Research hotspots and evolving trends of barrier dysfunction in acute lung injury and acute respiratory distress syndrome.

Endothelial and epithelial barrier dysfunction due to increased permeability and heightened inflammatory reactions influences the emergence of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Nevertheless, bibliometric research comparing endothelial and epithelial barriers is limited. Therefore, this bibliometric study analyzed the Web of Science Core Collection (WoSCC) of the Science Citation Index Expanded literature to explore present research priorities and development tendencies within this field. We conducted a comprehensive search (October 18, 2023) on WoSCC from January 1, 2010, to October 18, 2023, focusing on articles related to endothelial and epithelial barriers in ALI and ARDS. Retrieved data were visualized and analyzed using R-bibliometrix, VOS viewer 1.6.19, and CiteSpace 6.2. R4. Functional enrichment analysis of gene targets identified in the keyword list using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology databases, and based on the STRING database to construct a PPI network to predict core genes. A total of 941 original articles and reviews were identified. The United States had the highest number of publications and citations and the highest H-index and G-index. According to the Collaboration Network Analysis graph, the United States and China had the strongest collaboration. Birukova AA had the most publications and citations among all authors, while eight of the top ten institutions with mediator centrality were located in the United States. The American Journal of Physiology-Lung Cellular and Molecular Physiology was the leading journal and had the most well-established publication on endothelial and epithelial barriers in ALI and ARDS. Bibliometric analysis revealed that the most frequently used keywords were acute lung injury, ARDS, activation, expression, and inflammation. RHOA appeared most frequently among gene-related keywords, and the PI3K-AKT signaling pathway had the highest count in KEGG pathway enrichment. Research on endothelial versus epithelial barriers in ALI and ARDS remains preliminary. This bibliometric study examined cooperative network connections among countries, authors, journals, and network associations in the cited references. Investigation of the functions of the endothelial and epithelial barriers in ALI/ARDS associated with COVID-19 has recently gained significant attention.

Global study of anti-NMDA encephalitis: a bibliometric analysis from 2005 to 2023.

Background: Autoimmune diseases have always been one of the difficult diseases of clinical concern. Because of the diversity and complexity of its causative factors, unclear occurrence and development process and difficult treatment, it has become a key disease for researchers to study. And the disease explored in this paper, anti-NMDA encephalitis, belongs to a common type of autoimmune encephalitis. However, the quality of articles and research hotspots in this field are not yet known. Therefore, in this field, we completed a bibliometric and visualization analysis from 2005 to 2023 in order to understand the research hotspots and directions of development in this field. Materials and methods: We searched the SCI-expanded databases using Web of Science's core databases on January 22, 2024 and used tools such as VOS viewer, Cite Space, and R software to visualize and analyze the authors, countries, journals, institutions, and keywords of the articles. Results: A total of 1,161 literatures were retrieved and analyzed in this study. China was the country with the most total publications, and USA and Spain were the most influential countries in the field of anti-NMDA encephalitis. University of Pennsylvania from USA was the institution with the highest number of publications. While Dalmau Josep is the most prolific, influential and contributing author who published one of the most cited articles in Lancet Neurology, which laid the foundation for anti-NMDA encephalitis research, the top three appearances of keyword analysis were: "antibodies", "diagnosis", and "autoimmune encephalitis." Conclusion: Bibliometric analysis shows that the number of studies on anti-NMDA encephalitis is generally increasing year by year, and it is a hot disease pursued by researchers. USA and Spain are leading in the field of anti-NMDA encephalitis, while China should continue to improve the quality of its own research. The suspected causes of anti-NMDA encephalitis other than ovarian teratoma and herpes simplex, the specific clinical manifestations that are not masked by psychiatric symptoms, the diagnostic modalities that are faster and more accurate than antibody tests, and the improvement of treatment modalities by evaluating prognosis of various types of patients are the hotspots for future research.

The Top 100 Cited Articles Focusing on Acute Lung Injury and ARDS: Bibliometric and Visualization Analyses.

BACKGROUND: In recent years, acute lung injury (ALI) and ARDS have emerged as critical health concerns, drawing considerable attention from clinicians. The volume of published articles on ALI/ARDS is on the rise, indicating the expanding research interest in this field. However, the precise quantity and quality of studies on ALI/ARDS remain unclear. Consequently, we employed bibliometric and visual techniques to comprehensively analyze the patterns and focal points of these articles. METHODS: To investigate the characteristics of highly referenced papers on ALI/ARDS and offer insights into the progress and advancements in research on ALI/ARDS, we conducted a comprehensive search in the core Web of Science database for cited articles using the terms "ALI," "acute lung injury," "ARDS," or "acute respiratory distress syndrome." A total of 60,282 citations were retrieved by narrowing the scope to reviews, articles, and publications in English. From the obtained citations, we selected materials for analysis from the top 100 articles with the highest number of citations. Subsequently, the articles were visualized and analyzed using VOSviewer, CiteSpace, and bibliometric techniques. This analysis focused on identifying trends related to authors, journals, countries, institutions, collaborative networks, key words, and other relevant factors in the field of ALI/ARDS research. RESULTS: Among the top 100 cited articles, the highest and lowest number of citations were 6,957 and 451, respectively. A total of 100 articles were published between 1991-2020, with a peak in publications observed in 2004, 2005, and 2012 (no. = 7). Among 29 journals, The New England Journal of Medicine (no. = 21) had the highest number of publications, followed by the American Journal of Respiratory and Critical Care Medicine (no. = 14). Among the 29 countries represented in the top 100 cited articles, the United States (no. = 51) emerged as the leading country in the number of publications, followed by Canada (no. = 19) (there was some overlap in paper output between countries due to co-publication). The 3 predominant keywords identified in studies within the ALI/ARDS domain were ALI, mechanical ventilation, and PEEP. CONCLUSIONS: This study provides a historical perspective on the scientific advancements in ALI/ARDS research, highlighting the need for further investigation and development in specific areas within the field. Bibliometric analyses reveal that the United States is the predominant force in the field of ALI/ARDS, contributing significantly to its development. Through an examination of highly cited papers on ALI/ARDS, we have identified global research trends, assessed the quality of studies, and identified hot topics in the field of ALI/ARDS.

CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab.

PURPOSE: To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model. METHODS: This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI). RESULTS: VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750-0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595-0.815) vs. 0.799 (95% CI, 0.687-0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706-0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726-0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05). CONCLUSION: VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM. CRITICAL RELEVANCE STATEMENT: Our radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy. KEY POINTS: • Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy. • VAT features contribute to the prediction of IFX treatment efficacy. • Comprehensive model improved the performance compared with the bowel model alone.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

Knowledge and willingness toward SARS-CoV-2 rapid antigen testing among older adults in China: a nationwide cross-sectional study.

BACKGROUND AND AIMS: The end of the zero-COVID-19 policy placed a large number of older adults in China at increased risk of COVID-19 infection. SARS-CoV-2 rapid antigen testing (RAT) is a promising tool for scaling up testing and ensuring that patient management and public health measures can be implemented without delay. We aimed to understand the knowledge and willingness of RAT, and its correlates among older adults in China. METHODS: A nationwide cross-sectional survey on knowledge and willingness about RAT among older adults in China was conducted between January 14 and 28, 2023, shortly after the end of the zero-COVID-19 policy. An online questionnaire was used to collect information on sociodemographic characteristics, health characteristics, sources to access RAT information, and attitudes toward COVID-19 and its RAT. Logistic regression was used to assess correlates of knowledge of RAT and willingness to take RAT among older adults. RESULTS: A total of 1030 older adults (494 women and 536 men, mean age 68.7 ± 7.0 years) were recruited. 49.4% of the participants had a high level of RAT knowledge. After adjusting for sociodemographic characteristics, chronic diseases (0.70, 0.49-0.99), learning RAT from new media (5.46, 3.48-8.68) and traditional media (3.35, 2.13-5.34), and perceiving RAT as convenient (4.03, 2.80-5.85) were associated with levels of RAT knowledge. 53.3% of the participants were willing to take RAT. After adjusting for sociodemographic characteristics, learning RAT from new media (8.46, 5.26-14.0) and traditional media (1.63, 1.04-2.55), perceiving RAT as convenient (2.97, 2.10-4.22), and worrying about (re)infection with COVID-19 (2.12, 1.55-2.92) were associated with willingness to take RAT. CONCLUSION: The levels of RAT knowledge and willingness to take RAT among older adults in China may hinder the scale-up of RAT. Health education about RAT should be strengthened among older adults. Special efforts should be made to integrate traditional and new media to promote RAT among older adults, specifically, for virus susceptibility and the convenience of RAT. Given the reopening of society, our study could inform our response to future novel infectious diseases and aid in the timely scale-up of RAT.

Tanshinone⠡A inhibits excessive autophagy and protects myocardium against ischemia/reperfusion injury via 14-3-3η/Akt/Beclin1 pathway.

Excessive autophagy induced by reperfusion is one of the causes of severe myocardial injury. Tanshinone IIA (TSN) protects the myocardium against ischemia/reperfusion (I/R) injury. The mechanism by which the inhibition of excessive autophagy contributes to the myocardial protection by TSN is unclear. The protective effects and mechanisms of TSN were studied in H9c2 cells and rats after anoxia/reoxygenation (A/R)-or I/R-induced myocardial injury. The results showed that after the injury, cell viability decreased, lactate dehydrogenase and caspase 3 activity and apoptosis increased, and autophagy was excessively activated. Further, redox imbalance and energy stress, mitochondrial dysfunction, reduced myocardial function, increased infarct area, and severely damaged morphology were observed in rats. TSN increased 14-3-3η expression and regulated Akt/Beclin1 pathway, inhibited excessive autophagy, and significantly reversed the functional, enzymological and morphological indexes in vivo and in vitro. However, the protective effects of TSN were mimicked by 3-methyladenine (an autophagy inhibitor) and were attenuated by pAD/14-3-3η-shRNA, API-2 (an Akt inhibitor), and rapamycin (an autophagy activator). In conclusion, TSN could increase 14-3-3η expression and regulate Akt/Beclin1 pathway, inhibit excessive autophagy, maintain the mitochondrial function, improve energy supply and redox equilibrium, alleviate apoptosis, and ultimately protect myocardium against I/R injury.

Mega-city construction engineering to residential satisfaction: new insights from Yan' an of China.

Introduction: The contradiction among population, economy and urbanization has gradually intensified, and the Mountain Excavation and City Construction (MECC) project is one of the special solutions. Nevertheless, there are few comparative studies on the project index studies and effect of MECC projects on residential satisfaction. To remedy this deficiency, this study base on the Yan'an new district (YND) reconstruction project, attempting to analyze the specific influencing factors prerelocation and post-relocation from the perspective of residential satisfaction. Methods: After conducting reliability and validity analysis on each dimension, multiple linear regression and paired t-test were used to analyze and compare the questionnaire data. Results: The results show that the residential satisfaction index of the YND is indeed higher than that of the Yan'an old district (YOD). Concurrently, the decisive factors of residential satisfaction are also different. Specifically, the interpersonal communication, supporting facilities, community environment and economic income are significant in the YOD, but only the aspect of supporting facilities is negative significant. The supporting facilities, community environment, economic income and urban development are all positive significant in the YND. The satisfaction factors of middle-aged people in YOD and YND have the most significant differences, and the significance of each dimension is different. Discussion: The research results of this study provide a comparative perspective at the micro-level for evaluating China's urban construction, and it supplies specific directions for future urban development and the improvement of old cities through the new residential satisfaction index.

A screening method based on analytic hierarchy process for closed-loop DBS strategies of Parkinson's disease.

BACKGROUND: Closed-loop deep brain stimulation (DBS) is a research hotspot in the treatment of Parkinson's disease. However, a variety of stimulation strategies will increase the selection time and cost in animal experiments and clinical studies. Moreover, the stimulation effect is little difference between similar strategies, so the selection process will be redundant. OBJECTIVE: The objective was to propose a comprehensive evaluation model based on analytic hierarchy process (AHP) to select the best one among similar strategies. METHODS: Two similar strategies, namely, threshold stimulation (CDBS) and threshold stimulus after EMD feature extraction (EDBS), were used for analysis and screening. The values of Similar to Unified Parkinson's Disease Rating Scale estimates (SUE), ß power and energy consumption were calculated and analysed. The stimulation threshold with the best improvement effect was selected. The weights of the indices were allocated by AHP. Finally, the weights and index values were combined, and the comprehensive scores of the two strategies were calculated using the evaluation model. RESULTS: The optimal stimulation threshold for CDBS was 52% and for EDBS was 62%. The weights of the indices were 0.45, 0.45 and 0.1, respectively. According to comprehensive scores, different from the situation where either EDBS or CDBS can be called optimal stimulation strategies. But under the same threshold stimulation, the EDBS was better than the CDBS under the optimal level. CONCLUSION: The evaluation model based on AHP under the optimal stimulation conditions satisfied the screening conditions between the two strategies.

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. METHODS: We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. RESULTS: DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). CONCLUSION: DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM. KEY POINTS: • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

Effects of customized resin base on bonding strength of spherical self-ligating brackets.

PURPOSE: Fabricating resin bases has become an easy and economical method to achieve the customization of brackets. This study aimed to assess the effect of the resin base on bonding strength of spherical self-ligating brackets. METHODS: A defined amount of adhesive was bonded to the bracket base and constituted the new resin base. The thickness of the adhesive was measured and controlled at 0.5, 1.0, 1.5 and 2.0 mm, and a group without a resin base was used as a control. Sixty extracted human premolars were randomly divided into five groups. The brackets in each group were bonded to the specimen, and debonding tests were conducted. The shear bond strength (SBS) was calculated according to the measured debonding force in relation to the base area. The adhesive remnant index (ARI) score and the residual location of the fractured resin base were recorded. Enamel damage was also analyzed by scanning electron microscopy. After assessing for data normality and homogeneity, statistical comparisons between the groups and correlations among parameters were determined. P < 0.05 was regarded as significant. RESULTS: The correlation analysis revealed an inverse correlation between the resin base thickness and the SBS (Coeff = -0.719, P < 0.01). The highest SBS was 9.33 MPa, in the control group, which was significantly greater than the lowest SBS (6.03 MPa), in the 2.0-mm group (P < 0.05). Multiple comparisons analysis revealed no differences in SBS between the 1.0-, 1.5- and 2.0-mm groups. Nonparametric analysis found that only the ARI score in the 0.5-mm group (2.92) was significantly different (P < 0.05) from that in the control group (1.25). As the thickness of the resin base increased, the fractured resin base tended to remain at the bracket base, and the risk of enamel damage decreased. CONCLUSIONS: As the thickness of the resin base increased, the bonding strength of the spherical bracket decreased. However, the required clinical bonding strength was still satisfied when the thickness was less than 2.0 mm. The existence of a resin base could protect the enamel surface from damage caused by debonding. The customization of spherical brackets by tailoring a resin base can be applied in clinical practice because of the clinically acceptable bonding strength.

OANet: Learning Two-View Correspondences and Geometry Using Order-Aware Network.

Establishing correct correspondences between two images should consider both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential or fundamental matrix. Specifically, this proposed network is built hierarchically and comprises three operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in canonical order and invariant to input permutations. Next, the clusters are spatially correlated to encode the global context of correspondences. After that, the context-encoded clusters are interpolated back to the original size and position to build a hierarchical architecture. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts. Besides, based on the proposed method and advanced local feature, we won the first place in CVPR 2019 image matching workshop challenge and also achieve state-of-the-art results in the Visual Localization benchmark. Code is available at https://github.com/zjhthu/OANet.

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis.

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

Secretory IgA in Mucosa of Pharynx and Larynx Plays an Important Role against Influenza A Virus Infection in Kidney Yang Deficiency Syndrome Model.

OBJECTIVE: Influenza virus poses a major threat to human health and has serious morbidity and mortality which commonly occurs in high-risk populations. Pharynx and larynx of the upper respiratory tract mucosa is the first defense line against influenza virus infection. However, the ability of the pharynx and larynx organ to eliminate the influenza pathogen is still not clear under different host conditions. METHODS: In this study, a mouse model of kidney yang deficiency syndrome (KYDS) was used to mimic high-risk peoples. Two different methods of influenza A (H1N1) virus infection by nasal dropping or tracheal intubation were applied to these mice, which were divided into four groups: normal intubation (NI) group, normal nasal dropping (ND) group, model intubation (MI) group, and model nasal dropping (MD) group. The normal control (NC) group was used as a negative control. Body weight, rectal temperature, and survival rate were observed every day. Histopathologic changes, visceral index, gene expressions of H1N1, cytokine expressions, secretory IgA (SIgA) antibodies of tracheal lavage fluids in the upper respiratory tract, and bronchoalveolar lavage fluids were analyzed by ELISA. RESULTS: The MD group had an earlier serious morbidity and mortality than the others. MI and NI groups became severe only in the 6th to 7th day after infection. The index of the lung increased significantly in NI, MI, and MD groups. Conversely, indices of the thymus and spleen increased significantly in NC and ND groups. H&E staining showed severe tissue lesions in MD, MI, and NI groups. H1N1 gene expressions were higher in the MD group compared with the MI group on the 3rd day; however, the MD group decreased significantly on the 7th day. IL-6 levels increased remarkably, and SIgA expressions decreased significantly in the MD group compared with the NC group. CONCLUSIONS: SIgA secretions are influenced directly by different conditions of the host in the pharynx and larynx in the upper respiratory tract mucosa. In the KYDS virus disease mode, SIgA expressions could be inhibited severely, which leads to serious morbidity and mortality after influenza A virus infection. The SIgA expressions of the pharynx and larynx would be an important target in high-risk populations against the influenza A virus for vaccine or antiviral drugs research.

Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus.

The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy.

Synthesis and bioevaluation of thienopyrimidines bearing a pyrazoline unit as selective PI3Kα inhibitors.

A series of thienopyrimidines containing a pyrazoline unit (4a-d, 7a-d and 13a-l) were designed and synthesized. The target compounds were evaluated for antiproliferative activity against A549, HepG2 and MCF-7 cancer cell lines. Among the twenty target compounds, most of them exhibited excellent antiproliferative activity against one or several cancer cell lines. Compound 13f showed the best activity against A549, MCF-7 and HepG2 cancer cell lines, with IC50 values of 2.84 ± 0.09 µM, 2.88 ± 0.43 µM and 2.08 ± 0.36 µM, respectively. Four selected compounds (13c, 13f, 13g and 13h) were further evaluated for their inhibitory activity against the PI3Kα/mTOR protein kinase. Moreover, time-dependent and dose-dependent experiments, AO fluorescence staining, Annexin V-FITC/PI staining and docking studies were carried out in this study. The results indicated that compound 13f may be a potential selective PI3Kα inhibitor.