Tick-borne encephalitis epidemic in Hungary 1951–2021: The story and lessons learned

The authors analysed epidemiological data of the Hungarian tick-borne encephalitis epidemic from the past seven decades. A total of 911 meningitis serosa cases were described from 1930-1950 s by local hospital physicians, indicating that the virus had been present in the country decades before its official identification in 1952. The virus spread freely in the 1950s–1960s, occupying almost all habitats where ticks occurred in large numbers. The increasing number of cases drove authorities to classify this illness as a notifiable disease in 1977 and to organize the first measures to stop the epidemic. Statistical analysis revealed that the large-scale vaccination launched from the 1990s was responsible for the sharp decrease in the number of human cases from 1997. A significant negative correlation was found between the number of vaccine doses sold and human cases 6 years later. The TBEV endemic area covers 16.57% of the territory and 16.65% of the population of the country. In the last 10 years, 186,000 vaccine doses/year in average were enough to keep the incidence of human TBEV infections between 0.45 and 0.06/100,000 persons. A 20-year-long study found evidence for easing clinical signs in TBEV-infected hospitalized patients. Statistics found a sharp decrease in the number of samples sent for TBEV diagnosis after 1989. Male dominance of patients was characteristic of the epidemics since the 1940s, but now analysis of detailed data from the 1981–2021 period (60.5%–87.5%) proved the statistical significance of this dominance. Obviously, the voluntary vaccination programme was the tool which broke the spread of the epidemic. Widespread public awareness of the disease and the tick vector, probable evolutionary spread of less pathogenic virus strains supplemented with the vaccination campaign led to a negligible level of human TBE cases in Hungary in the last years.     

Fertility of Holstein cows and heifers submitted to timed artificial insemination and receiving one or two doses (12 h apart) of semen

The objective of this retrospective study was to assess the effect of receiving a single (n = 50,285) or double (n = 4392) artificial insemination (AI), 12 h apart, within a timed artificial insemination protocol on pregnancy per AI (P/AI) in nulliparous heifers (inseminated with either sex-sorted or conventional semen) and pluriparous Holstein cows in a commercial dairy herd. Also, this study aimed to investigate the relationship between temperature-humidity index (THI) and time of the first AI and fertility. Fertility of cows receiving two AI with normothermia (THI <68) was higher (p < .05) than cows receiving a single AI (42.9% vs. 36.4%). P/AI of cows receiving two AI with severe heat stress (THI >85) was higher (p < .05) than cows receiving a single AI (21.0% vs. 12.6%). Regardless of heat stress conditions, applying the first AI in the morning increased (p < .05) P/AI in cows with double AI than in cows whose first AI occurred in the afternoon (38.4 vs. 33.3%). With moderate heat stress, and sexed-sorted semen, P/AI to timed AI was higher (65.0 vs. 51.9%; p < .05) in heifers receiving double AI than those serviced once. It was concluded that double AI, 12 h apart, enhanced fertility at timed AI than herd mates with a single AI, particularly with heat stress at breeding.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Histomorphological changes between short and long days in ovary guanacos (Lama guanicoe)

This is the first morpho-histological comparison of guanaco ovaries between reproductive (long-days) and non-reproductive (short-days) seasons, and oestrogen receptor-alpha (ERα) and beta (ERβ) detection. Different stages of follicle development were found in the cortical area, but no corpus luteum was detected. The size and frequency of antral follicles and large atretic follicles were higher in long-day ovaries than short-days, consistent with ovarian activity in this season. Differential expression of ERα and ERβ was observed in follicles at different stages of development between short and long days. These data reveal histological and molecular differences between reproductive and non-reproductive seasons of guanaco ovaries.     

The effect of salinity on corn yield using the CERES-maize model

In most cases, when calculating soil water availability, only thewater content is considered. The effect of salinity on the wiltingpoint is neglected. The objective of this work is to use asimulation model (CERES-maize) in order to predict cornyields as a function of water salinity under severalenvironmental, agrotechnical, and plant characteristics. A modelis presented in which the wilting point is a function of the soilsalt content. At high salinity, the water content at wilting pointis higher than at low salinity, resulting in an insufficient amountof available water and, therefore, a reduced yield. The modelwas used to simulate several theoretical and experimentalsituations for forage corn and grain corn. Simulation resultsshowed that nitrogen fertilization increases the salinity thresholdvalue and the yield sensitivity (rate of yield reduction per unitof salinity). The also showed that forage corn is more sensitiveto salinity than grain corn. If the soil is not leached, a heaviersoil texture has a higher salinity threshold value. On the otherhand, if the soil is leached, the soil texture has no influence onthe salinity threshold value and the yield is less sensitive tosalinity in sandy soils. The determination coefficient (r²= 0.75) indicated that the results of the simulations were in goodagreement with the field data.     

Habitat quality,not habitat amount,drives mammalian habitat use in the Brazilian Pantanal

Landscape Ecology - An understanding of species-habitat relationships is required to assess the impacts of habitat fragmentation and degradation. To date, habitat modeling in fragmented landscapes...