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Background and Aims: The present study was designed to investigate SNP rs17300539 in the ADIPOQ gene and its relationships with obesity, metabolic syndrome (MS), and serum circulating adiponectin. Methods: The present design involved a Caucasian population of 329 subjects with obesity. Anthropometric and adiposity parameters, blood pressure, biochemical parameters, and the percentage of patients with metabolic syndrome were recorded. The ADIPOQ gene variant (rs17300539) genotype was evaluated. Results: The percentage of patients with different genotypes of the rs17300539 polymorphism in this sample was 86.0% (n = 283) (GG), 11.2% (n = 37) (GA), and 2.7% (n = 9) (AA). The allele frequency was G (0.76) and A (0.24). Applying the dominant genetic model (GG vs. GA + AA), we reported differences between genotype GG and genotype GA + AA for serum adiponectin levels (Delta: 7.5 ± 1.4 ng/mL; p = 0.03), triglycerides (Delta: 41.1 ± 3.4 mg/dL; p = 0.01), fastingcirculating insulin (Delta: 4.9 ± 1.1 mUI/L; p = 0.02), and insulin resistance as HOMA-IR (Delta: 1.4 ± 0.1 units; p = 0.02). The remaining biochemical parameters were not related to the genotype of obese patients. The percentages of individuals with MS (OR = 2.07, 95% CI = 1.3–3.88; p = 0.01), hypertriglyceridaemia (OR = 2.66, 95% CI = 1.43–5.01; p = 0.01), and hyperglycaemia (OR = 3.31, 95% CI = 1.26–8.69; p = 0.02) were higher in GG subjects than patients with A allele. Logistic regression analysis reported an important risk of the presence of metabolic syndrome in GG subjects (OR = 1.99, 95% CI = 1.21–4.11; p = 0.02) after adjusting for adiponectin, dietary energy intakes, gender, weight, and age. Conclusions: The GG genotype of rs17300539 is associated with hypertriglyceridaemia, insulin resistance, low adiponectin levels, and a high risk of metabolic syndrome and its components.

The Philippines is one of the most vulnerable countries in the world to the potential impacts of climate change. To fully understand these potential impacts, especially on future hydrological regimes and water resources (2010-2050), 24 river basins located in the major agricultural provinces throughout the Philippines were assessed. Calibrated using existing historical interpolated climate data, the STREAM model was used to assess future river flows derived from three global climate models (BCM2, CNCM3 and MPEH5) under two plausible scenarios (A1B and A2) and then compared with baseline scenarios (20th century). Results predict a general increase in water availability for most parts of the country. For the A1B scenario, CNCM3 and MPEH5 models predict an overall increase in river flows and river flow variability for most basins, with higher flow magnitudes and flow variability, while an increase in peak flow return periods is predicted for the middle and southern parts of the country during the wet season. However, in the north, the prognosis is for an increase in peak flow return periods for both wet and dry seasons. These findings suggest a general increase in water availability for agriculture, however, there is also the increased threat of flooding and enhanced soil erosion throughout the country.

Research summary: This article uses item response theory (IRT) to advance strategic management research, focusing on an application to corporate social responsibility (CSR). IRT explicitly models firms' and individuals' observable actions in order to measure unobserved, latent characteristics. IRT models have helped researchers improve measures in numerous disciplines. To demonstrate their potential in strategic management, we show how the method improves on a key measure of corporate social responsibility and corporate social performance (CSP), the KLD Index, by creating what we term D-SOCIAL-KLD scores, and associated estimates of their accuracy, from the underlying data. We show, for instance, that firms such as Apple may not be as \"good\" as previously thought, while firms such as Walmart may perform better than typically believed. We also show that the D-SOCIAL-KLD measure outperforms the KLD Index and factor analysis in predicting new CSR-related activity. Managerial summary: Corporate social responsibility (CSR) continues to grow in importance among the press, political activists, managers, analysts, and investors, yet measurement techniques have not kept up. We show that the most common approach for measuring CSR—adding up observable traits—is fundamentally flawed, even if these traits accurately capture CSR-related behavior. We introduce an improved measurement technique that treats these traits as test questions that are differentially weighted, so that \"hard\" CSR activities affect a company's score more than \"easy\" CSR activities. This approach produces a measure that offers a more reliable comparison of firms than standard measures. Our approach has a number of additional advantages, including differentiating firms that receive identical scores on an additive scale and accounting for how CSR-related behavior has evolved over time. Anybody who cares about CSR should consider using our measure (available at www.socialscores.org) as the basis for analyzing firms' CSR.

The role of corporations in the U.S. political process has received increased scrutiny in the wake of the U.S. Supreme Court's Citizens United decision, leading to calls for greater regulation. In this paper, we analyze whether policies mandating greater disclosure and shareholder approval of political contributions reduce risk and increase firm value, as proponents of such rules claim. Specifically, we examine the Neill Committee Report (NCR), which led to the passage of the United Kingdom's Political Parties, Elections, and Referendums Act 2000 mandating new disclosure and shareholder approval rules. We find that politically active firms did not benefit from the NCR in the days after its release and suffered a decline in value in the months and years that followed. Politically active firms also suffered an increase in risk, as proxied by stock return volatility, following the release of the NCR. We theorize that these findings are due to the reduced flexibility these rules impose on corporate strategy as well as the potential for these rules to facilitate political activism against corporations.

Flood magnitude and frequency estimation are essential for the design of structural and nature-based flood risk management interventions and water resources planning. However, the global geography of hydrological observations is uneven, with many regions, especially in the Global South, having spatially and temporally sparse data that limit the choice of statistical methods for flood estimation. To address this data scarcity, we pool all available annual maximum flood data for the Philippines to estimate flood magnitudes at the national scale. Available river discharge data were collected from publications covering 842 sites, with data spanning from 1908 to 2018. Of these, 466 sites met criteria for reliable estimation of the annual maximum flood. Using the index flood approach, a range of controls was assessed at both national and regional scales using modern land cover and rainfall data sets, as well as geospatial catchment characteristics. Predictive equations for 2 to 100 year recurrence interval floods using only catchment area as a predictor have R2≤0.59. Adding a rainfall variable, the median annual maximum 1 d rainfall, increases R2 to between 0.56 for Q100 and 0.66 for Q2. Very few other topographic or land use variables were significant when added to multiple regression equations. Relatively low R2 values in flood predictions are typical of studies from tropical regions. Although the Philippines exhibits regional climate variability, residuals from national predictive equations show limited spatial structure, and region-specific equations do not significantly outperform the national equations. The predictive equations are suitable for use as design equations in ungauged catchments for the Philippines, but statistical uncertainties must be reported. Our approach demonstrates how combining individually short historical records, after careful screening and exclusion of unreliable data, can generate large data sets that can produce consistent results. Extension of continuous flood records by continuous and rated monitoring is required to reduce uncertainties. However, the national-scale consistency in our results suggests that extrapolation from a small number of carefully selected catchments could provide nationally reliable predictive equations with reduced uncertainties.

Background Sarcopenia is characterized by the loss of muscle mass, quality and function. Ultrasonography provides a non‐invasive method for assessing sarcopenia. Its generalizability remains limited due to certain methodological and population‐specific challenges. This study evaluated the association between AI‐assisted muscle ultrasonography and sarcopenia in patients at risk of malnutrition. Methods This observational, cross‐sectional study included 647 patients at risk of malnutrition. Nutritional status was assessed via anthropometry, bioimpedanciometry, quadriceps rectus femoris (QRF) ultrasonography and handgrip strength. An AI‐based imaging system segmented the region of interest (ROI) in transverse QRF images to measure muscle thickness (RFMT), area (RFMA) and pennation angle (RFPA). The Multi‐Otsu algorithm extracted ROI biomarkers: low echogenicity (MiT) and medium echogenicity (FatiT), assumed as a surrogate of muscle and fat percentage of the ROI. Sarcopenia was diagnosed using European Working Group on Sarcopenia in Older People (EWGSOP2) criteria and malnutrition was assessed with Global Leadership Initiative on Malnutrition (GLIM) criteria. Results Most of the patients of the study were female (54.4%) and the mean age was 64.83 ± 15.79 years. Malnutrition was present in 530 patients (81.9%) and sarcopenia in 167 patients (25.8%) Among patients with sarcopenia 57.2% had low muscle mass, and 44% had low handgrip strength. Patients with sarcopenia had significantly lower values of RFMT (sarcopenia: 0.89 ± 0.27 cm; no sarcopenia: 1.03 + 0.29 cm; p < 0.01) and RFMA (sarcopenia: 2.77 + 1.02 cm²; no sarcopenia: 3.25 + 1.17 cm²; p < 0.01). In terms of muscle quality by AI‐assisted ultrasonography, we observed lower values of pennation angle (sarcopenia: 4.97 ± 2.91°; no sarcopenia: 5.50 ± 2.78°; p < 0.01), low echogenicity (MiT) (sarcopenia: 45 ± 10.80%; no sarcopenia: 47.39 ± 10.91%; p = 0.02) and a higher high echogenicity percentage (NMNFiT) (sarcopenia: 14.99 ± 5.52%; no sarcopenia: 14.76 ± 5.17%; p = 0.02). Multivariate analysis showed male sex as a risk factor for sarcopenia (OR = 1.85 (IC 95%: 1.23–2.77); p < 0.01), while higher RFMT was protective (OR: 0.18 (IC 95%: 0.04–0.86); p = 0.03). For low handgrip strength, higher MiT was protective (OR: 0.07 (IC 95%: 0.13–0.43); p < 0.01) after adjusting for age and sex. Conclusions In patients at risk of malnutrition, sarcopenia and dynapenia were associated with reduced muscle mass and quality. AI‐based ultrasound parameters, particularly RFMT and MiT, were significantly lower in individuals with sarcopenia and correlated with poorer muscle function, independent of age and sex.

Background: A nationwide, prospective, multicenter, cohort study (the Disease-Related caloric-protein malnutrition EChOgraphy (DRECO) study) was designed to assess the usefulness of ultrasound of the rectus femoris for detecting sarcopenia in hospitalized patients at risk of malnutrition and to define cut-off values of ultrasound measures. Methods: Patients at risk of malnutrition according to the Malnutrition Universal Screening Tool (MUST) underwent handgrip dynamometry, bioelectrical impedance analysis (BIA), a Timed Up and Go (TUG) test, and rectus femoris ultrasound studies. European Working Group on Sarcopenia in Older People (EWGSOP2) criteria were used to define categories of sarcopenia (at risk, probable, confirmed, severe). Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were used to determine the optimal diagnostic sensitivity, specificity, and predictive values of cut-off points of the ultrasound measures for the detection of risk of sarcopenia and probable, confirmed, and severe sarcopenia. Results: A total of 1000 subjects were included and 991 of them (58.9% men, mean age 58.5 years) were evaluated. Risk of sarcopenia was detected in 9.6% patients, probable sarcopenia in 14%, confirmed sarcopenia in 9.7%, and severe sarcopenia in 3.9%, with significant differences in the distribution of groups between men and women (p < 0.0001). The cross-sectional area (CSA) of the rectus femoris showed a significantly positive correlation with body cell mass of BIA and handgrip strength, and a significant negative correlation with TUG. Cut-off values were similar within each category of sarcopenia, ranging between 2.40 cm2 and 3.66 cm2 for CSA, 32.57 mm and 40.21 mm for the X-axis, and 7.85 mm and 10.4 mm for the Y-axis. In general, these cut-off values showed high sensitivities, particularly for the categories of confirmed and severe sarcopenia, with male patients also showing better sensitivities than women. Conclusions: Sarcopenia in hospitalized patients at risk of malnutrition was high. Cut-off values for the better sensitivities and specificities of ultrasound measures of the rectus femoris are established. The use of ultrasound of the rectus femoris could be used for the prediction of sarcopenia and be useful to integrate nutritional study into real clinical practice.

Researchers are often interested in the effects of state policies and institutions on individual behavior or other outcomes in sub-state-level observational units, such as election results in state legislative districts. In this article, we examine the issue of clustered data in state and local politics research and the analytical problems it can cause. Standard estimation methods applied in most regression models do not properly account for the clustering of observations within states, leading analysts to overstate the statistical significance of coefficient estimates, especially of state-level factors. We discuss the theory behind two approaches for dealing with clustering—clustered standard errors and multilevel modeling—and argue that calculating clustered standard errors is a more straightforward and practical approach, especially when working with large datasets or many cross-level interactions. We demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005).

Fresh groundwater lenses on karstic oceanic islands form a vital resource sustaining local populations. However, this resource is susceptible to saltwater intrusion through human drivers (over-abstraction) and natural processes (variable precipitation and storm surges). There is a paucity of means to assess the risks that freshwater lenses are exposed to. This is partly driven by a poor understanding of the root causes of saltwater intrusion, which leads to potentially inappropriate freshwater management strategies. Thus, effective management of these freshwater lenses requires a baseline understanding of the processes that drive saltwater intrusion and the degradation of freshwater lenses, and the temporal and spatial variability of these processes. Dynamics of such freshwater lenses involve an interplay between physical, chemical, and socio-economic processes; therefore, finding a solution necessitates an interdisciplinary approach and a range of data collection strategies. This approach was formalized in a Freshwater Lens Assessment Protocol (FLAP). Results from the research developed and tested on Bantayan Island in the Philippines reveals a sufficient freshwater lens to support the current and projected population; however, local officials are operating abstraction wells from the wrong locations on the island. Such locations are utilized due to ease of access to existing infrastructure and government boundaries, but do not consider technical factors that influence saltwater intrusion. FLAP is an appropriate, cost-effective, interdisciplinary tool that uses a pragmatic approach to data collection, interpretation, and integration into an observational model. Continuous adjustments are possible through ongoing monitoring of the model, offering opportunities to evaluate the efficacy of resource management strategies.

Background This study aimed to evaluate the effects of an oral nutritional supplement (ONS) enriched with hydroxy‐methyl‐butyrate (HMB) in subjects with disease‐related malnutrition (DRM) and to monitor these effects with an ultrasound Imaging System, based on artificial intelligence, in a real‐world study. Methods Fifty consecutive adult patients with DRM were enrolled. The malnutrition was diagnosed by Global Leadership Initiative on Malnutrition (GLIM) criteria. For 3 months, the patients received nutritional education, and medical nutrition therapy was started with an adapted oral diet and two servings of an oral nutritional supplementation (ONS) with a hyperproteic hypercaloric formula (HMB—enriched). All patients were studied at baseline, and 3 months after intervention, with a nutritional assessment (anthropometry, bioelectrical impedanciometry [BIA], muscle ultrasonography and biochemical parameters). Ultrasound images were automatically quantified using an AI‐based ultrasound imaging system. Results The study included 50 patients (21 men and 29 women) with a mean age of 57.8 ± 18.1 years. Following treatment with the HMB‐enriched ONS, the prevalence of sarcopenia decreased significantly (from 24% to 18%; p = 0.01) and severe malnutrition (from 14% to 2%; p = 0.01). An improvement in BIA parameters; phase angle (0.14 ± 0.02°; p = 0.02); phase angle index (0.07 ± 0.01°/m2; p = 0.01); fat free mass (1.31 ± 0.12 kg; p = 0.006); skeletal muscle mass (0.51 ± 0.11 kg; p = 0.01); and skeletal muscle mass index (0.31 ± 0.13 kg/m2; p = 0.01) were reported. Functional parameters as handgrip strength (1.36 ± 0.21 kg; p = 0.009) and seconds of time up and go test (1.16 ± 0.15 s; p = 0.02) showed also an improvement. The AI‐based ultrasound system detected a significant increase in the thickness of the vastus intermedius muscle (TVI) (0.09 ± 0.01 cm; p = 0.008) and an increase of the rectus femoris (TRF) (1.06 ± 0.4 cm; p = 0.07) with the consequence of a significant increase of the (TVITRF) (0.15 ± 0.02 cm; p = 0.008). The sarcopenia index is also significant (1.03 ± 0.3 cm; p = 0.03). Muscle quality, as assessed by echogenicity analysis, improved significantly: muscle index (Mi) (0.04 ± 0.01; p = 0.04) and (fat index) FATi (−0.03 ± 0.01; p = 0.03). Conclusions The use of an HMB‐enriched ONS in DRM patients, combined with AI‐based ultrasound imaging for follow‐up, significantly improved their nutritional status, muscle mass and muscle quality as assessed by echogenicity.