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Sarcocystis spp. are coccidian protozoans belonging to the Apicomplexa phylum. As with other members of this phylum, they are obligate intracellular parasites with complex cellular machinery for the invasion of host cells. Sarcocystis spp. display dixenous life cycles, involving a predator and a prey as definitive and intermediate hosts, respectively. Specifically, these parasites develop sarcocysts in the tissues of their intermediate hosts, ranging in size from microscopic to visible to the naked eye, depending on the species. When definitive hosts consume sarcocysts, infective forms are produced in the digestive system and discharged into the environment via feces. Consumption of oocyst-contaminated water and pasture by the intermediate host completes the parasitic cycle. More than 200 Sarcocystis spp. have been described to infect wildlife, domestic animals, and humans, some of which are of economic or public health importance. Interestingly, Old World camelids (dromedary, domestic Bactrian camel, and wild Bactrian camel) and New World or South American camelids (llama, alpaca, guanaco, and vicuña) can each be infected by two different Sarcocystis spp: Old World camelids by S. cameli (producing micro- and macroscopic cysts) and S. ippeni (microscopic cysts); and South American camelids by S. aucheniae (macroscopic cysts) and S. masoni (microscopic cysts). Large numbers of Old and New World camelids are bred for meat production, but the finding of macroscopic sarcocysts in carcasses significantly hampers meat commercialization. This review tries to compile the information that is currently accessible regarding the biology, epidemiology, phylogeny, and diagnosis of Sarcocystis spp. that infect Old and New World camelids. In addition, knowledge gaps will be identified to encourage research that will lead to the control of these parasites.

Background Urban parks may promote physical activity (PA); however, little is known about whether renovated urban parks contribute to overall PA similarly across diverse sociodemographic groups. In this cross-sectional study, we examined associations between park-based activities and overall PA among adults living near recently renovated parks in low-income New York City (NYC) neighborhoods, with particular attention to differences by race/ethnicity and sex. Methods A total of 1336 adult survey respondents who reported past-month use of a renovated park within 0.5 miles of their residence were included. Surveys captured past-month participation in activities at the renovated park (walking, exercising, taking children to the playground/park, relaxing, socializing, volunteering) and self-reported PA level during visits (sitting, light, moderate-to-vigorous). The outcome, overall past-week PA, was measured in metabolic equivalent of task (MET)-minutes using the International Physical Activity Questionnaire and log-transformed prior to analysis. Linear regression models estimated associations of park-based activities and park-based PA levels with log(MET-minutes) of overall PA in the full sample and stratified by race/ethnicity and sex. Results In the full sample, park-based activities explained 5.4% of the variability in overall PA of past-month renovated park users; self-reported park-based PA level explained 5.2% of the variability. Compared to past-month park users who did not engage in these activities, those who reported exercising at the study park had 47% higher overall PA and those who reported walking had 33% higher overall PA, while those who reported volunteering had 26% lower overall PA, after confounder adjustment. Activities at renovated parks explained more variability in overall PA among males (6.3%) compared with females (5.1%), and among minority groups (Latino/as = 6.5%, Blacks = 6.8%, other race/ethnicity = 11.2%) compared with Whites (4.3%). Conclusions Among adults with past-month renovated park use in low-income NYC neighborhoods, park-based activities explained a significant proportion of the variability in overall PA, with stronger contributions among minorities and males. These findings highlight the importance of considering sociodemographic differences when assessing the role of renovated urban parks in promoting PA and suggest that investments in high-quality green spaces may be particularly impactful for minority groups facing disproportionate barriers to PA.

The effect of two pretreatments on the antioxidant activity was evaluated in quinoa protein hydrolysate, using supercritical CO2 extraction and ethanol as cosolvent, this type of pretreatment was compared to a conventional petroleum ether extraction method without recovery of bioactive compounds. The extractions were carried out at a temperature of 55°C and a pressure of 23 MPa using ethanol (7–8 g quinoa/100 ml); the CO2 mass flow was 35 g/min, the extraction time was 240 min and the particle size was 500 µm, enzyme COROLASE® 7089 was applied for enzymatic hydrolysis, finally ABTS test assessed antioxidant activity. A significant effect was found on the degree of hydrolysis (23.93%) and antioxidant activity (1,181.64 μmol TE/g protein) compared to a conventional method (24.33%) and (1,448.84 μmol TE/g protein). In conclusion, our results suggest that the use of supercritical CO2 and the addition of ethanol as cosolvent are the interesting green technology, to recovery oil and separate phenolic compounds prior to enzymatic hydrolysis to avoid interference with biological activities from quinoa protein hydrolysates, and shows highest antioxidant activity to be incorporate in food products. Separate phenolic compounds to avoid interference on antioxidant activity was evaluated in protein hydrolyzate from quinoa, using Supercritical CO2 extraction and ethanol as cosolvent. Antioxidant activity of protein hydrolyzate from quinoa (Chenopodium quinoa Willd.)

Background: Mental disorders are alterations in several functional domains of human beings that trigger greater morbidity and mortality if not adequately addressed. The International Classification of Diseases 11th Revision (ICD11) is a recently approved global and modern system to guide clinical practice faced with these disorders and other conditions. State of the art: Despite the imminent implementation of this system in member states, the guidelines on its scientific basis, practice and importance in public health have been published in a scattered manner, with a mainly medical psychiatric target audience, hence, it is necessary to unify these guidelines in a unique text. Therefore, the objective of this review was to analyze three associated aspects: (a) current knowledge of the matter, (b) its application in psychological practice, and (c) reflection concerning the implications on public health policies. To do this, these aspects were divided into 10 sections with the most relevant topics, and examples to facilitate their use and comments to promote understanding were described. Conclusions: This paper presents a review that comprehensively addresses knowledge-practice-policy triad of mental disorders of the ICD-11. Authors’ contributions The authors participated equally in the preparation of this paper

Background: Personality disorder (PD) is an important predictor of the commission of crimes; however, there is a lack of clinical instruments adjusted to the characteristics of Peruvian convicts. Objective: To develop a reliable and valid comprehensive personality measurement instrument, the Integrative Dimensional Personality Inventory, ICD-11 version (IDPI-11), according to the standards of the International Classification of Diseases (ICD-11). Method: A stratified sample of prisoners from the Huancayo Penitentiary (HP) was selected (n study 1 = 60; n study 2 = 1095). Results: High reliability indices (McDonald’s ω ≥ .73) and adequate levels of content validity (CVI-S ≥ .87), construct validity, and criterion validity of the scales were found. This could explain the probability (a) of belonging to the group of inmates with instrumental or impulsive crimes (R2 N ≥ .52, OR ≥ 1.02, p ≤ .021), and (b) that recidivism, designated by the prison security level imposed, increases (R2 ≥ .53, β ≥ 1.16, p ≤ .008). Conclusions: The instrument is a valid and reliable measure that allows a dimensional and integrative assessment of the personality of convicts of the HP, according to ICD-11 standards.  Authors’ contributions LMHO, DNRC, and RMCA conceived the article; DNRC and PAAV collected the data; LMHO and PAAV performed the statistical analysis; LMHO reviewed the contents; and RMCA advised on the entire study and manuscript preparation process.

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m−3).

Land surface temperature (LST) plays an essential role in water and energy exchanges between the Earth's surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water and energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA CCI-LST product into the ORCHIDEE LSM (the continental part of the Institut Pierre-Simon Laplace Earth system model) with the aim of optimizing key parameters to improve the simulation of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for actual data availability and uncertainty from ESA CCI-LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating (i) two optimization methods (a random search technique and a gradient-based technique) and (ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, and morning and afternoon gradients). Upon identifying the optimal approach, we use ORCHIDAS to assimilate ESA CCI-LST data across 34 European sites provided by the Warm Winter database. Our results demonstrate the effectiveness of assimilating 3 h CCI-LST data in ORCHIDEE over a single year in 2018, thereby improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to a 60 % reduction in the root-mean-square deviation, with a median decrease of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales using the median of optimized parameters per vegetation type across sites. Notably, the performance for both LST and fluxes exhibits consistent stability over the years, comparable to using site-specific parameters, and indicates a significant improvement in the modelled fluxes. Future work will be focused on refining the utilization of the observation uncertainties provided by the ESA CCI-LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process.

Recently, an ongoing rise in temperature for both land and ocean areas is recorded resulting from the Earth’s warming climate. As a result, droughts we observe are getting more frequent, longer and more severe, exerting sustained impacts on humans, ecosystems leading to famine, poverty, mass migration, or agricultural and economic losses. The changes in climate are successfully monitored by analyzing Total Water Storage (TWS). For years, TWS has been successfully determined using geodetic techniques, such as gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) missions or station position changes monitored by the Global Positioning System (GPS). As well, geodetic-derived data can be applied successfully to study of hydrometeorological events. To quantify droughts characteristics at different temporal and spatial scales, we recalculate the vertical displacements to Drought Severity Indices (DSI). We find that DSI based on GPS and GRACE are positively correlated at over 80% of stations around the world, highlighting both Americas and Europe as the most correlated areas. To validate results, we compare DSI based on GPS/GRACE with the Global Land Water Storage (GLWS) hydrological model, the traditional climate indices, and temperature anomalies. We show that GPS-DSIs are strongly temporally consistent with both the Standardized Precipitation Index (SPI) and the Soil Moisture Index (SMI) climate indices at 85% of stations, indicating weakly correlated areas at mid-latitudes. We further show a high potential of geodetic data to assess drought characteristics within climate zones as well as global studies. We note that moderate conditions dominate for all climate zones, for which dry moderate conditions are observed for 40% of the months analyzed. As a result, we note warning conditions at least 52% of global stations with extreme drying DSI trends above a value of 2–3 per year. We note that the global water changes are dominated by 9 month droughts at over 72% of stations, indicating the average drought duration around 12, 14, and 15 months for GPS-, GRACE-, and GLWS-DSI, respectively. The obtained results from geodetic measurements more reliably characterize the type and phase of drought, as well as how these droughts cascade into freshwater, enabling appropriate mitigation strategies.

In order to obtain a highly nutritious extrudate, a combination of pseudocereals, vegetable protein, and banana flour, a fruit with high sensory acceptability, was used. The objective of the research was to produce a multi-component extrudate (ME) based on cañihua and quinoa with the addition of pea protein isolate and banana flour. The response variables evaluated were composition, expansion, hydration, colour, and hardness properties, as well as the microscopy and sensory characteristics of the flakes produced. These flakes were compared with three commercial extrudates, commercial quinoa-based extrudate (QE), commercial corn-based extrudate (CE), and commercial wheat-based extrudate (WE), which had similar characteristics. The ME showed a higher protein content compared to commercial extrudates (13.60%), and it had significant amounts of lipids, fibre, and ash. The expansion of the ME was like commercial quinoa but significantly lower than the CE and the WE in terms of expansion (p < 0.05). Regarding the absorption and solubility indices of the ME, these indicated that it had lower starch fragmentation compared to the commercial CE and WE. In addition, the instrumental hardness of the ME was higher than the commercial ones due to the complex nature of the product. Through scanning electron microscopy (SEM), it was observed that the ME showed some remaining extrusion-resistant starch granules from quinoa and cañihua with the presence of protein bodies. Finally, the flash profile described the ME as having a pronounced flavour, higher hardness, and lower sweetness, and the free sorting task allowed it to be differentiated from commercial extrudates based on its natural appearance and chocolate flavour.