Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Dysfunction of CD8+ T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients.

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

Spatial features of specific CD103+CD8+ tissue-resident memory T cell subsets define the prognosis in patients with non-small cell lung cancer.

BACKGROUND: Tissue-resident memory T (TRM) cells can reside in the tumor microenvironment and are considered the primary response cells to immunotherapy. Heterogeneity in functional status and spatial distribution may contribute to the controversial role of TRM cells but we know little about it. METHODS: Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of TRM cell subsets were recognized in the tissue from 274 patients with NSCLC after radical surgery. By integrating multiple machine learning methods, we constructed a TRM-based spatial immune signature (TRM-SIS) to predict the prognosis. Furthermore, we conducted a CD103-related gene set enrichment analysis (GSEA) and verified its finding by another mIF panel (CD8, CD103, CK, CD31, Hif-1α). RESULTS: The density of TRM cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of TRM cell subsets was defined, including TRM1 (PD-1-Tim-3-TRM), TRM2 (PD-1+Tim-3-TRM), TRM3 (PD-1-Tim-3+TRM) and TRM4 (PD-1+Tim-3+TRM). The cytotoxicity of TRM2 was the strongest while that of TRM4 was the weakest. Compare with TRM1 and TRM2, TRM3 and TRM4 had better infiltration and stronger interaction with cancer cells. The TRM-SIS was an independent prognostic factor for disease-free survival [HR = 2.43, 95%CI (1.63-3.60), P < 0.001] and showed a better performance than the TNM staging system for recurrence prediction. Furthermore, by CD103-related GSEA and mIF validation, we found a negative association between tumor angiogenesis and infiltration of TRM cells. CONCLUSIONS: These findings reveal a significant heterogeneity in the functional status and spatial distribution of TRM cells, and support it as a biomarker for the prognosis of NSCLC patients. Regulating TRM cells by targeting tumor angiogenesis may be a potential strategy to improve current immunotherapy.

Clustered surface amino acid residues modulate the acid stability of GH10 xylanase in fungi.

Acidic xylanases are widely used in industries such as biofuels, animal feeding, and fruit juice clarification due to their tolerance to acidic environments. However, the factors controlling their acid stability, especially in GH10 xylanases, are only partially understood. In this study, we identified a series of thermostable GH10 xylanases with optimal temperatures ranging from 70 to 90 °C, and among these, five enzymes (Xyn10C, Xyn10RE, Xyn10TC, Xyn10BS, and Xyn10PC) exhibited remarkable stability at pH 2.0. Our statistical analysis highlighted several factors contributing to the acid stability of GH10 xylanases, including electrostatic repulsion, π-π stacking, ionic bonds, hydrogen bonds, and Van der Waals interactions. Furthermore, through mutagenesis studies, we uncovered that acid stability is influenced by a complex interplay of amino acid residues. The key amino acid sites determining the acid stability of GH10 xylanases were thus elucidated, mainly concentrated in two surface regions behind the enzyme active center. Notably, the critical residues associated with acid stability markedly enhanced Xyn10RE's thermostability by more than sixfold, indicating a potential acid-thermal interplay in GH10 xylanases. This study not only reported a series of valuable genes but also provided a range of modification targets for enhancing the acid stability of GH10 xylanases. KEY POINTS: • Five acid stable and thermostable GH10 xylanases were reported. • The key amino acid sites, mainly forming two enriched surface regions behind the enzyme active center, were identified responsible for acid stability of GH10 xylanases. • The finding revealed interactive amino acid sites, offering a pathway for synergistic enhancement of both acid stability and thermostability in GH10 xylanase modifications.

The role of spatial interplay patterns between PD-L1-positive tumor cell and T cell in recurrence of locally advanced non-small cell lung cancer.

PURPOSE: To explore the relationship between the spatial interaction of programmed death-ligand 1(PD-L1)-positive tumor cell and T cell with specific functions and the recurrence of non-small cell lung cancer (NSCLC) and optimize prognostic stratification. MATERIALS AND METHODS: This study retrospectively included 104 patients with locally advanced NSCLC who underwent radical surgery. Tissue microarrays were constructed including tumor center (TC) and invasion margin (IM), and CK/CD4/CD8/PD-L1/programmed death-1 (PD-1) was labeled using multiplex immunofluorescence to decipher the counts and spatial distribution of tumor cells and T cells. The immune microenvironment and recurrence stratification were characterized using the Mann-Whitney U test and Cox proportional hazards model. RESULT: Compared with the IM, the proportion of tumor cells (especially PD-L1+) was increased in the TC, while T cells (especially PD-1+) were decreased. An increase in TC PD-1+ CD8 T cells promoted relapse (HR = 2.183), while PD-L1+ tumor cells alone or in combination with T cells had no predictive value for relapse. In addition, in both TC and IM, CD8 were on average closer to PD-L1+ tumor cells than CD4, especially exhausted CD8. The effective density and percentage of PD-1+ CD4 T cells interacting with PD-L1+ tumor cells in the IM were both associated with recurrence, and the HRs increased sequentially (HRs were 2.809 and 4.063, respectively). Patients with low PD-1+CD4 count combined high PD-1+CD4 effective density showed significantly poorer RFS compared to those with high PD-1+CD4 count combined low PD-1+CD4 effective density, in both the TC and IM regions (HRs were 5.810 and 8.709, respectively). CONCLUSION: Assessing the relative spatial proximity of PD-1/PD-L1 contributes to a deeper understanding of tumor immune escape and generates prognostic information in locally advanced NSCLC patients.

Functional status and spatial architecture of tumor-infiltrating CD8+ T cells are associated with lymph node metastases in non-small cell lung cancer.

BACKGROUND: Anti-PD-(L)1 immunotherapy has been recommended for non-small cell lung cancer (NSCLC) patients with lymph node metastases (LNM). However, the exact functional feature and spatial architecture of tumor-infiltrating CD8 + T cells remain unclear in these patients. METHODS: Tissue microarrays (TMAs) from 279 IA-IIIB NSCLC samples were stained by multiplex immunofluorescence (mIF) for 11 markers (CD8, CD103, PD-1, Tim3, GZMB, CD4, Foxp3, CD31, αSMA, Hif-1α, pan-CK). We evaluated the density of CD8 + T-cell functional subsets, the mean nearest neighbor distance (mNND) between CD8 + T cells and neighboring cells, and the cancer-cell proximity score (CCPS) in invasive margin (IM) as well as tumor center (TC) to investigate their relationships with LNM and prognosis. RESULTS: The densities of CD8 + T-cell functional subsets, including predysfunctional CD8 + T cells (Tpredys) and dysfunctional CD8 + T cells (Tdys), in IM predominated over those in TC (P < 0.001). Multivariate analysis identified that the densities of CD8 + Tpredys cells in TC and CD8 + Tdys cells in IM were significantly associated with LNM [OR = 0.51, 95%CI (0.29-0.88), P = 0.015; OR = 5.80, 95%CI (3.19-10.54), P < 0.001; respectively] and recurrence-free survival (RFS) [HR = 0.55, 95%CI (0.34-0.89), P = 0.014; HR = 2.49, 95%CI (1.60-4.13), P = 0.012; respectively], independent of clinicopathological factors. Additionally, shorter mNND between CD8 + T cells and their neighboring immunoregulatory cells indicated a stronger interplay network in the microenvironment of NSCLC patients with LNM and was associated with worse prognosis. Furthermore, analysis of CCPS suggested that cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) selectively hindered CD8 + T cells from contacting with cancer cells, and were associated with the dysfunction of CD8 + T cells. CONCLUSION: Tumor-infiltrating CD8 + T cells were in a more dysfunctional status and in a more immunosuppressive microenvironment in patients with LNM compared with those without LNM.

Adaptive Admixture of HLA Class I Allotypes Enhanced Genetically Determined Strength of Natural Killer Cells in East Asians.

Human natural killer (NK) cells are essential for controlling infection, cancer, and fetal development. NK cell functions are modulated by interactions between polymorphic inhibitory killer cell immunoglobulin-like receptors (KIR) and polymorphic HLA-A, -B, and -C ligands expressed on tissue cells. All HLA-C alleles encode a KIR ligand and contribute to reproduction and immunity. In contrast, only some HLA-A and -B alleles encode KIR ligands and they focus on immunity. By high-resolution analysis of KIR and HLA-A, -B, and -C genes, we show that the Chinese Southern Han (CHS) are significantly enriched for interactions between inhibitory KIR and HLA-A and -B. This enrichment has had substantial input through population admixture with neighboring populations, who contributed HLA class I haplotypes expressing the KIR ligands B*46:01 and B*58:01, which subsequently rose to high frequency by natural selection. Consequently, over 80% of Southern Han HLA haplotypes encode more than one KIR ligand. Complementing the high number of KIR ligands, the CHS KIR locus combines a high frequency of genes expressing potent inhibitory KIR, with a low frequency of those expressing activating KIR. The Southern Han centromeric KIR region encodes strong, conserved, inhibitory HLA-C-specific receptors, and the telomeric region provides a high number and diversity of inhibitory HLA-A and -B-specific receptors. In all these characteristics, the CHS represent other East Asians, whose NK cell repertoires are thus enhanced in quantity, diversity, and effector strength, likely augmenting resistance to endemic viral infections.

Cerebrovascular Dysregulation in Patients with Glioma Assessed with Time-shifted BOLD fMRI.

Background Microscopic vascular events, such as neovascularization and neurovascular uncoupling, are common in cerebral glioma. Mapping the cerebrovascular network remodeling at the macroscopic level may provide an alternative approach to assess hemodynamic dysregulation in patients with glioma. Purpose To investigate cerebrovascular dynamics and their relevance to tumor aggressiveness by using time-shift analysis (TSA) of the systemic low-frequency oscillation (sLFO) of the resting-state blood oxygenation level-dependent signal and a decision tree model. Materials and Methods In this retrospective study, 96 patients with histologically confirmed cerebral glioma were consecutively included (March 2012 to February 2017). TSA was performed to quantify the temporal properties of sLFO signals. Alteration in the time-shift properties was assessed in the tumor region and the contralesional hemisphere relative to the brains of healthy controls by using the Mann-Whitney U test. A decision tree model based on time-shift features was developed to predict the World Health Organization (WHO) glioma grade. Results A total of 88 patients with glioma (WHO grade II, 45; grade III, 21; grade IV, 22; mean age, 42 years; age range, 20-73 years; 51 men) and 40 healthy individuals from the 1000 Functional Connectomes Project (mean age, 32 years; age range, 24-49 years; 19 men) were included. The sLFO of the brain tissues was characterized by increased time shift in the tumor region and enhanced correlation with the global reference signal in the contralesional hemisphere compared with healthy brains. The proportion of tumor voxels with negative correlation to the reference signal significantly increased with the glioma malignancy grade. The decision tree model achieved an accuracy of 91% (80 of 88 patients) in predicting the glioma malignancy grade at the individual level (P = .004) based on the time-shift features. Conclusion Gliomas induced grade-specific cerebrovascular dysregulation in the entire brain, with altered time-shift features of systemic low-frequency oscillation signals. © RSNA, 2022 Online supplemental material is available for this article.

Effective Generation of Glucosylpiericidins with Selective Cytotoxicities and Insights into Their Biosynthesis.

Exploring unknown glycosyltransferases (GTs) is important for compound structural glycodiversification during the search for drug candidates. Piericidin glycosides have been reported to have potent bioactivities; however, the GT responsible for piericidin glucosylation remains unknown. Herein, BmmGT1, a macrolide GT with broad substrate selectivity and isolated from Bacillus methylotrophicus B-9987, was found to be able to glucosylate piericidin A1 in vitro. Next, the codon-optimized GT gene sbmGT1, which was designed based on BmmGT1, was heterologously expressed in the piericidin producer Streptomyces youssoufiensis OUC6819. Piericidin glycosides thus significantly accumulated, leading to the identification of four new glucopiericidins (compounds 3, 4, 6, and 7). Furthermore, using BmmGT1 as the probe, GT1507 was identified in the genome of S. youssoufiensis OUC6819 and demonstrated to be associated with piericidin glucosylation; the overexpression of this gene led to the identification of another new piericidin glycoside, N-acetylglucosamine-piericidin (compound 8). Compounds 4, 7, and 8 displayed cytotoxic selectivity toward A549, A375, HCT-116, and HT-29 solid cancer cell lines compared to the THP-1 lymphoma cell line. Moreover, database mining of GT1507 homologs revealed their wide distribution in bacteria, mainly in those belonging to the high-GC Gram-positive and Firmicutes clades, thus representing the potential for identification of novel tool enzymes for compound glycodiversification. IMPORTANCE Numerous bioactive natural products are appended with sugar moieties and are often critical for their bioactivities. Glycosyltransferases (GTs) are powerful tools for the glycodiversification of natural products. Although piericidin glycosides display potent bioactivities, the GT involved in glucosylation is unclear. In this study, five new piericidin glycosides (compounds 3, 4, 6, 7, and 8) were generated following the overexpression of GT-coding genes in a piericidin producer. Three of them (compounds 4, 7, and 8) displayed cytotoxic selectivity. Notably, GT1507 was demonstrated to be related to piericidin glucosylation in vivo. Furthermore, mining of GT1507 homologs from the GenBank database revealed their wide distribution across numerous bacteria. Our findings would greatly facilitate the exploration of GTs to glycodiversify small molecules in the search for drug candidates.

First identification and genomic characterization of equine hepacivirus subtype 2 in China.

Equine hepacivirus (EqHV) is a newly discovered hepatitis C virus-like virus that can infect equines. EqHV strains circulating worldwide have been classified into subtypes 1-3. In previous studies, we detected the presence of EqHV strains of subtype 1 and 3 in China. To determine whether EqHV strains of subtype 2 are prevalent in China, serum samples were collected from 133 racehorses in Guangdong province in 2021 and were tested for EqHV RNA by RT-PCR, and the positive rate was 9% (12/133). Sequencing of the NS3 gene revealed that one field strain (GD2021) had a high degree of genetic similarity to EqHV strains of subtype 2. Subsequent genome sequencing and analysis demonstrated that strain GD2021 belongs to subtype 2. The present study enriches our knowledge about the genetic diversity of EqHV in China.

Genomic sequencing and characterization of a novel group of canine bufaviruses from Henan province, China.

Canine bufavirus (CBuV) is a novel protoparvovirus of dogs that was first reported in 2018 in Italy. The prevalence and genetic diversity of CBuV in China are not clear. In this study, a total of 115 canine fecal samples were collected from northern China in 2019, and two of the samples tested positive for CBuV DNA by PCR. These two field CBuV strains were designated Henan38 and Henan44. The complete genomic sequences of Henan38 and Henan44 were obtained by gap-filling PCR, sequenced, and assembled. Phylogenetic analysis demonstrated that the two strains clustered together in a novel group that was distant from previously reported CBuV strains. This study will strengthen our understanding of the epidemiology and genetic diversity of CBuV in China.

First identification and genomic characterization of equine hepacivirus sub-type 3 strain in China.

Equine Hepacivirus (EqHV) is a newly discovered equine virus that is classified under the Hepacivirus genus of the Flaviviridae family. There are three sub-types of EqHV worldwide namely; sub-types 1-3. The majority of EqHV sub-type 1 strains were found in China. While different sub-types have been found in Japan and USA, therefore, to investigate whether the other sub-types of EqHV strains were present in China, a total of 60 horse serum samples were collected and screened for EqHV RNA through RT-PCR. The results revealed that 19 serum samples were RNA-positive (19/60) and the EqHV detection rate was 31.67%. One EqHV strain named GD23 was obtained and its near-complete genome sequence was acquired. Analysis of nucleotide p-distance with reference to the entire polyprotein gene revealed that GD23 was classified into sub-type 3. In addition, the phylogenetic analysis demonstrated that GD23 was clustered together with EqHV strains of sub-type 3 in other countries. The present study is the first to identify an EqHV sub-type 3 strain in China.

Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors.

BACKGROUND: Compensations are commonly observed in patients with stroke when they engage in reaching without supervision; these behaviors may be detrimental to long-term functional improvement. Automatic detection and reduction of compensation cab help patients perform tasks correctly and promote better upper extremity recovery. OBJECTIVE: Our first objective is to verify the feasibility of detecting compensation online using machine learning methods and pressure distribution data. Second objective was to investigate whether compensations of stroke survivors can be reduced by audiovisual or force feedback. The third objective was to compare the effectiveness of audiovisual and force feedback in reducing compensation. METHODS: Eight patients with stroke performed reaching tasks while pressure distribution data were recorded. Both the offline and online recognition accuracy were investigated to assess the feasibility of applying a support vector machine (SVM) based compensation detection system. During reduction of compensation, audiovisual feedback was delivered using virtual reality technology, and force feedback was delivered through a rehabilitation robot. RESULTS: Good classification performance was obtained in online compensation recognition, with an average F1-score of over 0.95. Based on accurate online detection, real-time feedback significantly decreased compensations of patients with stroke in comparison with no-feedback condition (p < 0.001). Meanwhile, the difference between audiovisual and force feedback was also significant (p < 0.001) and force feedback was more effective in reducing compensation in patients with stroke. CONCLUSIONS: Accurate online recognition validated the feasibility of monitoring compensations using machine learning algorithms and pressure distribution data. Reliable online detection also paved the way for reducing compensations by providing feedback to patients with stroke. Our findings suggested that real-time feedback could be an effective approach to reducing compensatory patterns and force feedback demonstrated a more enviable potential compared with audiovisual feedback.

[Identification of a novel KIR3DL3*064 allele by cDNA cloning and sequencing].

OBJECTIVE: To report on a novel KIR3DL3 allele identified in a southern Han Chinese individual. METHODS: Peripheral blood sample was collected from a voluntary blood donor with inconclusive result by KIR3DL3 sequence-based typing (SBT). Total mRNA was extracted and subjected to reverse transcription to obtain KIR3DL3 cDNA, which was then amplified by PCR with a pair of KIR3DL3-specific primers. The product was subjected to cDNA cloning and sequencing. RESULTS: cDNA cloning and sequencing have identified a wide-type KIR3DL3*00802 allele and a novel KIR3DL3*064 allele. The latter differed from KIR3DL3*00601 by a missense variant at codon 374[c.1184 C>T (p.Thr374Ile)] in exon 9. The novel KIR3DL3 allele has been officially assigned by the KIR subcommittee of World Health Organization Nomenclature Committee for factors of HLA system. CONCLUSION: cDNA cloning and sequencing may be used to distinguish inconclusive results in KIR3DL3 SBT in order to identify novel KIR alleles.

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms.

BACKGROUND: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. METHODS: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-and-down reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. RESULTS: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1-score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. CONCLUSIONS: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG). However, to our knowledge, topological relationships between individual channels have not yet been considered in any study. In this work, we introduced a novel architecture that exploits the topology of the human brain to perform auditory spatial attention detection (ASAD) from EEG signals. METHODS: We propose EEG-Graph Net, an EEG-graph convolutional network, which employs a neural attention mechanism. This mechanism models the topology of the human brain in terms of the spatial pattern of EEG signals as a graph. In the EEG-Graph, each EEG channel is represented by a node, while the relationship between two EEG channels is represented by an edge between the respective nodes. The convolutional network takes the multi-channel EEG signals as a time series of EEG-graphs and learns the node and edge weights from the contribution of the EEG signals to the ASAD task. The proposed architecture supports the interpretation of the experimental results by data visualization. RESULTS: We conducted experiments on two publicly available databases. The experimental results showed that EEG-Graph Net significantly outperforms the state-of-the-art methods in terms of decoding performance. In addition, the analysis of the learned weight patterns provides insights into the processing of continuous speech in the brain and confirms findings from neuroscientific studies. CONCLUSION: We showed that modeling brain topology with EEG-graphs yields highly competitive results for auditory spatial attention detection. SIGNIFICANCE: The proposed EEG-Graph Net is more lightweight and accurate than competing baselines and provides explanations for the results. Also, the architecture can be easily transferred to other brain-computer interface (BCI) tasks.

Attention-guided graph structure learning network for EEG-enabled auditory attention detection.

Objective: Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. This study aims to overcome the challenges of extracting discriminative feature representations from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, particularly focusing on the intrinsic relationships between different EEG channels.Approach: We propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals.Main result: We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.Significance: This study presents a novel approach for examining brain functional connections, improving AAD performance in low-latency settings, and supporting the development of neuro-steered hearing aids.

Modified spatial architecture of regulatory T cells after neoadjuvant chemotherapy in non-small cell lung cancer patients.

It is crucial to decipher the modulation of regulatory T cells (Tregs) in tumor microenvironment (TME) induced by chemotherapy, which may contribute to improving the efficacy of neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer (NSCLC). We retrospectively collected specimens from patients with II-III NSCLC, constituting two cohorts: a neoadjuvant chemotherapy (NAC) cohort (N = 141) with biopsy (N = 58) and postoperative specimens (N = 141), and a surgery-only cohort (N = 122) as the control group. Then, the cell density (Dens), infiltration score (InS), and Treg-cell proximity score (TrPS) were conducted using a panel of multiplex fluorescence staining (Foxp3, CD4, CD8, CK, CD31, ɑSMA). Subsequently, the association of Tregs with cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) was analyzed. Patients with NAC treatment have a higher density of Tregs in both paired (P < 0.001) and unpaired analysis (P = 0.022). Additionally, patients with NAC treatment showed higher infiltration score (paired, P < 0.001; unpaired, P = 0.014) and more CD8+T cells around Tregs (paired/unpaired, both P < 0.001). Subgroup analysis indicated that tumors with a diameter of ≤ 5 cm exhibited increase in both Dens(Treg) and InS(Treg), and gemcitabine, pemetrexed and taxel enhanced Dens(Treg) and TrPS(CD8) following NAC. Multivariate analysis identified that the Dens(Tregs), InS(Tregs) and TrPS(CD8) were significantly associated with better chemotherapy response [OR = 8.54, 95%CI (1.69, 43.14), P = 0.009; OR = 7.14, 95%CI (1.70, 30.08), P = 0.024; OR = 5.50, 95%CI (1.09, 27.75), P = 0.039, respectively] and positive recurrence-free survival [HR = 3.23, 95%CI (1.47, 7.10), P = 0.004; HR = 2.70; 95%CI (1.27, 5.72); P = 0.010; HR = 2.55, 95%CI (1.21, 5.39), P = 0.014, respectively]. Moreover, TrPS(CD8) and TrPS(CD4) were negatively correlated with the CMVs and CAFs. These discoveries have deepened our comprehension of the immune-modulating impact of chemotherapy and underscored that the modified spatial landscape of Tregs after chemotherapy should be taken into account for personalized immunotherapy, aiming to ultimately improve clinical outcomes in patients with NSCLC.

The STING-mediated antiviral effect of fucoidan from Durvillaea antarctica.

Fucoidans have attracted increasing attention due to their minimal toxicity and various biological activities, such as antioxidant, anti-inflammatory, anti-tumor and immunomodulatory effects. In this study, the antiviral effect and mechanism of fucoidan (FU) derived from Durvillaea antarctica were explored in vitro. The results demonstrated that FU effectively inhibited the infection of both RNA virus (VSV) and DNA virus (HSV-1). The potential antiviral mechanism of FU is to trigger the production of type I IFN (IFN-I) and IFN-stimulated genes dependent on the cytoplasmic DNA adaptor STING (stimulator of interferon genes), and to enhance innate immune response via activating the STING-TBK1-IRF3 pathway. FU possesses the potential to be an antiviral and immunomodulatory agent in the future.

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain.

Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory attention detection (AAD) has attracted considerable interest recently. Despite much progress, the performance of traditional AAD decoders remains to be improved, especially in low-latency settings. State-of-the-art AAD decoders based on deep neural networks generally lack the intrinsic temporal coding ability in biological networks. In this study, we first propose a bio-inspired spiking attentional neural network, denoted as BSAnet, for decoding auditory attention. BSAnet is capable of exploiting the temporal dynamics of EEG signals using biologically plausible neurons and an attentional mechanism. Experiments on two publicly available datasets confirm the superior performance of BSAnet over other state-of-the-art systems across various evaluation conditions. Moreover, BSAnet imitates realistic brain-like information processing, through which we show the advantage of brain-inspired computational models.