Molecular insight into T cell exhaustion in hepatocellular carcinoma.

Hepatocellular carcinoma is one of the leading causes of cancer-related mortality globally. The emergence of immunotherapy has been shown to be a promising therapeutic approach for hepatocellular carcinoma in recent years. It has been well known that T cell plays a key role in current immunotherapy. However, sustained exposure to antigenic stimulation within the tumor microenvironment may lead to T cell exhaustion, which may cause treatment ineffectiveness. Therefore, reversing T cell exhaustion has been an important issue for the clinical application of immunotherapy, and a comprehensive understanding of the intricacies surrounding T cell exhaustion and its underlying mechanisms is imperative for devising strategies to overcome the T cell exhaustion during treatment. In this review, we summarized the reported drivers of T cell exhaustion in hepatocellular carcinoma and delineate potential ways to reverse it. Additionally, we discussed the interplay among metabolic plasticity, epigenetic regulation, and transcriptional factors in exhausted T cells in hepatocellular carcinoma, and their implication for future clinical applications.

Recent advances in phyto-combined remediation of heavy metal pollution in soil.

The global industrialization and modernization have witnessed a rapid progress made in agricultural production, along with the issue of soil heavy metal (HM) pollution, which has posed severe threats to soil quality, crop yield, and human health. Phytoremediation, as an alternative to physical and chemical methods, offers a more cost-effective, eco-friendly, and aesthetically appealing means for in-situ remediation. Despite its advantages, traditional phytoremediation faces challenges, including variable soil physicochemical properties, the bioavailability of HMs, and the slow growth and limited biomass of plants used for remediation. This study presents a critical overview of the predominant plant-based HM remediation strategies. It expounds upon the mechanisms of plant absorption, translocation, accumulation, and detoxification of HMs. Moreover, the advancements and practical applications of phyto-combined remediation strategies, such as the addition of exogenous substances, genetic modification of plants, enhancement by rhizosphere microorganisms, and intensification of agricultural technologies, are synthesized. In addition, this paper also emphasizes the economic and practical feasibility of some strategies, proposing solutions to extant challenges in traditional phytoremediation. It advocates for the development of cost-effective, minimally polluting, and biocompatible exogenous substances, along with the careful selection and application of hyperaccumulating plants. We further delineate specific future research avenues, such as refining genetic engineering techniques to avoid adverse impacts on plant growth and the ecosystem, and tailoring phyto-combined strategies to diverse soil types and HM pollutants. These proposed directions aim to enhance the practical application of phytoremediation and its integration into a broader remediation framework, thereby addressing the urgent need for sustainable soil decontamination and protection of ecological and human health.

Copper removal from aqueous solutions by white rot fungus Pleurotus ostreatus GEMB-PO1 and its potential in co-remediation of copper and organic pollutants.

Addressing the environmental contamination from heavy metals and organic pollutants remains a critical challenge. This study explored the resilience and removal potential of Pleurotus ostreatus GEMB-PO1 for copper. P. ostreatus GEMB-PO1 showed significant tolerance, withstanding copper concentrations up to 2 mM. Its copper removal efficiency ranged from 64.56 % at 0.5 mM to 22.90 % at 8 mM. Transcriptomic insights into its response to copper revealed a marked upregulation in xenobiotic degradation-related enzymes, such as laccase and type II peroxidases. Building on these findings, a co-remediation system using P. ostreatus GEMB-PO1 was developed to remove both copper and organic pollutants. While this approach significantly enhanced the degradation efficiency of organic contaminants, it concurrently exhibited a diminished efficacy in copper removal within the composite system. This study underscores the potential of P. ostreatus GEMB-PO1 in environmental remediation. Nevertheless, further investigation is required to optimize the simultaneous removal of organic pollutants and copper.

Endophytic Enterobacter sp. YG-14 mediated arsenic mobilization through siderophore and its role in enhancing phytostabilization.

Soil arsenic (As) phytoremediation has long faced the challenge of efficiently absorbing As by plant accumulators while maintaining their health and fast growth. Even at low doses, arsenic is highly toxic to plants. Therefore, plant growth-promoting microorganisms that can mediate As accumulation in plants are of great interest. In this study, the endophyte Enterobacter sp. YG-14 (YG-14) was found to have soil mobilization activity. By constructing a siderophore synthesis gene deletion mutant (ΔentD) of YG-14, the endophyte was confirmed to effectively mobilize Fe-As complexes in mining soil by secreting enterobactin, releasing bioavailable Fe and As to the rhizosphere. YG-14 also enhances As accumulation in host plants via extracellular polymer adsorption and specific phosphatase transfer protein (PitA) absorption. The root accumulation of As was positively correlated with YG-14 root colonization. In addition, YG-14 promoted plant growth and alleviated oxidative damage in R. pseudoacacia L. under arsenic stress. This is the first study, from phenotype, physiology, and molecular perspectives, to determine the role of endophyte in promoting As phytostabilization and maintaining the growth of the host plant. This demonstrated the feasibility of using endophytes with high siderophore production to assist host plants in As phytoremediation.

A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study.

The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.

The Rice Endophyte-Derived α-Mannosidase ShAM1 Degrades Host Cell Walls To Activate DAMP-Triggered Immunity against Disease.

Endophytes play an important role in shaping plant growth and immunity. However, the mechanisms for endophyte-induced disease resistance in host plants remain unclear. Here, we screened and isolated the immunity inducer ShAM1 from the endophyte Streptomyces hygroscopicus OsiSh-2, which strongly antagonizes the pathogen Magnaporthe oryzae. Recombinant ShAM1 can trigger rice immune responses and induce hypersensitive responses in various plant species. After infection with M. oryzae, blast resistance was dramatically improved in ShAM1-inoculated rice. In addition, the enhanced disease resistance by ShAM1 was found to occur through a priming strategy and was mainly regulated through the jasmonic acid-ethylene (JA/ET)-dependent signaling pathway. ShAM1 was identified as a novel α-mannosidase, and its induction of immunity is dependent on its enzyme activity. When we incubated ShAM1 with isolated rice cell walls, the release of oligosaccharides was observed. Notably, extracts from the ShAM1-digested cell wall can enhance the disease resistance of the host rice. These results indicated that ShAM1 triggered immune defense against pathogens by damage-associated molecular pattern (DAMP)-related mechanisms. Our work provides a representative example of endophyte-mediated modulation of disease resistance in host plants. The effects of ShAM1 indicate the promise of using active components from endophytes as plant defense elicitors for the management of plant disease. IMPORTANCE The specific biological niche inside host plants allows endophytes to regulate plant disease resistance effectively. However, there have been few reports on the role of active metabolites from endophytes in inducing host disease resistance. In this study, we demonstrated that an identified α-mannosidase protein, ShAM1, secreted by the endophyte S. hygroscopicus OsiSh-2 could activate typical plant immunity responses and induce a timely and cost-efficient priming defense against the pathogen M. oryzae in rice. Importantly, we revealed that ShAM1 enhanced plant disease resistance through its hydrolytic enzyme (HE) activity to digest the rice cell wall and release damage-associated molecular patterns. Taken together, these findings provide an example of the interaction mode of endophyte-plant symbionts and suggest that HEs derived from endophytes can be used as environmentally friendly and safe prevention agent for plant disease control.

Multi-omics joint analysis reveals how Streptomyces albidoflavus OsiLf-2 assists Camellia oleifera to resist drought stress and improve fruit quality.

Camellia oleifera (C. oleifera) is a unique edible oil crop in China cultivated in the hilly southern mountains. Although C. oleifera is classified as a drought-tolerant tree species, drought remains the main factor limiting the growth of C. oleifera in summer and autumn. Using endophytes to improve crop drought tolerance is one effective strategy to meet our growing food crop demand. In this study, we showed that endophyte Streptomyces albidoflavus OsiLf-2 could mitigate the negative impact of drought stress on C. oleifera, thus improving seed, oil, and fruit quality. Microbiome analysis revealed that OsiLf-2 treatment significantly affected the microbial community structure in the rhizosphere soil of C. oleifera, decreasing both the diversity and abundance of the soil microbe. Likewise, transcriptome and metabolome analyses found that OsiLf-2 protected plant cells from drought stress by reducing root cell water loss and synthesizing osmoregulatory substances, polysaccharides, and sugar alcohols in roots. Moreover, we observed that OsiLf-2 could induce the host to resist drought stress by increasing its peroxidase activity and synthesizing antioxidants such as cysteine. A multi-omics joint analysis of microbiomes, transcriptomes, and metabolomes revealed OsiLf-2 assists C. oleifera in resisting drought stress. This study provides theoretical and technical support for future research on endophytes application to enhance the drought resistance, yield, and quality of C. oleifera.

A reduced latency regional gap-filling method for SMAP using random forest regression.

The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R2 ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with in situ and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.

Construction Industry-Associated Penetrating Craniocerebral Injuries.

BACKGROUND: Various high-energy tasks in the construction industry can lead to craniocerebral injuries. Construction industry-associated penetrating craniocerebral injuries due to metal foreign bodies have unique characteristics. However, no norms exist for removing metal foreign bodies and preventing secondary trauma. This study aimed to explore the characteristics and treatment of construction industry-associated penetrating craniocerebral injuries due to metal foreign bodies. METHODS: Data of patients who suffered from penetrating injuries due to metal foreign bodies and were treated in the Zhongshan People's Hospital from 2001 to 2021 were collected based on the causes of injuries to explore disease characteristics and therapeutic effects. RESULTS: A total of six patients with penetrating craniocerebral injuries due to metal foreign bodies, who underwent surgeries, were included in the study. Five patients recovered well after the surgery, and one patient died. In four patients, intracranial infection complicated the course after surgery, and two patients had delayed intracranial hematoma. CONCLUSION: Patients with construction industry-associated penetrating craniocerebral injuries due to metal foreign bodies are prone to coma and intracranial vascular injuries. Early surgical removal and prevention of intracranial infection are key to achieving good therapeutic effects.

Landscape pattern change and its correlation with influencing factors in semiarid areas, northwestern China.

The West Liaohe Plain (WLP) is a typical crop-pastoral ecotone of the semiarid area in Northwestern, China. Land use/cover change (LUCC) of the WLP might endanger this ecosystem, triggering long-lasting environmental concerns. LUCC data for China (1980-2020) and hydrometeorological data were analyzed to reveal factors contributing to change and explore sustainable development opportunities. The results show that characteristics of the main land-use types in the WLP have changed significantly, especially cultivated land area, which increased by 15.2% and 6.79% during the periods 1980-1995 and 2000-2020, respectively. Response relationships were observed due to natural (precipitation, temperature, and runoff) and anthropogenic (economy) factors and LUCC. Between 2000 and 2020, the impact of anthropogenic factors on cultivated land was stronger than on grassland at the class and landscape level, using the landscape indices which were selected, including percent of landscape (PLAND), number of patches (NP), largest patch index (LPI), and Shannon's evenness index (SHEI). Expansion of cultivated land from 1990 to 1995 was not only related to anthropogenic factors but also to hydrological & climatic factors. The results of this study have the potential to influence sustainable land resource development.

Cyanobacteria bloom hazard function and preliminary application in lake taihu, China.

In recent years, due to the intensification of human activities, water ecological problems are gradually increasing. As the third largest freshwater lake in China, Lake Taihu is an important drinking water source for several densely populated cities in China. The prominent water ecological problem in this area is mainly Cyanobacteria Bloom. Cyanobacterial blooms have been erupting which have affected local residents' drinking water and caused losses to the national economy. Based on the interpretation results of MODIS data in the Lake Taihu region from 2004 to 2014, this paper analyzes the main driving factors of cyanobacterial bloom are phosphorus and potassium through the correlation analysis of nitrogen, phosphorus, potassium and cyanobacteria area, normalizes nutrient, and identifies that the water level of Lake Taihu is the influencing factor of cyanobacterial bloom. A Lake Taihu cyanobacteria bloom hazard function is constructed to quantitatively assess the losses (economic losses) caused by cyanobacterial blooms from 2001 to 2012, supporting for cyanobacteria control management in Lake Taihu.

The Vital Role of ShTHIC from the Endophyte OsiSh-2 in Thiamine Biosynthesis and Blast Resistance in the OsiSh-2-Rice Symbiont.

Endophytes can benefit the growth and stress resistance of host plants by secreting bioactive components. Thiamine is an essential vitamin involved in many metabolic pathways and can only be synthesized by microbes and plants. In this study, we found that thiamine could inhibit the development of the phytopathogen Magnaporthe oryzae and decrease the rice blast index under field conditions. In the thiamine biosynthesis pathway, the key enzyme ShTHIC of an endophyte Streptomyces hygroscopicus OsiSh-2 and OsTHIC of rice (Oryza sativa) were highly homologous. Gene overexpression or knockout approaches revealed that both THIC contributed to thiamine synthesis and resistance to M. oryzae. Furthermore, S. hygroscopicus OsiSh-2 colonization led to a decrease in the thiamine synthesis level of rice but still maintained thiamine homeostasis in rice. However, inoculation with the ShTHIC knockout strain ΔTHIC reduced the thiamine content in rice, although the thiamine synthesis level of rice was increased. After infection with M. oryzae, blast resistance was dramatically improved in OsiSh-2-inoculated rice but decreased in ΔTHIC-inoculated rice compared with non-inoculated rice. This result demonstrated that ShTHIC could regulate thiamine biosynthesis and consequently assist blast resistance in the OsiSh-2-rice symbiont. Our results revealed a novel blast-resistance mechanism mediated by a key thiamine biosynthetic enzyme from an endophyte OsiSh-2.

Multigraph Fusion for Dynamic Graph Convolutional Network.

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustness is sensitive to the quality of both the feature matrix and the initial graph. In this article, we propose a novel multigraph fusion method to produce a high-quality graph and a low-dimensional space of original high-dimensional data for the GCN model. Specifically, the proposed method first extracts the common information and the complementary information among multiple local graphs to obtain a unified local graph, which is then fused with the global graph of the data to obtain the initial graph for the GCN model. As a result, the proposed method conducts the graph fusion process twice to simultaneously learn the low-dimensional space and the intrinsic graph structure of the data in a unified framework. Experimental results on real datasets demonstrated that our method outperformed the comparison methods in terms of classification tasks.

Endophytic Streptomyces hygroscopicus OsiSh-2-Mediated Balancing between Growth and Disease Resistance in Host Rice.

Plants fine-tune the growth-defense trade-off to survive when facing pathogens. Meanwhile, plant-associated microbes, such as the endophytes inside plant tissues, can benefit plant growth and stress resilience. However, the mechanisms for the beneficial microbes to increase stress resistance with little yield penalty in host plants remain poorly understood. In the present study, we report that endophytic Streptomyces hygroscopicus OsiSh-2 can form a sophisticated interaction with host rice, maintaining cellular homeostasis under pathogen-infection stress, and optimize plant growth and disease resistance in rice. Four-year field trials consistently showed that OsiSh-2 could boost host resistance to rice blast pathogen Magnaporthe oryzae while still maintaining a high yield. The integration of the proteomic, physiological, and transcriptional profiling analysis revealed that OsiSh-2 induced rice defense priming and controlled the expression of energy-consuming defense-related proteins, thus increasing the defense capability with the minimized costs of plant immunity. Meanwhile, OsiSh-2 improved the chloroplast development and optimally maintained the expression of proteins related to plant growth under pathogen stress, thus promoting the crop yield. Our results provided a representative example of an endophyte-mediated modulation of disease resistance and fitness in the host plant. The multilayer effects of OsiSh-2 implicate a promising future of using endophytic actinobacteria for disease control and crop yield promotion. IMPORTANCE Under disease stress, activation of defense response in plants often comes with the cost of a reduction in growth and yield, which is referred as the growth-defense trade-off. The microorganisms which can be recruited by plants to mitigate the growth-defense trade-off are of great value in crop breeding. Here, we reported a rice endophytic actinomycetes Streptomyces hygroscopicus OsiSh-2, which can improve host performances on resistance to rice blast while still sustaining high yield in the 4-year field trials. The proteomic, physiological, and transcriptional profiling data offer insights into the molecular basis underlying the balancing between defense and growth in OsiSh-2-rice symbiont. The findings provide an example for the endophyte-mediated modulation of growth-defense trade-offs in plants and indicated the promising application of endophytic actinobacterial strains in agriculture to breed "microbe-optimized crops."

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l1 -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.

Enhanced Cd phytostabilization and rhizosphere bacterial diversity of Robinia pseudoacacia L. by endophyte Enterobacter sp. YG-14 combined with sludge biochar.

Robinia pseudoacacia L., a pioneer woody legume grown in mining areas, has been recognized as a remarkable accumulator of various heavy metals. Compared with other hazardous heavy metals (HMs), it is of low capacity in accumulating Cd, which, as a result, may hinder the phytoremediation efficiency. To enhance R. pseudoacacia's uptake efficiency of Cd, the individual effects of various rhizobia and arbuscular mycorrhizal fungi have been reported, however, the combined influence of endophytes and biochar receives little attention. In the current study, a Cd-adsorbing endophyte Enterobacter sp. YG-14 was inoculated to R. pseudoacacia, and its extraordinary effect on increasing R. pseudoacacia's Cd uptake was found, which was ascribed to the reinforced root Cd chelation by the strain through secreting siderophores/LMWOAs. Further, P-enriched sludge biochar was applied along with YG-14 to form a combined biochar-endophyte-accumulator system, in which biomineralization were reinforced (i.e. CdCO3 and Cd2P2O7 were generated), as the total and acid-soluble Cd in rhizosphere were reduced by 61.75% and 69.01% respectively, and soil's bacterial diversity was further improved with diversified N2-fixing microbial biomarkers. Multiple synergistic effects (E > 0) were also found, with the optimum performance on plant growth parameters (increased by 39.61%-561.91%) in comparison to the control group. Moreover, the system exhibited a preferable Cd phytostabilization capacity with the highest increase (81.42%) in Cd accumulation and a significant reduction (72.73%) in Cd root-to-shoot translocation.

In vitroadipogenesis and long-term adipocyte culture in adipose tissue-derived cell banks.

There is a critical need to developin vitroculture systems appropriate for the expansion of adipose tissue, in order to gain new insights into metabolic diseases and to assist in the restoration of tissue defects. Conventional two- or three-dimensional (2D or 3D)in vitromodels of adipocytes require a combination of supplements to induce adipocyte maturation that greatly increases the cost of large-scale industrial production. In the present study, a microporous, perforated bacterial cellulose (BC)-assisted culture system was developed that promoted the adhesion, proliferation, and adipogenic differentiation of preadipocytes. Additionally, the system maintained the cells as mature unilocular adipocytesex vivoin normal cell culture medium in long-term culture. All cells were derived from isolated adipose tissue without the use of expensive enzymes for tissue digestion. In contrast to culture in hard tissue culture plates, preadipocytes in the soft 3D environments formed multidimensional interlaced cell contacts, undergoing significant spontaneous lipid accumulation and could be cultured for up to threemonths in maintenance medium. More importantly, the cultured adipose tissue-derived cell bank created here was able to produce injury repair activators that promoted the proliferation of fibroblasts with little fibrosis and the functional differentiation of myoblasts, displaying the potential for use in adipose reconstruction. Thus, the present study demonstrates the potential of a mechanically flexible BC scaffold to generate volume tunable adipose constructs and provides a low-cost and user-friendly strategy for large-scale industrial production of adipose tissue.

Predictive Factors of Postoperative Peritumoral Brain Edema after Meningioma Resection.

BACKGROUND: Postoperative peritumoral brain edema (PTBE) is the progressively exacerbating cerebral edema following meningiomas resection. OBJECTIVE: The study aims to identify the predictive factors of postoperative PTBE. MATERIALS AND METHODS: A retrospective study was conducted on the 117 cases of patients who underwent meningioma. The histopathological features of the tumors were re-assessed according to WHO 2016 classification. Clinical and pathohistological features were analyzed. RESULTS: Thirteen patients (11.1%) were diagnosed having postoperative PTBE. Preoperative seizure (odds ratio [OR] = 6.125, P = 0.039) and histological prominent nucleoli (OR = 3.943, P = 0.039) were the independent risk factors for postoperative PTBE. Meningiomas with a parietal localization were more likely to develop postoperative PTBE (OR = 3.576, P = 0.054). Brain invasion and large tumor volume did not increase complication rate. Preoperative edema index was significantly higher in brain invasive meningiomas (3.0 ± 2.2 versus 1.8 ± 1.7, P = 0.001). Patients having moderate preoperative PTBE were prone to the complication (21.4% versus 7.9%, P = 0.100). CONCLUSIONS: Preoperative seizure were the predictive factors for postoperative PTBE. Careful venous protection during the operation may be helpful, especially for tumors locating in the parietal lobe. Prominent nucleoli observed in postoperative pathology should warrant surgeons' attention. Comprehensive perioperative management is essential for these patients.

Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data.

Unsupervised feature selection (UFS) is a popular technique of reducing the dimensions of high-dimensional data. Previous UFS methods were often designed with the assumption that the whole information in the data set is observed. However, incomplete data sets that contain unobserved information can be often found in real applications, especially in industry. Thus, these existing UFS methods have a limitation on conducting feature selection on incomplete data. On the other hand, most existing UFS methods did not consider the sample importance for feature selection, i.e., different samples have various importance. As a result, the constructed UFS models easily suffer from the influence of outliers. This article investigates a new UFS method for conducting UFS on incomplete data sets to investigate the abovementioned issues. Specifically, the proposed method deals with unobserved information by using an indicator matrix to filter it out the process of feature selection and reduces the influence of outliers by employing the half-quadratic minimization technique to automatically assigning outliers with small or even zero weights and important samples with large weights. This article further designs an alternative optimization strategy to optimize the proposed objective function as well as theoretically and experimentally prove the convergence of the proposed optimization strategy. Experimental results on both real and synthetic incomplete data sets verified the effectiveness of the proposed method compared with previous methods, in terms of clustering performance on the low-dimensional space of the high-dimensional data.