Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning have raised higher demands for network architectures considering multiple design criteria: number of parameters/floating-point operations, and inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed $\texttt{EvoXBench}$, to generate benchmark test problems for EMO algorithms to run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of $\texttt{EvoXBench}$ is available from $\href{https://github.com/EMI-Group/EvoXBench}{\rm{here}}$

Numerical Simulation Analysis of Mechanical Properties on Rock Brittle–Ductility Transformation Under Different Loading Rates

At present, a large number of physical tests and numerical simulations have been carried out to study the effect of confining pressure on rock deformation mechanism, and some achievements have been achieved; however, the mechanism of rock deformation in actual mine engineering needs to be further studied, for example, rock-burst is actually a unilateral unloading process of rock mass, and this process can not be completed by physical test. RFPA3D was used to simulate the brittle–ductility transformation mechanical properties of rock under different confining pressures in this paper. The damage constitutive equation of rock was derived from continuum damage mechanics; the damage coefficients of different rocks were determined based on the numerical results of stress acoustic emission, so the correctness of rock damage constitutive equation was verified. According to the derived brittle–ductility damage equation and the fitting results of ductility cumulative damage data, it was found that the development trend of rock brittleness stage was almost the same, and the extended separation occurred after entering ductility stage. The larger the Poisson’s ratio was, the longer the ductility stage was. The smaller the Poisson’s ratio was, the shorter the ductility stage was, but the larger the bearing capacity was. At the late loading stage, the ductility cumulative damage of rock showed a linear upward trend, the bearing capacity sharply decreased, the rock stability failure occurred, and the ductility damage coefficient increased gradually. The study on the brittle–ductile mechanical properties of rocks can help to deep mine’s rock-burst prediction and prevention and has significant engineering significance

Towards a framework for better understanding of quiescent cancer cells

Quiescent cancer cells (QCCs) are cancer cells that are reversibly suspended in G0 phase with the ability to re-enter the cell cycle and initiate tumor growth, and, ultimately, cancer recurrence and metastasis. QCCs are also therapeutically challenging due to their resistance to most conventional cancer treatments that selectively act on proliferating cells. Considering the significant impact of QCCs on cancer progression and treatment, better understanding of appropriate experimental models, and the evaluation of QCCs are key questions in the field that have direct influence on potential pharmacological interventions. Here, this review focuses on existing and emerging preclinical models and detection methods for QCCs and discusses their respective features and scope for application. By providing a framework for selecting appropriate experimental models and investigative methods, the identification of the key players that regulate the survival and activation of QCCs and the development of more effective QCC-targeting therapeutic agents may mitigate the consequences of QCCs

Misuse of Statistical Methods in 10 Leading Chinese Medical Journals in 1998 and 2008

Statistical methods are vital to biomedical research. Our aim was to find out whether progress has been made in the last decade in the use of statistical methods in Chinese medical research. We reviewed 10 leading Chinese medical journals published in 1998 and in 2008. Regarding statistical methods, using a multiple t-test for multiple group comparison was the most common error in the t-test in both years, which significantly decreased in 2008. In contingency tables, no significant level adjustment for multiple comparison significantly decreased in 2008. In ANOVA, over a quarter of articles misused the method of multiple pair-wise comparison in both years, and no significant difference was seen between the two years. In the rank transformation nonparametric test, the error of using multiple pair-wise comparison for multiple group comparison became less common. Many mistakes were found in the randomised controlled trial (56.3% in 1998; 67.9% in 2008), non- randomised clinical trial (57.3%; 58.6%), basic science study (72.9%; 65.5%), case study or case series study (48.4%; 47.2%), and cross-sectional study (57.1%; 44.2%). Progress has been made in the use of statistical methods in Chinese medical journals, but much is yet to be done

The effect of prehabilitation on the postoperative outcomes of patients undergoing colorectal surgery: A systematic review and meta-analysis

Study objectivePrehabilitation is analogous to marathon training and includes preoperative preparation for exercise, as well as nutrition and psychology. However, evidence-based recommendations to guide prehabilitation before colorectal surgery are limited. We aimed to evaluate the effect of prehabilitation on the postoperative outcomes of patients undergoing colorectal surgery.DesignThis study is a systematic review and meta-analysis.MethodsThe PubMed, Embase, and Cochrane databases were searched for studies reporting the effect of prehabilitation strategies versus standard care or rehabilitation in patients undergoing colorectal surgery. The primary outcomes were overall postoperative complications and length of hospital stay (LOS), and the secondary outcome was functional capacity (measured using the 6-min walk test [6MWT]) at 4 and 8 weeks after surgery.Main resultsFifteen studies with 1,306 participants were included in this meta-analysis. The results showed no significant reduction in the number of overall postoperative complications (risk ratio = 1.02; 95% confidence interval [CI] = 0.79–1.31; p = 0.878) or LOS (standardized mean difference = 0.04; 95% CI = −0.11 to 0.20; p = 0.589) in patients who underwent colorectal surgery with or without prehabilitation strategy. Additionally, there were no significant differences in the functional capacity estimated using the 6MWT at 4 and 8 weeks postoperatively.ConclusionsPrehabilitation did not significantly affect the number of postoperative complications, LOS, or functional capacity of patients undergoing colorectal surgery. Whether prehabilitation should be recommended deserves further consideration.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=290108, identifier CRD4202129010

Leptomeningeal enhancement of myelin oligodendrocyte glycoprotein antibody-associated encephalitis: uncovering novel markers on contrast-enhanced fluid-attenuated inversion recovery images

BackgroundMyelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a newly defined autoimmune inflammatory demyelinating central nervous system (CNS) disease characterized by antibodies against MOG. Leptomeningeal enhancement (LME) on contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) images has been reported in patients with other diseases and interpreted as a biomarker of inflammation. This study retrospectively analyzed the prevalence and distribution of LME on CE-FLAIR images in children with MOG antibody-associated encephalitis (MOG-E). The corresponding magnetic resonance imaging (MRI) features and clinical manifestations are also presented.MethodsThe brain MRI images (native and CE-FLAIR) and clinical manifestations of 78 children with MOG-E between January 2018 and December 2021 were analyzed. Secondary analyses evaluated the relationship between LME, clinical manifestations, and other MRI measures.ResultsForty-four children were included, and the median age at the first onset was 70.5 months. The prodromal symptoms were fever, headache, emesis, and blurred vision, which could be progressively accompanied by convulsions, decreased level of consciousness, and dyskinesia. MOG-E showed multiple and asymmetric lesions in the brain by MRI, with varying sizes and blurred edges. These lesions were hyperintense on the T2-weighted and FLAIR images and slightly hypointense or hypointense on the T1-weighted images. The most common sites involved were juxtacortical white matter (81.8%) and cortical gray matter (59.1%). Periventricular/juxtaventricular white matter lesions (18.2%) were relatively rare. On CE-FLAIR images, 24 (54.5%) children showed LME located on the cerebral surface. LME was an early feature of MOG-E (P = 0.002), and cases without LME were more likely to involve the brainstem (P = 0.041).ConclusionLME on CE-FLAIR images may be a novel early marker among patients with MOG-E. The inclusion of CE-FLAIR images in MRI protocols for children with suspected MOG-E at an early stage may be useful for the diagnosis of this disease