Atomic Dynamics of Multi-Interfacial Migration and Transformations.

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H2 gas, a reaction pathway of CuO → monoclinic m-Cu4 O3 → Cu2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu2 O/m-Cu4 O3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu4 O3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu2 O and m-Cu4 O3 , and activate the interfacial transformations. These results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

Higher glucose and insulin responses to a mixed meal are associated with increased risk of diabetic retinopathy in Indigenous Americans.

PURPOSE: Prior research has focused on glucose/insulin responses to meal challenges to create personalized diets to improve health, though it is unclear if these responses predict chronic diseases. We aimed to identify glucose and insulin responses to a mixed meal tolerance test (MMTT) that predict the development of diabetic retinopathy (DR) and compare the predictive abilities with the oral glucose tolerance test (OGTT). METHODS: Indigenous American adults without diabetes (n = 168) underwent a 4-h MMTT, body composition assessment, and a 3-h OGTT at baseline. During follow-up (median 13.4 years), DR was diagnosed by direct ophthalmoscopy (n = 28) after onset of type 2 diabetes. Total and incremental area under the curve (AUC and iAUC) were calculated from glucose/insulin responses after the MMTT and OGTT. RESULTS: In separate Cox proportional hazards models adjusted for age, sex, and body fat (%), MMTT glucose AUCs (180-min and 240-min) and iAUC (180-min) predicted DR (HR 1.50, 95% CI 1.06, 2.12; HR 1.50, 95% CI 1.05, 2.14; HR 1.58, 95% CI 1.01, 2.46). The predictive abilities were better than the fasting OGTT glucose (p < 0.01) but similar to the 120-min OGTT glucose (p = 0.53). MMTT insulin AUCs (180-min and 240-min) and iAUC (180-min) also predicted DR (HR 1.65, 95% CI 1.09, 2.51; HR 1.58, 95% CI 1.00, 2.35; HR 1.53 95% CI 1.06, 2.22) while insulin AUC and iAUC from the OGTT did not (p > 0.05). CONCLUSIONS: Higher MMTT glucose and insulin responses predicted DR and were comparable to the OGTT, supporting the use of a meal challenge for precision nutrition. TRIAL REGISTRATIONS: Clinical Trial Registry: ClinicalTrials.gov identifier: NCT00340132, NCT00339482.

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification.

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in which the defect occurred, but automating pattern classification is difficult to apply to actual manufacturing sites unless processing speed and resource efficiency are supported. The purpose of this study was to classify these defect patterns with a small amount of resources and time. To this end, we explored an efficient convolutional neural network model that can incorporate three properties: (1) state-of-the-art performances, (2) less resource usage, and (3) faster processing time. In this study, we dealt with classifying nine types of frequently found defect patterns: center, donut, edge-location, edge-ring, location, random, scratch, near-full type, and None type using open dataset WM-811K. We compared classification performance, resource usage, and processing time using EfficientNetV2, ShuffleNetV2, MobileNetV2 and MobileNetV3, which are the smallest and latest light-weight convolutional neural network models. As a result, the MobileNetV3-based wafer map pattern classifier uses 7.5 times fewer parameters than ResNet, and the training speed is 7.2 times and the inference speed is 4.9 times faster, while the accuracy is 98% and the F1 score is 89.5%, achieving the same level. Therefore, it can be proved that it can be used as a wafer map classification model without high-performance hardware in an actual manufacturing system.

[Research progress on biomarkers and detection methods for Alzheimer's disease diagnosis in vitro].

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

[The incidence of artificial stone-related silicosis among interior decoration workers in different countries].

In this paper, 177 cases of artificial stone-related silicosis in interior decoration workers from Israel, Spain, USA, Italy and Australia were analyzed. Interior decoration workers were from small businesses (or workshops), engaged in kitchen and/or bathroom artificial stone countertops cutting, grinding, polishing and other reprocessing. In the working environment, the content of crystalline silica in artificial stone was more than 70%, and the concentration of silica dust exceeded the relevant standards. Most workplaces used dry cutting without ventilation and dust removal and other dust-proof measures, and most workers did not wear qualified respiratory protective equipment. Taking comprehensive measures such as wet operation, ventilation and dust removal, and individual protection can effectively prevent the occurrence of artificial stone-related silicosis.

Endothermic reaction at room temperature enabled by deep-ultraviolet plasmons.

Metallic nanoparticles have been used to harvest energy from a light source and transfer it to adsorbed gas molecules, which results in a reduced chemical reaction temperature. However, most reported reactions, such as ethylene epoxidation, ammonia decomposition and H-D bond formation are exothermic, and only H-D bond formation has been achieved at room temperature. These reactions require low activation energies (<2 eV), which are readily attained using visible-frequency localized surface plasmons (from ~1.75 eV to ~3.1 eV). Here, we show that endothermic reactions that require higher activation energy (>3.1 eV) can be initiated at room temperature by using localized surface plasmons in the deep-UV range. As an example, by leveraging simultaneous excitation of multiple localized surface plasmon modes of Al nanoparticles by using high-energy electrons, we initiate the reduction of CO2 to CO by carbon at room temperature. We employ an environmental transmission electron microscope to excite and characterize Al localized surface plasmon resonances, and simultaneously measure the spatial distribution of carbon gasification near the nanoparticles in a CO2 environment. This approach opens a path towards exploring other industrially relevant chemical processes that are initiated by plasmonic fields at room temperature.

Cortical thickness across the cingulate gyrus in schizophrenia and its association to illness duration and memory performance.

Schizophrenia has been associated with structural brain abnormalities and cognitive deficits that partly change during the course of illness. In the present study, cortical thickness in five subregions of the cingulate gyrus was assessed in 44 patients with schizophrenia-spectrum disorder and 47 control persons and related to illness duration and memory capacities. In the patients group, cortical thickness was increased in the posterior part of the cingulate gyrus and related to illness duration whereas cortical thickness was decreased in anterior parts unrelated to illness duration. In contrast, cortical thickness was related to episodic and working memory performance only in the anterior but not posterior parts of the cingulate gyrus. Our finding of a posterior cingulate increase may point to either increased parietal communication that is accompanied by augmented neural plasticity or to effects of altered neurodegenerative processes in schizophrenia.

Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation.

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification. Each labeled input image is used to generate two synthetic replicas: one under factual assumptions about the sensitive attribute and one under counterfactual. The proposed causal graph-based attribute translation generates realistic counterfactual images that consider the complicated causal relationship among the attributes with an encoder-decoder framework. A causal graph represents complex relationships among the attributes and is used to sample factual and counterfactual facial attributes of the given face image. The encoder-decoder architecture translates the given facial image to have sampled factual or counterfactual attributes while preserving its identity. The attribute classifier is trained for fair prediction with counterfactual regularization between factual and corresponding counterfactual translated images. Extensive experimental results on the CelebA dataset demonstrate the effectiveness and interpretability of the proposed learning method for classifying multiple face attributes.

Dual-Scale Doppler Attention for Human Identification.

This paper considers a Deep Convolutional Neural Network (DCNN) with an attention mechanism referred to as Dual-Scale Doppler Attention (DSDA) for human identification given a micro-Doppler (MD) signature induced as input. The MD signature includes unique gait characteristics by different sized body parts moving, as arms and legs move rapidly, while the torso moves slowly. Each person is identified based on his/her unique gait characteristic in the MD signature. DSDA provides attention at different time-frequency resolutions to cater to different MD components composed of both fast-varying and steady. Through this, DSDA can capture the unique gait characteristic of each person used for human identification. We demonstrate the validity of DSDA on a recently published benchmark dataset, IDRad. The empirical results show that the proposed DSDA outperforms previous methods, using a qualitative analysis interpretability on MD signatures.

Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning.

In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively. In comparison to the prior unsupervised methods, the performance of VPCPM matches or outperforms all the baselines. We further demonstrate that the learned representations successfully generalize to the new tasks that share a similar observation and action space.

CE-BART: Cause-and-Effect BART for Visual Commonsense Generation.

"A Picture is worth a thousand words". Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation has the aim of generating three cause-and-effect captions for a given image: (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after. However, this task is challenging for machines, owing to two limitations: existing approaches (1) directly utilize conventional vision-language transformers to learn relationships between input modalities and (2) ignore relations among target cause-and-effect captions, but consider each caption independently. Herein, we propose Cause-and-Effect BART (CE-BART), which is based on (1) a structured graph reasoner that captures intra- and inter-modality relationships among visual and textual representations and (2) a cause-and-effect generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on the VisualCOMET and AVSD benchmarks. CE-BART achieved SOTA performance on both benchmarks, while an extensive ablation study and qualitative analysis demonstrated the performance gain and improved interpretability.

[Recent progress of aptasensors for tumor exosome-associated protein detection].

Exosomes are phospholipid bilayer membrane-enclosed vesicles released from cells with diameters of 30-150 nm, exosomes can directly reflect the physiological and functional state of secretory cells, participate in material transport and information communication between cells, which are of great significance as biomarkers for early tumor diagnosis and treatment evaluation. There are many detection methods for exosomes, among which aptasensor technology with the properties of low price and easy operation, fast response, high sensitivity, remarkable specificity helps tumor patients to find, diagnose and treat early, improve the survival rate, and provide important basis for the evaluation of the prognosis. There are seven types of common aptasensors: fluorescent, electrochemical, colorimetric, luminescence, lateral flow strips, surface-enhanced Raman scattering and surface plasmon resonance sensors. Different aptasensors have different characteristics, this article focuses on the research progress of several common aptasensor for tumor exosomes detection.

[Research progress on the treatment of presbyopia].

Presbyopia is a physiological aging situation that the plasticity and elasticity of the lens and the function of the ciliary muscle become weaker, resulting in a decreased accommodation and inability to focus on near objects. Nowadays, there are many clinical strategies to correct presbyopia, each of which has its own advantages and disadvantages, however, there is no true sense of way to restore accommodation function. This article reviews both worldwide and domestic research on presbyopia, and analyzes and summaries the status quo as well as research progress of presbyopia correction modalities, surgical approaches, and drug therapies, hoping to provide a reference for clinical works.

Reshaping preoperative treatment of pancreatic cancer in the era of precision medicine.

This review summarises the recent evidence on preoperative therapeutic strategies in pancreatic cancer and discusses the rationale for an imminent need for a personalised therapeutic approach in non-metastatic disease. The molecular diversity of pancreatic cancer and its influence on prognosis and treatment response, combined with the failure of 'all-comer' treatments to significantly impact on patient outcomes, requires a paradigm shift towards a genomic-driven approach. This is particularly important in the preoperative, potentially curable setting, where a personalised treatment allocation has the substantial potential to reduce pancreatic cancer mortality.

Quantum Nonlinear Optics Based on Two-Dimensional Rydberg Atom Arrays.

We propose the combination of subwavelength, two-dimensional atomic arrays and Rydberg interactions as a powerful platform to realize strong, coherent interactions between individual photons with high fidelity. The atomic spatial ordering guarantees efficient atom-light interactions without the possibility of scattering light into unwanted directions, allowing the array to act as a perfect mirror for individual photons. In turn, Rydberg interactions enable single photons to alter the optical response of the array within a potentially large blockade radius R_{b}, which can effectively punch a large "hole" for subsequent photons. We show that such a system enables a coherent photon-photon gate or switch, with a significantly better error scaling (∼R_{b}^{-4}) than in a disordered ensemble. We also investigate the optical properties of the system in the limit of strong input intensities and show that this many-body quantum driven dissipative system can be modeled well by a semiclassical model based on holes punched in a classical mirror.

ESMO recommendations on predictive biomarker testing for homologous recombination deficiency and PARP inhibitor benefit in ovarian cancer.

BACKGROUND: Homologous recombination repair deficiency (HRD) is a frequent feature of high-grade serous ovarian, fallopian tube and peritoneal carcinoma (HGSC) and is associated with sensitivity to PARP inhibitor (PARPi) therapy. HRD testing provides an opportunity to optimise PARPi use in HGSC but methodologies are diverse and clinical application remains controversial. MATERIALS AND METHODS: To define best practice for HRD testing in HGSC the ESMO Translational Research and Precision Medicine Working Group launched a collaborative project that incorporated a systematic review approach. The main aims were to (i) define the term 'HRD test'; (ii) provide an overview of the biological rationale and the level of evidence supporting currently available HRD tests; (iii) provide recommendations on the clinical utility of HRD tests in clinical management of HGSC. RESULTS: A broad range of repair genes, genomic scars, mutational signatures and functional assays are associated with a history of HRD. Currently, the clinical validity of HRD tests in ovarian cancer is best assessed, not in terms of biological HRD status per se, but in terms of PARPi benefit. Clinical trials evidence supports the use of BRCA mutation testing and two commercially available assays that also incorporate genomic instability for identifying subgroups of HGSCs that derive different magnitudes of benefit from PARPi therapy, albeit with some variation by clinical scenario. These tests can be used to inform treatment selection and scheduling but their use is limited by a failure to consistently identify a subgroup of patients who derive no benefit from PARPis in most studies. Existing tests lack negative predictive value and inadequately address the complex and dynamic nature of the HRD phenotype. CONCLUSIONS: Currently available HRD tests are useful for predicting likely magnitude of benefit from PARPis but better biomarkers are urgently needed to better identify current homologous recombination proficiency status and stratify HGSC management.

Site-selective CO disproportionation mediated by localized surface plasmon resonance excited by electron beam.

Recent reports of hot-electron-induced dissociation of small molecules, such as hydrogen, demonstrate the potential application of plasmonic nanostructures for harvesting light to initiate catalytic reactions. Theories have assumed that plasmonic catalysis is mediated by the energy transfer from nanoparticles to adsorbed molecules during the dephasing of localized surface plasmon (LSP) modes optically excited on plasmonic nanoparticles. However, LSP-induced chemical processes have not been resolved at a sub-nanoparticle scale to identify the active sites responsible for the energy transfer. Here, we exploit the LSP resonance excited by electron beam on gold nanoparticles to drive CO disproportionation at room temperature in an environmental scanning transmission electron microscope. Using in situ electron energy-loss spectroscopy with a combination of density functional theory and electromagnetic boundary element method calculations, we show at the subparticle level that the active sites on gold nanoparticles are where preferred gas adsorption sites and the locations of maximum LSP electric field amplitude (resonance antinodes) superimpose. Our findings provide insight into plasmonic catalysis and will be valuable in designing plasmonic antennas for low-temperature catalytic processes.

Topological Quantum Optics Using Atomlike Emitter Arrays Coupled to Photonic Crystals.

We propose an experimentally feasible nanophotonic platform for exploring many-body physics in topological quantum optics. Our system is composed of a two-dimensional lattice of nonlinear quantum emitters with optical transitions embedded in a photonic crystal slab. The emitters interact through the guided modes of the photonic crystal, and a uniform magnetic field gives rise to large topological band gaps, robust edge states, and a nearly flat band with a nonzero Chern number. The presence of a topologically nontrivial nearly flat band paves the way for the realization of fractional quantum Hall states and fractional topological insulators in a topological quantum optical setting.

Barriers to Women Entering Surgical Careers: A Global Study into Medical Student Perceptions.

BACKGROUND: Barriers to female surgeons entering the field are well documented in Australia, the USA and the UK, but how generalizable these problems are to other regions remains unknown. METHODS: A cross-sectional survey was developed by the International Federation of Medical Students' Associations (IFMSA)'s Global Surgery Working Group assessing medical students' desire to pursue a surgical career at different stages of their medical degree. The questionnaire also included questions on students' perceptions of their education, resources and professional life. The survey was distributed via IFMSA mailing lists, conferences and social media. Univariate analysis was performed, and statistically significant exposures were added to a multivariate model. This model was then tested in male and female medical students, before a further subset analysis by country World Bank income strata. RESULTS: 639 medical students from 75 countries completed the survey. Mentorship [OR 3.42 (CI 2.29-5.12) p = 0.00], the acute element of the surgical specialties [OR 2.22 (CI 1.49-3.29) p = 0.00], academic competitiveness [OR 1.61 (CI 1.07-2.42) p = 0.02] and being from a high or upper-middle-income country (HIC and UMIC) [OR 1.56 (CI 1.021-2.369) p = 0.04] all increased likelihood to be considering a surgical career, whereas perceived access to postgraduate training [OR 0.63 (CI 0.417-0.943) p = 0.03], increased year of study [OR 0.68 (CI 0.57-0.81) p = 0.00] and perceived heavy workload [OR 0.47 (CI 0.31-0.73) p = 0.00] all decreased likelihood to consider a surgical career. Perceived quality of surgical teaching and quality of surgical services in country overall did not affect students' decision to pursue surgery. On subset analysis, perceived poor access to postgraduate training made women 60% less likely to consider a surgical career [OR 0.381 (CI 0.217-0.671) p = 0.00], whilst not showing an effect in the men [OR 1.13 (CI 0.61-2.12) p = 0.70. Concerns about high cost of training halve the likelihood of students from low and low-middle-income countries (LICs and LMICs) considering a surgical career [OR 0.45 (CI 0.25-0.82) p = 0.00] whilst not demonstrating a significant relationship in HIC or UMIC countries. Women from LICs and LMICs were 40% less likely to consider surgical careers than men, when controlling for other factors [OR 0.59 CI (0.342-1.01 p = 0.053]. CONCLUSION: Perceived poor access to postgraduate training and heavy workload dissuade students worldwide from considering surgical careers. Postgraduate training in particular appears to be most significant for women and cost of training an additional factor in both women and men from LMICs and LICs. Mentorship remains an important and modifiable factor in influencing student's decision to pursue surgery. Quality of surgical education showed no effect on student decision-making.

[Evaluation of the efficacy of Chinese Children's Asthma Action Plan on the long-term management of children with asthma at home].

Objective: During the COVID-19 pandemic, the electronic "questionnaire star" was employed to investigate the general situation, medication situation and pandemic impact of children diagnosed with asthma in our hospital and enrolled in the electronic platform of the Chinese Children's Asthma Action Plan (CCAAP). The internet technology and big data were used to grasp the trend and asthma control of children who had been at home for a long time due to the pandemic, so as to facilitate the management. Methods: By random cluster sampling survey method, according to the needs and medication adherence score, the electronic "questionnaire star" was designed to conduct a survey among children (0 to 14 years old) who diagnosed with asthma and joined the CCAAP on the basis of bronchial asthma medication adherence scale. Finally, the results of electronic questionnaire survey were analyzed. Results: A total of 423 questionnaires were sent out, 422 of which were valid, with an effective response rate of 99.7%. The results of questionnaire survey showed that 296 cases were male, accounting for 70.1%, and 126 cases were female, accounting for 29.9%, with an average age of (5.4±2.6) years old. The average age of males and females was (5.3±2.6) and (5.4±2.6) years old, respectively. There were more children aged ≥5 years than children who were younger than 5 years. Additionally, 13.95% of the parents thought that the pandemic had more than moderate impact on children with asthma, and 76.12% of the children were in the green zone and had no asthma attack. The proportion of green zone inhaled drugs (79.8%) was higher than yellow zone and red zone (49.8%). After using the CCAAP platform, the dissatisfaction rate was only 1.42%. Moreover, 71.87% of the children's medical expenses decreased, and the proportion of frequent use and intermittent use of antibiotics reduced, however, the proportion of occasional use and never use of antibiotics increased significantly (all P<0.05). The average score of drug compliance was 4.56, and the more frequently the platform was used, the higher the score of medication compliance was (P<0.05). Conclusions: After using CCAAP management with the aid of internet technology, children with asthma who had been isolated at home for a long time were less affected by COVID-19, with high medication compliance, generally lower medical expenses, significantly reduced use of antibiotics, and high satisfaction. This management mode provides a new idea for internet medicine.