Time-Dependent Extension of Grimme's Continuous Chirality Measure for Electronic Chirality Flips in Femto- and Attosecond Time Domains.

Grimme's Continuous Chirality Measure ( C C M ${CCM}$ ) was developed for comparisons of the chirality of the electronic wave functions of molecules, typically in their ground states. For example, C C M = 14 . 5 ${CCM=14.5}$ , 1 . 2 ${1.2}$ and 0 . 0 ${0.0}$ for alanine, hydrogen-peroxide, and for achiral molecules, respectively. Well-designed laser pulses can excite achiral molecules from the electronic ground state to time-dependent chiral superposition states, with chirality flips in the femto- or even attosecond (fs or as) time domains. Here we provide a time-dependent extension C C M t ${CCM\left(t\right)}$ of Grimme's C C M ${CCM}$ for trailing the electronic chirality flips. As examples, we consider two laser driven electronic wavefunctions which represent flips between opposite electronic enantiomers of oriented NaK within 4 . 76 f s ${4.76\ {\rm f}{\rm s}}$ and 433 a s ${433\ {\rm a}{\rm s}}$ . The corresponding C C M t ${CCM\left(t\right)}$ vary respectively from 14 . 5 ${14.5}$ or from 13 . 3 ${13.3}$ to 0 . 0 ${0.0}$ , and back.

Efficacy and safety of nab-paclitaxel or combined nab-paclitaxel and immune checkpoint inhibitors in relapsed small-cell lung cancer.

Background: Most patients with small-cell lung cancer (SCLC) experience disease progression after first-line chemotherapy. Notably, nab-paclitaxel monotherapy has antitumor activity in relapsed SCLC. Objective: This study evaluated the efficacy and safety of combined of nab-paclitaxel and immune checkpoint inhibitors (ICIs) in relapsed SCLC. Design: We retrospectively analyzed patients with relapsed SCLC who received nab-paclitaxel or combined nab-paclitaxel and ICIs (anti-programmed death-1, PD-1 or anti-programmed cell death 1 ligand, PD-L1) between February 2017 and September 2021. Methods: Efficacy and safety data were collected from electronic health records. Progression-free survival (PFS) and overall survival (OS) were assessed using the Kaplan-Meier method and a standard log-rank test. Results: We included 56 patients with relapsed SCLC, of which 29 received nab-paclitaxel alone (Group A), and 27 received combined nab-paclitaxel and ICIs (Group B). Baseline characteristics were similar between the two groups. Group B had a numerically higher objective response rate than Group A (40.7% versus 17.2%; p = 0.052). However, combined nab-paclitaxel and ICIs failed to demonstrate survival superiority over nab-paclitaxel monotherapy [median PFS: 3.2 months versus 2.8 months (p = 0.5225); median OS: 11.0 months versus 9.3 months (p = 0.7298)]. The safety profiles of Groups A and B were both tolerable. Conclusion: This study indicated that compared with nab-paclitaxel monotherapy, combined nab-paclitaxel and ICIs failed to improve survival in relapsed SCLC.

Few-Shot Partial Multi-View Learning.

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://github.com/zhouyuan888888/UGDA.

Comparability of JAK2 p.V617F allele burden in peripheral blood and that in bone marrow in patients with suspected myeloproliferative neoplasms: a single-center prospective study.

PURPOSE: To detect JAK2 p.V617F and measure allele burden in peripheral blood (PB) and bone marrow (BM) aspirates in patients with suspected myeloproliferative neoplasms (MPNs). METHODS: Patients with suspected MPNs were prospectively enrolled between August 2017 and May 2019, and their PB and BM were collected during the same period. Quantitative fluorescence polymerase chain reaction (PCR) was used to detect the copy number of JAK2 wild type and the V617F mutant; the JAK2 V617F proportion was also calculated. The JAK2 p.V617F proportion in PB was compared to that in BM by Chi-square test. RESULTS: Among 54 patients with suspected MPNs, 43 of them were eligible for analysis. The JAK2 p.V617F in PB had the same sensitivity and specificity as BM (all P>0.05). The Chi-square test suggested that the JAK2 p.V617F allele burden of PB was comparable to that of BM (Spearman Correlation =0.986; P=0.000). CONCLUSION: PB could be used as an alternative to BM for JAK2 p.V617F measurement in patients with suspected MPNs.

MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection.

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

Clinical value of alveolar lavage supernatant specimens in the detection of the EGFR gene mutation in patients with non-small cell lung carcinoma.

Background: This study sought to compare the consistency of the epidermal growth factor receptor (EGFR) gene mutation detection results in the supernatant of alveolar lavage specimens to the tissue sample results, and the consistency of the blood EGFR gene mutation detection results to the tissue detection results. Methods: In total, 29 patients with non-small cell lung carcinoma (NSCLC) were selected, and their bronchoalveolar lavage fluid (BALF) was collected. The supernatant and precipitate were separated by centrifugation. Deoxyribonucleic acid (DNA) was extracted from the supernatant, and blood and tumor tissues were collected to detect patients' EGFR gene mutation status. Results: Of the 29 enrolled patients, 12 of the 23 tissue-biopsy patients (52.2%) were positive for EGFR mutations, 11 of the 28 blood-test patients (39.2%) were positive for EGFR mutations, and 13 of the 29 cases of the BALF-test patients (44.8%) were positive for EGFR mutations. The most common mutations were the exon 19 deletion mutation and the L858R point mutation. The EGFR gene mutation rate was higher in female, young, non-smoker, and stage IIIB patients (than stage IV patients), but the differences were not statistically significant (all P>0.05). Of the 29 NSCLC patients tested for the EGFR gene mutation, the BALF supernatant and blood results were the same for 27 patients (coincidence rate: 93.10%). Of the 23 of the 29 enrolled patients tested for the EGFR gene mutation, the BALF supernatant and tissue test results were the same for 21 patients (coincidence rate: 91.30%). Further, the blood-test and the tissue test results were the same for 20 patients (coincidence rate: 86.96%). Conclusions: The EGFR gene mutation rate was high in NSCLC patients. The coincidence rate of the EGFR gene mutation detection results between BALF supernatant and tumor tissues was slightly higher than that of the blood and tumor tissue EGFR gene mutation detection results.

A systematic review and meta-analysis on the risk factors of acute myeloid leukemia.

Background: Acute myeloid leukemia (AML) is one of the main types of leukemia that threatens the life and health of patients. A large number of clinical studies have been conducted on the etiology of the disease. However, there are few evidence-based medical studies and no definitive treatment guidelines. Methods: Related articles were searched from Medline, Excerpta Medica Database (EMBASE), EBSCO, OVID, Chinese Biology Medicine Disc (CBMDISC), and Wanfang databases. The search time limit was from the establishment of the database to September 2021. The search terms were as follows: acute myeloid leukemia, AML, electromagnetic field, case-control study, cohort study, and risk factors. All literatures were included according to PICOS standards, and the risk of deviation and literature quality were assessed. RevMan 5.3 software was used for meta-analysis. Results: The 10 articles included were of high quality and low bias risk. The research results showed that compared with healthy people, among the risk factors for AML, family tumor history [risk ratio (RR) =0.98; 95% confidence interval (CI): (0.57, 1.69); Z=0.08; P=0.94] and the hepatitis B virus (HBV) infection rate [odds ratio (OR) =1.34; 95% CI: (0.57, 3.13); Z=0.68; P=0.50] showed no significant differences, but the hepatitis C virus (HCV) infection rate [OR =1.60; 95% CI: (1.17, 2.19); Z=2.92; P=0.003] and environmental exposure rate [OR =1.49; 95% CI: (1.01, 2.21); Z=2.02; P=0.04] increased significantly. Conclusions: A total of 10 articles were included to analyze AML risk factors and related content. The results suggested that HCV infection and environmental exposure history such as home decoration were risk factors for AML.

Hierarchical Prototype Refinement With Progressive Inter-Categorical Discrimination Maximization for Few-Shot Learning.

Metric-based few-shot learning categorizes unseen query instances by measuring their distance to the categories appearing in the given support set. To facilitate distance measurement, prototypes are used to approximate the representations of categories. However, we find prototypical representations are generally not discriminative enough to represent the discrepancy of inter-categorical distribution of queries, thereby limiting the classification accuracy. To overcome this issue, we propose a new Progressive Hierarchical-Refinement (PHR) method, which effectively refines the discrimination of prototypes by conducting the Progressive Discrimination Maximization strategy based on the hierarchical feature representations. Specifically, we first encode supports and queries into the representation space of spatial level, global level, and semantic level. Then, the refining coefficients are constructed by exploring the metric information contained in these hierarchical embedding spaces simultaneously. Under the guidance of the refining coefficients, the meta-refining loss progressively maximizes the discrimination degree of inter-categorical prototypical representations. In addition, the refining vectors are adopted to further enhance the representations of prototypes. In this way, the metric-based classification can be more accurate. Our PHR method shows the competitive performance on the miniImagenet, CIFAR-FS, FC100, and CUB datasets. Moreover, PHR presents good compatibility. It can be incorporated with other few-shot learning models, making them more accurate.

Real-Time Semantic Segmentation via Spatial-Detail Guided Context Propagation.

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g., semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the spatial-detail guided context propagation network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Cityscapes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768 x 1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://github.com/zhouyuan888888/SGCPNet.

FGF2 is overexpressed in asthma and promotes airway inflammation through the FGFR/MAPK/NF-κB pathway in airway epithelial cells.

BACKGROUND: Airway inflammation is the core pathological process of asthma, with the key inflammatory regulators incompletely defined. Recently, fibroblast growth factor 2 (FGF2) has been reported to be an inflammatory regulator; however, its role in asthma remains elusive. This study aimed to investigate the immunomodulatory role of FGF2 in asthma. METHODS: First, FGF2 expression was characterised in clinical asthma samples and the house dust mite (HDM)-induced mouse chronic asthma model. Second, recombinant mouse FGF2 (rm-FGF2) protein was intranasally delivered to determine the effect of FGF2 on airway inflammatory cell infiltration. Third, human airway epithelium-derived A549 cells were stimulated with either HDM or recombinant human interleukin-1ß (IL-1ß) protein combined with or without recombinant human FGF2. IL-1ß-induced IL-6 or IL-8 release levels were determined using enzyme-linked immunosorbent assay, and the involved signalling transduction was explored via Western blotting. RESULTS: Compared with the control groups, the FGF2 protein levels were significantly upregulated in the bronchial epithelium and alveolar areas of clinical asthma samples (6.70 ± 1.79 vs. 16.32 ± 2.40, P = 0.0184; 11.20 ± 2.11 vs. 21.00 ± 3.00, P = 0.033, respectively) and HDM-induced asthmatic mouse lung lysates (1.00 ± 0.15 vs. 5.14 ± 0.42, P < 0.001). Moreover, FGF2 protein abundance was positively correlated with serum total and anti-HDM IgE levels in the HDM-induced chronic asthma model (R2 = 0.857 and 0.783, P = 0.0008 and 0.0043, respectively). Elevated FGF2 protein was mainly expressed in asthmatic bronchial epithelium and alveolar areas and partly co-localised with infiltrated inflammatory cell populations in HDM-induced asthmatic mice. More importantly, intranasal instillation of rm-FGF2 aggravated airway inflammatory cell infiltration (2.45 ± 0.09 vs. 2.88 ± 0.14, P = 0.0288) and recruited more subepithelial neutrophils after HDM challenge [(110.20 ± 29.43) cells/mm2 vs. (238.10 ± 42.77) cells/mm2, P = 0.0392] without affecting serum IgE levels and Th2 cytokine transcription. In A549 cells, FGF2 was upregulated through HDM stimulation and promoted IL-1ß-induced IL-6 or IL-8 release levels (up to 1.41 ± 0.12- or 1.44 ± 0.14-fold change vs. IL-1ß alone groups, P = 0.001 or 0.0344, respectively). The pro-inflammatory effect of FGF2 is likely mediated through the fibroblast growth factor receptor (FGFR)/mitogen-activated protein kinase (MAPK)/nuclear factor kappa B (NF-κB) pathway. CONCLUSION: Our findings suggest that FGF2 is a potential inflammatory modulator in asthma, which can be induced by HDM and acts through the FGFR/MAPK/NF-κB pathway in the airway epithelial cells.