Metabolome and Transcriptome Association Analysis Reveals Mechanism of Synthesis of Nutrient Composition in Quinoa (Chenopodium quinoa Willd.) Seeds.

Quinoa (Chenopodium quinoa Willd.) seeds are rich in nutrition, superior to other grains, and have a high market value. However, the biosynthesis mechanisms of protein, starch, and lipid in quinoa grain are still unclear. The objective of this study was to ascertain the nutritional constituents of white, yellow, red, and black quinoa seeds and to employ a multi-omics approach to analyze the synthesis mechanisms of these nutrients. The findings are intended to furnish a theoretical foundation and technical support for the biological breeding of quinoa in China. In this study, the nutritional analysis of white, yellow, red, and black quinoa seeds from the same area showed that the nutritional contents of the quinoa seeds were significantly different, and the protein content increased with the deepening of color. The protein content of black quinoa was the highest (16.1 g/100 g) and the lipid content was the lowest (2.7 g/100 g), among which, linoleic acid was the main fatty acid. A combined transcriptome and metabolome analysis exhibited that differentially expressed genes were enriched in "linoleic acid metabolism", "unsaturated fatty acid biosynthesis", and "amino acid biosynthesis". We mainly identified seven genes involved in starch synthesis (LOC110716805, LOC110722789, LOC110738785, LOC110720405, LOC110730081, LOC110692055, and LOC110732328); five genes involved in lipid synthesis (LOC110701563, LOC110699636, LOC110709273, LOC110715590, and LOC110728838); and nine genes involved in protein synthesis (LOC110710842, LOC110720003, LOC110687170, LOC110716004, LOC110702086, LOC110724454 LOC110724577, LOC110704171, and LOC110686607). The data presented in this study based on nutrient, transcriptome, and metabolome analyses contribute to an enhanced understanding of the genetic regulation of seed quality traits in quinoa, and provide candidate genes for further genetic improvements to improve the nutritional value of quinoa seeds.

ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM.

BACKGROUND: Glioblastoma (GBM) is a fatal primary brain malignancy in adults. Previous studies have shown that cytomegalovirus (CMV) is a risk factor for tumorigenesis and aggressiveness for glioblastoma. However, little is known about how CMV infection affects immune cells in the tumor microenvironment of GBM. Furthermore, there has been almost no engineered T-cell receptor (TCR)-T targeting CMV for GBM research to date. METHODS: We evaluated the CMV infection status of patients with GBM's tumor tissue by immune electron microscopy, immunofluorescence, and droplet digital PCR. We performed single-cell RNA sequencing for CMV-infected GBM to investigate the effects of CMV on the GBM immune microenvironment. CellChat was applied to analyze the interaction between cells in the GBM tumor microenvironment. Additionally, we conducted single-cell TCR/B cell receptor (BCR) sequencing and Grouping of Lymphocyte Interactions with Paratope Hotspots 2 algorithms to acquire specific CMV-TCR sequences. Genetic engineering was used to introduce CMV-TCR into primary T cells derived from patients with CMV-infected GBM. Flow cytometry was used to measure the proportion and cytotoxicity status of T cells in vitro. RESULTS: We identified two novel immune cell subpopulations in CMV-infected GBM, which were bipositive CD68+SOX2+ tumor-associated macrophages and FXYD6+ T cells. We highlighted that the interaction between bipositive TAMs or cancer cells and T cells was predominantly focused on FXYD6+ T cells rather than regulatory T cells (Tregs), whereas, FXYD6+ T cells were further identified as a group of novel immunosuppressive T cells. CMV-TCR-T cells showed significant therapeutic effects on the human-derived orthotopic GBM mice model. CONCLUSIONS: These findings provided an insight into the underlying mechanism of CMV infection promoting the GBM immunosuppression, and provided a novel potential immunotherapy strategy for patients with GBM.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

Thyroid Hormone Suppresses Medulloblastoma Progression Through Promoting Terminal Differentiation of Tumor Cells.

Hypothyroidism is commonly detected in patients with medulloblastoma (MB). A possible link between thyroid hormone (TH) signaling and MB pathogenicity has not been reported. Here, we find that TH plays a critical role in promoting tumor cell differentiation. Reduction in TH levels frees the TH receptor, TRα1, to bind to EZH2 and repress expression of NeuroD1, a transcription factor that drives tumor cell differentiation. Increased TH reverses EZH2-mediated repression of NeuroD1 by abrogating the binding of EZH2 and TRα1, thereby stimulating tumor cell differentiation and reducing MB growth. Importantly, TH-induced differentiation of tumor cells is not restricted by the molecular subgroup of MB. These findings establish an unprecedented association between TH signaling and MB pathogenicity, providing solid evidence for TH as a promising modality for MB treatment.

Effect of Siphon Morphology on the Risk of C7 Segment Aneurysm Formation : A Case-control CFD Study.

PURPOSE: Tortuosity of the internal carotid artery (ICA) is associated with intracranial aneurysms (IAs). The siphon is the most curved segment of the ICA, but its morphology has controversial effects on IAs. This study aimed to explore the morphometric features of the siphon and the potential hemodynamic mechanisms that may affect C7 aneurysm formation. METHODS: In this study 32 patients with C7 aneurysms diagnosed at Xiangya Hospital between 2019 and 2021 and 32 control subjects were enrolled after propensity score matching. Computed tomography angiography (CTA) images were acquired to measure morphologic features, and then, by combining clinical data, simplified carotid siphon models were constructed, and computational fluid dynamics (CFD) analysis was performed. RESULTS: The presence of C7 aneurysms was associated with the height of the C4-C6 curved arteries (odds ratio [OR] 0.028, 95% confidence interval [CI] 0.003-0.201; P < 0.001). The heights of the C4-C6 curved arteries in the aneurysm group were significantly shorter than those in the control group. The CFD analysis revealed that shorter C4-C6 bends led to greater blood velocity and pressure in the C7 segment arteries. CONCLUSION: A shorter C4-C6 bend was associated with distal C7 aneurysm formation, and an elaborate hemodynamic mechanism may underlie this association.

Comparative efficacy of sodium thiosulfate, bisphosphonates, and cinacalcet for the treatment of vascular calcification in patients with haemodialysis: a systematic review and network meta-analysis.

BACKGROUND: Up to now, there is no unequivocal intervention to mitigate vascular calcification (VC) in patients with hemodialysis. This network meta-analysis aimed to systematically evaluate the clinical efficacy of sodium thiosulfate, bisphosphonates, and cinacalcet in treating vascular calcification. METHODS: A comprehensive study search was performed using PubMed, Web of Science, the Cochrane Library, EMBASE and China National Knowledge Internet (CNKI) to collect randomized controlled trials (RCTs) of sodium thiosulfate, bisphosphonates, and cinacalcet for vascular calcification among hemodialysis patients. Then, network meta-analysis was conducted using Stata 17.0 software. RESULTS: In total, eleven RCTs including 1083 patients were qualified for this meta-analysis. We found that cinacalcet (SMD - 0.59; 95% CI [-0.95, -0.24]) had significant benefit on vascular calcification compared with conventional therapy, while sodium thiosulfate or bisphosphonates did not show such efficiency. Furthermore, as for ranking the efficacy assessment, cinacalcet possessed the highest surface under the cumulative ranking curve (SUCRA) value (88.5%) of lessening vascular calcification and was superior to sodium thiosulfate (50.4%) and bisphosphonates (55.4%). Thus, above results suggested that cinacalcet might be the most promising drug for vascular calcification treatment in hemodialysis patients. Mechanistically, our findings illustrated that cinacalcet reduced serum calcium (SMD - 1.20; 95% CI [-2.08, - 0.33]) and showed the tendency in maintaining the balance of intact Parathyroid Hormone (iPTH) level. CONCLUSIONS: This network meta-analysis indicated that cinacalcet appear to be more effective than sodium thiosulfate and bisphosphonates in mitigating vascular calcification through decreasing serum calcium and iPTH. And cinacalcet might be a reasonable option for hemodialysis patients with VC in clinical practice. SYSTEMATIC REVIEW REGISTRATION: [ http://www.crd.york.ac.uk/PROSPERO ], identifier [CRD42022379965].

Low-complexity characterized-long-short-term-memory-aided channel modeling for optical fiber communications.

In this work, a low-complexity data-driven characterized-long-short-term-memory (C-LSTM)-aided channel modeling technique is proposed for optical single-mode fiber (SMF) communications. To fully utilize the sequence correlation learning ability of traditional long short-term memory (LSTM) networks and solve the gradient explosion problem, the feature information is introduced into the traditional LSTM input layer to better characterize the intersymbol interference caused by dispersion in SMF modeling. The simulation results show that the proposed C-LSTM can effectively alleviate the gradient explosion problem with a stable and ultimately lower mean square error (MSE) than traditional LSTM. Compared with the split-step Fourier method (SSFM) and the conditional generative adversarial network (CGAN), the proposed C-LSTM has superior computational complexity. Moreover, due to the sequence correlation learning ability inherent to C-LSTM, coupled with the flexibility of feature information selection, the proposed C-LSTM-aided modeling technique has a higher modeling accuracy than traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be effectively extended to other channel modeling applications with strong sequence correlations.

Short-term restoration effects of ecological projects detected using the turning point method in the Three River Headwater Region, China.

The Three River Headwater Region (TRHR) is an important river source area providing important ecological functions. Decades ago, climate change and human activities severely degraded the ecosystem in the TRHR. To restore vegetation, a series of ecological projects have been implemented since 1989. Using net primary productivity (NPP) data from 1988 to 2012, a sequential Mann-Kendall trend test (SQ-MK) method was applied to identify the turning point of vegetation NPP. This approach was able to represent the critical response time of the vegetation to important disturbances. A 3-year time window was set after the implementation of one ecological project to detect and analyze its short-term effects. The ecological projects included the Yangtze River Basin Shelterbelt System Construction Project (YRCP), the TRHR Nature Reserve Construction Project (TNR), the Returning Grazing Land to Grassland Project (RGLGP), and the first phase of the Ecological Conservation and Restoration Project of the TRHR (ECRP). Our results showed that the vegetation in the TRHR responded positively to restoration: 89% of pixels showed an increasing trend and 54% of pixels underwent an abrupt change. The accelerated growth type accounted for the highest proportion among all types of detected turning points. In the ECRP's window, the positive turns rose rapidly, from 41% in 2005 to 86% in 2008, and it showed the most balanced restoration effects across grasslands. The alpine meadow and montane meadow restoration was largely influenced by the ECRP and the RGLGP (both >40%). The alpine steppe restoration was mainly attributed to the ECRP (68%). On the county scale, the positive turns in Yushu at the source of the Yangtze River mainly benefited from the RGLGP (56%), while the positive turns in Maduo at the source of the Yellow River benefited from the ECRP (77%). Nangqian, Tanggula and Zaduo County were still in need of intervention for restoration (< 3%). The results of the study can enhance our understanding of the spatio-temporal distribution of the short-term ecological benefits of different ecological projects, thus provide a scientific and timely reference for future planning and adjustment of the conservation and restoration projects.

Analysis of volatile organic compounds and potential odour compounds in food contact paperboard using headspace two-dimensional GC-QTOF-MS.

The objective was to establish a robust and reliable approach for the characterisation of volatile organic compounds (VOCs) present in food contact paperboard. This was achieved through the utilisation of headspace solid-phase microextraction in tandem with comprehensive two-dimensional (2D) gas chromatography (GC) and quadrupole time-of-flight mass spectrometry (HS-SPME-GC × GC-QTOF-MS). The experimental parameters were optimised, involving the use of a DVB/C-WR/PDMS fibre at a temperature of 80 °C for a duration of 30 min. A total of 344 VOCs comprising aldehydes, ketones, alcohols, ethers, esters, alkanes and aromatic compounds, were tentatively identified in the samples. Twelve compounds believed to be from biogenic sources had a high odour impact making them major contributors to potential taint from the paperboard samples. Significant attention should be devoted to five compounds namely, 2-methylnaphthalene, 2-pentyl-furan, furfural, 1-octen-3-one and 1-octen-3-ol due to their potential adverse impact on the organoleptic qualities of packaged food items and their potential toxicity.Abbreviations: C-WR: carbon wide range; DVB: divinylbenzene; GC-MS: gas chromatography - mass spectrometry; GCxGC-QTOF-MS: comprehensive two-dimensional gas chromatography coupled to quadrupole-time-of-flight - mass spectrometry; HS-SPME: headspace - solid phase microextraction; LOD: limit of detection; LOQ: limit of quantification; OAV: odor activity values; PDMS: polydimethylsiloxane; RI: retention index; TTC: threshold of toxicological concern; VOC: volatile organic compound.

Event-Triggered Adaptive Antidisturbance Switching Control for Switched Systems With Dynamic Neural Network Disturbance Modeling.

In this article, a dynamic event-triggered adaptive antidisturbance (ETAAD) switching control strategy is proposed for switched systems subject to multisource disturbances. The disturbances are divided into two categories: the available unmodeled disturbance and the unavailable dynamic neural network modeled disturbance. First, a dynamic ET criterion is set based on the system state. Then, a novel dynamic ETA disturbance estimator is introduced to observe the modeled disturbance. Furthermore, according to the ET rule and adaptive disturbance observer, a switched controller is designed. Next, under the controller and switching criterion with the average dwell time limitation, sufficient conditions are given to force the switched systems to realize multisource disturbance suppression (DS), trajectory tracking, and communication resource (CR) saving simultaneously. Meanwhile, the Zeno phenomenon may be caused by the ET rule being excluded. In addition, the presented ETAAD approach is also applicable to the nonswitched systems case. Finally, a simulation case is given to validate the effectiveness of the dynamic ETAAD switching control method.

Enhanced H2O2 Upcycling into Hydroxyl Radicals with GO/Ni:FeOOH-Coated Silicon Nanowire Photocatalysts for Wastewater Treatment.

There remains continued interest in improving the advanced water oxidation process [e.g., ultraviolet (UV)/hydrogen peroxide (H2O2)] for more efficient and environmentally friendly wastewater treatment. Here, we report the design, fabrication, and performance of graphene oxide (GO, on top)/nickel-doped iron oxyhydroxide (Ni:FeOOH, shell)/silicon nanowires (SiNWs, core) as a new multifunctional photocatalyst for the degradation of common pollutants like polystyrene and methylene blue through enhancing the hydroxyl radical (•OH) production rate of the UV/H2O2 system. The photocatalyst combines the advantages of a large surface area and light absorption characteristics of SiNWs with heterogeneous photo-Fenton active Ni:FeOOH and photocatalytically active/charge separator GO. In addition, the built-in electric field of GO/Ni:FeOOH/SiNWs facilitates the charge separation of electrons to GO and holes to Ni:FeOOH, thus boosting the photocatalytic performance. Our photocatalyst increases the •OH yield by 5.7 times compared with that of a blank H2O2 solution sample and also extends the light absorption spectrum to include visible light irradiation.

[Impacts of Climate Change and Human Activities on Vegetation Restoration in Typical Grasslands of China].

Grasslands, as one of the key ecosystems relevant to the terrestrial ecosystem carbon and water cycles as well as the ecological security in China, are very sensitive to climate change and human activities. However, the relative contributions of climate change and human activities on the vegetation restoration in those regions are still controversial. Using ecosystem net primary production (NPP) as an ecological indicator, this study quantified the relative roles of climate change and human activities on vegetation restoration in Chinese typical grasslands (northern temperate grasslands and Qinghai-Tibet Plateau alpine grasslands) by comparing the trends of actual NPP derived from MODIS and potential NPP estimated by the Thornthwaite Memorial model during 2000-2020. The results showed that approximately 93% of the grasslands in the study area experienced a recovering tendency, with an average increase of NPP (carbon) by 2.12 g·(m2·a)-1(P<0.01). Therein, nearly half of the vegetation-restored areas were jointly-dominated by climate change and human activities, whereas approximately 36% and 10% of the restored areas were controlled individually by climate change and human activities, respectively. In addition, the share of climate-change dominated areas differed greatly by grassland types, characterized by a much larger area percentage in the alpine grasslands than that in the temperate grasslands and an increasing area share with a drying background climate. This study suggested that human activities were not primarily responsible for the vegetation restoration in northern temperate grasslands and Qinghai-Tibet Plateau alpine grasslands, but they could decrease and even cancel the possible vegetation degeneration caused by worsening climate in a few regions. Long-term monitoring of vegetation dynamics and a multi-method comparison are needed in future studies.

Sparse Actuator Attack Detection and Identification: A Data-Driven Approach.

This article aims to investigate the data-driven attack detection and identification problem for cyber-physical systems under sparse actuator attacks, by developing tools from subspace identification and compressive sensing theories. First, two sparse actuator attack models (additive and multiplicative) are formulated and the definitions of I/O sequence and data models are presented. Then, the attack detector is designed by identifying the stable kernel representation of cyber-physical systems, followed by the security analysis of data-driven attack detection. Moreover, two sparse recovery-based attack identification policies are proposed, with respect to sparse additive and multiplicative actuator attack models. These attack identification policies are realized by the convex optimization methods. Furthermore, the identifiability conditions of the presented identification algorithms are analyzed to evaluate the vulnerability of cyber-physical systems. Finally, the proposed methods are verified by the simulations on a flight vehicle system.

Experiences and perceptions of self-management in people with prediabetes: A qualitative meta-synthesis.

BACKGROUND: Diabetes has become a global public health problem. Strengthening the self-management ability of people with prediabetes plays an important role in preventing the occurrence and development of type 2 diabetes. The aim of this study is to synthesise the self-management experiences and perceptions of people with prediabetes, which can contribute to the development of self-management programs. METHODS: This review adheres to the ENTREQ Guide. Evidence-based medicine database (JBI and Cochrane) and original literature database (PubMed, Medline, EMbase, Web of Science, Wanfang, CNKI and VIP) were searched up to 31 May 2022. Both Chinese and English literature of qualitative research on self-management experiences and perceptions of prediabetic patients were included. The quality of the included studies was evaluated, and the data were synthesised and analysed by thematic synthesis method. RESULTS: A total of 23 primary studies containing 504 participants were included. After repeated reading and coding of the literature, three analytical themes were finally identified: coping with role management, success and failure in medical management, seeking and perceiving support. CONCLUSION: Role management for people with prediabetes needs more attention. Healthcare providers should identify problems from patients' self-management experiences and improve professional skills to assist program modifications. Integrating the self-management program into community activities under the guidance of medical staff and inviting family members and peers to participate can increase involvement and improve the self-management ability. RELEVANCE TO CLINICAL PRACTICE: These findings describe the different stages and issues in the self-management process of prediabetic patients. The practice of prediabetes self-management should incorporate the psychosocial, physical, and financial issues of the patients. As the main provider of health services, nurses should make patients aware of the susceptibility and severity of prediabetes and help them improve their self-management skills. NO PATIENT OR PUBLIC CONTRIBUTION: This is a meta-synthesis without direct participation of patients.

Novel Human Meningioma Organoids Recapitulate the Aggressiveness of the Initiating Cell Subpopulations Identified by ScRNA-Seq.

High-grade meningioma has an unsatisfactory outcome despite surgery and postoperative radiotherapy; however, the factors driving its malignancy and recurrence remain largely unknown, which limits the development of systemic treatments. Single-cell RNA sequencing (scRNA-Seq) technology is a powerful tool for studying intratumoral cellular heterogeneity and revealing the roles of various cell types in oncogenesis. In this study, scRNA-Seq is used to identify a unique initiating cell subpopulation (SULT1E1+ ) in high-grade meningiomas. This subpopulation modulates the polarization of M2-type macrophages and promotes meningioma progression and recurrence. A novel patient-derived meningioma organoid (MO) model is established to characterize this unique subpopulation. The resulting MOs fully retain the aggressiveness of SULT1E1+ and exhibit invasiveness in the brain after orthotopic transplantation. By targeting SULT1E1+ in MOs, the synthetic compound SRT1720 is identified as a potential agent for systemic treatment and radiation sensitization. These findings shed light on the mechanism underlying the malignancy of high-grade meningiomas and provide a novel therapeutic target for refractory high-grade meningioma.

Reduced expression of APLP2 in spinal GABAergic inhibitory neurons contributed to nerve injury-induced microglial activation and pain sensitization.

The amyloid precursor protein (APP) is critical for the pathogenesis of Alzheimer's disease (AD). The AD patients usually have lower pain sensitivity in addition to cognitive impairments. However, considerably less is known as yet about the role of APP and its two mammalian homologues, amyloid precursor-like protein 1 and 2 (APLP1, APLP2), in spinal processing of nociceptive information. Here we found that all APP family members were present in spinal cord dorsal horn of adult male C57BL/6J mice. Peripheral nerve injury specifically reduced the expression of spinal APLP2 that correlated with neuropathic mechanical allodynia. The loss of APLP2 was confined to inhibitory GABAergic interneurons. Targeted knockdown of APLP2 in GABAergic interneurons of GAD2-Cre mice evoked pain hypersensitivity by means of microglia activation. Our data showed that GABAergic terminals expressed APLP2, a putative cell adhesion protein that interacted with microglia-specific integrin molecule CD11b. Knocking down APLP2 in GAD2-positive neurons to disrupt the trans-cellular interaction led to microglia-dependent pain sensitization. Our data thus revealed an important role of APLP2 for GABAergic interneurons to control microglial activity and pain sensitivity.

Learning-Based DoS Attack Power Allocation in Multiprocess Systems.

We study the denial-of-service (DoS) attack power allocation optimization in a multiprocess cyber-physical system (CPS), where sensors observe different dynamic processes and send the local estimated states to a remote estimator through wireless channels, while a DoS attacker allocates its attack power on different channels as interference to reduce the wireless transmission rates, and thus degrading the estimation accuracy of the remote estimator. We consider two attack optimization problems. One is to maximize the average estimation error of different processes, and the other is to maximize the minimal one. We formulate these problems as Markov decision processes (MDPs). Unlike the majority of existing works where the attacker is assumed to have complete knowledge of the CPS, we consider an attacker with no prior knowledge of the wireless channel model and the sensor information. To address this uncertainty issue and the curse of dimensionality, we provide a learning-based attack power allocation algorithm stemming from the double deep Q-network (DDQN) method. First, with a defined partial order, the maximal elements of the action space are determined. By investigating the characteristic of the MDP, we prove that the optimal attack allocations of both problems belong to the set of these elements. This property reduces the entire action space to a smaller subset and speeds up the learning algorithm. In addition, to further improve the data efficiency and learning performance, we propose two enhanced attack power allocation algorithms which add two auxiliary tasks of MDP transition estimation inspired by model-based reinforcement learning, i.e., the next state prediction and the current action estimation. Experimental results demonstrate the versatility and efficiency of the proposed algorithms in different system settings compared with other algorithms, such as the conventional value iteration, double Q-learning, and deep Q-network.

Rosai-Dorfman disease of the central nervous system: A clinical, radiological, and prognostic study of 12 cases.

Background: Rosai-Dorfman disease (RDD) is a rare benign non-Langerhans cell histiocytic proliferative disease. RDD with central nervous system (CNS) involvement (CNS-RDD) is extremely rare. Its etiology is unclear, and there are no consensus recommendations for its treatment. More studies are needed to elucidate the clinical and radiological manifestations and prognosis of CNS-RDD. Methods: From January 2012 to June 2022, 12 patients with CNS-RDD (intracranial or spinal) were retrospectively evaluated, including collecting clinical data, imaging data, and pathological findings; summarizing imaging characteristics; and conducting follow-up studies on CND-RDD patient treatment and prognosis. Results: Twelve CNS-RDD patients (nine male and three female patients, aged 12-67 years) were enrolled in this study. Nine patients represented convex and/or skull base RDD (eight with edema, six with lobulation and/or pseudopodium sign, four with multiple intracranial lesions), two patients had parenchymal RDD, and one patient had spinal cord subdural lesions. Symptoms of patients would vary according to the locations of the lesion, including but not limited to headaches, dizziness, seizures, cranial nerve dysfunction, and visual impairment. The immunohistochemistry of RDD showed positive expression of S100 and CD68 but not CD1a. Total resection (n = 7), subtotal resection (n = 3), partial resection (n = 1), and stereotaxic biopsy (n = 1) were achieved, respectively. A combination of chemotherapy plus steroid therapy was performed on two patients (relapsing case and residual lesion) and showed a remarkable effect. Conclusion: CNS-RDD, as a rare disease, presents a significant diagnostic challenge for clinicians. Solitary CNS-RDD are easily misdiagnosed as meningioma. However, when the MRI imaging of the disease represents dura-based masses with significant edema, homogeneous enhancement, lobulation, and/or pseudopodium sign, we should consider it might be the CNS-RDD. Surgery is an important and effective therapy for CNS-RDD. Steroids and chemotherapy are safe and effective for the postoperative treatment of relapsing cases or residual lesions.

Invariance Principles for Nonlinear Discrete-Time Switched Systems and Its Application to Output Synchronization of Dynamical Networks.

In this article, we develop two invariance principles for nonlinear discrete-time switched systems based on multiple Lyapunov functions and multiple weak Lyapunov functions, respectively, which allow the first differences of multiple weak Lyapunov functions to be positive on certain sets. It is shown that the solution of the system is attracted to the largest weakly invariant set in a certain specific region. Then, based on the invariance principle developed and geometrical dissipativity, we obtain the generalized output synchronization for discrete-time dynamical networks with nonidentical nodes by an appropriate switching among several communication topologies. Finally, two examples are provided to demonstrate the effectiveness of the main results.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.