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"Weiming Li"
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مناشدة الحلم الفني
يتناول كتاب (مناشدة الحلم الفني) والذي قام بتأليفه (يوان مو، وانغ ويمينغ، بنغ بوه) في حوالي (56، 76) صفحة من القطع المتوسط موضوع (الفنون في الصين ودراسة وتعليم اللغة الصينية) مستعرضا ما يحويه الكتاب من موضوع سطع وهج النفوذ الصيني على الصعيد الدولي، وبات كثير من الناس يرغب في التعرف إلى الصين وإلى حياة شعبها اليومية، ومع انتشار معاهد كونفوشيوس في شتى أرجاء المعمورة، ازداد عدد الطالب وال سيما الصغار الذين يرغبون بتعلم اللغة الصينية.
Book
Analysis of the influencing factors of vitality and built environment of shopping centers based on mobile-phone signaling data
Nowadays, shopping centers not only provides commercial function but also serve as a public space. In this article, we take Nanshan district of Shenzhen as an example, based on the characteristics information of people activities provided by Mobile-phone Signaling Data, using the standard deviation ellipse method to classify the direction of people in shopping centers, and then applying the entropy weighting method to analyze the vitality factors of shopping centers from three perspectives: visitors’ density, revisit rate, and the average length of stay. Finally, we analyzed the influence factors of the surrounding built environment based on correlation analysis to discuss the results with field survey data. The results show that (1) shopping centers in Nanshan District are classified into wide-area type and geo-regional type according to the gathering of visitors. The shopping centers with high comprehensive vitality are basically wide-area type. (2) The factors influencing the vitality of shopping centers are different between wide-area type and geo-regional type. The vitality of wide-area type is mainly influenced by the traffic accessibility and whether they are located in adjacent to large public spaces such as squares and green public; the vitality of geo-regional type shopping centers is mainly influenced by the number of people within a 15-minute walking circle, and the high-vitality of geo-regional shopping centers are generally located in densely populated areas.
Journal Article
High-Precision Vital Signs Monitoring Method Using a FMCW Millimeter-Wave Sensor
The method of using millimeter-wave radar sensors to detect human vital signs, namely respiration and heart rate, has received widespread attention in non-contact monitoring. These sensors are compact, lightweight, and able to sense and detect various scenarios. However, it still faces serious problems of noisy interference in hardware, which leads to a low signal-to-noise ratio (SNR). We used a frequency-modulated continuous wave (FMCW) radar sensor operating at 77 GHz in an office environment to extract the respiration and heart rate of a person accustomed to sitting in a chair. Indeed, the proposed signal processing includes novel impulse denoising operations and the spectral estimation decision method, which are unique in terms of noise reduction and accuracy improvement. In addition, the proposed method provides high-quality, repeatable respiration and heart rates with relative errors of 1.33% and 1.96% on average compared with the reference values measured by a reliable smart bracelet.
Journal Article
Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals
Sleep staging is crucial for assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets; Sleep-EDF Expanded consists of 153 overnight PSG recordings collected from 78 healthy subjects and ISRUC-Sleep includes 100 PSG recordings collected from 100 subjects diagnosed with various sleep disorders, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms existing sleep staging models in terms of overall performance metrics and per-class F1-scores for several sleep stages, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model’s performance. The results demonstrate the effectiveness and robustness of the proposed model in classifying sleep stages, and highlights its potential to reduce human clinicians’ workload, making sleep assessment and diagnosis more effective. However, the proposed model is subject to several limitations. Firstly, the model is a sequence-to-sequence network, which requires input sequences of EEG epochs. Secondly, the weight coefficients in the loss function could be further optimized to balance the classification performance of each sleep stage. Finally, apart from the channel attention mechanism, incorporating more advanced attention mechanisms could enhance the model’s effectiveness.
Journal Article
On structure testing for component covariance matrices of a high dimensional mixture
By studying the family of p-dimensional scale mixtures, the paper shows for the first time a non-trivial example where the eigenvalue distribution of the corresponding sample covariance matrix does not converge to the celebrated Marčenko–Pastur law. A different and new limit is found and characterized. The reasons for failure of the Marčenko–Pastur limit in this situation are found to be a strong dependence between the p-co-ordinates of the mixture. Next, we address the problem of testing whether the mixture has a spherical covariance matrix. To analyse the traditional John's-type test we establish a novel and general central limit theorem for linear statistics of eigenvalues of the sample covariance matrix. It is shown that John's test and its recent high dimensional extensions both fail for high dimensional mixtures, precisely because of the different spectral limit above. As a remedy, a new test procedure is constructed afterwards for the sphericity hypothesis. This test is then applied to identify the covariance structure in model-based clustering. It is shown that the test has much higher power than the widely used integrated classification likelihood and Bayesian information criteria in detecting non-spherical component covariance matrices of a high dimensional mixture.
Journal Article
Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest
Background
Mature fruit cracking during the normal season in African Pride (AP) atemoya is a major problem in postharvest storage. Our current understanding of the molecular mechanism underlying fruit cracking is limited. The aim of this study was to unravel the role starch degradation and cell wall polysaccharide metabolism in fruit ripening and cracking after harvest through transcriptome analysis.
Results
Transcriptome analysis of AP atemoya pericarp from cracking fruits of ethylene treatments and controls was performed. KEGG pathway analysis revealed that the starch and sucrose metabolism pathway was significantly enriched, and approximately 39 DEGs could be functionally annotated, which included starch, cellulose, pectin, and other sugar metabolism-related genes. Starch, protopectin, and soluble pectin contents among the different cracking stages after ethylene treatment and the controls were monitored. The results revealed that ethylene accelerated starch degradation, inhibited protopectin synthesis, and enhanced the soluble pectin content, compared to the control, which coincides with the phenotype of ethylene-induced fruit cracking. Key genes implicated in the starch, pectin, and cellulose degradation were further investigated using RT-qPCR analysis. The results revealed that alpha-amylase 1 (
AMY1
), alpha-amylase 3 (
AMY3
), beta-amylase 1 (
BAM1
), beta-amylase 3 (
BAM3
), beta-amylase 9 (
BAM9)
, pullulanase (
PUL)
, and glycogen debranching enzyme (
glgX
), were the major genes involved in starch degradation.
AMY1
,
BAM3
,
BAM9
,
PUL
, and
glgX
all were upregulated and had higher expression levels with ethylene treatment compared to the controls, suggesting that ethylene treatment may be responsible for accelerating starch degradation. The expression profile of alpha-1,4-galacturonosyltransferase (
GAUT
) and granule-bound starch synthase (
GBSS
) coincided with protopectin content changes and could involve protopectin synthesis. Pectinesterase (
PE
), polygalacturonase (
PG
), and pectate lyase (
PEL
) all involved in pectin degradation;
PE
was significantly upregulated by ethylene and was the key enzyme implicated pectin degradation.
Conclusion
Both KEGG pathway enrichment analysis of DEGs and material content analysis confirmed that starch decomposition into soluble sugars and cell wall polysaccharides metabolism are closely related to the ripening and cracking of AP atemoya. A link between gene up- or downregulation during different cracking stages of atemoya fruits and how their expression affects starch and pectin contents were established by RT-qPCR analysis.
Journal Article
MicroRNA-93 promotes proliferation and metastasis of gastric cancer via targeting TIMP2
MicroRNAs (miRNAs) are important regulators of pathobiological processes in various cancer. In the present study, we demonstrated that miR-93 expression was significantly up-regulated in gastric cancer tissues compared with that in matched normal mucosal tissues. High expression of miR-93 was significantly associated with lymph node metastasis and tumor-node-metastasis (TNM) stage. Functionally, ectopic expression of miR-93 promoted cell proliferation, migration, invasion, EMT phenotypes, and repressed apoptosis and G1 cell cycle arrest in vitro, and promoted tumor formation in vivo. We further identified that tissue inhibitor of metalloproteinase 2 (TIMP2) was a direct target of miR-93 by using luciferase reporter assay, qRT-PCR, and immunoblotting assay. Furthermore, knockdown of TIMP2 with specific siRNA showed similar oncogenic effects in gastric cancer cells with that transfected with miR-93 mimics. Our findings indicated that miR-93 serves as a tumor promoter in human gastric carcinogenesis by targeting TIMP2, suggesting that miR-93 might be a promising biomarker and therapeutic target for treatment of gastric cancer.
Journal Article
Olverembatinib (HQP1351), a well-tolerated and effective tyrosine kinase inhibitor for patients with T315I-mutated chronic myeloid leukemia: results of an open-label, multicenter phase 1/2 trial
Background
BCR-ABL1
T315I
mutations confer resistance to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML). Olverembatinib is a new potent
BCR-ABL1
TKI with preclinical activity against T315I-mutated CML. In phase 1/2 studies, we explored the safety and efficacy of olverembatinib in Chinese adults with TKI-resistant CML in the chronic phase (CML-CP) and accelerated phase (CML-AP).
Methods
In the phase 1 study, olverembatinib was orally administered once every other day in 28-day cycles at 11 dose cohorts ranging from 1 to 60 mg, and we evaluated the maximum tolerated dose, recommended phase 2 dose (RP2D), safety, efficacy, and pharmacokinetics of olverembatinib. In the phase 2 studies, olverembatinib was administered at the RP2D of 40 mg orally on alternate days for 28-day cycles. The primary outcome measure is major cytogenetic response (MCyR) and major hematologic response by the end of Cycle 12 in CML-CP and CML-AP, respectively. Fine and Gray's hazard models were used to identify covariates associated with responses.
Results
A total of 165 patients (> 80.0% of whom had received ≥ 2 TKIs) were enrolled in this study. Among 127 patients with CML-CP, the 3-year cumulative incidences of achieving MCyR, complete cytogenetic response (CCyR), major molecular response (MMR), MR
4.0
, and MR
4.5
were 79.0, 69.0, 56.0, 44.0 and 39.0%, respectively. The highest response rates were observed in patients with a single T315I mutation. Among 38 patients with CML-AP, the 3-year cumulative incidences of achieving MCyR, CCyR, MMR, MR
4.0
, and MR
4.5
were 47.4%, 47.4%, 44.7%, 39.3%, and 32.1%, respectively. In multivariate analyses, baseline
BCR-ABL1
mutation status was significantly associated with cytogenetic and molecular responses. Common treatment-related adverse events included skin hyperpigmentation, hypertriglyceridemia, proteinuria, and severe thrombocytopenia.
Conclusions
Olverembatinib was well tolerated, with significant antileukemic activity in adults with TKI-resistant CML-CP and CML-AP, especially those with the T315I mutation.
Trial registration
: The phase 1 trial is registered at CTR20220566, and the two single-arm, open-label phase 2 studies are registered at ClinicalTrials.gov: NCT03883087 (CML-CP) and NCT03883100 (CML-AP).
Journal Article