Apple Autotetraploids—Phenotypic Characterisation and Response to Drought Stress

Polyploidization is an important source of variability for plant breeding. Polyploids are often characterised by increased resistance to biotic and abiotic stresses. Since drought and pathogen attack are the main threats to apple cultivation, obtaining new sources of resistance is an important issue for apple breeding. The newly obtained autotetraploid clones of apple cv. ‘Redchief’ showed superior resistance to fire blight. The aim of the presented research was the in-depth phenotypic characterisation of ‘Redchief’ tetraploids and assessment of their response to drought at the physiological and genetic level. The growth of own-rooted five-year-old trees of ‘Redchief’ tetraploids was poor compared with diploids; all growth parameters—the number and length of current season shoots, the total length of current season shoots per tree and the cross-section area of the trunk—were reduced in tetraploid clones. Grafting on M9 rootstock improved the growth characteristics of ‘Redchief’ tetraploids. Compared with diploid plants, the leaves of tetraploids were thicker, with altered shape, higher chlorophyll content, and larger stomata, but the stomatal density decreased. The leaf anatomical structure of tetraploids was changed, the adaxial and abaxial epidermis and both types of mesophyll were significantly thicker than in diploids. Moreover, the pollen grains of tetraploids were larger, but their viability and germination were reduced. Under conditions of limited water supply, the reduction in growth parameters was smaller and the physiological parameters were higher in the ‘Redchief’ tetraploid clone 4x-25 than in diploid plants. The expression of APX gene was higher in tetraploids than in diploids 15 days after drought stress induction. The results suggest the enhanced drought tolerance of the studied ‘Redchief’ autotetraploid clone compared with its diploid counterpart.

Охота на куликов на Камчатке и ее влияние на популяции видов, внесенных в Красную книгу России.

The first in Russia large-scale study on the impact of hunting on wader populations was carried out in Kamchatka in 2019. The main research method was anonymous hunters questioning. In Kamchatka Krai, about 3.400 hunters take part in the wader hunt. Of the 402 hunters, who participated in the anonymous questioning, 55 % were hunting Whimbrels in the current season; 9 %, other large and medium-size wader species; 14 %, smaller waders. The total of about 45.000 waders are shot annually in Kamchatka, including 37.000 Whimbrels 1.600 waders of other large species, and 6 thousand waders of smaller species. From the species listed in the Red Data Book of the Russian Federation, up to 20 Oystercatchers, 100 Far Eastern Curlews, 200 Bar-tailed Godwits, 300 Grater Knots, and 100 Red Knots are shot. Hunting smaller waders is not popular, but it causes the death of up to 12.000 thousand smaller waders, with Spoon-billed Sandpiper also occasionally killed. Special educational activities for hunters and hunting officers are required for conservation of wader populations included in the Red Data Books of the Russian Federation; first, a special field guide of waders in the Far East should be published. It is also necessary to restore the Moroshechnaya River sanctuary reserve, liquidated in 2009 yet included in the official list of areas of international importance for waders on the East Asian-Australasian Flyway.

Influence of Prior Influenza Vaccination on Current Influenza Vaccine Effectiveness in Children Aged 1 to 5 Years

Background: Although annual influenza vaccination is an important strategy used to prevent influenza-related morbidity and mortality, some studies have reported the negative influence of prior vaccination on vaccine effectiveness (VE) for current seasons. Currently, the influence of prior vaccination is not conclusive, especially in children. Methods: We evaluated the association between current-season VE and prior season vaccination using a test-negative design in children aged 1–5 years presenting at nine outpatient clinics in Japan during the 2016/17 and 2017/18 influenza seasons. Children with influenza-like illness were enrolled prospectively and tested for influenza using real-time RT-PCR. Their recent vaccination history was categorized into six groups according to current vaccination doses (0/1/2) and prior vaccination status (unvaccinated = 0 doses/vaccinated = 1 dose or 2 doses): (1) 0 doses in the current season and unvaccinated in prior seasons (reference group); (2) 0 doses in the current season and vaccinated in a prior season; (3) 1 dose in the current season and unvaccinated in a prior season; (4) 1 dose in the current season and vaccinated in a prior season; (5) 2 doses in the current season and unvaccinated in a prior season, and (6) 2 doses in the current season and vaccinated in a prior season. Results: A total of 799 cases and 1196 controls were analyzed. The median age of the subjects was 3 years, and the proportion of males was 54%. Overall, the vaccination rates (any vaccination in the current season) in the cases and controls were 36% and 53%, respectively. The VEs of the groups were: (2) 29% (95% confidence interval: −25% to 59%); (3) 53% (6% to 76%); (4) 70% (45% to 83%); (5) 56% (32% to 72%), and (6) 61% (42% to 73%). The one- and two-dose VEs of the current season were significant regardless of prior vaccination status. The results did not differ when stratified by influenza subtype/lineage. Conclusion: Prior vaccination did not attenuate the current-season VE in children aged 1 to 5 years, supporting the annual vaccination strategy.

Determinants of the adoption of injury risk reduction programmes in athletics (track and field): an online survey of 7715 French athletes

ObjectivesTo identify individual characteristics associated with the adoption of injury risk reduction programmes (IRRP) and to investigate the variations in sociocognitive determinants (ie, attitudes, subjective norms, perceived behavioural control and intentions) of IRRP adoption in athletics (track and field) athletes.MethodsWe conducted a cross-sectional study using an online survey sent to athletes licensed with the French Federation of Athletics to investigate their habits and sociocognitive determinants of IRRP adoption. Sociodemographic characteristics, sports practice and history of previous injuries were also recorded. Logistic regression analyses and group comparisons were performed.ResultsThe final sample was composed of 7715 athletes. From the multivariable analysis, competing at the highest level was positively associated with IRRP adoption (adjusted OR (AOR)=1.66; 99.9% CI 1.39 to 1.99 and AOR=1.48; 99.9% CI 1.22 to 1.80) and presenting a low number of past injuries was negatively associated with IRRP adoption (AOR=0.48; 99.9% CI 0.35 to 0.65 and AOR=0.61; 99.9% CI 0.44 to 0.84), both during their lifetime and the current season, respectively. These results were supported by higher scores of sociocognitive determinants among athletes who reported IRRP adoption compared with other athletes.ConclusionSome characteristics of athletes seem to be associated with IRRP adoption either positively (competing at the highest level) or negatively (presenting a lower number of past injuries), whereas all the sociocognitive determinants tested appear to be linked to IRRP adoption. Since many athlete characteristics are difficult or impossible to change, IRRP promotion may be enhanced by targeting athletes’ beliefs and intentions to adopt an IRRP.

Harmonizing sowing dates with onset of rains to guarantee nutri-millets grain yield and quality under rain dependent farming

Nutri-millets offer copious micronutrients like vitamins, beta-carotene etc. In this present day, all the millets are amazingly superior and are therefore, the result for the malnutrition and obesity that affects a vast majority of the Indian population. They have numerous beneficial properties like drought resistant, good yielding in areas where water is limited and they possess good nutritive values. The prospective water scarcity in semi-arid regions disturbs both normal as well as managed environments, which limits the cultivation of crops, fodder, and other plants. The issues faced by the rain-dependent farming of these semi-arid regions are primarily the unpredictability of the monsoon. Probability analysis of rainfall events are believed to contribute in deciding sowing dates for the current season and for successful crop production in semi-arid environments. The present study was carried out in semi-arid condition to quantify the performance of nutri-millets in the rain dependent farming. The experiment was laid out under factorial randomized block design with 3 replications. The treatments comprises of crop factor viz., Sorghum [Sorghum bicolor (L.) Moench] (C1) and, little millet [Panicum sumatrense Roth ex Roem. & Schult] (C2) and sowing window factor viz., sowing based farmer’s practice (M1) i.e. on 31st standard meteorological week (SMW); Sowing at 33rd SMW based on 50% rainfall probability (M2); Sowing at 38th SMW based on 75% rainfall probability (M3), Sowing window as per the current weather forecast, for this season on 35th SMW (M4).It is evident from the study that Sowing sorghum at 38th standard meteorological week based on 75% rainfall probability recorded higher grain yield, rain water use efficiency with elevated iron and calcium content. This shows that different sowing dates have significant influence on grain yield and quality of nutri-millets.

Simple models to include influenza vaccination history when evaluating the effect of influenza vaccination

Background Most reports of influenza vaccine effectiveness consider current-season vaccination only. Aim We evaluated a method to estimate the effect of influenza vaccinations (EIV) considering vaccination history. Methods We used a test-negative design with well-documented vaccination history to evaluate the average EIV over eight influenza seasons (2011/12–2018/19; n = 10,356). Modifying effect was considered as difference in effects of vaccination in current and previous seasons and current-season vaccination only. We also explored differences between current-season estimates excluding from the reference category people vaccinated in any of the five previous seasons and estimates without this exclusion or only for one or three previous seasons. Results The EIV was 50%, 45% and 38% in people vaccinated in the current season who had previously received none, one to two and three to five doses, respectively, and it was 30% and 43% for one to two and three to five prior doses only. Vaccination in at least three previous seasons reduced the effect of current-season vaccination by 12 percentage points overall, 31 among outpatients, 22 in 9–65 year-olds, and 23 against influenza B. Including people vaccinated in previous seasons only in the unvaccinated category underestimated EIV by 9 percentage points on average (31% vs 40%). Estimates considering vaccination of three or five previous seasons were similar. Conclusions Vaccine effectiveness studies should consider influenza vaccination in previous seasons, as it can retain effect and is often an effect modifier. Vaccination status in three categories (current season, previous seasons only, unvaccinated) reflects the whole EIV.

Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Characteristics of Healthcare Workers Vaccinated against Influenza in the Era of COVID-19

Understanding the potential impact of COVID-19 on receiving influenza vaccination among healthcare workers (HCWs) is of utmost importance. The purposes of the present cross-sectional study were to describe the characteristics and to explore the predictors of receiving influenza vaccination among a large cohort of Italian HCWs in hospital settings. Information was collected through an anonymous questionnaire from December 2020 through January 2021. General and practice characteristics, perceived risk of seasonal influenza, attitudes towards efficacy and safety of influenza vaccination, and reasons behind the decision to be vaccinated against influenza were explored. Fewer than half (46.2%) of HCWs agreed that influenza is a serious illness and perceived the risk of getting infected with influenza, and concerns about the safety of the vaccination were significant positive predictors. Fewer than half of the respondents were not concerned at all about the efficacy (48.6%) and safety (49.8%) of influenza vaccination, and 51.9% reported that they have not received a seasonal influenza vaccine during the previous season. The most mentioned reason for receiving the influenza vaccine in the current season was that influenza and COVID-19 share some similar symptoms. Study results will aid policymakers in developing vaccination education programs, promotion of trust to address negative misconceptions, and to achieve future high coverage among this high-risk group.

Seasonal forecasting of mountain snow resources: evaluation of a climate service prototype

&lt;p&gt;Warming trends in the past decades in mountain regions have resulted in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff (IPCC, 2019), and we expect droughts to become more severe in the future (Haslinger et al., 2014) with consequences for both mountain and downstream economies. Effective adaptation strategies to address and reduce negative climate change impacts involve multiple time scales, from the long-term support of mountain water resource management and the diversification of mountain tourism activities, to the seasonal scale, for the optimization of the available snow resources.&amp;#160;&lt;/p&gt;&lt;p&gt;In the frame of the MEDSCOPE project we developed a prototype to generate seasonal forecasts of mountain snow resources, in order to estimate the temporal evolution of the depth and the water content of the snowpack with lead times of several months. The prototype has been tailored on the needs of water and hydropower plant managers and of mountain ski resorts managers. We present the modelling chain, based on the seasonal forecasts of ECMWF and M&amp;#233;t&amp;#233;o-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S). Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are bias-corrected and downscaled to the site of Bocchetta delle Pisse 2410 m a.s.l. in the North-Western Italian Alps, and finally used as input for a physically-based multi-layer snow model (SNOWPACK, Bartelt and Lehning, 2002). The RainFARM stochastic downscaling procedure (Terzago et al., 2018) is used for precipitation data in order to allow an estimate of uncertainties linked to small-scale variability in the forcing.&lt;/p&gt;&lt;p&gt;The skills of the prototype in predicting the snow depth evolution from November 1st to May 31st in each season of the hindcast period 1995-2015 are demonstrated using station measurements as a reference. We show the correlation between forecast and observed snow depth anomalies and we quantify the forecast quality in terms of reliability, resolution, discrimination and sharpness using a set of probabilistic measures (Brier Skill Score, the Area Under the ROC Curve Skill Score and the Continuous Ranked Probability Skill Score). Implications of the forecast quality at different lead times on climate services are discussed.&amp;#160;&lt;/p&gt;&lt;p&gt;Real-time snow forecasts for the current season (2020-2021) are available at this link: http://wilma.to.isac.cnr.it/diss/snowpack/snowseas-eng.html&lt;/p&gt;

Smart Agro - Improving Prediction Accuracy of Soil pH Values and Appropriate Crop Selection using ML

Indian economy is mostly influenced by the agriculture sector and Soil is a valuable natural resource for agriculture. The soil quality mainly depends on pH value. To achieve the requirement of the growing population, farmers are using more pesticides and fertilizers to maximize yield, which subsequently reduces soil infertility by increasing toxicity and decreasing the holding capacity as well. Maintaining the proper pH value of the soil is important before plantation and it can be determined by using image processing techniques. This paper provides a system that can bridge the gap between farmers and technology by predicting the pH value by the soil image and recommend the crop. The Feed forward Neural Network algorithm is used for crop recommendation. The crop recommendation is mostly reliant on the attributes like soil pH value, soil texture, soil type, current season, temperature, etc.