The mango tree in central and northern Benin: cultivar inventory, yield assessment, infested stages and loss due to fruit flies (Diptera Tephritidae)

Introduction. The mango tree is of prime importance to the rural economy of central and northern Benin since it provides food and crucial nutriments at the end of the dry season. However, mango producers in Benin are confronted with two problems that are closely connected: deterioration of fruit quality by fruit flies and the inadequacy of postharvest methods. In the Sudanian zone of Benin, fruit flies (Diptera Tephritidae) are highly responsible for major production losses. Materials and methods. An inventory of all present cultivars and yields of main mango cvs. (Gouverneur, Eldon, Dabshar, Kent, Smith, Keitt and Brooks) was carried out in Benin on 7 000 fruits near Parakou (Borgou) during the years 2005 and 2006. Sampling of 3 000 young fruits (length range 13–26 mm) was carried out in 2006 in order to detect some very early tephritid attacks. A loss assessment of pre-ripening and ripening fruits was also carried out on 7 750 fruits in 2006. Results and discussion. We identified 29 cultivars in the district of Borgou where 75% of the mango orchards of Benin are situated. Most of these use the ‘gatherer’ production system. The early Gouverneur cultivar had the lowest yield (1.8 t·ha–1), and the late Brooks cultivar had the highest yield (10.4 t·ha–1). Small and immature young fruits allowed development of both C. cosyra and B. invadens in February and March, i.e., before the mango season itself: this result could be a useful result for pest control. For the pre-ripening and ripening stages, average losses due to tephritid varied from 0.34 t·ha–1 to 6.5 t·ha–1 depending on cultivar type, resulting in considerable loss of income for small planters. Taking all cultivars together, losses stood at 17% in early April and exceeded 70% at mid-June. By the middle of the crop year, over 50% losses were recorded. The seasonal cultivar Eldon and late cultivars (Keitt and Brooks) were the most infested. Conclusion. In the Sudanian zone of Benin, the two main species of Tephritidae that have a high economic impact on mango trees are B. invadens and C. cosyra. Our preliminary observations and calculations will be used in a forthcoming article to calculate the economic injury level of these fruit flies.Introduction. Le manguier occupe une place paniculitrement importante dans l'dconotnierumle des zones centrales et. septentrionales du Bdnin. Pendant la fin de la saison siche, l:r mangue cons-titue un apport nutritionnel fondamental par sa forte teneur en nutriments. NIais. au Bdnin, les producteursde mangues sont confront6s i deux contraintes €troitement li6es l'une ir I'autre : ddfaut de <.;ualit6 dtr fruitimputable aux motrches des fnrits et insuffisance de techniqtres ad6quates de . post-recolte ". I)ans la zonesoudanienne b€ninoise, les mouches des fhrits (Diptera'l'ephriticlae) sont la contminte majeure respon-sable de pertes consid6rables de producrion. Materieletm€thodes. L'inventaire des cultivars presentsau Mali ei les estinations de rendentents des principaux cultivars de manguier ((louverneut, Eldon, Dabs-har. I{ent, Smith, Keitt, Brooks) ont 6t6 men6s sur 7000 fruils autour de Parakou durant lcs ann€es 2(X)5et 2006. Des 6chantillonnages de 3000 petits fruils (de 13 mm l 26 r.r.rm) ont 6t6 r6alis6s en 2006 atln desavoir quels 6taient les stades les plus pr€coces i 6tre atrrqu€s. Des estimntions de pertes au nivcau clesstacles de pr€raturit6 err maturit6 de 7 750 fruits ont €t6 6galerncnt l)ites en 2006. Resultats €t discus-slon. Notis avons identifi€ 29 cultivars dans le ddpartement clu f3orgou qui concentrc environ 75 o'n desvergers de manguiers du B€nin. La pluparr d'entrc euK +.pllartiennent ) un.systdme de production de . type,cueillette '. Le cultivar pr€coce Gouverneur (1,8 t ha-r) e eu le plus faible endement t le cultivar tardifBrooks a eu le renclement le plus €lev€ (10,,1 f ha-l). Les petititiuits immaturcs ()nt pcnnis le d6vekrp-pernent complet <Je C. cosj,ra comrne de B. inuadens clurant les rnois de f6i,rier et mars, soit bien:lvantla campagne mangue ; cela pourrait avoir des applications sur la lutte. Pour les stadqs dc pr€-mrturiteet niattirii6 cles fiuiis, les pe.tbs t.oyennes dues aL" 'l'.ph.iti,1"" ont vari€ de 0.34 t htl ) 6,5't ha-l sclonles cultivars et elles ont occasionnd rlne perte de revenus consid6rable pour Ies petits planteurs. En prellallten compte la moyenne de tous les cultivars, les prtes atteigtrcnt 17 94r en d6but d'avril pttur d€passer700,odIa mi-juin. Plus de 50 9zo de pertes ont 6t€ enregistr6s au milieu de la campagne de mangue. Lecv. de saison Eldon et les cv. tllrdifs (Kein, Brooks) ont 6t6les plus infest€s. Conclusion. Les cleux espicesma jeures de'ltphritidae cl'inta:ret €conornique pour le rnanguier dans le Norcl du B€nin sont ts. inuaderset C. cosyra. Cette 6tude prdlirninaire devrait servir de base au calcul d'un seuil 6conomique de nuisibilitdde ces Tephritidae du manguier dans un prochain anicle

Long-Term Effects of Cancer Survivorship on the Employment of Older Workers

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good

Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths

Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-α and/or IFN-ω are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-α and/or IFN-ω (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients &gt;80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-β. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-α and/or IFN-ω are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% &gt;80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals &lt;70 years, 2.3% between 70 and 80 years, and 6.3% &gt;80 years. By contrast, auto-Abs neutralizing IFN-β do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases. © 2021 The Authors, some rights reserved

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran