Global Λ\Lambda Polarization in high energy collisions

With a Yang-Mills flux-tube initial state and a high resolution (3+1)D Particle-in-Cell Relativistic (PICR) hydrodynamics simulation, we calculate the $\Lambda$ polarization for different energies. The origination of polarization in high energy collisions is discussed, and we find linear impact parameter dependence of the global $\Lambda$ polarization. Furthermore, the global $\Lambda$ polarization in our model decreases very fast in the low energy domain, and the decline curve fits well the recent results of Beam Energy Scan (BES) program launched by the STAR collaboration at the Relativistic Heavy Ion Collider (RHIC). The time evolution of polarization is also discussed

Students' evacuation behavior during an emergency at schools:A systematic literature review

Disasters and emergencies frequently happen, and some of them require population evacuation. Children can be severely affected during evacuations due to their lower capability to analyze, perceive, and answer disaster risks. Although several studies attempted to address the safety of children during the evacuation, the existing literature lacks a systematic review of students' evacuation behavior during school time. Therefore, this study aims to conduct a systematic literature review to explore how students' evacuation behavior during school time has been addressed by previous scholars and identify gaps in knowledge. The review process included three steps: formulating the research question, establishing strategic search strategies, and data extraction and analysis. The studies have been identified by searching academic search engines and refined the recognized publications unbiasedly. The researchers have then thematically analyzed the objectives and findings of the selected studies resulting in the identification of seven themes in the field of students' evacuation behavior during school time. Finally, the study put forward suggestions for future research directions to efficiently address the recognized knowledge gaps.</p

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.</p

EGR1, EGFR and IGF1R protein expressions in non-small cell lung cancer and their clinical significances

The purpose of this study was to investigate the incidence and clinical significance of alterations in EGFR, IGF1R and the cell signaling pathway activities induced by them, as well as EGR1 expression in resected non-small cell lung cancer (NSCLC). The protein expressions of  biomarker were evaluated by Western blotting in tissues from 19 surgically resected NSCLCs. High expressions of EGR1, EGFR and  IGF1R were detected in more than 30% tumor tissues. High expressions of pErk and pAkt were detected in more than 50% paracancer tissues. There were significant correlations between the NSCLC target factors detected (p<0.05). Alterations of protein expressions of target factor detected in NSCLC were significantly associated with alterations in pathological subtype, differentiation, pathological stage, and smoking history. Positive EGR1 might be  associated with good survival, while positive pErk might be associated with poor prognosis.

Clinical Significance of Elevated S100A8 Expression in Breast Cancer Patients

Breast cancer is the leading cause of female cancer-related death; however, novel biomarkers for predicting cancer recurrence still need to be explored. Aberrant expression of S100A8 has been reported to be related to tumor progression in various cancer types. This study aims to evaluate the clinical significance of S100A8 expression in breast cancer patients. In this study, data from 140 breast cancer patients were retrospectively collected to examine the association between S100A8 expression and clinical prognosis. Increased S100A8 expression was detected in breast cancer patients with relapse. The patients with increased S100A8 levels had significantly shorter disease-free survival (DFS) and overall survival (OS). In a multivariate survival analysis, a high histological grade and an elevated S100A8 level were independent factors associated with poor DFS and OS. Moreover, S100A8 expression was correlated with clinical subtype in breast cancer patients. The results showed that ER-negative and triple-negative breast cancer (TNBC) patients had significantly higher expression of S100A8 than patients with other subtypes. In conclusion, this study identified S100A8 as a potential biomarker for relapse in breast cancer patients

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor

A supersensitive silicon nanowire array biosensor for quantitating tumor marker ctDNA

Cancer has become one of the major diseases threatening human health and life. Circulating tumor DNA (ctDNA) testing, as a practical liquid biopsy technique, is a promising method for cancer diagnosis, targeted therapy and prognosis. Here, for the first time, a field effect transistor (FET) biosensor based on uniformly sized high-response silicon nanowire (SiNW) array was studied for real-time, label-free, super-sensitive detection of PIK3CA E542K ctDNA. High-response 120-SiNWs array was fabricated on a (111) silicon-on-insulator (SOI) by the complementary metal oxide semiconductor (CMOS)-compatible microfabrication technology. To detecting ctDNA, we modified the DNA probe on the SiNWs array through silanization. The experimental results demonstrated that the as-fabricated biosensor had significant superiority in ctDNA detection, which achieved ultralow detection limit of 10 aM and had a good linearity under the ctDNA concentration range from 0.1 fM to 100 pM. This biosensor can recognize complementary target ctDNA from one/two/full-base mismatched DNA with high selectivity. Furthermore, the fabricated SiNW-array FET biosensor successfully detected target ctDNA in human serum samples, indicating a good potential in clinical applications in the future

Fluid dynamics study of the

With a Yang–Mills field, stratified shear flow initial state and a high resolution ($$3+1$$)D particle-in-cell relativistic (PICR) hydrodynamic model, we calculate the $$\varLambda$$ polarization for peripheral Au + Au collisions at RHIC energy of $$\sqrt{S_{NN}}=200$$ GeV. The obtained longitudinal polarization in our model agrees with the experimental signature and the quadrupole structure on transverse momentum plane. It is found that the relativistic correction (2nd term), arising from expansion and from the time component of the thermal vorticity, plays a crucial role in our results. This term is changing sign and exceeds the first term, arising from the classical vorticity. Finally, the global polarization in our model shows no significant dependence on rapidity, which agrees with the experimental data. It is also found that the second term flattens the sharp peak arising from the classical vorticity (1st term)

Interpretability-Based Multimodal Convolutional Neural Networks for Skin Lesion Diagnosis

Wang S, Yin Y, Wang D, Wang Y, Jin Y. Interpretability-Based Multimodal Convolutional Neural Networks for Skin Lesion Diagnosis. IEEE Transactions on Cybernetics. 2022;52(12):12623-12637.Skin lesion diagnosis is a key step for skin cancer screening, which requires high accuracy and interpretability. Though many computer-aided methods, especially deep learning methods, have made remarkable achievements in skin lesion diagnosis, their generalization and interpretability are still a challenge. To solve this issue, we propose an interpretability-based multimodal convolutional neural network (IM-CNN), which is a multiclass classification model with skin lesion images and metadata of patients as input for skin lesion diagnosis. The structure of IM-CNN consists of three main paths to deal with metadata, features extracted from segmented skin lesion with domain knowledge, and skin lesion images, respectively. We add interpretable visual modules to provide explanations for both images and metadata. In addition to area under the ROC curve (AUC), sensitivity, and specificity, we introduce a new indicator, an AUC curve with a sensitivity larger than 80% (AUC_SEN_80) for performance evaluation. Extensive experimental studies are conducted on the popular HAM10000 dataset, and the results indicate that the proposed model has overwhelming advantages compared with popular deep learning models, such as DenseNet, ResNet, and other state-of-the-art models for melanoma diagnosis. The proposed multimodal model also achieves on average 72% and 21% improvement in terms of sensitivity and AUC_SEN_80, respectively, compared with the single-modal model. The visual explanations can also help gain trust from dermatologists and realize man–machine collaborations, effectively reducing the limitation of black-box models in supporting medical decision making