Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.

The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models

Monopoles, confinement and charge localization in the t-J model with dilute holes

We present a quantum field theoretic description on the t$-$J model on a square lattice with dilute holes (i.e. near half-filling), based on the compact mutual Chern-Simons gauge theory. We show that, due to the presence of non-perturbative monopole plasma configuration from the antiferromagnetic background, holons (carrying electric charge) are linearly confined and strongly localized even without extrinsic disorder taken into account. Accordingly, the translation symmetry is spontaneously broken at ground state. Such an exotic localization is distinct from Anderson localization and essentially rooted in intrinsic Mott physics of the t$-$J model. Finally, a finite-temperature phase diagram is proposed. The metal-insulator transition observed in in-plane resistivity measurement is identified to a confinement-deconfinement transition from the perspective of gauge theory. The transition is characterized by the order parameter "Polyakov-line".Comment: 8 papges, 1 figure, accepted by Nucl. Phys.

Rapid prediction of multidrug-resistant klebsiella pneumoniae through deep learning analysis of sers spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-To-Treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenemsensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE: This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings

Psoriasin promotes invasion, aggregation and survival of pancreatic cancer cells; association with disease progression

Psoriasin (S100A7) is an 11-kDa small calcium binding protein initially isolated from psoriatic skin lesions. It belongs to the S100 family of proteins which play an important role in a range of cell functions including proliferation, differentiation, migration and apoptosis. Aberrant Psoriasin expression has been implicated in a range of cancers and is often associated with poor prognosis. This study examined the role of Psoriasin on pancreatic cancer cell functions and the implication in progression of the disease. Expression of Psoriasin was determined in a cohort of pancreatic tissues comprised of 126 pancreatic tumours and 114 adjacent non-tumour pancreatic tissues. Knockdown and overexpression of Psoriasin in pancreatic cancer cells was performed using specifically constructed plasmids, which either had anti-Psoriasin ribozyme transgene or the full length human Psoriasin coding sequence. Psoriasin knockdown and overexpression was verified using conventional RT-PCR and qPCR. The effect of manipulating Psoriasin expression on pancreatic cancer cell functions was assessed using several in vitro cell function assays. Local invasive pancreatic cancers extended beyond the pancreas expressed higher levels of Psoriasin transcripts compared with the cancers confined to the pancreas. Primary tumours with distant metastases exhibited a reduced expression of Psoriasin. Psoriasin overexpression cell lines exhibited significantly increased growth and migration compared to control cells. In addition, Psoriasin overexpression resulted in increased pancreatic cancer cell invasion which was associated with upregulation of matrix metalloproteinase-2 (MMP-2) and MMP-9. Overexpression of Psoriasin also promoted aggregation and survival of pancreatic cancer cells when they lost anchorage. Taken together, higher expression of Psoriasin was associated with local invasion in pancreatic cancers. Psoriasin expression is associated with pancreatic cancer cell growth, migration, cell-matrix adhesion, and invasion via regulation of MMPs. As such, the proposed implications of Psoriasin in invasion, disease progression and as a potential therapeutic target warrant further investigation

Estrogen receptor α (ERα) mediates 17β-estradiol (E2)-activated expression of HBO1

BACKGROUND: HBO1 (histone acetyltransferase binding to ORC1) is a histone acetyltransferase (HAT) which could exert oncogenic function in breast cancer. However, the biological role and underlying mechanism of HBO1 in breast cancer remains largely unknown. In the current study, we aimed to investigate the role of HBO1 in breast cancer and uncover the underlying molecular mechanism. METHODS: Immunohistochemistry was applied to detect HBO1 protein expression in breast cancer specimens (n = 112). The expression of protein level was scored by integral optical density (IOD) for further statistical analyses using SPSS. Real-time PCR was used to simultaneously measure mRNA levels of HBO1. The HBO1 protein expression in breast cancer cells was confirmed by western blot. RESULTS: HBO1 was highly expressed in breast cancer tissues and significantly correlated with estrogen receptor α (ERα) (p < 0.001) and progestational hormone (PR) (p = 0.002). HBO1 protein level also correlated positively with histology grade in ERα positive tumors (p = 0.016) rather than ERα negative tumors. 17β-estradiol (E2) could upregulate HBO1 gene expression which was significantly inhibited by ICI 182,780 or ERα RNAi. E2-increased HBO1 protein expression was significantly suppressed by treatment with inhibitor of MEK1/2 (U0126) in T47 D and MCF-7 cells. CONCLUSIONS: HBO1 was an important downstream molecule of ERα, and ERK1/2 signaling pathway may involved in the expression of HBO1 increased by E2

Atomic-layered Au clusters on α-MoC as catalysts for the low-temperature water-gas shift reaction

The water-gas shift (WGS) reaction (where carbon monoxide plus water yields dihydrogen and carbon dioxide) is an essential process for hydrogen generation and carbon monoxide removal in various energy-related chemical operations. This equilibrium-limited reaction is favored at a low working temperature. Potential application in fuel cells also requires a WGS catalyst to be highly active, stable, and energy-efficient and to match the working temperature of on-site hydrogen generation and consumption units. We synthesized layered gold (Au) clusters on a molybdenum carbide (α-MoC) substrate to create an interfacial catalyst system for the ultralow-temperature WGS reaction. Water was activated over α-MoC at 303 kelvin, whereas carbon monoxide adsorbed on adjacent Au sites was apt to react with surface hydroxyl groups formed from water splitting, leading to a high WGS activity at low temperatures