Differences In Weight Research Articles

Development of a Large-Aperture Coordinate Precision Measurement Instrument Using Differential Geometric Error Weighting

The accuracy of traditional measuring machines is affected by the measuring range and sensitive geometric errors, and it is not possible to combine large caliber and high-precision measurements. This study proposes a differential geometric error-weighting method for designing a high-precision, large-diameter measuring machine. The machine utilized a zero-Abbe arm structure and applied the rigid body theory and small-angle hypothesis to model geometric errors. Weights were calculated for 23 geometric errors, identifying eight sensitive ones. A picometer-precision laser interferometer (quDIS) with a theoretical positioning accuracy of 0.2 nm/mm and standard flat rulers are used to ensure highly accurate positioning of the Y-axis/Z-axis of the measuring platform and reduce the straightness of both axes by approximately 75%, with radial and axial runout of the rotary table under 100 nm. The development and design method of the high-precision measuring machine proposed in this study is applicable to large-diameter high-precision flexible measurement, and the accurate control of measuring machine movement accuracy is realized by calculating the geometric error weights.

A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained more than 0.5% of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6% using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18%) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

The contribution of vulnerability to emotional contagion to the expression of psychological distress in older adults

This study examines the differential weight of a wide range of factors—sociodemographic factors, indicators of autonomy, social support, coping styles, vulnerability to emotional contagion, and empathy—in the presence of two profiles of psychological distress and in their absence. This cross-sectional study included 170 older adults. As assessed by the Hospital Anxiety and Depression Scale (HADS), 65.9% of the individuals in the sample had a clinical or subthreshold level of anxiety and depression (score > 1). Based on the HADS’s clinical cutoff scores for the anxiety and depression subscales, three profiles were created for the no distress, anxiety, and anxious depression groups. The profiles did not differ on demographic indicators except for sex. Vulnerability to emotional contagion, satisfaction with the social network and coping styles emerged as factors weighing the likelihood of being in either of the psychological distress groups relative to individuals with no distress. After controlling for adversity and psychotropic treatment, vulnerability to emotional contagion had the strongest relationship with both psychological distress profiles. Future research, such as a prospective longitudinal study, may provide an opportunity to explain the direction of the relationship between psychological distress and the factors studied, particularly vulnerability to emotional contagion.

ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification

Synthetic Aperture Radar (SAR) images have proven to be a valuable cue for multimodal Land Cover Classification (LCC) when combined with RGB images. Most existing studies on cross-modal fusion assume that consistent feature information is necessary between the two modalities, and as a result, they construct networks without adequately addressing the unique characteristics of each modality. In this paper, we propose a novel architecture, named the Asymmetric Semantic Aligning Network (ASANet), which introduces asymmetry at the feature level to address the issue that multi-modal architectures frequently fail to fully utilize complementary features. The core of this network is the Semantic Focusing Module (SFM), which explicitly calculates differential weights for each modality to account for the modality-specific features. Furthermore, ASANet incorporates a Cascade Fusion Module (CFM), which delves deeper into channel and spatial representations to efficiently select features from the two modalities for fusion. Through the collaborative effort of these two modules, the proposed ASANet effectively learns feature correlations between the two modalities and eliminates noise caused by feature differences. Comprehensive experiments demonstrate that ASANet achieves excellent performance on three multimodal datasets. Additionally, we have established a new RGB-SAR multimodal dataset, on which our ASANet outperforms other mainstream methods with improvements ranging from 1.21% to 17.69%. The ASANet runs at 48.7 frames per second (FPS) when the input image is 256 × 256 pixels.

Brain natriuretic peptide as a predictor of 30-day mortality after return of spontaneous circulation in cardiac arrest patients

PurposeTo determine the predictive value of brain natriuretic peptide (BNP) levels for 30-day mortality after return of spontaneous circulation (ROSC) in patients with cardiac arrest (CA) of presumed cardiac etiology. MethodsThis retrospective study included 260 patients with CA of presumed cardiac etiology who regained ROSC and was conducted between November 2013 and June 2022 at two tertiary comprehensive hospitals. Cox regression and nomogram models were used to demonstrate the value of BNP level in predicting 30-day mortality rates. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the ability of the two models to predict 30-day mortality risk. ResultsBNP level was a predictive factor for 30-day mortality (hazard ratio [HR] = 1.441; 95 % confidence interval [CI] = 1.198–1.734). The area under curves (AUCs) of BNP level alone and model 2 (male sex, age, non-shockable rhythm, epinephrine, and time to ROSC >30 min) for predicting 30-day mortality were similar(0.813 versus 0.834). Model 1 that included the variables in model 2 and BNP level showed good predictive value (area under curve = 0.887; 95 % CI = 0.836–0.939). Compared to Model 2, Model 1 showed improved comprehensive differentiation and net weight classification of mortality prediction, further demonstrating the predictive value of BNP for 30-day mortality (NRI = 0.451, 95 % CI = 0.267–0.577; IDI = 0.109, 95 % CI = 0.035–0.191). ConclusionBNP level was a predictive factor for 30-day mortality after ROSC in patients with CA of presumed cardiac etiology who regained ROSC. The nomogram model included BNP may provide a reference for predicting 30-day mortality.

Distinct, common and synergistic effects of insulin and IGF-1 receptors on healthy murine ageing

ObjectiveReduced IGF-1 signalling is an evolutionarily conserved mediator of longevity, yet the magnitude of this effect is substantially larger in organisms retaining a common insulin and IGF-1 receptor. Whether this reflects the failure to simultaneously reduce IGF-1 and insulin signalling in mammalian model systems remains unexplored, as is the associated impact on markers of healthy ageing. We set out to address these uncertainties. MethodsWe compared the duration of healthy life (healthspan) in male mice with haploinsufficiency of the insulin receptor (IRKO), IGF-1 receptor (IGF-1RKO), or both (DKO), versus wildtype (WT) littermates. Cognitive performance was defined using nesting studies at 3- and 24-months of age. Brain transcriptome was characterised at 3- and 18-months of age using RNA-seq. ResultsHealthspan was longer in DKO versus WT, with IRKO and IGF-1RKO being intermediate. At 2 years of age, DKO also exhibited preserved nesting behaviour in contrast with all other genotypes. Differential insulin sensitivity or weight gain during ageing did not explain the preserved healthspan of DKO, since these were comparable to IRKO littermates. Brain transcriptomics at 18 months of age revealed lower expression of canonical ageing-associated genes in DKO versus WT, although many of these findings were replicated in IRKO versus WT or IGF-1RKO vs WT. ConclusionsReduced insulin and IGF-1 receptor expression have both common and synergistic effects upon elements of healthy mammalian ageing, suggesting future ageing studies should consider targeting both insulin and IGF-1 signalling.

Research and Application of Destriping Algorithm based on Weighted Block Sparse Representation to FengYun Satellite Imager Data

Abstract In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the MCP regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D satellite, the Multi-channel Scanning Radiometer (AGRI) onboard FengYun-4A satellite and Precipitation Microwave Radiometer (MWRI–RM) onboard FengYun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.

Efficacy of antiobesity medications among breast cancer survivors taking aromatase inhibitors.

Aromatase inhibitors (AI) block estrogen synthesis and are used as long-term adjuvant treatment for breast cancer in postmenopausal women. AI use can be associated with weight gain that can lead to increased cardiometabolic risk. The response to anti-obesity medications (AOM) in patients using AI has yet to be studied. We sought to investigate weight loss outcomes of AOM in patients taking AI for breast cancer treatment. This is a matched retrospective cohort study of breast cancer survivors on AI using AOM (AOM/AI group). We compared their weight loss outcomes with a group of female patients with obesity, without a history of breast cancer or AI use, on AOM (AOM group). The primary endpoint was total body weight loss percentage (TBWL %) at the last follow-up. We performed mixed linear regression models, including diabetes status at baseline, to assess associations between use of AOM with/without AI with total body weight loss percentage (TBWL%). We included 124 patients: 62 in the AOM/AI group (63.6 ± 10years, body mass index [BMI] 34.3 ± 7.1kg/m2) and 62 in the AOM group (62.8 ± 9.9years, BMI 34.6 ± 6.5kg/m2). The mean time of follow up was 9.3 ± 3.5months, with no differences among the two groups. The AOM/AI group had a lower TBWL% compared to the AOM group at the last follow-up -5.3 ± 5.0 vs. -8.2 ± 6.3 (p = 0.005). The results remained significant after adjusting for diabetes status (p = 0.0002). At 12months, the AOM/AI group had a lower TBWL% compared to the AOM group 6.4 ± 0.8% vs. 9.8 ± 0.9% (p = 0.04). The percentage of patients achieving ≥ 5%, ≥ 10%, and ≥ 15% of weight loss at 12months was greater in the AOM compared to the AOM/AI group. Although the weight loss response was suboptimal, patients in the AOM/AI group had improvement in fasting glucose, glycated hemoglobin, systolic blood pressure, and low-density lipoprotein cholesterol. The use of AI in breast cancer survivors is associated with less weight loss response to AOM compared to patients without breast cancer history and who do not take AI. Studies are needed to assess the mechanisms behind the differential weight loss response to AOM in women taking AI.

On the mathematical properties of spatial Rao’s Q to compute ecosystem heterogeneity

Spatio-ecological heterogeneity has a significant impact on various ecosystem properties, such as biodiversity patterns, variability in ecosystem resources, and species distributions. Given this perspective, remote sensing has gained widespread recognition as a powerful tool for assessing the spatial heterogeneity of ecosystems by analyzing the variability among different pixel values in both space and, potentially, time. Several measures of spatial heterogeneity have been proposed, broadly categorized into abundance-related measures (e.g., Shannon’s H) and dispersion-related measures (e.g., Variance). A measure that integrates both abundance and distance information is the Rao’s quadratic entropy (Rao’s Q index), mainly used in ecology to measure plant diversity based on in-situ based functional traits. The question arises as to why one should use a complex measure that considers multiple dimensions and couples abundance and distance measurements instead of relying solely on simple dispersion-based measures of heterogeneity. This paper sheds light on the spatial version of the Rao’s Q index, based on moving windows for its calculation, with a particular emphasis on its mathematical and statistical properties. The main objective is to theoretically demonstrate the strength of Rao’s Q index in measuring heterogeneity, taking into account all its potential facets and applications, including (i) integrating multivariate data, (ii) applying differential weighting to pixels, and (iii) considering differential weighting of distances among pixel reflectance values in spectral space.

MDEFC: Automatic recognition of human activities using modified differential evolution based fuzzy clustering method

In the present scenario, automatic Human Activity Recognition (HAR) is an emerging research topic, particularly in the applications of healthcare, Human Computer Interaction (HCI), and smart homes. By reviewing existing literature, the majority of the HAR methods achieved limited performance, while trained and tested utilizing unseen Internet of Things (IoT) data. In order to achieve higher recognition performance in the context of HAR, a new clustering method named Modified Differential Evolution based Fuzzy Clustering (MDEFC) is proposed in this article. The proposed MDEFC method incorporates an asymptotic termination rule and a new differential weight for enhancing the termination condition and improving this method’s ability in exploring the solution space of the objective function. The extensive empirical analysis states that the proposed MDEFC method achieved impressive recognition results with minimal training time by using both spatial and temporal features of the individual. The proposed MDEFC method’s effectiveness is tested on a real time dataset and an online Wireless Sensor Data Mining (WISDM) v1.1 dataset. The result findings demonstrate that the proposed MDEFC method averagely obtained 99.73 % of precision and 99.86 % of recall on the WISDM v1.1 dataset. Similarly, the proposed MDEFC method averagely obtained 93.46 % of f1-measure, 94.60 % of recall, and 93.88 % of precision on the real time dataset. These obtained experimental results are significantly higher in comparison to the traditional HAR methods.

Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.

Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodological framework that transforms qualitative information into quantitative information, establishing differential weights in relation to symptoms according to the medical evidence and the GRADE scale based on recommendation 1 of the said guide. To achieve this, common variables from the dataset were identified using the PAHO guide, and quality rules were established. A linear interpolation function was then parameterised to assign weights to the symptoms according to the evidence. Machine learning was used to compare the different models, achieving 99% accuracy compared with 79% without the methodology. This proposal represents a significant advancement, allowing the direct application of the PAHO recommendations to the dataset and improving the differential classification of arboviruses.

Radioactivity and GMO-Free Sterile Insect Technology for the Sustainable Control of the Invasive Pest Drosophila suzukii.

Drosophila suzukii (D. suzukii), commonly known as the spotted wing drosophila, is a highly invasive crop pest that is difficult to control using chemical insecticides. To address the urgent need for alternative and more sustainable control strategies, the sterile insect technique (SIT) is improved, which involves the release of sterilized male insects to mate with fertile conspecifics, thereby reducing the size of the pest population in the subsequent generation. The three critical aspects that influence the success of SIT programs in D. suzukii are addressed. First, an accurate and nondestructive method is established to determine the sex of individual insects based on the differential weight of male and female pupae. Second, conditions for X-ray sterilization are systematically tested and an optimal dose (90kV/40Gy) is identified that ensures the efficient production of sterile D. suzukii for release. Finally, the inherent thermosensitivity of D. suzukii males is exploited to develop a temperature-based sterilization technique, offering an alternative or additional SIT method for this pest. These advances will contribute to the development of a comprehensive and effective strategy for the management of D. suzukii populations, reducing their impact on agriculture and helping to safeguard crop yields.

Isocaloric replacement of ultraprocessed foods was associated with greater weight loss in the POUNDS Lost trial.

Higher intake of ultraprocessed foods (UPFs) is associated with obesity. We examined whether replacing UPFs (NOVA 4) with minimally processed foods and culinary ingredients (NOVA 1 + 2) was associated with differential weight change in this secondary prospective analysis of the Preventing Overweight Using Novel Dietary Strategies (POUNDS) Lost trial. We estimated percent energy intake (%kcal) from the four NOVA groups using 24-h dietary recalls in a subset of 356 participants. Multivariable-adjusted substitution models examined whether replacing %kcal from UPFs with NOVA 1 + 2 was associated with greater weight, body fat percentage, trunk fat, and waist circumference reduction at 6 months; changes in parameters were compared among NOVA 1 + 2 tertiles (T). Participants were on average 52.3 years of age, 85% White, 55% female, and 58.2% nonsmoking, with a mean BMI of 32.7 kg/m2. Replacing 10%kcal of UPFs with NOVA 1 + 2 was associated with greater 6-month weight (ß = 0.51, 95% CI: -0.93 to -0.09, p = 0.02), body fat percentage (ß = 2.7, 95% CI: -5.10 to -0.43, p = 0.02), and trunk fat reduction (ß = 3.9, 95% CI: -7.01 to -0.70, p = 0.02), but not waist circumferencereduction. Participants in T3 (-8.33 kg) versus T1 (-5.32 kg) of NOVA 1 + 2 had greater weight loss (p < 0.001). Isocaloric substitution of UPFs with NOVA 1 + 2 was associated with marginally greater weight loss under energy restriction. These modest findings support more research exploring the mechanisms linking UPFs with body weight regulation beyond energy intake.

Transition of dome formation to sudden explosive eruptions at Popocatépetl, Mexico: magnetic indicators

Transitions from effusive to explosive activity can increase hazards making it crucial to define early indicators such as changes in the magnetic signals. After more than 80 cycles of crater-dome extrusion and destruction from 1996 on, Popocatépetl volcano (Mexico) experienced changes in its behavior from March 15 to 18 July 2019, when no lava domes were observed. Some of the domes behaved as contained lava flows within the crater floor (pancakes) while others were more irregular-shaped. Activity decreased considerably over this 2019 interval except for the unexpected explosions in March and June, that produced ash plumes reaching up to 14,000 m a.s.l. In order to investigate the causes of the transition from effusive to explosive behavior in March and June, we analyzed the time series from the magnetic monitoring network at Popocatépetl volcano between October 2018 and December 2019. The raw signals were analyzed by weighted differences (WD) based on the elimination of non-local changes from the total intensity values of the geomagnetic field and the discrete-time continuous wavelet transform was used to evaluate the local variations of energy within the time series. The high energy periods (linked to negative magnetic anomalies) are induced by magma ascent associated with movement within the conduit. They indicate that the sudden explosions were due to the ascent of several magma batches that were slowed during ascent and were not able to reach the surface. Changes in the rheology of the lava are linked to the influx of several batches of magma with different compositions as well as to compaction by gas loss when ascending andesitic magma pushed out overlying more viscous degassed magma clearing the conduit, which can explain why these sudden explosions were more energetic. Several geophysical data sets as well as tephra compositions were integrated to support this conclusion. The correlated multiparameters also confirm that geomagnetic volcano monitoring has been essential in understanding the processes that drive the observed changes in eruptive behavior. We present new evidence for the detection of transient events produced by magma ascent and changes in the feeding system of Popocatépetl volcano with wavelet analysis. Detailed vulcanomagnetic processing, especially when it is correlated with other monitoring parameters, provides information on ascending magma and several conduit processes that would otherwise be camouflaged. Ascending batches may precede an eruption but they can also ascend in several pulses indicating how dome growth occurs.

Physical Activity and Depression: Nationwide Evaluation of Depression and Physical Activity in South Korea

PURPOSE: The relationship between depression and physical activity levels in adults in Korea was determined using data from the National Health and Nutrition Survey (K-NAESE).METHODS: Data collected from K-NAESE between 2014 and 2020, comprising 29,716 (male: 13,416, female: 16,300) participants, were analyzed using a complex sample statistical analysis by applying differential weight to the variables to analyze the relationship between depression and physical activity levels in an estimated South Korean population (50,881,242). Demographic factors were used as control variables while constructing an independent variable-by-variable fit model for the zero-inflated Poison Regression analysis of PHQ-9 depression scores, and a meaningfully interpretable fit model was used in the final model.RESULTS: A significant relationship was observed between the total PHQ-9 score and commute physical activity (OR = 1.042; SE, 0.006; p &lt; .001) and moderate-intensity leisure and physical activity time (OR = 1.116; SE, 0.008; p &lt; .001). A significant association was found between the PHQ-9 scores and physical factors (grip strength; OR = 0.985; SE, 0.001; p &lt; .001). Using Binomial Logistic Model for Depression Classification, a significant association was observed for classification as low/high-risk depression in individuals without moderate-intensity physical activity (OR = 1.190; SE, 0.046; p &lt; .001). Furthermore, individuals with a high grip strength were classified as low/high-risk depression compared to a normal grip strength (OR = 0.980; SE, 0.004; p &lt; .001).CONCLUSIONS: These findings indicate that a negative association exists between depression and the availability of moderate-intensity leisure and structured physical activity. Furthermore, a negative association was also found between depression and grip strength in the general population of South Korea aged in individuals over the age of 18.

Differences in weight-loss outcomes among race-gender subgroups by behavioural intervention delivery mode: An analysis of the POWER trial.

Prior in-person behavioural intervention studies have documented differential weight loss between men and women and by race, with Black women receiving the least benefit. Remotely delivered interventions are now commonplace, but few studies have compared outcomes by race-gender groups and delivery modality. We conducted a secondary analysis of POWER, a randomized trial (NCT00783315) designed to determine the effectiveness of 2 active, lifestyle-based, weight loss interventions (remote vs. in-person) compared to a control group. Participants with obesity and at least one cardiovascular disease risk factor (N = 415) were recruited in the Baltimore, MD area. Data from 233 white and 170 Black individuals were used for this analysis. Following an intention-to-treat approach, we compared the mean percent weight loss at 24 months by race-gender subgroups using repeated-measures, mixed-effects models. Everyone lost weight in the active interventions however, weight loss differed by race and gender. white and Black men had similar results for both interventions (white: in-person (-7.6%) remote (-7.4%); Black: in-person (-4.7%) remote (-4.4%)). In contrast, white women lost more weight with the in-person intervention (in-person (-7.2%) compared to the remote (-4.4%)), whereas Black women lost less weight in the in-person group compared to the remote intervention at 24 months (-2.0% vs. -3.0%, respectively; p for interaction <.001). We found differences between the effectiveness of the 2 weight loss interventions-in-person or remote-in white and Black women at 24 months. Future studies should consider intervention modality when designing weight loss interventions for women.

Cracking the Deck: National Origins and Promotions in the Dutch East India Company, 1700-1796

Organizational scholars highlight challenges in reducing inequality within organizations. Due to unanticipated consequences, many programs launched by organizations fail to accomplish these goals. We leverage historical data from the Dutch East India Company to claim that training programs may be an effective tool against inequality when coupled with teamwork incentives and included in broader programs for change. We study the effects of these programs on the unequal promotion rates between Dutchmen and non-Dutchmen across periods of time in which those programs were offered and in which they were not. Our analyses of promotions to top-ranked positions on the Company’s ships suggest a two-fold conclusion: first, the introduction of leadership training and teamwork incentives reduced the differential weights given to the experience of Dutchmen and non-Dutchmen; and, second, those benefits disappeared when the policies were rescinded after a change in top leadership. The results underscore the opportunities and difficulties that organizations face in implementing durable interventions against inequality.

Attachment style to body dissatisfaction, restrained eating, and disordered eating in adolescent girls: Differential weight communication mediators

Body dissatisfaction and eating behaviors are disproportionately elevated amongst adolescent girls. Family relationships represent a context in which adolescent girls’ body image issues emerge. Thus, we integrated attachment and confirmation theories to examine whether weight related supportive messages (i.e., acceptance and challenge) mediated the relationship between attachment style (i.e., anxiety and avoidance) and body image outcomes (i.e., body dissatisfaction (BD), restrained eating (RE), and disordered eating (DE)). Acceptance refers to weight related support that is characterized by warmth and accepting messages; challenge refers to weight related support that is characterized by instrumental assistance and problem-solving messages. A sample of 106 adolescent girls, ages 11 to 21, completed self-report measures on attachment (Relationship Structures Questionnaire), acceptance and challenge (weight related Parental Behavior Questionnaire), body dissatisfaction (Eating Disorders Inventory), and eating behaviors (Dutch Eating Behavior Questionnaire; Eating Disorder Diagnostic Scale). Attachment anxiety directly related to all body image outcomes, whereas attachment avoidance only directly related to RE. Attachment avoidance only related to BD and DE indirectly through perceived acceptance. Those with high anxiety endorsed lower perceived challenge, whereas avoidant individuals endorsed lower perceived acceptance and challenge. We concluded that attachment dimension characteristics to be either hyperactivated regarding relationships (i.e., anxiety), or deactivated and distant (i.e., avoidance), are associated with how individuals perceive support, and in turn, are related to body image and eating behavior outcomes. Further, we clarify the differential roles of acceptance and challenge. Although both supportive, acceptance encompasses a layer of warmth that is more meaningful in body image, particularly for avoidantly attached individuals. By integrating qualities of attachment and supportive weight communication, we reveal that potential pathways of attachment to body image and eating behaviors.

Order effects in stimulus discrimination challenge established models of comparative judgement: A meta-analytic review of the Type B effect.

This paper provides a comprehensive review of the TypeB effect (TBE), a phenomenon reflected in the observation that discrimination sensitivity varies with the order of stimuli in comparative judgment tasks, such as the two-alternative forced-choice (2AFC) paradigm. Specifically, when the difference threshold is lower (higher) with the constant standard preceding rather than following the variable comparison, one speaks of a negative (positive) TBE. Importantly, prominent psychophysical difference models such as signal detection theory (Green & Swets, 1966) cannot easily account for the TBE, and are hence challenged by it. The present meta-analysis provides substantial evidence for the TBE across various stimulus attributes, suggesting that the TBE is a general feature of discrimination experiments when standard and comparison are presented successively. Thus, inconsistent with psychophysical difference models, subjective differences between stimuli are not merely a function of their physical differences but rather also depend on their temporal order. From the literature, we identify four classes of potential candidate theories explaining the origin of the TBE, namely (1) differential weighting of the stimulus magnitudes at the two positions (e.g., Hellström, Psychological Research, 39, 345-388 1977), (2) internal reference formation (e.g., Dyjas, Bausenhart, & Ulrich, Attention, Perception, & Psychophysics, 74, 1819-1841 2012), (3) Bayesian updating (e.g., de Jong, Akyürek, & van Rijn, Psychonomic Bulletin and Review, 28, 1183-1190 2021), and (4) biased threshold estimation (García-Pérez & Alcalá-Quintana, Attention, Perception & Psychophysics, 72, 1155-1178 2010). As these models, to some extent, make differential predictions about the direction of the TBE, investigating the respective boundary conditions of positive and negative TBEs might be a valuable perspective for diagnostic future research.