Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy

BACKGROUND Improved adenoma detection rate(ADR)has been demonstrated with artificial intelligence(AI)-assisted colonoscopy.However,data on the real-world appli-cation of AI and its effect on colorectal cancer(CRC)screening outcomes is limited.AIM To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or sympt-oms.METHODS AI software(GI Genius,Medtronic)was implemented into the standard proced-ure protocol in November 2022.Data was collected on patient demographics,procedure indication,polyp size,location,and pathology.CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.RESULTS We evaluated 1008 colonoscopies(278 pre-AI,255 early post-AI,285 established post-AI,and 190 late post-AI).The ADR was 38.1%pre-AI,42.0%early post-AI(P=0.77),40.0%established post-AI(P=0.44),and 39.5%late post-AI(P=0.77).There were no significant differences in polyp detection rate(PDR,baseline 59.7%),advanced ADR(baseline 16.2%),and non-neoplastic PDR(baseline 30.0%)before and after AI introduction.CONCLUSION In patients with an increased pre-test probability of having an abnormal colonoscopy,the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy.Although the potential of AI in colonoscopy is undisputed,current AI technology may not universally elevate screening metrics across all situations and patient populations.Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-company features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We added handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.

Genomic Mutations Within the Host Microbiome: Adaptive Evolution or Purifying Selection

Next-generation sequencing technology has transformed our ability to assess the taxonomic composition functions of host-associated microbiota and microbiomes. More human microbiome research projects—particularly those that explore genomic mutations within the microbiome—will be launched in the next decade. This review focuses on the coevolution of microbes within a microbiome, which shapes strain-level diversity both within and between host species. We also explore the correlation between microbial genomic mutations and common metabolic diseases, and the adaptive evolution of pathogens and probiotics during invasion and colonization. Finally, we discuss advances in methods and algorithms for annotating and analyzing microbial genomic mutations.

有高温相变的电工钢热轧起浪的有限元分析

针对电工钢自由规程轧制过程起浪问题,开展电工钢不同冷却条件下的连续冷却相变转变温度分析测定和Gleeble热模拟实验,发现电工钢在975~875℃的奥氏体-铁素体两相区,随着轧制温度降低,变形抗力反而减小;电工钢大量同宽自由规程轧制下的工作辊出现严重的不均匀的箱型磨损和明显的热胀,综合辊形变化显著.考虑电工钢两相区变形抗力差异和综合辊形变化,建立电工钢热轧过程轧辊轧件的三维弹塑性耦合有限元模型,仿真分析轧制力、弯辊与窜辊对承载辊缝形状的影响,研究摩擦系数、压下量与轧制速度对带钢宽度方向内应力变化规律的影响,采用Shohet板形判据确定电工钢比例凸度残差变化路径,明确电工钢在“平坦死区”较大的上游机架出现“异常起浪”的生成过程.该方法为电工钢热轧板形的浪形控制提供了依据.

基于CAD和CFD技术的高效叶片泵流场仿真与优化分析

为了提高高效节能叶片泵的设计效率和降低实际工况的不确定性产生的"无效能耗",基于CAD和CFD技术,对高效叶片泵流场进行仿真与优化分析.以SS300-560型离心泵为对象,采用Pro/E和Fluent软件分别建立了高效叶片泵三维造型和CFD模型,结合实际条件仿真分析了叶轮内部流动的压力场和速度场,明确了具体叶型产生的局部湍流及汽蚀现象;建立了以离心泵效率最大、汽蚀余量最小为目标,以无驼峰曲线和无过载为约束的多目标函数优化模型,采用遗传算法对叶轮设计参数进行求解,提高了泵体效率并降低了汽蚀余量.

Multi-Material ALE with AMR for Modeling Hot Plasmas and Cold Fragmenting Materials

We have developed a new 3D multi-physics multi-material code, ALE-AMR, which combines Arbitrary Lagrangian Eulerian （ALE） hydrodynamics with Adaptive Mesh Refinement （AMR） to connect the continuum to the microstructural regimes. The code is unique in its ability to model hot radiating plasmas and cold fragmenting solids. New numerical techniques were developed for many of the physics packages to work efficiently on a dynamically moving and adapting mesh. We use interface reconstruction based on volume fractions of the material components within mixed zones and reconstruct interfaces as needed. This interface reconstruction model is also used for void coalescence and fragmentation. A flexible strength/failure framework allows for pluggable material models, which may require material history arrays to determine the level of accumulated damage or the evolving yield stress in J2 plasticity models. For some applications laser rays are propagating through a virtual composite mesh consisting of the finest resolution representation of the modeled space. A new 2nd order accurate diffusion solver has been implemented for the thermal conduction and radiation transport packages. One application area is the modeling of laser/target effects including debris/shrapnel generation. Other application areas include warm dense matter, EUV lithography, and material wall interactions for fusion devices.

Seismic hazard mitigation for nuclear power plant

The seismic safety of nuclear power plan(tNPP)has always been a major consideration in the site selection,design,operation,and more recently recertification of existing installations. In addition to the actual NPP and all their operational and safety related support systems,the storage of spent fuel in temporary or permanent storage facilities also poses a seismic risk. This seismic risk is typically assessed with state-of-the-art modeling and analytical tools that capture everything from the ground rupture or source of the earthquake to the site specific ground shaking,taking geotechnical parameters and soilfoundationstructureinteraction (SFSI) into account to the non-linear structural response of the reactor core,the containment structure,the core cooling system and the emergency cooling system(s),to support systems,piping systems and non-structural components,and finally the performance of spent fuel storage in the probabilistically determined operational basis earthquake (OBE) or the safe shutdown earthquake (SSE) scenario. The best and most meaningful validation and verification of these advanced analytical tools is in the form of full or very large scale experimental testing,designed and conducted in direct support of model and analysis tool calibration. This paper outlines the principles under which such calibration testing should be conducted and illustrates with examples the kind of testing and parameter evaluation required.

Additively manufactured metallic biomaterials

Metal additive manufacturing(AM)has led to an evolution in the design and fabrication of hard tissue substitutes,enabling personalized implants to address each patient’s specific needs.In addition,internal pore architectures integrated within additively manufactured scaffolds,have provided an opportunity to further develop and engineer functional implants for better tissue integration,and long-term durability.In this review,the latest advances in different aspects of the design and manufacturing of additively manufactured metallic biomaterials are highlighted.After introducing metal AM processes,biocompatible metals adapted for integration with AM machines are presented.Then,we elaborate on the tools and approaches undertaken for the design of porous scaffold with engineered internal architecture including,topology optimization techniques,as well as unit cell patterns based on lattice networks,and triply periodic minimal surface.Here,the new possibilities brought by the functionally gradient porous structures to meet the conflicting scaffold design requirements are thoroughly discussed.Subsequently,the design constraints and physical characteristics of the additively manufactured constructs are reviewed in terms of input parameters such as design features and AM processing parameters.We assess the proposed applications of additively manufactured implants for regeneration of different tissue types and the efforts made towards their clinical translation.Finally,we conclude the review with the emerging directions and perspectives for further development of AM in the medical industry.