scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.

Silencing hsa_circ_0032449 inhibits the pancreatic differentiation of human embryonic stem cells via the hsa_miR-195-5p/CCND1/PI3K/AKT signaling pathway.

Stem cell-derived ß cells (SC-ß cells) differentiated from stem cell-derived pancreatic progenitor (PP) cells are promising tools for enabling normal glucose control of islet transplants and have therapeutic potential for type 1 diabetes treatment. Pancreatic specification is essential for SC-ß cell induction in vitro and low-quality PP cells may convert into derivatives of non-pancreatic lineages both in vivo and in vitro, impeding PP-derived ß cell safety and differentiation efficiency. Circular RNA (circRNA) commonly determines the fate of stem cells by acting as competing endogenous RNA (ceRNA). Currently, the relationships between endogenous circRNA and pancreatic specification remain elusive. Herein, we used whole transcriptome sequencing analysis and functional experiments to reveal that deficiency of hsa_circ_0032449 resulted in posterior foregut-derived PP cells with a weakened the progenitor state with decreased expression of PDX1, NKX6.1 and CCND1. As differentiation processed into maturation, silencing of hsa_circ_0032449 suppressed PP cell development into functionally mature and glucose-responsive SC-ß cells. These SC-ß cells exhibited lower serum C-peptide levels compared with those of control groups in nude mice and had difficulties in reversing hyperglycemia in STZ-induced diabetic nude mice. Mechanistically, loss of hsa_circ_0032449 participated in PI3K-AKT signaling transduction by acting as a ceRNA to sponge miR-195-5p and by influencing the expression of the downstream target CCND1 at transcription and translation levels. Overall, our findings identified hsa_circ_0032449 as an essential PP cell-fate specification regulator, indicating a promising potential in clinical applications and basic research.

The mitochondrial quality control system: a new target for exercise therapeutic intervention in the treatment of brain insulin resistance-induced neurodegeneration in obesity.

Obesity is a major global health concern because of its strong association with metabolic and neurodegenerative diseases such as diabetes, dementia, and Alzheimer's disease. Unfortunately, brain insulin resistance in obesity is likely to lead to neuroplasticity deficits. Since the evidence shows that insulin resistance in brain regions abundant in insulin receptors significantly alters mitochondrial efficiency and function, strategies targeting the mitochondrial quality control system may be of therapeutic and practical value in obesity-induced cognitive decline. Exercise is considered as a powerful stimulant of mitochondria that improves insulin sensitivity and enhances neuroplasticity. It has great potential as a non-pharmacological intervention against the onset and progression of obesity associated neurodegeneration. Here, we integrate the current knowledge of the mechanisms of neurodegenration in obesity and focus on brain insulin resistance to explain the relationship between the impairment of neuronal plasticity and mitochondrial dysfunction. This knowledge was synthesised to explore the exercise paradigm as a feasible intervention for obese neurodegenration in terms of improving brain insulin signals and regulating the mitochondrial quality control system.

Chemical derivatization strategy for mass spectrometry-based lipidomics.

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.

Phytochrome B mediates dim-light-reduced insect resistance by promoting the ethylene pathway in rice.

Increasing planting density is one of the most effective ways to improve crop yield. However, one major factor that limits crop planting density is the weakened immunity of plants to pathogens and insects caused by dim light (DL) under shade conditions. The molecular mechanism underlying how DL compromises plant immunity remains unclear. Here, we report that DL reduces rice (Oryza sativa) resistance against brown planthopper (BPH; Nilaparvata lugens) by elevating ethylene (ET) biosynthesis and signaling in a Phytochrome B (OsPHYB)-dependent manner. The DL-reduced BPH resistance is relieved in osphyB mutants, but aggravated in OsPHYB overexpressing plants. Further, we found that DL reduces the nuclear accumulation of OsphyB, thus alleviating Phytochrome Interacting Factor Like14 (OsPIL14) degradation, consequently leading to the up-regulation of 1-Aminocyclopropane-1-Carboxylate Oxidase1 (OsACO1) and an increase in ET levels. In addition, we found that nuclear OsphyB stabilizes Ethylene Insensitive Like2 (OsEIL2) by competitively interacting with EIN3 Binding F-Box Protein (OsEBF1) to enhance ET signaling in rice, which contrasts with previous findings that phyB blocks ET signaling by facilitating Ethylene Insensitive3 (EIN3) degradation in other plant species. Thus, enhanced ET biosynthesis and signaling reduces BPH resistance under DL conditions. Our findings provide insights into the molecular mechanism of the light-regulated ET pathway and host-insect interactions and potential strategies for sustainable insect management.

Genome-Wide Association Analysis of Protein-Coding Variants in IgA Nephropathy.

SIGNIFICANCE STATEMENT: Genome-wide association studies have identified nearly 20 IgA nephropathy susceptibility loci. However, most nonsynonymous coding variants, particularly ones that occur rarely or at a low frequency, have not been well investigated. The authors performed a chip-based association study of IgA nephropathy in 8529 patients with the disorder and 23,224 controls. They identified a rare variant in the gene encoding vascular endothelial growth factor A (VEGFA) that was significantly associated with a two-fold increased risk of IgA nephropathy, which was further confirmed by sequencing analysis. They also identified a novel common variant in PKD1L3 that was significantly associated with lower haptoglobin protein levels. This study, which was well-powered to detect low-frequency variants with moderate to large effect sizes, helps expand our understanding of the genetic basis of IgA nephropathy susceptibility. BACKGROUND: Genome-wide association studies have identified nearly 20 susceptibility loci for IgA nephropathy. However, most nonsynonymous coding variants, particularly those occurring rarely or at a low frequency, have not been well investigated. METHODS: We performed a three-stage exome chip-based association study of coding variants in 8529 patients with IgA nephropathy and 23,224 controls, all of Han Chinese ancestry. Sequencing analysis was conducted to investigate rare coding variants that were not covered by the exome chip. We used molecular dynamic simulation to characterize the effects of mutations of VEGFA on the protein's structure and function. We also explored the relationship between the identified variants and the risk of disease progression. RESULTS: We discovered a novel rare nonsynonymous risk variant in VEGFA (odds ratio, 1.97; 95% confidence interval [95% CI], 1.61 to 2.41; P = 3.61×10 -11 ). Further sequencing of VEGFA revealed twice as many carriers of other rare variants in 2148 cases compared with 2732 controls. We also identified a common nonsynonymous risk variant in PKD1L3 (odds ratio, 1.16; 95% CI, 1.11 to 1.21; P = 1.43×10 -11 ), which was associated with lower haptoglobin protein levels. The rare VEGFA mutation could cause a conformational change and increase the binding affinity of VEGFA to its receptors. Furthermore, this variant was associated with the increased risk of kidney disease progression in IgA nephropathy (hazard ratio, 2.99; 95% CI, 1.09 to 8.21; P = 0.03). CONCLUSIONS: Our study identified two novel risk variants for IgA nephropathy in VEGFA and PKD1L3 and helps expand our understanding of the genetic basis of IgA nephropathy susceptibility.

Effect of atorvastatin calcium plus clopidogrel in the treatment of patients with transient ischemic attacks and its effect on blood lipids and platelets.

OBJECTIVE: To explore the clinical effect of atorvastatin calcium combined with clopidogrel in the treatment of patients with transient ischemic attacks (TIAs) and its effect on blood lipids and platelets. METHODS: Low-density lipoprotein cholesterol (LDL-C)], platelet-related parameters [prothrombin time (PT), activated partial thromboplastin time (APTT), platelet count (PLT)], incidence of cerebral infarction, and adverse reactions. RESULTS: The clinical outcomes of the experimental group patients were significantly better than those of the control group patients (p < 0.05). The experimental group exhibited notably lower levels of TG, TC, and LDL-C compared to the control group (p < 0.05). Platelet-related indices-PT, APTT, and PLT-showed no significant differences between groups before and after treatment (p > 0.05). The incidence of cerebral infarction was notably lower in the experimental group (p < 0.005), while the occurrence of adverse reactions showed no significant difference between groups (p > 0.05). CONCLUSION: Atorvastatin calcium combined with clopidogrel demonstrates a positive impact on individuals with TIAs by significantly lowering levels of LDL, total cholesterol, and triglycerides. However, it is noteworthy that platelet-related indices did not exhibit significant differences between the experimental and control groups. While the observed improvements in blood lipids are attributed to the effects of atorvastatin, the combination with clopidogrel did not show a substantial influence on platelet-related parameters. Thus, the overall therapeutic impact, particularly on platelet-related indices, may require further investigation and clarification. Despite these nuances, our findings suggest potential benefits in reducing the risk of adverse reactions and cerebral infarction, supporting the consideration of this approach for wider clinical use.

Dibutyl phthalate adsorbed on multi-walled carbon nanotubes can aggravate liver injury in mice via the Jak2/STAT3 pathway.

Phthalic acid esters (PAEs) and carbon nanotubes (CNTs) are common environmental pollutants and may degrade differently with different resulting biotoxicity, when present together. This study investigated the toxicological effects of singular or combined exposure to dibutyl phthalate (DBP) and multi-walled carbon nanotubes (MWCNTs) in KM mice. Results indicated that combined exposure led to slower weight gain and an increased leukocyte count in the blood, as well as liver tissue lesions and downregulation of organ coefficients. Additionally, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were elevated in the liver, and glucose, pyruvate, triglyceride (TG), and total cholesterol (T-CHO) were significantly reduced, suggesting compromised liver function. Furthermore, mRNA levels of genes related to hepatic glucose and lipid metabolism were significantly altered. These findings suggest that combined exposure to DBP and MWCNTs can have severe impacts on liver function in mice, highlighting the importance of considering interactions between multiple contaminants in environmental risk assessments.

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

High Moisture-Barrier Performance of Double-Layer Graphene Enabled by Conformal and Clean Transfer.

Graphene films that can theoretically block almost all molecules have emerged as promising candidate materials for moisture barrier films in the applications of organic photonic devices and gas storage. However, the current barrier performance of graphene films does not reach the ideal value. Here, we reveal that the interlayer distance of the large-area stacked multilayer graphene is the key factor that suppresses water permeation. We show that by minimizing the gap between the two monolayers, the water vapor transmission rate of double-layer graphene can be as low as 5 × 10-3 g/(m2 d) over an A4-sized region. The high barrier performance was achieved by the absence of interfacial contamination and conformal contact between graphene layers during layer-by-layer transfer. Our work reveals the moisture permeation mechanism through graphene layers, and with this approach, we can tailor the interlayer coupling of manually stacked two-dimensional materials for new physics and applications.

Integrating diverse plant bioactive ingredients with cyclodextrins to fabricate functional films for food application: a critical review.

The popularity of plant bioactive ingredients has become increasingly apparent in the food industry. However, these plant bioactive ingredients have many deficiencies, including low water solubility, poor stability, and unacceptable odor. Cyclodextrins (CDs), as cyclic molecules, have been extensively studied as superb vehicles of plant bioactive ingredients. These CD inclusion compounds could be added into various film matrices to fabricate bioactive food packaging materials. Therefore, in the present review, we summarized the extraction methods of plant bioactive ingredients, the addition of these CD inclusion compounds into thin-film materials, and their applications in food packaging. Furthermore, the release model and mechanism of active film materials based on various plant bioactive ingredients with CDs were highlighted. Finally, the current challenges and new opportunities based on these film materials have been discussed.

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction.

Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.

Biotransformation of the saponins in Panax notoginseng leaves mediated by gut microbiota from insomniac patients.

Saponins extracted from Panax notoginseng leaves by methanol or water could be orally administrated for insomnia with very low bioavailability, which might be bio-converted by gut microbiota to generate potential bioactive products. Moreover, gut microbiota profiles from insomniac patients are very different from healthy subjects. We aimed to compare the metabolic characteristics and profiles of the two saponins extract by incubation with gut microbiota from insomniac patients. The ginsenosides, notoginsenosides, and metabolites were identified and relatively quantified by high-performance liquid chromatography-tandem mass spectrometry. Gut microbiota was profiled by 16S ribosomal RNA gene sequencing. The results showed that saponins were very different between methanol or water extract groups, which were metabolized by gut microbiota to generate similar yields. The main metabolites included ginsenoside Rd, ginsenoside F2 , ginsenoside C-Mc or ginsenoside C-Y, ginsenoside C-Mx, ginsenoside compound K, and protopanaxadiol in both groups, while gypenoside XVII, notoginsenoside Fe, ginsenoside Rd2 , and notoginsenoside Fd were the intermediates in the methanol group. Moreover, the microbial, Faecalibacterium prausnitzi, could bio-convert the saponins to obtain the corresponding metabolites. Our study implied that saponins extracted from P. notoginseng leaves by methanol or water could be used for insomniac patients due to gut microbiota biotransformation.

A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.

CsPbI3-Based Phase-Stable 2D Ruddlesden-Popper Perovskites for Efficient Solar Cells.

Inorganic CsPbI3 perovskite has shown great promise in highly stable perovskite solar cells due to the lack of volatile organic components. However, the inferior phase stability in ambient conditions resulted from the very small Cs+, limiting their practical applications. Here, CsPbI3-based 2D Ruddlesden-Popper (RP) perovskites were developed using two thiophene-based aromatic spacers, namely, 2-thiophenemethylamine hydroiodide (ThMA) and 2-thiopheneformamidine hydroiodide (ThFA), which significantly improved the phase stability by releasing the large inner stress of black-phase CsPbI3. The optimized ThFA-based 2D RP perovskite (n = 5, ThFA-Cs) device achieves a record efficiency of 16.00%. Importantly, the ThFA-Cs devices could maintain an average of 98% of their initial efficiencies after being stored in N2 at room temperature for 3000 h and 92% of their initial value at 80 °C for 960 h. This work provides a new perspective for exploration of the phase-stable CsPbI3-based perovskite with reduced dimensions for high-performance solar cells.

Systematic Identification of Microproteins during the Development of Drosophila melanogaster.

Short open reading frame-encoded peptides (SEPs) are microproteins with less than 100 amino acids that play an essential role in the growth and development of organisms. There are plenty of short open reading frames in Drosophila melanogaster that potentially code polypeptides. We chose 11 time points during the life cycle of Drosophila to investigate microproteins, particularly those related to development. Finally, we identified a total of 410 microproteins, of which 27 were noncoding RNA-encoded proteins. Of the 410 microproteins, 74 were expressed in all stages from embryo to adults, whereas 300 microproteins were only found in one or two time points. Approximately, one-third of the microproteins were not reported previously and 44 were obtained from de novo sequencing, validated by synthetic peptides. These microproteins are related to the main bioprocesses of growth and development, such as multicellular organism reproduction, postmating behavior, and oviposition. Over half of the microproteins have predicted functional domains and are conserved across species, suggesting that these microproteins have critical functions in fly development. This work enriches the D. melanogaster proteome and provides a significant data resource for growth and development research.

Prevalence of inflammatory bowel disease in patients with primary sclerosing cholangitis: A systematic review and meta-analysis.

BACKGROUND AND AIM: Previous studies have established an association between primary sclerosing cholangitis (PSC) and inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis (UC). The disease burden of IBD in PSC patients was not well estimated. The study aimed to quantify the pooled prevalence of IBD in PSC and to investigate whether subtypes of PSC and sex influence the prevalence of IBD. METHODS: PubMed, Embase, and Web of Science were searched through November 2021 for studies reporting data on IBD among PSC patients. The outcomes were the prevalence of IBD in patients with PSC, as well as the association (odds ratio [OR]) of IBD in PSC according to subtype and sex. RESULTS: Based on the analysis of 25 studies, the prevalence of IBD in patients with PSC was 71.1% (95% CI 68.2-75.1%), most commonly in UC (55.9%, 95% CI 52.5-59.3%). The pooled prevalence of IBD was 76.9% in Australia (95% CI 71.2-82.6%, 1 study), 75.9% (95% CI 69.5-82.3%, 4 studies) in North America, 70.9% (95% CI 65.8-76.0%, 17 studies) in Europe and 67.0% (95% CI 57.9-76.0%, 2 studies) in Asia. Male PSC patients had a higher prevalence of IBD (OR 1.67, 95% CI 1.52-1.83) and UC (OR 2.02, 95% CI 1.56-2.63) and a lower prevalence of CD (OR 0.77, 95% CI 0.67-0.88) than female patients. Large duct PSC patients had a higher prevalence of IBD (OR 2.57, 95% CI 2.03-3.25) and UC (OR 4.51, 95% CI 1.22-16.71) than small duct PSC patients. CONCLUSIONS: The study provided the first pooled estimates of the burden of IBD in patients with PSC and could be used as the basis for risk stratification of PSC patients.

Status Epilepticus in Patients with Anti-NMDAR Encephalitis Requiring Intensive Care: A Follow-Up Study.

BACKGROUND: To date, specialized studies focusing on status epilepticus (SE) in anti-N-methyl D-aspartate receptor (anti-NMDAR) encephalitis are limited, and the association between the occurrence of SE and clinical outcome is controversial. This study aims to investigate the differences between patients with critical anti-NMDAR encephalitis with SE and patients who experienced epileptic seizures without SE and to evaluate the long-term disease outcomes of patients with anti-NMDAR encephalitis with SE who were admitted to the neurological intensive care unit (neuro-ICU). METHODS: In this retrospective study based on a prospective registry, patients with anti-NMDAR encephalitis with neuro-ICU admission from 2014 to 2019 were analyzed and divided into two groups based on whether they had SE. Their clinical characteristics during the neuro-ICU stay were assessed and compared. The neurological and seizure outcomes were evaluated every 3 months. RESULTS: Of 83 patients with anti-NMDAR encephalitis, 24 required intensive care. In the SE group, 38.5% (5 of 13) of patients developed refractory SE (RSE), and 21.3% (3 of 13) of patients developed super RSE. More patients in the SE group presented with seizures as the initial symptoms (53.8% vs. 9.1%, p = 0.033) and had a strong positive NMDAR antibody titer in the cerebrospinal fluid (76.9% vs. 27.3%, p = 0.043). More patients in the non-SE group had a good neurological outcome (modified Rankin Scale (mRS) score < 2) at 3 months after disease onset (60.0% vs. 15.4%, p = 0.039), but 83.3% of patients with SE had a mRS score < 2 at 9 months after disease onset, which was similar to the rate in the non-SE group. A total of 41.7% of patients with SE had their last seizure within 1 month from disease onset, which was significantly lower than the rate in the non-SE group (90%), but all the patients with SE became seizure free after the acute phase of disease. CONCLUSIONS: Patients with critical anti-NMDAR encephalitis who present with SE have a high rate of RSE/super RSE and recover more slowly than patients without SE, but most of them will eventually achieve good long-term neurological outcomes and live seizure free after the acute phase.

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT.

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.

Multiple-Noncovalent-Interaction-Stabilized Layered Dion-Jacobson Perovskite for Efficient Solar Cells.

Two-dimensional Dion-Jacobson (DJ) perovskites have shown improved structure stability in comparison with Ruddlesden-Popper (RP) perovskites. However, the mechanism behind the improved stability is still largely unexplored. Here a multifluorinated aromatic spacer, namely, 4F-PhDMA, has been successfully developed for 2D DJ perovskites. It is found that the 2D DJ perovskite with a 4F-PhDMA spacer exhibits a high dissociation energy due to the multiple noncovalent interactions. The optimized 2D DJ device based on the 4F-PhDMA spacer (n = 4) exhibits a champion efficiency of 16.62% with much improved light and thermal stability. This efficiency is much higher than that of the control device using an unfluorinated spacer (n = 4, PCE = 10.11%) and is among the highest efficiencies in aromatic-spacer-based 2D DJ perovskite solar cells (PSCs). Our work highlights the importance of incorporating multiple noncovalent interactions in the 2D DJ perovskite by employing a multifluorinated aromatic spacer to achieve DJ PSCs with both high efficiency and high stability.