bluuebunny/crossref_metadata_2025_split huggingface dataset:
The intergenerational transmission of filial norms and children’s provision of long-term care to parents
Authors: Diederich Freya , König Hans-Helmut , Brettschneider Christian 
Published: May 2023

AbstractIn the light of an increasing future demand for long-term care services in ageing societies, families’ provision for current and future long-term care needs has been subject to debate. Within this context, there is little discussion about parents’ incentives to socialise their children to their own traits to achieve a desired child behaviour. Our study contributes to the literature by analysing to what extent parents transmit filial norms to their children and whether this transmission process affects children’s care-giving behaviour if a parent needs long-term care. Using data from the German Family Panel, we initially analyse the transmission of filial norms from mothers and fathers to their children. Second, we examine the importance of filial norms that prevail in the child’s local environment. Third, we assess whether an intergenerational transmission of norms transmits into child behaviour if a parent needs long-term care. We estimate linear regression models and account for child and parent characteristics as well as for the living environment. The results show that children have stronger filial norms when both parents report stronger filial norms. But, children also tend to be influenced by average norms in their local environment. Furthermore, children are more likely to provide support to a parent in need when their filial norms are stronger. We conclude that children’s filial norms are at least partially shaped via a process of socialisation and that these norms transmit into children’s care-giving behaviour if a parent needs long-term care. It is, therefore, important that filial norms that prevail in a society are in accordance with the institutional long-term care system that prevails within that country.
CBT for difficult-to-treat depression: self-regulation model
Authors: Barton Stephen B. , Armstrong Peter V. , Robinson Lucy J. , Bromley Elizabeth H.C. 
Published: May 2023

AbstractBackground:Cognitive behavioural therapy (CBT) is an effective treatment for depression but a significant minority of clients do not complete therapy, do not respond to it, or subsequently relapse. Non-responders, and those at risk of relapse, are more likely to have adverse childhood experiences, early-onset depression, co-morbidities, interpersonal problems and heightened risk. This is a heterogeneous group of clients who are currently difficult to treat.Aim:The aim was to develop a CBT model of depression that will be effective for difficult-to-treat clients who have not responded to standard CBT.Method:The method was to unify theory, evidence and clinical strategies within the field of CBT to develop an integrated CBT model. Single case methods were used to develop the treatment components.Results:A self-regulation model of depression has been developed. It proposes that depression is maintained by repeated interactions of self-identity disruption, impaired motivation, disengagement, rumination, intrusive memories and passive life goals. Depression is more difficult to treat when these processes become interlocked. Treatment based on the model builds self-regulation skills and restructures self-identity, rather than target negative beliefs. A bespoke therapy plan is formed out of ten treatment components, based on an individual case formulation.Conclusions:A self-regulation model of depression is proposed that integrates theory, evidence and practice within the field of CBT. It has been developed with difficult-to-treat cases as its primary purpose. A case example is described in a concurrent article (Barton et al., 2022) and further empirical tests are on-going.
Integrated growth assessment in the first 1000 d of life: an interdisciplinary conceptual framework
Authors: Nel Sanja , Pattinson Robert C , Vannevel Valerie , Feucht Ute D , Mulol Helen , Wenhold Friede AM 
Published: May 2023

AbstractObjectives:Prenatal growth affects short- and long-term morbidity, mortality and growth, yet communication between prenatal and postnatal healthcare teams is often minimal. This paper aims to develop an integrated, interdisciplinary framework for foetal/infant growth assessment, contributing to the continuity of care across the first 1000 d of life.Design:A multidisciplinary think-tank met regularly over many months to share and debate their practice and research experience related to foetal/infant growth assessment. Participants’ personal practice and knowledge were verified against and supplemented by published research.Setting:Online and in-person brainstorming sessions of growth assessment practices that are feasible and valuable in resource-limited, low- and middle-income country (LMIC) settings.Participants:A group of obstetricians, paediatricians, dietitians/nutritionists and a statistician.Results:Numerous measurements, indices and indicators were identified for growth assessment in the first 1000 d. Relationships between foetal, neonatal and infant measurements were elucidated and integrated into an interdisciplinary framework. Practices relevant to LMIC were then highlighted: antenatal Doppler screening, comprehensive and accurate birth anthropometry (including proportionality of weight, length and head circumference), placenta weighing and incorporation of length-for-age, weight-for-length and mid-upper arm circumference in routine growth monitoring. The need for appropriate, standardised clinical records and corresponding policies to guide clinical practice and facilitate interdisciplinary communication over time became apparent.Conclusions:Clearer communication between prenatal, perinatal and postnatal health care providers, within the framework of a common understanding of growth assessment and a supportive policy environment, is a prerequisite to continuity of care and optimal health and development outcomes.
Genetic and Environmental Correlation Analysis of Serum Creatinine Levels in Chinese Twins
Authors: Wang Anni , Zhang Tianhao , Li Jingxian , Wang Weijing , Xu Chunsheng , Duan Haiping , Tian Xiaocao , Zhang Dongfeng 
Published: May 2023

AbstractAlmost all creatinine is excreted by the kidney in individuals. Serum creatinine concentration, a widely used renal function index in clinical practice, can be affected by both genetic and environmental factors, as evidenced by current research exploring the relationship between these factors and kidney function. However, few studies have explored the heritability of serum creatinine in Asian populations. Therefore, we explored the genetic and environmental factors that affect the serum creatinine level in Asian populations. Participants in this study came from the Qingdao Twin Registry in China, and 374 pairs of twins were included, of which 139 pairs were dizygotic twins, whose ages ranged from 40 to 80 years old, and the serum creatinine level ranged from 10 to 126 μmol/L. Structural equation models were constructed using Mx software to calculate heritability, with adjusted covariates being age, sex, and body mass index. The results of heritability analysis showed that ACE was the best fit model. Serum creatinine level is influenced by genetic and environmental factors. The result of heritability was 35.44%, and the influence of shared environmental factors accounted for 52.13%. This study provided the relevant basis for future research on genetic and environmental factors affecting serum creatinine levels in Asian populations.
Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)
Authors: Yan Hongxiang , Sun Ning , Eldardiry Hisham , Thurber Travis B. , Reed Patrick M. , Malek Keyvan , Gupta Rohini , Kennedy Daniel , Swenson Sean C. , Hou Zhangshuan , Cheng Yanyan , Rice Jennie S. 
Published: May 2023

AbstractLand surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.
Optimization and Evaluation of Stochastic Unified Convection Using Single‐Column Model Simulations at Multiple Observation Sites
Authors: Shin Jihoon , Baik Jong‐Jin 
Published: May 2023

AbstractWe extend the previously developed stochastic unified convection scheme (UNICON) for shallow convection to deep convection by parameterizing the impact of mesoscale organized flow on updraft properties. The extended stochastic UNICON parameterizes thermodynamic properties of updrafts at the near‐surface as a multivariate Gaussian distribution, where the variances of the distribution are the summation of variances from non‐organized turbulence and mesoscale organized flow. The distribution of updraft radius is parameterized as a power‐law distribution with a scale break which is parameterized as a linear function of the strength of mesoscale organized flow. The proposed parameterization is validated using a series of large‐eddy simulations of deep convection. The free parameters introduced in the formulation of stochastic UNICON are optimized using 10 cases of single‐column model simulations over the ocean. Stochastic UNICON with the optimized parameters significantly reduces the biases of thermodynamic profiles and surface precipitation rates simulated in the original UNICON for tropical convection cases. The simulation of the variation in anomalies of temperature and moisture associated with the Madden‐Julian oscillation is also improved. The overall improvements in simulated thermodynamic profiles are found to be due to the increased heating and drying tendencies by convective processes in stochastic UNICON. An additional simulation of an idealized deep convection case shows that stochastic UNICON produces enhanced cloud variabilities with dependency on updraft radius, indicating its ability to represent the coexistence of shallow and deep convection.
Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models
Authors: Simms L. E. , Ganushkina N. Yu. , Van der Kamp M. , Balikhin M. , Liemohn M. W. 
Published: May 2023

AbstractWe screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. Value‐predicting models developed using ARMAX (autoregressive moving average transfer function), RNN (recurrent neural network), or stepwise‐reduced regression produced roughly similar results. Including magnetic local time as a categorical variable to describe both the differing levels of flux and the differing influence of parameters improved the models (r as high as 0.814; Heidke Skill Score (HSS) as high as 0.663), however value‐predicting models did a poor job at predicting highs and lows. Diagnostic tests are introduced (cubic fit to observation‐prediction relationship and Lag1 correlation) that better assess predictions of extremes than single metrics such as root mean square error, mean absolute error, or median symmetric accuracy. Classifier models (RNN and logistic regression) were equally able to predict flux rise above the 75th percentile (HSS as high as 0.667). Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. Only predictors from 1 or 3 hr before were necessary and a detailed description of flux time series behavior was not needed. Stepwise selection of these variables trimmed non‐contributing parameters for a more parsimonious and portable logistic regression model that predicted as well as neural network‐derived models. We provide a logistic regression model (LL3: LogisticLag3) based on inputs measured 3 hr previous, along with optimal probability thresholds, for future predictions.
Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis
Authors: Sjaarda Jennifer , Delacrétaz Aurélie , Dubath Céline , Laaboub Nermine , Piras Marianna , Grosu Claire , Vandenberghe Frederik , Crettol Séverine , Ansermot Nicolas , Gamma Franziska , Plessen Kerstin Jessica , von Gunten Armin , Conus Philippe , Kutalik Zoltan , Eap Chin B. 
Published: May 2023

AbstractPatients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance (p < 5 × 10−8): rs7736552 (near MAN2A1), rs11074029 (in SLCO3A1), rs117496040 (near DEFB1) and rs7647863 (in IQSEC1). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope (p = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.
A kidney-brain neural circuit drives progressive kidney damage and heart failure
Authors: Cao Wei , Yang Zhichen , Liu Xiaoting , Ren Siqiang , Su Huanjuan , Yang Bihui , Liu Youhua , Wilcox Christopher S. , Hou Fan Fan 
Published: May 2023

AbstractChronic kidney disease (CKD) and heart failure (HF) are highly prevalent, aggravate each other, and account for substantial mortality. However, the mechanisms underlying cardiorenal interaction and the role of kidney afferent nerves and their precise central pathway remain limited. Here, we combined virus tracing techniques with optogenetic techniques to map a polysynaptic central pathway linking kidney afferent nerves to subfornical organ (SFO) and thereby to paraventricular nucleus (PVN) and rostral ventrolateral medulla that modulates sympathetic outflow. This kidney-brain neural circuit was overactivated in mouse models of CKD or HF and subsequently enhanced the sympathetic discharge to both the kidney and the heart in each model. Interruption of the pathway by kidney deafferentation, selective deletion of angiotensin II type 1a receptor (AT1a) in SFO, or optogenetic silence of the kidney-SFO or SFO-PVN projection decreased the sympathetic discharge and lessened structural damage and dysfunction of both kidney and heart in models of CKD and HF. Thus, kidney afferent nerves activate a kidney-brain neural circuit in CKD and HF that drives the sympathetic nervous system to accelerate disease progression in both organs. These results demonstrate the crucial role of kidney afferent nerves and their central connections in engaging cardiorenal interactions under both physiological and disease conditions. This suggests novel therapies for CKD or HF targeting this kidney-brain neural circuit.
Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian
Authors: Li Limin , Wang Weixing , Wang Mengfei , Feng Sheng , Khatoon Amna 
Published: May 2023

AbstractTo detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%.
Maximum gain enhancement in wireless power transfer using anisotropic metamaterials
Authors: Harris William Carter , Ricketts David S. 
Published: May 2023

AbstractWe present an analysis for metamaterial (MM) enhanced wireless power transfer (WPT) that includes new results revealing the impact of magnetostatic surface waves and their degradation of WPT efficiency. Our analysis shows that the commonly used fixed loss model used by previous works leads to the incorrect conclusion regarding the highest efficeincy MM configuration. Specifically, we show that the “perfect lens” configuration provides lower WPT efficiency enhancement in comparison to many other MM configurations and operating conditions. To understand why, we introduce a model for quantifying loss in MM-enhanced WPT and introduce a new figure of merit on efficiency enhancement, $$G_{\rho }$$

G
ρ

. Using both simulation and experimental prototypes, we show that while the “perfect-lens” MM achieves a field enhancement of four times the other configurations considered, its internal loss due to magnetostatic waves significantly reduces its efficiency-enhancement. Surprisingly, all the MM configurations analyzed other than the “perfect-lens” achieved higher efficiency enhancement in simulation and in experiment than the perfect lens.
Converting N2 molecules into NH3 with TiO2/Fe3O4 composite covered with a thin water layer under ambient condition
Authors: Moriya Ichiro 
Published: May 2023

AbstractAs ammonia manufacture today require huge energy and very pure hydrogen gas and moreover emit large quantities of CO2, researches for new ammonia synthesis methods are actively performed. Here, author reports the novel method through which N2 molecules in air is reduced into ammonia with TiO2/Fe3O4 composite having thin water layer on composite’s surface under ambient condition (less than 100 °C and atmospheric pressure). The composites were composed of both nm-sized TiO2 particles and μm-sized Fe3O4 ones. First, composites were held in refrigerator, mainly at that time, N2 molecules in air adsorbed onto surface of composite. Next, the composite was irradiated with various lights including solar light, 365 nm LED light and tungsten light through thin water layer formed by condensation of water vapour in air. Reliable amount of ammonia was obtained under 5 min’s irradiation of solar light or of both 365 m LED light and 500 W tungsten light. This reaction was catalytic reaction promoted by photocatalytic one. In addition, holding in freezer instead of refrigerator provided larger amount of ammonia. Maximum ammonia yield was approximately 18.7 μmol/g 5 min under irradiation of 300 W tungsten light only.
Transcutaneous auricular vagus nerve stimulation as a potential novel treatment for polycystic ovary syndrome
Authors: Zhang Shike , He Hui , Wang Yu , Wang Xiao , Liu Xiaofang 
Published: May 2023

AbstractPolycystic ovary syndrome (PCOS) is a common endocrine disorder in women of childbearing age. The etiology of PCOS is multifactorial, and current treatments for PCOS are far from satisfactory. Recently, an imbalanced autonomic nervous system (ANS) with sympathetic hyperactivity and reduced parasympathetic nerve activity (vagal tone) has aroused increasing attention in the pathogenesis of PCOS. In this paper, we review an innovative therapy for the treatment of PCOS and related co-morbidities by targeting parasympathetic modulation based on non-invasive transcutaneous auricular vagal nerve stimulation (ta-VNS). In this work, we present the role of the ANS in the development of PCOS and describe a large number of experimental and clinical reports that support the favorable effects of VNS/ta-VNS in treating a variety of symptoms, including obesity, insulin resistance, type 2 diabetes mellitus, inflammation, microbiome dysregulation, cardiovascular disease, and depression, all of which are also commonly present in PCOS patients. We propose a model focusing on ta-VNS that may treat PCOS by (1) regulating energy metabolism via bidirectional vagal signaling; (2) reversing insulin resistance via its antidiabetic effect; (3) activating anti-inflammatory pathways; (4) restoring homeostasis of the microbiota-gut-brain axis; (5) restoring the sympatho-vagal balance to improve CVD outcomes; (6) and modulating mental disorders. ta-VNS is a safe clinical procedure and it might be a promising new treatment approach for PCOS, or at least a supplementary treatment for current therapeutics.