A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction

Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems. As the foundation to achieve such an optimization, a spatiotemporal method for EV charging demand prediction in urban areas is required. Although several solutions have been proposed by using data-driven deep learning methods, it can be found that these performance-oriented methods may suffer from misinterpretations to correctly handle the reverse relationship between charging demands and prices. To tackle the emerging challenges of training an accurate and interpretable prediction model, this paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction and the usage of physic-informed meta-learning in the model pre-training step for knowledge transfer. Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China, show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance and the ability in understanding the adaptive changes in charging demands caused by price fluctuations.Comment: Preprint. This work has been submitted to the IEEE Transactions on ITS for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

UV-Based 3D Hand-Object Reconstruction with Grasp Optimization

We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art.Comment: BMVC 2022 Spotligh

Construction of a novel clinical nomogram to predict cancer-specific survival in patients with primary malignant adrenal tumors: a large population-based retrospective study

BackgroundPrimary malignant adrenal tumors were rare and had a poor prognosis. This investigation aimed to create a useful clinical prediction nomogram to anticipate cancer-specific survival (CSS) of patients with a primary malignant adrenal tumor.MethodThis study included 1748 patients with malignant adrenal tumor diagnoses subjects from 2000 to 2019. These subjects were allocated randomly into training (70%) and validation (30%) cohorts. Patients with adrenal tumors underwent univariate and multivariate Cox regression analyses to identify the CSS-independent predictive biomarkers. Therefore, a nomogram was created depending on those predictors, and calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to assess the calibration capacity of the nomogram, discriminative power, and clinical efficiency, respectively. Afterward, a risk system for categorizing patients with adrenal tumors was established.ResultThe univariate and multivariate Cox analysis demonstrated the CSS-independent predictive factors, including age, tumor stage, size, histological type, and surgery. As a result, a nomogram was developed using these variables. For the 3-, 5-, and 10-year CSS of this nomogram, the values of the area under the curve (AUC) of the ROC curves were 0.829, 0.827, and 0.822, respectively. Furthermore, the AUC values of the nomogram were higher than those of the individual independent prognostic components of CSS, indicating that the nomogram had stronger prognostic prediction reliability. A novel risk stratification method was created to further improve patient stratification and give clinical professionals a better reference for clinical decision-making.ConclusionThrough the developed nomogram and risk stratification method, the CSS of patients with malignant adrenal tumors could be predicted more precisely, assisting physicians to differentiate patients better and creating personalized treatment strategies to optimize patient benefits

Burden of disease resulting from chronic mountain sickness among young Chinese male immigrants in Tibet

BACKGROUND: In young Chinese men of the highland immigrant population, chronic mountain sickness (CMS) is a major public health problem. The aim of this study was to measure the disease burden of CMS in this population. METHODS: We used disability-adjusted life years (DALYs) to estimate the disease burden of CMS. Disability weights were derived using the person trade-off methodology. CMS diagnoses, symptom severity, and individual characteristics were obtained from surveys collected in Tibet in 2009 and 2010. The DALYs of individual patients and the DALYs/1,000 were calculated. RESULTS: Disability weights were obtained for 21 CMS health stages. The results of the analyses of the two surveys were consistent with each other. At different altitudes, the CMS rates ranged from 2.1-37.4%; the individual DALYs of patients ranged from 0.13-0.33, and the DALYs/1,000 ranged from 3.60-52.78. The age, highland service years, blood pressure, heart rate, smoking rate, and proportion of the sample working in engineering or construction were significantly higher in the CMS group than in the non-CMS group (p < 0.05). These variables were also positively associated with the individual DALYs (p < 0.05). Among the symptoms, headaches caused the largest proportion of DALYs. CONCLUSION: The results show that CMS imposes a considerable burden on Chinese immigrants to Tibet. Immigrants with characteristics such as a higher residential altitude, more advanced age, longer highland service years, being a smoker, and working in engineering or construction were more likely to develop CMS and to increase the disease burden. Higher blood pressure and heart rate as a result of CMS were also positively associated with the disease burden. The authorities should pay attention to the highland disease burden and support the development and application of DALYs studies of CMS and other highland diseases

A genome-wide association study identifies FSHR rs2300441 associated with follicle-stimulating hormone levels

Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play critical roles in female reproduction, while the underlying genetic basis is poorly understood. Genome-wide association studies (GWASs) of FSH and LH levels were conducted in 2590 Chinese females including 1882 polycystic ovary syndrome (PCOS) cases and 708 controls. GWAS for FSH level identified multiple variants at FSHR showing genome-wide significance with the top variant (rs2300441) located in the intron of FSHR. The A allele of rs2300441 led to a reduced level of FSH in the PCOS group (β = −.43, P = 6.70 × 10−14) as well as in the control group (β = −.35, P = 6.52 × 10−4). In the combined sample, this association was enhanced after adjusting for the PCOS status (before: β = −.38, P = 1.77 × 10−13; after: β = −.42, P = 3.33 × 10−16), suggesting the genetic effect is independent of the PCOS status. The rs2300441 explained sevenfold higher proportion of the FSH variance than the total variance explained by the two previously reported FSHR missense variants (rs2300441 R2 = 1.40% vs rs6166 R2 = 0.17%, rs6165 R2 = 0.03%). GWAS for LH did not identify any genome-wide significant associations. In conclusion, we identified genome-wide significant association between variants in FSHR and circulating FSH first, with the top associated variant rs2300441 might be a primary contributor at the population level

Serum vitamin levels in multiple system atrophy: A case-control study

AimThere is increasing evidence suggesting that vitamins may play important roles in the pathogenesis of multiple system atrophy (MSA). The purpose of this study was to detect the changes of serum vitamin levels and investigate their correlation with disease severity in MSA patients.MethodsIn this cross-sectional study, 244 MSA patients, 200 Parkinson’s disease (PD) patients and 244 age-gender matched healthy controls were recruited. Serum vitamin levels were measured, including vitamin A, B1, B2, B9 (folate), B12, C, D, and E. Relevant clinical scales were used to assess the disease severity of MSA patients.ResultsCompared with the healthy controls, decreased serum folate levels and increased serum vitamin A and C levels were detected in MSA patients. Similar differences were also observed in the gender-based subgroup analysis. There were no differences detected between MSA and PD patients. In MSA patients, significant correlation was found between vitamin A, folate, or vitamin C and relevant clinical scales or laboratory findings. In addition, ROC analysis showed potential diagnostic value of the combination of vitamin A, folate, and vitamin C in distinguishing MSA patients from healthy controls.ConclusionThere were significant changes in the blood vitamin spectrums of MSA patients, suggesting that dysregulation of vitamins homeostasis might play an important role in the pathogenesis of MSA

Effects of Reproductive Status, Social Rank, Sex and Group Size on Vigilance Patterns in Przewalski's Gazelle

Quantifying vigilance and exploring the underlying mechanisms has been the subject of numerous studies. Less attention has focused on the complex interplay between contributing factors such as reproductive status, social rank, sex and group size. Reproductive status and social rank are of particular interest due to their association with mating behavior. Mating activities in rutting season may interfere with typical patterns of vigilance and possibly interact with social rank. In addition, balancing the tradeoff between vigilance and life maintenance may represent a challenge for gregarious ungulate species rutting under harsh winter conditions. We studied vigilance patterns in the endangered Przewalski's gazelle (Procapra przewalskii) during both the rutting and non-rutting seasons to examine these issues.Field observations were carried out with focal sampling during rutting and non-rutting season in 2008-2009. Results indicated a complex interplay between reproductive status, social rank, sex and group size in determining vigilance in this species. Vigilance decreased with group size in female but not in male gazelles. Males scanned more frequently and thus spent more time vigilant than females. Compared to non-rutting season, gazelles increased time spent scanning at the expense of bedding in rutting season. During the rutting season, territorial males spent a large proportion of time on rutting activities and were less vigilant than non-territorial males. Although territorial males may share collective risk detection with harem females, we suggest that they are probably more vulnerable to predation because they seemed reluctant to leave rut stands under threats.Vigilance behavior in Przewalski's gazelle was significantly affected by reproductive status, social rank, sex, group size and their complex interactions. These findings shed light on the mechanisms underlying vigilance patterns and the tradeoff between vigilance and other crucial activities

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function