A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography

The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the amygdala for studying its subdivisions. In this work, we present a deep clustering pipeline to perform automated, fine-scale parcellation of the amygdala using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the amygdala directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the amygdala. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable amygdala parcellation at a finer scale. With the proposed method, we obtain nine unique amygdala parcels. Experiments show that these parcels can be consistently identified across subjects and have good correspondence to the widely used coarse-scale amygdala parcellation

Rare Copy Number Variations and Predictors in Children With Intellectual Disability and Epilepsy

Introduction: The concurrence of intellectual disability/global developmental delay and epilepsy (ID/GDD-EP) is very common in the pediatric population. The etiologies for both conditions are complex and largely unknown. The predictors of significant copy number variations (CNVs) are known for the cases with ID/GDD, but unknown for those with exclusive ID/GDD-EP. Importantly, the known predictors are largely from the same ethnic group; hence, they lack replication.Purpose: We aimed to determine and investigate the diagnostic yield of CNV tests, new causative CNVs, and the independent predictors of significant CNVs in Chinese children with unexplained ID/GDD-EP.Materials and methods: A total of 100 pediatric patients with unexplained ID/GDD-EP and 1,000 healthy controls were recruited. The American College of Medical Genetics guideline was used to classify the CNVs. Additionally, clinical information was collected and compared between those with significant and non-significant CNVs.Results: Twenty-eight percent of the patients had significant CNVs, 16% had variants of unknown significance, and 56% had non-significant CNVs. In total, 31 CNVs were identified in 28% (28/100) of cases: 25 pathogenic and 6 likely pathogenic. Eighteen known syndromes were diagnosed in 17 cases. Thirteen rare CNVs (8 novel and 5 reported in literature) were identified, of which three spanned dosage-sensitive genes: 19q13.2 deletion (ATP1A3), Xp11.4-p11.3 deletion (CASK), and 6q25.3-q25.3 deletion (ARID1B). By comparing clinical features in patients with significant CNVs against those with non-significant CNVs, a statistically significant association was found between the presence of significant CNVs and speech and language delay for those aged above 2 years and for those with facial malformations, microcephaly, congenital heart disease, fair skin, eye malformations, and mega cisterna magna. Multivariate logistic regression analysis allowed the identification of two independent significant CNV predictors, which are eye malformations and facial malformations.Conclusion: Our study supports the performance of CNV tests in pediatric patients with unexplained ID/GDD-EP, as there is high diagnostic yield, which informs genetic counseling. It adds 13 rare CNVs (8 novel), which can be accountable for both conditions. Moreover, congenital eye and facial malformations are clinical markers that can aid clinicians to understand which patients can benefit from the CNV testing and which will not, thus helping patients to avoid unnecessary and expensive tests

The Influences of Acoustic and Pulsed Corona Discharge Coupling Field on Agglomeration of Monodisperse Fine Particles

In view of the low efficiency of traditional electrostatic precipitators in removing fine particles, acoustic and pulsed corona discharge coupling fields were proposed to increase particle size. In this paper, monodisperse particles with three different sizes (0.5 μm, 2 μm, and 4 μm) were generated to investigate the agglomeration effect under different parameters in external fields. A larger reduction ratio of particle number concentration resulted in a higher agglomeration efficiency. Results indicated that, in the range from 800 to 2400 Hz, the acoustic agglomeration effect on 4-μm particles was better than that on 0.5-μm and 2-μm particles. In the pulsed corona discharge field, agglomeration efficiencies of the three particle sizes were lower than those in the acoustic field. However, application of the coupling field highly improved agglomeration efficiency compared with the single field. When a pulse input voltage of 50 kV with acoustic sound pressure level (SPL) of 143 dB and frequency of 1600 Hz was selected, the corresponding number reduction ratio of 0.5-μm, 2-μm, and 4-μm particles increased to 0.464, 0.526, and 0.918 from 0.254, 0.438, and 0.814 in the acoustic wave field and 0.226, 0.385, and 0.794 in the pulsed corona discharge field

Equity of Elderly Care Facility Allocation in a Multi-Ethnic City under the Aging Background

Societal concerns in ethnic minority areas are global issues. Paying close attention to the equitable allocation of social resources in an aging population is crucial to preserving the cultural diversity and social stability of multi-ethnic countries. This study took a multi-ethnic city—Kunming (KM), China—as an example. The population aging and the comprehensive service level of elderly care institutions at the township (subdistrict) scale were evaluated to discuss the equity of elderly care facility allocation. This study revealed that the overall convenience of elderly care institutions was low. The coupling coordination between the degree of aging and the service level of elderly care institutions in the majority of areas in KM showed poor adaptation. There is spatial differentiation in population aging and an imbalanced distribution of elderly care facilities and relevant service facilities among ethnic minority communities and other areas in KM. We also attempted to provide optimization recommendations for existing problems. This study, on the degree of population aging, the service level of elderly care institutions, and the degree of coupling coordination between them at the township (subdistrict) scale, offers a theoretical foundation for planning elderly care facilities in multi-ethnic cities

Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments

Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and feature extraction, the development of AMC can be greatly promoted. In this paper, we propose a deep learning-based modulation classification method with 2D time-frequency signal representation. In our proposed method, signals which have been received are first analyzed by time-frequency based on continuous wavelet transform (CWT). Then, CWT images of received signals are obtained and input to the deep learning model for classifying. We create a new CWT image dataset including 12 modulation types of signals under various signal-to-noise ratio (SNR) environment to verify the effectiveness of the proposed method. The experimental results demonstrate that our proposed method can reach to a high classification accuracy over the SNR of −11 dB

Enabling Efficient and Malicious Secure Data Aggregation in Smart Grid With False Data Detection

As the next-generation power grid, the smart grid has significantly improved dependability, flexibility, and efficiency compared with the traditional power grid. However, due to increasingly diverse application requirements, it faces challenges on balancing data privacy, efficiency, and robustness. In this paper, we present a fog computing-based smart grid model. In addition, based on the proposed model, we construct an efficient and privacy-preserving scheme that supports malicious secure smart grid usage data aggregation communication. To our best knowledge, this is the first concrete smart grid solution that concurrently achieves secure aggregation communication, data privacy, and data robustness (e.g., false data detection). Specifically, benefiting from Boolean/Arithmetic secret-sharing methods, our proposed scheme allows home users to report their electricity usage data to the cloud and fogs securely. Besides, a false data detection protocol is proposed to resist false data injection attacks launched by malicious home users. Theoretical analysis and experimental implementation show that our scheme efficiently achieves data security, anonymity, and robustness

A novel decanedioic hydroxamic acid collector for the flotation separation of bastnäsite from calcite

In this work, decanedioic hydroxamic acid (DDHA) was introduced as a novel collector for the flotation separation of bastnӓsite and calcite, and the results were compared with those obtained for the conventional hydroxamic acid collectors benzohydroxamic acid (BHA) and octyl hydroxamic acid (OHA). The microflotation results showed that DDHA had better selectivity than BHA and OHA for the flotation of bastnӓsite from calcite. The flotation of artificially mixed minerals confirmed that the effective separation of bastnӓsite from calcite could be realized in the presence of DDHA. The zeta potentials of mineral surfaces with or without these collectors were measured, and the results indicated that DDHA strongly interacted with the bastnӓsite surface but was not adsorbed on the calcite surface

PtNi Alloy Coated in Porous Nitrogen-Doped Carbon as Highly Efficient Catalysts for Hydrogen Evolution Reactions

The development of low platinum loading hydrogen evolution reaction (HER) catalysts with high activity and stability is of great significance to the practical application of hydrogen energy. This paper reports a simple method to synthesize a highly efficient HER catalyst through coating a highly dispersed PtNi alloy on porous nitrogen-doped carbon (MNC) derived from the zeolite imidazolate skeleton. The catalyst is characterized and analyzed by physical characterization methods, such as XRD, SEM, TEM, BET, XPS, and LSV, EIS, it, v-t, etc. The optimized sample exhibits an overpotential of only 26 mV at a current density of 10 mA cm−2, outperforming commercial 20 wt% Pt/C (33 mV). The synthesized catalyst shows a relatively fast HER kinetics as evidenced by the small Tafel slope of 21.5 mV dec−1 due to the small charge transfer resistance, the alloying effect between Pt and Ni, and the interaction between PtNi alloy and carrier

Peri- and postoperative outcomes of laparoscopic adrenalectomy in nonobese versus obese patients: a systematic review and meta-analysis

INTRODUCTION: Obesity is generally thought to increase the difficulty and complications of surgery. Laparoscopic adrenalectomy has become the standard operation for adrenal tumors at present. AIM: To assess whether laparoscopic adrenalectomy (LA) can be used for obese patients with adrenal tumor. MATERIAL AND METHODS: We systematically searched PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Science databases and Cochrane Library, and the search time is up to January 2022. We used STATA 16.0 and RevMan 5.4 software for data processing and statistical analysis. RESULTS: Eight studies were included in the meta-analysis. The meta-analysis results showed that compared with the nonobese group, the obese group had a significantly longer operation time (OT) (weighted mean difference (WMD): –10.02, 95% confidence interval (CI): –19.16 to 0.87, p = 0.03). It also had higher estimated blood loss (WMD: –13.15, 95% CI: –35.92 to 9.63, p = 0.26) and conversion rate (odds ratio (OR): 0.70, 95% CI: 1.31 to 1.60, p = 0.40), longer length of hospital stay (LOS) (WMD: –0.04, 95% CI: –0.47 to 0.39, p = 0.86), and a higher number of complications (odds ratio (OR) = 0.71, 95% CI: 0.49 to 1.02, p = 0.06), but these differences were not statistically significant. Additionally, in subgroup analysis longer OT (p = 0.0001) and LOS (p = 0.007) were associated with retroperitoneal laparoscopic adrenalectomy for obesity. CONCLUSIONS: Our meta-analysis suggests that LA is feasible and effective in patients with obesity