Predicting the occurrence of surplus and deficit net radiation in Ibadan, Nigeria

This study aims at predicting the occurrence of surplus and deficit net radiation in Ibadan, Nigeria. Thirty-four (34) years data (1977-2010) on daily maximum and minimum Relative-Humidity, Solar irradiance and maximum and minimum Air temperature were sourced from the International Institute of Tropical Agriculture (IITA) and used in the analysis. The Penman-Monteith (FAO-56) step by step method was used to compute net radiation in Ibadan. A two – state (surplus and deficit net radiation) Markov Chain model was developed and used in this work. The monthly transition counts, transition probability matrix, n-step transition matrix (power matrix), steady state probability vector and the vector of mean reoccurrence times (in days) were determined each for the two states. The model was also used in predicting the chance occurrence of surplus and deficit net radiation for one year. The average monthly net radiation is surplus (positive) in the months of February, March, April, May, June, October, November and December, while it is deficit (negative) in January, July, August, and September. The study also reveals a 69%, 76%, 76%, 74%, 63%, 63%, 70% and 52% chance occurrence of surplus net radiation in the months of February, March, April, May, June, October, November and December, while a 54%, 64%, 76% and 55% chance of deficit net radiation occurring in the months of January, July, August and September respectively using the Markov Chain model (steady states probabilities). The mean reoccurrences times (in days) analysis reveals that, on the average it takes: 1.4 days for surplus net radiation and 3.5 days for deficit net radiation to reoccur in the months of February, March, April, May, June, October, November and December; 3.9 days for deficit net radiation and 1.3 days for surplus net radiation to reoccur in the months of January, July, August and September. This explains why the air temperature of Ibadan is warmer in the months of February, March, April, May, June, October, November and December, and colder in January, July, August and September. The weather/climate is extremely warm in March and April, and extremely cold in August as revealed by the proportions of surplus and deficit net radiation for each month of the year

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Distribution and intensity of airborne diseases in Benue State of Nigeria

The prevalence of airborne diseases in Benue State of Nigeria has been studied for over a period of eight years from 1993 – 2000. The study was based on a review of epidemiological data collected from clinics and hospitals in major cities of the state. Diseases prevalent include allergic asthma, pulmonary tuberculosis, pneumonia and upper respiratory tract infection (URTI). Out of the 5,431 patients treated for the diseases in the state, 257 died. Gboko, Otukpo, Katsina-Ala and Makurdi recorded the highest number with 53% of the dead caused by pneumoconiosis. The ambient air quality in the state is worse than national and international ambient air quality standards. This gives an indication of a strong correlation between the diseases and the air pollutants. The resulting impact of the diseases on the quality of life and productivity of the people is discussed. For a robust economic development and clean environment, we recommend the introduction of counseling on the prevention and control of air pollution, as well as environmental ethics, into the primary and secondary education curricula. Keywords: airborne diseases, Benue State, Nigeria Nigerian Journal of Physics Vol. 17, 2005: 50-5

Serum 8,12-iso-iPF2α-VI isoprostane marker of oxidative damage and cognition deficits in children with konzo.

We sought to determine whether motor and cognitive deficits associated with cassava (food) cyanogenic poisoning were associated with high concentrations of F2-isoprostanes, well-established indicators of oxidative damage. Concentrations of serum F2-isoprostanes were quantified by LC-MS/MS and anchored to measures of motor proficiency and cognitive performance, which were respectively assessed through BOT-2 (Bruininks/Oseretsky Test, 2nd Edition) and KABC-II (Kaufman Assessment Battery for Children, 2nd edition) testing of 40 Congolese children (21 with konzo and 19 presumably healthy controls, overall mean age (SD): 9.3 (3.2) years). Exposure to cyanide was ascertained by concentrations of its main metabolite thiocyanate (SCN) in plasma and urine. Overall, SCN concentrations ranged from 91 to 325 and 172 to 1032 µmol/l in plasma and urine, respectively. Serum isoprostanes ranged from 0.1 to 0.8 (Isoprostane-III), 0.8 to 8.3 (total Isoprostane-III), 0.1 to 1.5 (Isoprostane-VI), 2.0 to 9.0 (total Isoprostane-VI), or 0.2 to 1.3 ng/ml (8,12-iso-iPF2α-VI isoprostane). Children with konzo poorly performed at the BOT-2 and KABC-II testing relative to presumably healthy children (p<0.01). Within regression models adjusting for age, gender, motor proficiency, and other biochemical variables, 8,12-iso-iPF2α-VI isoprostane was significantly associated with the overall cognitive performance (β = -32.36 (95% CI: -51.59 to -13.03; P<0.001). This model explained over 85% of variation of the KABC-II score in children with konzo, but was not significant in explaining the motor proficiency impairment. These findings suggest that cognitive deficits and, possibly, brain injury associated with cassava poisoning is mediated in part by oxidative damage in children with konzo. 8,12-iso-iPF2α-VI isoprostane appears to be a good marker of the neuropathogenic mechanisms of konzo and may be used to monitor the impact of interventional trials to prevent the neurotoxic effects of cassava cyanogenic poisoning

Correlations between motor/cognition performance scores and levels of serum isoprostanes.

<p>Low motor or cognition performance significantly correlated with high concentrations of 8,12-iso-iPF2alpha-VI isoprostane in children affected by konzo. (A) MPI (mental processing index) also referred to as KABC-II scores in main text versus serum level of 8,12-iso-iPF2α-VI isoprostane (triangles  =  konzo children, r = −0.78, p = 0.00; circles  =  non-konzo children, r = −0.24, p = 0.47). (B) BOT-2 scores versus serum level of 8,12-iso-iPF2α-VI isoprostane (triangles  =  konzo children, r = −0.63, p<0.01; circles  =  non-konzo children, r = −0.06, p = 0.86).</p

Gender-specific concentrations of serum isoprostanes (ng/ml).

<p>Females appeared to have higher concentrations of total isoprostane-VI relative to males.</p><p>Gender-specific concentrations of serum isoprostanes (ng/ml).</p

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Illustrative example of neurodisability in konzo.

<p>(A) Spastic stance in a boy severely affected by konzo. (B) Depiction of deficits at KABC-II cognition testing. (C) Deficits in fine motor control at the BOT-2 testing.</p