked mouse polyclonal IgG antibody sample were tested by both methods, and both methods demonstrated a good degree of precision. Therefore, the residual mouse DNA quantitation methods described here represented rapid, accurate, precise, and sensitive procedures that can be used in quality control testing for regulatory compliance in the pharmaceutical industry.

The evaluation of a novel approach for the profiling and identification of N-linked glycan with a procainamide tag by HPLC with fluorescent and mass spectrometric detection.

Procainamide was investigated as a multifunctional oligosaccharide label for glycan profiling and identification in a HPLC-FL/ESI-QTOF system. Addition of this aromatic amine to glycans through reductive amination improves fluorescence detection and ESI ionization efficiency. Both procainamide and 2-AB derivatives of N-linked glycans released from three glycoproteins (Human IgG, Mouse IgG, and RNase B) were quantitatively profiled with HPLC-FL and identified with ESI-QTOF. The procainamide derivatives produced FL glycan profiles comparable to the 2-AB derivatives, but with a few extra minor peaks, which suggests better labeling efficiency for procainamide derivatives for minor peaks. The procainamide derivatives also improve ESI ionization efficiency by 10-50 times over the respective 2-AB derivatives and the ESI-QTOF method sensitivity is at the low picomole to high femtomole level. Using the procainamide tag, all N-linked glycans released from three tested glycoproteins can be quantitatively detected with HPLC-FL and identified with ESI-QTOF at the same time. Monosaccharide sequence confirmation was also demonstrated in this study.