Serological assays for differentiating natural COVID-19 infection from vaccine induced immunity

BACKGROUND: Natural SARS-CoV-2 infection may elicit antibodies to a range of viral proteins including non-structural protein ORF8. RNA, adenovirus vectored and sub-unit vaccines expressing SARS-CoV-2 spike would be only expected to elicit S-antibodies and antibodies to distinct domains of nucleocapsid (N) protein may reliably differentiate infection from vaccine-elicited antibody. However, inactivated whole virus vaccines may potentially elicit antibody to wider range of viral proteins, including N protein. We hypothesized that antibody to ORF8 protein will discriminate natural infection from vaccination irrespective of vaccine type. METHODS: We optimized and validated the anti-ORF8 and anti-N C-terminal domain (NCTD) ELISA assays using sera from pre-pandemic, RT-PCR confirmed natural infection sera and BNT162b2 (BNT) or CoronaVac vaccinees. We then applied these optimized assays to a cohort of blood donor sera collected in April-July 2022 with known vaccination and self-reported infection status. RESULTS: We optimized cut-off values for the anti-ORF8 and anti-N-CTD IgG ELISA assays using receiver-operating-characteristic (ROC) curves. The sensitivity of the anti-ORF8 and anti-N-CTD ELISA for detecting past infection was 83.2% and 99.3%, respectively. Specificity of anti-ORF8 ELISA was 96.8 % vs. the pre-pandemic cohort or 93% considering the pre-pandemic and vaccine cohorts together. The anti-N-CTD ELISA specificity of 98.9% in the pre-pandemic cohort, 93% in BNT vaccinated and only 4 % in CoronaVac vaccinated cohorts. Anti-N-CTD antibody was longer-lived than anti-ORF8 antibody after natural infection. CONCLUSIONS: Anti-N-CTD antibody assays provide good discrimination between natural infection and vaccination in BNT162b2 vaccinated individuals. Anti-ORF8 antibody can help discriminate infection from vaccination in either type of vaccine and help estimate infection attack rates (IAR) in communities

Medium term load demand forecast of Kano zone using neural network algorithms

Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand

Weed Persistence, Crop Resistance and Phytotonic Effects of Herbicides in Maize (Zea mays) Production Under Different Weed Control Method and Poultry Manure in Kano State Nigeria

The research is financed by centre for dry land Agriculture Bayero University Kano for providing financial support and the management of Agronomy Department Bayero University, for providing technical support throughout the duration of the study (Sponsoring information). Abstract Results of experiment conducted during rainy season of 2016 and 2017 at teaching and research farm of Bayero university Kano, Livestock development centre Dangora with two different pre-emergence herbicides and two post-emergence herbicides all applied at two different rates, (S-Metolachlor 290 g/l + Atrazine 370 g/l at 2.0 kg a.i. ha-1 followed by Nicosulfuron at 100 g a.i.ha-1, S-Metolachlor 290 g/l + Atrazine 370 g/l 2.0 kg a.i. ha-1 followed by Bentazone at 2.5 kg a.i. ha-1, Metolachlor 375 g/l + Terbuthylazine 125 g/l + Mesotrione 37.5 g/l at 2.5 kg a.i ha-1 followed by Nicosulfuron at 100 g a.i. ha-1, Metolachlor 375 g/l + Terbuthylazine 125 g/l + Mesotrione 37.5 g/l at 2.5 kg a.i ha-1 followed by Bentazone at 2.5 kg a.i. ha-1, Two hoe weeded at 3 and 6 WAS and weedy check) three levels of poultry manure (0, 4, and 8 t ha-1) and NPK at the rate of 120kg N, 60kg P and 60kg K ha-1 and three maize varieties (SAMMAZ 15, SAMMAZ 21 and SAMMAZ 35). The experiments was laid out in a split-split plot design with variety allocated to the main plot, poultry manure to the sub-plot and weed control method to the sub-sub plot, and was replicated three times. The result from the study showed that two hoe weeding at 3 and 6WAS and Application of 3Maizeforce at 2.5 kg a.i. ha-1 followed by Bentazone at 2.5 kg a.i. ha-1, significantly revealed higher crop resistance index with medium persistence index of the weeds indicating broad spectrum effect in controlling the weeds, the said treatments were best herbicides for maize production. Furthermore the said treatment is recommended for weed control that can improve maize grain yield. Keywords: Weed Occurrence level, Weed persistence index, Crop resistance index and Phytotonic effect. DOI: 10.7176/JBAH/10-10-03 Publication date:May 31st 202

Estimation of turbidity in water treatment plant using hammerstein-wiener and neural network technique

Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant

Estimation of pH and MLSS using neural network

The main challenges to achieving a reliable model which can predict well the process are the nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent approaches revolved as better alternative in predicting the system. Typical measured variables for effluent quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feed- forward neural network techniques as nonlinear function approximators have demonstrated the capability of predicting nonlinear behaviour of the system. The data for the period of two years and nine months sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The ANFIS model may serves as a valuable prediction tool for the plant

An upstream open reading frame modulates ebola virus polymerase translation and virus replication

Ebolaviruses, highly lethal zoonotic pathogens, possess longer genomes than most other non-segmented negative-strand RNA viruses due in part to long 5' and 3' untranslated regions (UTRs) present in the seven viral transcriptional units. To date, specific functions have not been assigned to these UTRs. With reporter assays, we demonstrated that the Zaire ebolavirus (EBOV) 5'-UTRs lack internal ribosomal entry site function. However, the 5'-UTRs do differentially regulate cap-dependent translation when placed upstream of a GFP reporter gene. Most dramatically, the 5'-UTR derived from the viral polymerase (L) mRNA strongly suppressed translation of GFP compared to a β-actin 5'-UTR. The L 5'-UTR is one of four viral genes to possess upstream AUGs (uAUGs), and ablation of each uAUG enhanced translation of the primary ORF (pORF), most dramatically in the case of the L 5'-UTR. The L uAUG was sufficient to initiate translation, is surrounded by a "weak" Kozak sequence and suppressed pORF translation in a position-dependent manner. Under conditions where eIF2α was phosphorylated, the presence of the uORF maintained translation of the L pORF, indicating that the uORF modulates L translation in response to cellular stress. To directly address the role of the L uAUG in virus replication, a recombinant EBOV was generated in which the L uAUG was mutated to UCG. Strikingly, mutating two nucleotides outside of previously-defined protein coding and cis-acting regulatory sequences attenuated virus growth to titers 10-100-fold lower than a wild-type virus in Vero and A549 cells. The mutant virus also exhibited decreased viral RNA synthesis as early as 6 hours post-infection and enhanced sensitivity to the stress inducer thapsigargin. Cumulatively, these data identify novel mechanisms by which EBOV regulates its polymerase expression, demonstrate their relevance to virus replication and identify a potential therapeutic target

Pushing the Frontiers of Biodiversity Research: Unveiling the Global Diversity, Distribution, and Conservation of Fungi

Fungi comprise approximately 20% of all eukaryotic species and are connected to virtually all life forms on Earth. Yet, their diversity remains contentious, their distribution elusive, and their conservation neglected. We aim to flip this situation by synthesizing current knowledge. We present a revised estimate of 2–3 million fungal species with a “best estimate” at 2.5 million. To name the unknown >90% of these by the end of this century, we propose recognition of species known only from DNA data and call for large-scale sampling campaigns. We present an updated global map of fungal richness, highlighting tropical and temperate ecoregions of high diversity. We call for further Red List assessments and enhanced management guidelines to aid fungal conservation. Given that fungi play an inseparable role in our lives and in all ecosystems, and considering the fascinating questions remaining to be answered, we argue that fungi constitute the next frontier of biodiversity research

Brazilian cross-cultural adaptation and validation of the List of Threatening Events Questionnaire (LTE-Q)

Objective: To perform a construct validation of the List of Threatening Events Questionnaire (LTE-Q), as well as convergence validation by identifying its association with drug use in a sample of the Brazilian population. Methods: This is a secondary analysis of the Second Brazilian National Alcohol and Drugs Survey (II BNADS), which used a cross-cultural adaptation of the LTE-Q in a probabilistic sample of 4,607 participants aged 14 years and older. Latent class analysis was used to validate the latent trait adversity (which considered the number of events from the list of 12 item in the LTE experienced by the respondent in the previous year) and logistic regression was performed to find its association with binge drinking and cocaine use. Results: The confirmatory factor analysis returned a chi-square of 108.341, weighted root mean square residual (WRMR) of 1.240, confirmatory fit indices (CFI) of 0.970, Tucker-Lewis index (TLI) of 0.962, and root mean square error approximation (RMSEA) score of 1.000. LTE-Q convergence validation showed that the adversity latent trait increased the chances of binge drinking by 1.31 time and doubled the chances of previous year cocaine use (adjusted by sociodemographic variables). Conclusion: The use of the LTE-Q in Brazil should be encouraged in different research fields, including large epidemiological surveys, as it is also appropriate when time and budget are limited. The LTE-Q can be a useful tool in the development of targeted and more efficient prevention strategies.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)Univ Fed Sao Paulo UNIFESP, Inst Nacl Pesquisa Alcool & Outras Drogas INCT IN, Sao Paulo, SP, BrazilUniv Fed Sao Paulo UNIFESP, Dept Psiquiatria, Sao Paulo, SP, BrazilUniv Municipal Sao Caetano USCS, Escola Saude, Sao Caetano do Sul, SP, BrazilPacific Inst Res & Evaluat, Oakland, CA USAUniv Fed Sao Paulo UNIFESP, Inst Nacl Pesquisa Alcool & Outras Drogas INCT IN, Sao Paulo, SP, BrazilUniv Fed Sao Paulo UNIFESP, Dept Psiquiatria, Sao Paulo, SP, BrazilWeb of Scienc

Human And Murine Ifit1 Proteins Do Not Restrict Infection Of Negative-sense Rna Viruses Of The Orthomyxoviridae, Bunyaviridae, And Filoviridae Families

Interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) is a host protein with reported cell-intrinsic antiviral activity against several RNA viruses. The proposed basis for the activity against negative-sense RNA viruses is the binding to exposed 5'-triphosphates (5'-ppp) on the genome of viral RNA. However, recent studies reported relatively low binding affinities of IFIT1 for 5'-ppp RNA, suggesting that IFIT1 may not interact efficiently with this moiety under physiological conditions. To evaluate the ability of IFIT1 to have an impact on negative-sense RNA viruses, we infected Ifit1(-/-) and wild-type control mice and primary cells with four negative-sense RNA viruses (influenza A virus [IAV], La Crosse virus [LACV], Oropouche virus [OROV], and Ebola virus) corresponding to three distinct families. Unexpectedly, a lack of Ifit1 gene expression did not result in increased infection by any of these viruses in cell culture. Analogously, morbidity, mortality, and viral burdens in tissues were identical between Ifit1(-/-) and control mice after infection with IAV, LACV, or OROV. Finally, deletion of the human IFIT1 protein in A549 cells did not affect IAV replication or infection, and reciprocally, ectopic expression of IFIT1 in HEK293T cells did not inhibit IAV infection. To explain the lack of antiviral activity against IAV, we measured the binding affinity of IFIT1 for RNA oligonucleotides resembling the 5' ends of IAV gene segments. The affinity for 5'-ppp RNA was approximately 10-fold lower than that for non-2'-O-methylated (cap 0) RNA oligonucleotides. Based on this analysis, we conclude that IFIT1 is not a dominant restriction factor against negative-sense RNA viruses. IMPORTANCE Negative-sense RNA viruses, including influenza virus and Ebola virus, have been responsible for some of the most deadly outbreaks in recent history. The host interferon response and induction of antiviral genes contribute to the control of infections by these viruses. IFIT1 is highly induced after virus infection and reportedly has antiviral activity against several RNA and DNA viruses. However, its role in restricting infection by negative-sense RNA viruses remains unclear. In this study, we evaluated the ability of IFIT1 to inhibit negative-sense RNA virus replication and pathogenesis both in vitro and in vivo. Detailed cell culture and animal studies demonstrated that IFIT1 is not a dominant restriction factor against three different families of negative-sense RNA viruses.891894659476Division of Intramural Research, NIAID, NIHNIH grant [F32 AI112274][U54 AI057160][R01 AI104972][R01 AI104002