Footshock drives remodeling of perineuronal nets in retrosplenial cortex during contextual fear memory formation.

The retrosplenial cortex (RSC) plays a critical role in complex cognitive functions such as contextual fear memory formation and consolidation. Perineuronal nets (PNNs) are specialized structures of the extracellular matrix that modulate synaptic plasticity by enwrapping the soma, proximal neurites and synapsis mainly on fast spiking inhibitory GABAergic interneurons that express parvalbumin (PV). PNNs change after contextual fear conditioning (CFC) in amygdala or hippocampus, yet it is unknown if similar remodeling takes place at RSC. Here, we used Wisteria floribunda agglutinin (WFA), a ubiquitous marker of PNNs, to study the remodeling of PNNs in RSC during the acquisition or retrieval of contextual fear conditioning (CFC). Adult male mice were exposed to paired presentations of a context and footshock, or to either of these stimuli alone (control groups). The mere exposure of animals to the footshock, either alone or paired with the context, evoked a significant expansion of PNNs, both in the number of WFA positive neurons and in the area occupied by WFA staining, across the entire RSC. This was not associated with c-Fos expression in RSC nor correlated with c-Fos expression in individual PNNs-expressing neurons in RSC, suggesting that PNNs remodeling is triggered by inputs external to the RSC. We also found that PNNs remodeling was independent of the level of PV expression. Notably, PNNs in RSC remained expanded long-after CFC. These results suggest that, in male mice, the threatening experience is the main cause of PNNs remodeling in the RSC.

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23 MJ m-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14 MJ m-2 d-1 and 4.00 ± 0.99 MJ m-2 d-1 for λE; 3.80 ± 1.12 MJ m-2 d-1 and 5.00 ± 1.00 MJ m-2 d-1 for H; 0.50 ± 0.12 MJ m-2 d-1 and 0.40 ± 0.10 MJ m-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

Net primary productivity and litter decomposition rates in two distinct Amazonian peatlands.

Measurements of net primary productivity (NPP) and litter decomposition from tropical peatlands are severely lacking, limiting our ability to parameterise and validate models of tropical peatland development and thereby make robust predictions of how these systems will respond to future environmental and climatic change. Here, we present total NPP (i.e., above- and below-ground) and decomposition data from two floristically and structurally distinct forested peatland sites within the Pastaza Marañón Foreland Basin, northern Peru, the largest tropical peatland area in Amazonia: (1) a palm (largely Mauritia flexuosa) dominated swamp forest and (2) a hardwood dominated swamp forest (known as 'pole forest', due to the abundance of thin-stemmed trees). Total NPP in the palm forest and hardwood-dominated forest (9.83 ± 1.43 and 7.34 ± 0.84 Mg C ha-1 year-1, respectively) was low compared with values reported for terra firme forest in the region (14.21-15.01 Mg C ha-1 year-1) and for tropical peatlands elsewhere (11.06 and 13.20 Mg C ha-1 year-1). Despite the similar total NPP of the two forest types, there were considerable differences in the distribution of NPP. Fine root NPP was seven times higher in the palm forest (4.56 ± 1.05 Mg C ha-1 year-1) than in the hardwood forest (0.61 ± 0.22 Mg C ha-1 year-1). Above-ground palm NPP, a frequently overlooked component, made large contributions to total NPP in the palm-dominated forest, accounting for 41% (14% in the hardwood-dominated forest). Conversely, Mauritia flexuosa litter decomposition rates were the same in both plots: highest for leaf material, followed by root and then stem material (21%, 77% and 86% of mass remaining after 1 year respectively for both plots). Our results suggest potential differences in these two peatland types' responses to climate and other environmental changes and will assist in future modelling studies of these systems.

Mediciones de la productividad primaria neta (PPN) y la descomposición de materia orgánica de las turberas tropicales son escasas, lo que limita nuestra capacidad para parametrizar y validar modelos de desarrollo de las turberas tropicales y, en consecuencia, realizar predicciones sólidas sobre la respuesta de estos sistemas ante futuros cambios ambientales y climáticos. En este estudio, presentamos datos de PPN total (es decir, biomasa aérea y subterránea) y descomposición de la materia orgánica colectada en dos turberas boscosas con características florísticas y estructurales contrastantes dentro de la cuenca Pastaza Marañón al norte del Perú, el área de turberas tropicales más grande de la Amazonia: (1) un bosque pantanoso dominado por palmeras (principalmente Mauritia flexuosa) y (2) un bosque pantanosos dominado por árboles leñosos de tallo delgado (conocido como 'varillal hidromórfico'). La PPN total en el bosque de palmeras y el varillal hidromórfico (9,83 ± 1,43 y 7,34 ± 0,84 Mg C ha­1 año­1 respectivamente) fue baja en comparación con los valores reportados para los bosques de tierra firme en la región (14,21­15,01 Mg C ha­1 año­1) y para turberas tropicales en otros lugares (11,06 y 13,20 Mg C ha­1 año­1). A pesar de que la PPN total fue similar en ambos tipos de bosque, hubo diferencias considerables en la distribución de la PPN. La PPN de las raíces finas fue siete veces mayor en el bosque de palmeras (4,56 ± 1,05 Mg C ha­1 año­1) que en el varillal hidromórfico (0,61 ± 0,22 Mg C ha­1 año­1). La PPN de la biomasa aérea de las palmeras, un componente ignorado frecuentemente, contribuyó en gran medida a la PPN total del bosque de palmeras, representando el 41% (14% en el varillal hidromórfico). Por el contrario, la tasa de descomposición de materia orgánica de Mauritia flexuosa fue la misma en ambos sitios: la más alta corresponde a la hojarasca, seguida por las raíces y luego el tallo (21%, 77% y 86% de la masa restante después de un año, respectivamente para ambos sitios). Nuestros resultados sugieren diferencias potenciales en la respuesta de estos dos tipos de turberas al clima y otros cambios ambientales, y ayudarán en futuros estudios de modelamiento de estos sistemas.

A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.

Genomic selection (GS) is changing plant breeding by significantly reducing the resources needed for phenotyping. However, its accuracy can be compromised by mismatches between training and testing sets, which impact efficiency when the predictive model does not adequately reflect the genetic and environmental conditions of the target population. To address this challenge, this study introduces a straightforward method using binary-Lasso regression to estimate ß coefficients. In this approach, the response variable assigns 1 to testing set inputs and 0 to training set inputs. Subsequently, Lasso, Ridge, and Elastic Net regression models use the inverse of these ß coefficients (in absolute values) as weights during training (WLasso, WRidge, and WElastic Net). This weighting method gives less importance to features that discriminate more between training and testing sets. The effectiveness of this method is evaluated across six datasets, demonstrating consistent improvements in terms of the normalized root mean square error. Importantly, the model's implementation is facilitated using the glmnet library, which supports straightforward integration for weighting ß coefficients.

Motor Control in Parkinson's Disease During the Performance of Multi-Joint Reversal Movements.

We tested if the movement slowness of individuals with Parkinson's disease is related to their decreased ability to generate adequate net torques and linearly coordinate them between joints. This cross-sectional study included ten individuals with Parkinson's disease and ten healthy individuals. They performed planar movements with a reversal over three target distances. We calculated joint kinematics of the elbow and shoulder using spatial orientation. The muscle, interaction, and net torques were integrated into the acceleration/deceleration phases of the fingertip speed. We calculated the linear correlations of those torques between joints. Both groups modulated the elbow and shoulder net torques with target distances. They linearly coupled the production of torques. Both groups did not modulate the interaction torques. The movement slowness in Parkinson's disease was related to the difficulty in generating the appropriate muscle and net torques in the task. The interaction torques do not seem to play any role in movement control.

Net costs of breast cancer in Colombia: a cost-of-illness study based on administrative claims databases.

BACKGROUND: Breast Cancer (BC) is associated with substantial costs of healthcare; however, real-world data regarding these costs in Colombia is scarce. The contributory regime provides healthcare services to formal workers and their dependents and covers almost half of the population in Colombia. This study aims to describe the net costs of healthcare in women with BC covered by the contributory regime in Colombia in 2019 from the perspective of the Colombian Health System. METHODS: The main data source was the Capitation Sufficiency Database, an administrative database that contains patient-level data on consumption of services included in the National Formulary (PBS, in Spanish Plan de Beneficios en Salud). Data on consumption of services not included in the PBS (non-PBS) were calculated using aggregated data from MIPRES database. All direct costs incurred by prevalent cases of BC, from January 1 to December 31, 2019, were included in the analysis. The net costs of the disease were estimated by multiplying the marginal cost and the expected number of cases with BC by region and age group. Marginal costs were defined as the costs of services delivered to patients with BC after subtracting the expected costs of health services due to age, comorbidity burden or region of residence. To calculate these costs, we used Propensity Score Matching in the main analysis. All costs were expressed in 2019 international dollars. Productivity losses, transportation expenses, and caregiving costs were not included. RESULTS: A total of 46,148 patients with BC were identified. Total net costs were $387 million (95% CI $377 to $396 million), 60% associated with non-PBS services. Marginal costs were $8,366 (95% Confidence Interval $8,170 to $8,573), with substantial variations between regions age groups (from $3,919 for older patients in the Amazonia region to $10,070 for younger patients in the Pacific region). The costs for PBS services were higher for ambulatory services and for patients who died during 2020. CONCLUSIONS: BC imposes a substantial economic burden for the Colombian Health System with important variations in net costs between regions and age groups. Patients near death and ambulatory services were associated with higher costs of healthcare.

Descriptive study of perineuronal net in enteric nervous system of humans and mice.

Perineuronal nets (PNN) are highly specialized structures of the extracellular matrix around specific groups of neurons in the central nervous system (CNS). They play functions related to optimizing physiological processes and protection neurons against harmful stimuli. Traditionally, their existence was only described in the CNS. However, there was no description of the presence and composition of PNN in the enteric nervous system (ENS) until now. Thus, our aim was to demonstrate the presence and characterize the components of the PNN in the enteric nervous system. Samples of intestinal tissue from mice and humans were analyzed by RT-PCR and immunofluorescence assays. We used a marker (Wisteria floribunda agglutinin) considered as standard for detecting the presence of PNN in the CNS and antibodies for labeling members of the four main PNN-related protein families in the CNS. Our results demonstrated the presence of components of PNN in the ENS of both species; however its molecular composition is species-specific.

Assessing the predictive capability of N, P, and B diagnosis in cotton crop.

The compositional nutrient diagnosis-CND method is a standard tool for evaluating plant nutritional status. Adjustments are crucial to elevate accuracy. The effectiveness of such methodological refinements should be rigorously assessed through accuracy tests that are benchmarked against the prescient diagnostic analysis-PDA methodology. The objective of this investigation was to refine the CND technique for a more precise evaluation of N, P, and B nutrient status in cotton. The study's database encompasses 144 data points pertaining to crop yield and foliar nutrient concentrations from cotton plantations in the Cerrado biome of Brazil. Subsequently, the CND norms were established through rigorous calibration. Three separate nutrient-dose trials, each featuring four levels of N, P and B, were carried out to assess plant true nutritional status. Adjustments were made to the nutrient responsiveness range-NRr (0.5 and 1.0), while yield response-YR were scrutinized at threshold levels (5% and 10%). The prerequisites for achieving high diagnostic accuracy were nutrient specific. For N, maximal accuracy was linked only to the YR parameter (YR = 10%). For P, the most precise outcomes were attained with a NRr = 0.5 and YI = 5%. For B, highest diagnostic accuracy when the NRr = 1.0 and YI = 10%. These insights highlight the need to fine-tune the CND method for reliable nutritional evaluations and cotton crop productivity optimization.

Climate change dominates the interannual variation of carbon export efficiency in each season in the Northern Gulf of Mexico during 1998-2011.

Carbon export efficiency is a key indicator of the capacity of biological pump, but the controlling mechanism of the efficiency remains unclear. Our findings revealed that interannual variations in seasonal carbon export efficiency are determined by direct factors including riverine nutrient fluxes, stratification, residence time. These direct factors are finally attributed to two indirect factors (human activities and climate change). We quantified the absolute contributions of direct and indirect factors to carbon export efficiency. The results showed that the carbon export efficiency in the northern Gulf of Mexico in spring (summer; autumn; winter) was driven by human activities, which accounted for an absolute contribution of 16.02% (7.20%; 4.00%; 8.49%, respectively) through riverine nutrient fluxes, and by climate change, which accounted for an absolute contribution of 33.51% (21.43%; 25.73%; 15.80%, respectively) through stratification and water residence time. Moreover, carbon export efficiency could be predicted by MEI of 8 months earlier.

Does commercialization lead to more intensive management strategies? Decision-making for the utilization of non-timber forest products in a Nahua area of the Sierra Negra, Mexico.

BACKGROUND: The commercialization of non-timber forest products (NTFPs) provides income for rural indigenous households. The integration of NTFPs into formal markets tends to intensify management practices to ensure production and monetary benefits. However, more research is needed to understand the motivations for managing of commercialized species. We examine the influence of social, ecological, and economic factors on traditional management and how they drive the adoption of more or less intensive practices for subsistence and commercially traded NTFPs. METHODS: The study was conducted in the Nahua community of Ixtacxochitla, in the Sierra Negra of central Mexico, where we conducted free lists and semi-structured interviews in 32% of the 88 households to assess socio-ecological variables related to management practices. In addition, we interviewed local traders to assess commercial variables used in a cost-benefit model to calculate the net annual income of commercialized species. Non-metric multidimensional scaling was used to analyze relationships between socio-ecological variables and management practices. We also explored the relationship between management and commercial factors using principal component analysis. RESULTS: We recorded 64 plant and mushroom species of NTFPs used for medicinal, ornamental, ceremonial, and edible purposes, 36 of which are commercialized in the municipal market of Coyomeapan. The commercialized species generated an average annual net income of MXN 67,526 (USD 3924) per family, with five species contributing the most. Species both used for both subsistence and commercialization were managed through incipient in situ gathering, tolerance in ex situ anthropogenic areas, and intensive protection and propagation efforts in ex situ environments. Even the five species with the highest commercial returns were managed across this gradient of practices. Key factors influencing the adoption of more intensive species management practices were feasibility of management, type of species use, ecological abundance, frequency of consumption, and cultural importance. CONCLUSIONS: The intensification of NTFPs management is not solely driven by the commercial value of the products or the level of income generated. Instead, the interaction between socio-ecological and economic factors determines the extent of management practices. The main constraint to the implementation of intensive practices has been the inability to manage species outside their natural habitats, despite their cultural significance and frequent consumption. Understanding the factors involved in the harvesting of NTFPs can serve as the basis for future research aimed at analyzing the conditions for successful and sustainable NTFPs commercialization.

Nitric Oxide Mitigates the Deleterious Effects Caused by Infection of Pseudomonas syringae pv. syringae and Modulates the Carbon Assimilation Process in Sweet Cherry under Water Stress.

Bacterial canker is an important disease of sweet cherry plants mainly caused by Pseudomonas syringae pv. syringae (Pss). Water deficit profoundly impairs the yield of this crop. Nitric oxide (NO) is a molecule that plays an important role in the plant defense mechanisms. To evaluate the protection exerted by NO against Pss infection under normal or water-restricted conditions, sodium nitroprusside (SNP), a NO donor, was applied to sweet cherry plants cv. Lapins, before they were exposed to Pss infection under normal or water-restricted conditions throughout two seasons. Well-watered plants treated with exogenous NO presented a lower susceptibility to Pss. A lower susceptibility to Pss was also induced in plants by water stress and this effect was increased when water stress was accompanied by exogenous NO. The lower susceptibility to Pss induced either by exogenous NO or water stress was accompanied by a decrease in the internal bacterial population. In well-watered plants, exogenous NO increased the stomatal conductance and the net CO2 assimilation. In water-stressed plants, NO induced an increase in the leaf membranes stability and proline content, but not an increase in the CO2 assimilation or the stomatal conductance.

Assessment of Covid-19 vaccine confidence among healthcare personnel in the safety-net sector in the United States and Puerto Rico.

BACKGROUND: This study aimed to assess COVID-19 vaccine confidence among healthcare personnel in the safety net sector of the United States and Puerto Rico. This study aimed to examine the extent to which increased knowledge and positive attitudes toward COVID-19 vaccine safety and efficacy were associated with healthcare workers' COVID-19 vaccination status and their recommendation of the vaccine to all patients. METHODS: Online survey data were collected from health care workers working in Free and Charitable Clinics across the United States and Federally Qualified Health Centers in Puerto Rico. The survey consisted of 62 questions covering various demographic measures and constructs related to healthcare workers' vaccination status, beliefs, and recommendations for COVID-19 vaccination. Statistical analyses, including multivariate analysis, were conducted to identify the factors associated with the COVID-19 vaccine status and recommendations among healthcare personnel. RESULTS: Among the 2273 respondents, 93% reported being vaccinated against COVID-19. The analysis revealed that respondents who believed that COVID-19 vaccines were efficacious and safe were three times more likely to be vaccinated and twice as likely to recommend them to all their patients. Respondents who believed they had received adequate information about COVID-19 vaccination were 10 times more likely to be vaccinated and four times more likely to recommend it to all their patients. CONCLUSIONS: The study results indicate that healthcare workers' confidence in COVID-19 vaccines is closely tied to their level of knowledge, positive beliefs, and attitudes about vaccine safety and efficacy. The study emphasizes the significance of healthcare workers feeling well informed and confident in their knowledge to recommend the vaccine to their patients. These findings have important implications for the development of strategies to boost COVID-19 vaccine confidence among healthcare workers and increase vaccine uptake among patients.

Optimal Rotation Age in Fast Growing Plantations: A Dynamical Optimization Problem.

Forest plantations are economically and environmentally relevant, as they play a key role in timber production and carbon capture. It is expected that the future climate change scenario affects forest growth and modify the rotation age for timber production. However, mathematical models on the effect of climate change on the rotation age for timber production remain still limited. We aim to determine the optimal rotation age that maximizes the net economic benefit of timber volume in a negative scenario from the climatic point of view. For this purpose, a bioeconomic optimal control problem was formulated from a system of Ordinary Differential Equations (ODEs) governed by the state variables live biomass volume, intrinsic growth rate, and area affected by fire. Then, four control variables were associated to the system, representing forest management activities, which are felling, thinning, reforestation, and fire prevention. The existence of optimal control solutions was demonstrated, and the solutions of the optimal control problem were also characterized using Pontryagin's Maximum Principle. The solutions of the model were approximated numerically by the Forward-Backward Sweep method. To validate the model, two scenarios were considered: a realistic scenario that represents current forestry activities for the exotic species Pinus radiata D. Don, and a pessimistic scenario, which considers environmental conditions conducive to a higher occurrence of forest fires. The optimal solution that maximizes the net benefit of timber volume consists of a strategy that considers all four control variables simultaneously. For felling and thinning, regardless of the scenario considered, the optimal strategy is to spend on both activities depending on the amount of biomass in the field. Similarly, for reforestation, the optimal strategy is to spend as the forest is harvested. In the case of fire prevention, in the realistic scenario, the optimal strategy consists of reducing the expenses in fire prevention because the incidence of fires is lower, whereas in the pessimistic scenario, the opposite is true. It is concluded that the optimal rotation age that maximizes the net economic benefit of timber volume in P. radiata plantations is 24 and 19 years for the realistic and pessimistic scenarios, respectively. This corroborates that the presence of fires influences the determination of the optimal rotation age, and as a consequence, the net economic benefit.

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy. METHOD: To optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis. RESULT: In this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%. CONCLUSION: Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.

Equivalent biodiversity area: A novel metric for No Net Loss success in Brazil's changing biomes.

This study presents a new method to incorporate the No Net Loss (NNL) principle within corporate Environmental, Social, and Governance (ESG) frameworks. This principle aims to ensure that biodiversity losses from human activities are fully offset. In this context, we tackle two main challenges: managing epistemic uncertainties in environmental modeling and accurately assessing compensatory areas needed to replace lost habitats. Focusing on Brazil's diverse biomes, which are undergoing rapid changes, we highlight the role of expert opinion surveys in addressing the uncertainties of the InVEST Habitat Quality, a model that simulates changes in landscape integrity under different land use scenarios. Our analysis across three of Brazil's regions - Caatinga Semi-arid, Cerrado Savanna, and Atlantic Forest - leverages open-source data to reveal substantial habitat losses due to activities like wind farm development, mining, and intensive agriculture, leading to a widespread decline in habitat quality. We introduce the Equivalent Biodiversity Area (EBA) metric to support NNL and Net Gain of Biodiversity efforts, measured in hectares. Findings show a reduction in EBA across all studied areas, highlighting the need for effective compensation strategies. Such strategies should merge Legal Reserves and ecological restoration into ESG policies, encourage landholder collaboration, and align with larger environmental efforts, such as watershed revitalization and Biodiversity Credits markets.

Modulation of Drought-Induced Stress in Cowpea Genotypes Using Exogenous Salicylic Acid.

Plant endogenous mechanisms are not always sufficient enough to mitigate drought stress, therefore, the exogenous application of elicitors, such as salicylic acid, is necessary. In this study, we assessed the mitigating action of salicylic acid (SA) in cowpea genotypes under drought conditions. An experiment was conducted with two cowpea genotypes and six treatments of drought stress and salicylic acid (T1 = Control, T2 = drought stress (stress), T3 = stress + 0.1 mM of SA, T4 = stress + 0.5 mM of SA, T5 = stress + 1.0 mM of SA, and T6 = stress + 2.0 mM of SA). Plants were evaluated in areas of leaf area, stomatal conductance, photosynthesis, proline content, the activity of antioxidant enzymes, and dry grain production. Drought stress reduces the leaf area, stomatal conductance, photosynthesis, and, consequently, the production of both cowpea genotypes. The growth and production of the BRS Paraguaçu genotype outcompetes the Pingo de Ouro-1-2 genotype, regardless of the stress conditions. The exogenous application of 0.5 mM salicylic acid to cowpea leaves increases SOD activity, decreases CAT activity, and improves the production of both genotypes. The application of 0.5 mM of salicylic acid mitigates drought stress in the cowpea genotype, and the BRS Paraguaçu genotype is more tolerant to drought stress.

Understanding the effect of sex on energy requirements of hair sheep.

Differences and/or similarities in the influence of sex class for hair sheep requirements remain inconclusive. Knowledge of energy requirements allows well-formulated diets to be provided which is crucial for improving animal production. We aimed to determine the effect of sex class on the net energy requirements of growing hair sheep in a multi-study approach. We used a data set composed of individual measurements of 382 hair sheep (299 non-castrated and 83 castrated males) from 11 studies that used the methodology of comparative slaughter. Net energy requirements for maintenance (NEm) were obtained by the regression between heat production and metabolizable energy intake. The metabolizable energy requirements for maintenance (MEm) were calculated by the iterative method, and the efficiency of use of metabolizable energy for maintenance (km) was obtained by NEm divided by MEm. The net energy requirements for gain (NEg) were estimated from retained energy (RE) against empty BW gain (EBWG). The efficiency of energy use for weight gain (kg) was obtained from the relationship between RE and the energy metabolizable intake for gain, removing the intercept. There was an effect of sex on NEg and two equations were generated: NEg (MJ/day) = 1.040 (±0.04055) × EBW0.75 × EBWG0.8767(±0.03293) and NEg (MJ/day) = 1.040 (±0.04055) × EBW0.75 × EBWG0.8300(±0.03468) (R2 = 0.86; MSE = 0.0037; AIC = -468.0) for non-castrated and castrated males, respectively. Sex class did not affect kg (P > 0.05) and one kg was generated (0.29). Sex did not affect kprotein (P = 0.14) and kfat (P = 0.32), assuming an average deposition efficiency of 0.27 for protein and 0.78 for fat. The NEm and MEm did not differ (P > 0.05) between sex classes, with a value of 0.272 and 0.427 MJ/kg0.75 EBW per day, respectively. The km observed was 0.64. In conclusion, non-castrated and castrated male hair sheep have similar maintenance energy requirements although energy requirements for gain differed among them. The Committees overestimate the gain and maintenance requirements for hair sheep. Therefore, the equations generated in this study are recommended.

Greenhouse gas emissions from petroleum production in Guyana: An examination of the implications for the country's net carbon sink status.

The emerging petroleum production sector has been positively impacting Guyana's economic prospects while contributing to an anticipated increase in the country's greenhouse gas emissions. This article presents a case study that adopts a convergent mixed methods approach. The methods selected for data collection consisted of in-depth interviews, document review and quantitative analysis to examine the implications of the GHG emissions from Guyana's emerging petroleum production sector for the country's net carbon sink status. The article explores measures to enable Guyana to remain a net carbon sink. The study reveals that fugitive emissions were the highest component of greenhouse gas emissions, mostly accounted for by flaring and venting from well testing and flaring from conventional petroleum production. The annual GHG emissions from petroleum production for 2025, 2027 and 2030 were 9034, 13,397 and 20,516 kilotons of CO2e, respectively. Moreover, the combination of the emissions from the oil and gas production and those from three scenarios of growth in Guyana's energy sector, the total annual GHG emissions could vary from 4445 kilotons of CO2e by 2025 to the largest amount of 24,888 kilotons of CO2e by 2030 across various scenarios and conditions. Further, the highest total GHG emissions for 2025 would be 11,015 kilotons CO2e compared to a sequestration rate of 154,060 kilotons CO2 (7%) for 2025. In 2027, the highest total GHG emissions would be 16,234 kilotons CO2e as compared to a sequestration rate of 153,860 kilotons CO2 (11%). No negative implication for Guyana's net carbon sink is projected. However, Guyana should review, update and implement policies to mitigate GHG emissions and offset unavoidable ones. This research highlights the efforts of Guyana to adopt a development path that seeks to fulfil obligations to the UNFCCC and the Paris Accord while improving the social and economic well-being of its citizens.