The phase transfer of arsenic by As-POMs could significantly increase the As adsorption capacity. Specifically, the composites achieved the conversion of S atoms at the interface of biochar into SO4•- radicals to enhance the As(III) photooxidation performance.

The aim of this study is to describe the epidemiological characteristics about regional and age difference of human rabies in the past fourteen years in China, and provide a reliable epidemiology basis for further control and prevention of human rabies.

The database of “China Public Health Science Data Center” affiliated Chinese CDC was searched with the key words of “rabies” or “epidemiology” or “morbidity” or “mortality” from 2004 to 2018 and the corresponding data about human rabies cases was collected referred to regional and age difference for describing the epidemiological characteristics of human rabies.

In this study, a total of nearly 26,315 rabies cases (1754 ± 253) and 25,691 rabies-related deaths (1712 ± 255) (Mean ± SE) were reported, and a decreasing trend about the morbidity and mortality of human rabies existed from 0.2039 and 0.2039 (1/100,000) in 2004 to 0.0304 and 0.0295 in 2018. Otherwise, regional difference of human rabies prevalence significantly existed, and juvenile and middle-aged population especially in 50-60 years old were more easily attacked and infected with rabies (all p < 0.05).

This study proved that human rabies still is a major public health problem in China though a decreasing trend about the morbidity and mortality of human rabies existed in the past fourteen years.

This study proved that human rabies still is a major public health problem in China though a decreasing trend about the morbidity and mortality of human rabies existed in the past fourteen years.Male dairy calves are exposed to an accumulation of transport, social and environmental stressors while transferred to fattening units. As a consequence, calves show high cortisol concentrations upon arrival at the veal facility. Whether cortisol levels as measured on arrival can be associated with animal health, welfare and production results is currently unknown. The first objective of this prospective cohort study was to determine possible associations of arrival serum cortisol concentration with health and production variables of veal calves and other arrival predictors like body weight and γ-globulin concentration. The second aim was to investigate potential clustering of arrival risk factors in veal calves for developing bovine respiratory disease (BRD) based on arrival body weight, serum cortisol concentration, total protein and protein fractions. In total, 105 male Holstein calves from two consecutive production cycles in a single, commercial white veal farm were blood sampled directly at arrival on td) antimicrobial treatment and production losses.Annual peaks in reproductive activity have been identified in multiple domestic dog populations. However, there is little evidence to describe how these peaks may be associated with environmental factors such as daylength, which plays a well-established role in breeding patterns of seasonally-reproductive species. Data were collected 2016-2020 during 7,743 and 4,681 neuter surgeries on adult female unowned free-roaming dogs in veterinary clinics in Goa and Tamil Nadu respectively. Temperature, precipitation, relative humidity, and daylength data were gathered for time periods preceding the neuter surgery that may have influenced the likelihood of pregnancy (potential influence periods). A multivariable generalised additive model was used to assess the relationship between these factors and pregnancy. The prevalence of pregnancy varied by month in both locations indicating seasonality of reproduction in these groups. The annual pattern was more distinct in Goa with a peak in pregnancies between September and December. In Goa, decreasing daylength was associated with a higher probability of pregnancy (p = 0.040). Decreasing temperature was associated with decreasing probability of pregnancy in the Nilgiris (p = 0.034). Bitches had a median of 6 foetuses, with no evidence of seasonal variation. Environmental factors were associated with patterns of pregnancy in free-roaming dogs, however statistically-significant factors varied by geographical location. Establishing local seasonal patterns of breeding in free-roaming dogs and assessing their relationship with environmental influences is recommended to facilitate effective and efficient population management strategies, which aim to reduce conflict between human and free-roaming dog populations.In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. click here To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.