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IntroductionTo study the utility of A Body Shape Index (ABSI) alongside body mass index (BMI) to predict mortality and frailty in an aging community population.Materials and methodsParticipants (n = 1,580) were drawn from the first Israeli national health and nutrition survey of older adults (“Mabat Zahav”) conducted from 2005 to 2006, constituting adults aged ≥65 years. Socio-demographic, clinical, behavioral, and psychosocial data were collected. Baseline weight, height, and waist circumference (WC) were measured and expressed as the allometric indices BMI (kg/m2) and ABSI, a BMI-independent measure of abdominal obesity [WC/(BMI2/3*m1/2)]. Mortality follow-up lasted through 2019. Frailty was assessed in 2017–2019 by the Fried Biological Phenotype in a sub-cohort of 554 survivors. Cox and logistic regression models assessed associations of BMI and ABSI with mortality and frailty.ResultsAt baseline, mean [SD] age was 74.5 [6.1] years, and 52.4% were women. The correlation between BMI and WC Z scores was 0.71, reduced to −0.11 for BMI and ABSI. Over a median follow-up of 13 years, 757 deaths occurred. The multivariable-adjusted hazard ratios (95% CIs) for mortality per standard deviation increase in BMI and ABSI were 1.07 (0.99;1.17) and 1.13 (1.05;1.21), respectively. Among participants assessed for frailty, 77 (14%) met the frailty criteria. After multivariable adjustment, the odds ratios (95% CIs) for frailty were 0.83 (0.69–1.01) for BMI and 1.55 (1.34–1.79) for ABSI.DiscussionIn a nationwide cohort of older adults, ABSI was independently associated with mortality risk. Furthermore, ABSI, but not BMI, was a strong predictor of frailty.

Background: Type 2 diabetes (T2D) is viewed as a progressive chronic condition, yet recent research has raised hopes for reversal of this trajectory through innovative approaches. Aim: This audit assessed the impact of a very low carbohydrate ketogenic diet (VLCKD) on glucose control, weight and medication usage in T2D and prediabetes patients. The Glandt Center for Diabetes Care, in Tel Aviv, Israel, from 2015 to 2022. Setting: The Glandt Center for Diabetes Care, in Tel Aviv, Israel, from 2015 to 2022. Methods: A cohort of 344 T2D or prediabetes patients following a VLCKD diet for 6 months at a specialised diabetes centre was analysed. Patient records were reviewed for glucose control, weight, blood pressure, lipid profile, liver function and medication usage, with paired t-tests used for analysis. Results: Patients (mean age: 62 years; T2D duration: 12.3 years) showed significant improvements. Among patients with diabetes (N = 244), median HbA1c dropped from 59 mmol/mol (7.6%) to 45 mmol/mol (6.3%), with 96.3% showing improvement. Prediabetes patients (N = 100) experienced a drop from 42 mmol/mol (6%) to 38.7 mmol/mol (5.7%), with 84% improving. Weight loss occurred in both groups (median changes: −6.5 kg and −5.7 kg). Blood pressure, triglycerides and liver enzymes also improved. Initially, 78 patients were on insulin, reduced to 16 patients at 6 months, with average dose of those remaining on insulin reduced by 72%. Conclusion: Very low carbohydrate ketogenic diet is effective in enhancing glucose control, weight loss and cardiovascular risk factors in T2D. Most patients achieved insulin independence, with others significantly reducing insulin dosage. The study underscores the potential of integrating a VLCKD with medication management in comprehensive T2D treatment. Contribution: The audit shows the application of a KD in patients with long-standing diabetes.

This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen’s slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months.

The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid point and the region scale) as well as station observations. This assessment used forecast negative log-likelihood as the main performance measure, which is particularly sensitive to the most extreme heat waves. It was found that the computationally simpler normal distribution outperforms the GEVD in providing probabilistic year-ahead forecasts of temperature extremes. Given these findings, it is suggested to consider alternatives to the GEVD for assessing the risk of extreme heat. [ABSTRACT FROM AUTHOR]

Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions.

This study analyzes the spatiotemporal variability of precipitation at the scale of the Moulouya watershed in eastern Morocco, which is very vulnerable to the increasing water shortage. For this purpose, we opted for wavelet transformation, a method based on the spectral analysis of data which allows for periodic components of a rainfall time series to change with time. The results obtained from this work show spectral power across five frequency ranges of variability: 1 to 2 years, 2 to 4 years, 4 to 8 years, 8 to 16 years, and 16 to 32 years. The duration of significant power at these frequencies is generally not homogeneous and varies from station to station. The most widespread frequency over the entire study area was found in the 4- to 8-year range. This mode of variability can last up to 27 consecutive years. In most of the basin, this mode of variability was observed around the period between 1990 and 2010. Oscillations at 8 to 16 years in frequency appear in only five series and over different time periods. The 16- to 32-year mode of variability appears in 15 stations and extends over the period from 1983 to 2008. At this level, signal strength is very weak compared to other higher-frequency modes of variability. On the other hand, the mode of variability at the 1- to 2-year frequency range appeared to be continuous in some stations and intermittent in others. This allowed us to regionalize our study basin into two homogeneous clusters that only differ in variability and rainfall regime.

The aim of this paper was to present a precipitation trend analysis using gridded data at annual, seasonal and monthly time scales over the Fez-Meknes region (northern Morocco) for the period 1961–2019. Our results showed a general decreasing trend at an annual scale, especially over the mountain and the wetter parts of the region, which was statistically significant in 72% of the grid points, ranging down to −30 mm per decade. A general upward trend during autumn, but still non-significant in 95% of the grid points, was detected, while during winter, significant negative trends were observed in the southwest (−10 to −20 mm per decade) and northeast areas (more than −20 mm per decade) of the region. Spring rainfall significantly decreased in 86% of the grid points, with values of this trend ranging between 0 and −5 mm per decade in the upper Moulouya and −5 to −10 mm per decade over the rest of the region (except the northwest). At a monthly time scale, significant negative trends were recorded during December, February, March and April, primarily over the northeast Middle Atlas and the northwest tip of the region, while a significant upward trend was observed during the month of August, especially in the Middle Atlas. These results could help decision makers understand rainfall variability within the region and work out proper plans while taking into account the effects of climate change.

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution (in-situ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.

Forests play a vital role in atmospheric CO2 sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R2 and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%. [ABSTRACT FROM AUTHOR]

The lack of a complete and reliable data series often represents the main difficulty in carrying out climate studies. Diverse causes, such as human and instrumental errors, false and incomplete records, and the use of obsolete equipment in some meteorological stations, give rise to inhomogeneities that do not represent climatic reality. This work in the northern part of the Moroccan Middle Atlas used 22 meteorological stations with sometimes-incomplete monthly precipitation data from 1970 to 2019. The homogenization and estimation of the missing data were carried out with the R software package Climatol version 3.1.1. The trends in the series were quantified by the Mann–Kendall nonparametric test. The results obtained show a low root mean square error (RMSE), between the original and homogenized data, of between 0.5 and 38.7 mm per month, with an average of 8.5 mm. Rainfall trends for the months of December through June are generally downward. These negative trends are significantly stronger in the southern and eastern parts of the study area, especially during the month of April (the wettest month). On the other hand, July shows positive trends, with 71% of stations having an increasing precipitation tendency, although only five (or 1/3) of these are statistically significant. From August to November, generally positive trends were also observed. For these months, the percentage of series with a positive and significant trend varied between 55 and 77%.

During 2011 the LHCb experiment at CERN collected 1.0 fb−1 of √s = 7 TeV pp collisions. Due to the large heavy quark production cross-sections, these data provide unprecedented samples of heavy flavoured hadrons. The first results from LHCb have made a significant impact on the flavour physics landscape and have definitively proved the concept of a dedicated experiment in the forwardregion at a hadron collider. This document discusses the implications of these first measurements on classes of extensions to the Standard Model, bearing in mind the interplay with the results of searches for on-shell production of new particles at ATLAS and CMS. The physics potential of an upgrade to the LHCb detector, which would allow an order of magnitude more data to be collected, is emphasised.

During 2011 the LHCb experiment at CERN collected 1.0 fb−1 of √s = 7 TeV pp collisions. Due to the large heavy quark production cross-sections, these data provide unprecedented samples of heavy flavoured hadrons. The first results from LHCb have made a significant impact on the flavour physics landscape and have definitively proved the concept of a dedicated experiment in the forwardregion at a hadron collider. This document discusses the implications of these first measurements on classes of extensions to the Standard Model, bearing in mind the interplay with the results of searches for on-shell production of new particles at ATLAS and CMS. The physics potential of an upgrade to the LHCb detector, which would allow an order of magnitude more data to be collected, is emphasised.

Radiocarbon levels in annual plants provide a means to map out regional and continental-scale fossil fuel plumes in surface air. We collected corn (Zea mays) across North America during the summer of 2004. Plants from mountain regions of western North America showed the smallest influence of fossil fuel-derived CO2 with a mean Δ14C of 66.3‰ ±1.7‰. Plants from eastern North America and from the Ohio-Maryland region showed a larger fossil fuel influence with a mean Δ14C of 58.8‰ ± 3.9‰ and 55.2‰ ± 2.3‰, respectively, corresponding to 2.7 ppm ± 1.5 ppm and 4.3 ppm ± 1.0 ppm of added fossil fuel CO2 relative to the mountain west. A model–data comparison suggests that surveys of annual plant Δ14C can provide a useful test of atmospheric mixing in transport models that are used to estimate the spatial distribution of carbon sources and sinks.

Estimating CO2 fluxes from the pattern of atmospheric CO2 concentrations with atmospheric transport models is an ill-posed inverse problem, whose solution is stabilized using prior information. Weights assigned to prior information and to CO2 concentrations at different locations are quantified by parameters that are not well known, and differences in the choice of these parameters contribute to differences among published estimates of the regional partitioning of CO2 fluxes. Following the TransCom 3 protocol to estimate CO2 fluxes for 1992–1996, we find that the partitioning of the CO2 sink between land and oceans and between North America and Eurasia depends on parameters that quantify the relative weight given to prior flux estimates and the extent to which CO2 concentrations at different stations are differentially weighted. Parameter values that minimize an estimated prediction error can be chosen by generalized cross-validation (GCV). The GCV parameter values yield fluxes in northern regions similar to those obtained with the TransCom parameter values, but the GCV fluxes are smaller in the poorly constrained equatorial and southern regions.

Low growth rates of atmospheric CO2 were observed following the 1991 Pinatubo (Luzon) volcanic eruption. One hypothesis for this CO2 anomaly is that since diffuse light is more efficiently used by forests than direct light, the increase in the diffuse fraction of sunlight due to scattering by volcanic sulfur aerosol in the years following the eruption substantially increased forest net primary production (NPP). However, other observations suggest a decrease in northern forest NPP because of the cooler conditions following the eruption. Here we used a global database of dated tree ring widths (which correlate with forest NPP) to test this hypothesis. Ice core records of sulfur deposition allowed us to identify the timing and magnitude of 23 Pinatubo-scale eruptions since 1000 CE. We found a significant decrease in ring width for trees in middle to high northern latitudes (north of 45°N) following eruption sulfur peaks. Decreases in tree ring widths were in the range of 2–8% and persisted for ∼8 years following sulfur peaks, with minima at around 4–6 years. Ring width changes at lower latitudes in the Northern Hemisphere (30°N to 45°N) and in the Southern Hemisphere (30°S to 56°S) were not significant. In the tropics (30°N to 30°S) the paucity of tree ring records did not permit the evaluation of NPP changes. Given that elevated aerosol levels and summer cooling last only ∼2–3 years after an eruption, the persistence of declines in northern tree growth for up to 8 years after eruptions implies some additional mechanism that links these shorter-lived global eruption effects to sustained changes in tree physiology, biogeochemistry, or microclimate. At least for this sample of trees, the beneficial effect of aerosol light scattering appears to be entirely offset by the deleterious effect of eruption-induced climate change.

Abstract Background Obesity is known to be related to the development of type 2 diabetes mellitus (T2D). The most commonly used anthropometric indicator (body mass index [BMI]) presents several limitations such as the lack of possibility to distinguish adipose tissue distribution. Thus, this study examines the suitability of a body shape index (ABSI) for prediction of body composition and sarcopenic obesity in obese or overweight T2D subjects. Methods Cross-sectional study in 199 overweight/obese T2D adults. Anthropometric (BMI, ABSI) and body composition (fat mass [FM], fat-free mass [FFM], fat mass index [FMI] and fat-free mass index, and the ratio FM/FFM as an index of sarcopenic obesity) data was collected, as well as metabolic parameters (glycated haemoglobin [HbA1c], mean blood glucose, fasting plasma glucose [FPG], high-density-lipoprotein cholesterol [HDL], low-density-lipoprotein cholesterol, total cholesterol, and triglycerides [TG] levels; the ratio TG/HDL was also calculated as a surrogate marker for insulin resistance). Results ABSI was significantly associated with age and waist circumference. It showed a statistically significant correlation with BMI exclusively in women. Regarding body composition, in men, ABSI was associated with FM (%), while in women it was associated with both FM and FFM. Both males and females groups with high ABSI scores were significantly older (men: 59.3 ± 10.8 vs 54.6 ± 10.1, p ≤ 0.05; women: 65.1 ± 9.8 vs 58.1 ± 13.3, p ≤ 0.005) and showed lower FFM values (men: 62.3 ± 9.0 vs 66.2 ± 9.3, p ≤ 0.05; women: 48.7 ± 5.6 vs 54.5 ± 8.9, p ≤ 0.001) compared with low-ABSI groups. Multiple linear regression revealed that ABSI independently predict FMI and the FM/FFM ratio in women. Sarcopenic obesity was identified in 70 (36.5%) individuals according to the FM/FFM ratio. The AUROC of ABSI was 63.1% (95% CI 54.6–71.6%; p = 0.003) and an ABSI value of 0.083 m11/6 kg−2/3 was the optimal threshold in discriminating patients with sarcopenic obesity (sensitivity: 48%, specificity: 73%). Moreover, a significant association between ABSI and FPG was found in men. Conclusions ABSI could be useful to identify visceral and sarcopenic obesity in overweight/obese adults with T2D, adding some relevant clinical information to traditional anthropometric measures.

The Mediterranean Basin, located in a transition zone between the temperate and rainy climate of central Europe and the arid climate of North Africa, is considered a major hotspot of climate change, subject to water scarcity and drought. In this work, dry and wet spells have been analyzed in the Wadi Cheliff basin (Algeria) by means of annual precipitation observed at 150 rain gauges in the period 1970–2018. In particular, the characteristics of dry and wet spells (frequency, duration, severity, and intensity) have been evaluated by means of the run theory applied to the 12-month standardized precipitation index (SPI) values. Moreover, in order to detect possible tendencies in the SPI values, a trend analysis has been performed by means of two non-parametric tests, the Theil–Sen and Mann–Kendall test. The results indicated similar values of frequency, severity, duration, and intensity between the dry and the wet spells, although wet events showed higher values in the extreme. Moreover, the results of the trend analysis evidenced a different behavior between the northern side of the basin, characterized by a negative trend in the 12-month SPI values, and the southern side, in which positive trends were detected.