Connectomics: A New Direction in Research to Understand the Mechanism of Acupuncture

Acupuncture has been used to treat various disorders in China and some other eastern countries for thousands of years. Nowadays, acupuncture is gradually accepted as an alternative and complementary method in western countries for its undeniable therapeutic effects. However, its central mechanism is still unclear. It is especially difficult to reveal how different regions in the brain influence one another and how the relationship is among these regions responding to acupuncture treatment. Recently, by applying neuroimaging techniques and network theory, acupuncture studies can make further efforts to investigate the influence of acupuncture on regional cerebral functional connectivity (FC) and the modulation on “acupuncture-related” networks. Connectomics appears to be a new direction in research to further understand the central mechanism underlying acupuncture. In this paper, an overview of connectomics application in acupuncture research will be discussed, with special emphasis on present findings of acupuncture and its influence on cerebral FC. Firstly, the connectomics concept and its significance on acupuncture will be outlined. Secondly, the commonly used brain imaging techniques will be briefly introduced. Thirdly, the influence of acupuncture on FC will be discussed in greater detail. Finally, the possible direction in forthcoming research will be reviewed by analyzing the limitation of present studies

Discovery of potential biomarkers for osteoporosis using LC/GC−MS metabolomic methods

PurposeFor early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC−MS in a Chinese elderly population to find possible diagnostic biomarkers.MethodsA total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC−MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.ResultsWe screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.ConclusionThese findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals

Influence of load partial factors adjustment on reliability design of RC frame structures in China

Abstract The partial factor method has been widely used in building design and the partial factors to ensure the safety of structures are specified in the adopted codes. The load partial factors in the design expressions have been increased in the lasted code in China, which leads to theoretically increment in reliability and a growth in the consumption of construction materials. However, the influence of load partial factors adjustment on design of building structures arises different points among scholars. Some believe that it has a great impact on the design, some think the influence is small. This makes designers have doubts in the safety of structures and investors are also confused about the cost. In order to illustrate the influence of load partial factor adjustment on safety level and material consumption of RC (Reinforced Concrete) frame structures, reliability analysis and material consumption analysis are performed using First Order Reliability Method (FORM). The approach is carried out according to the load partial factors in Chinese codes of (GB50153-2008) and (GB50068-2018), respectively. Then, the influence of load partial factors adjustment is demonstrated with a case design of RC frame structures with different load partial factors in codes. The results show that the partial factor has a noticeable influence on the reliability index. The adjustment of load partial factors in design leads to an increase of the reliability index, which is about 8–16%. The increase of material consumption used in RC structures is about 0.75–6.29%. And the case indicated that the adjustment of load partial factors mainly result in the increase of reinforcement consumption, while have little effect on the concrete consumption. This study provides an analytical and conclusive insight into the influence of load partial factor adjustment on safety level and material consumption, which is can be applied to a wide range of structures

Study on Partial Factor of Load for Reinforced Concrete Columns

At present, the design method of components is still a partial factor design method, and the partial factor value is related to the load value. Because the partial factor has a great influence on the safety of engineering structure, it has been adjusted many times in the process of organization of the code. In order to be basically equivalent to European and American reliability standards and to conform to China’s national conditions and national policies, the Unified standard for reliability design of building structures is revised (i.e., the partial factor of permanent action and variable action was adjusted). Although the concept of factor of safety is commonly used in structure design practice to cover all the unexpected risks, there are some disadvantages to its direct use in structural reliability analysis. For example, the eccentricity of compression members is random, which will lead to the change in resistance parameters of compression members, rather than the fixed value specified in the code. However, the random variation in eccentricity is not considered in the code. So, in this paper, the partial factors of eccentrically loaded members are studied by considering the statistical parameter information of members with random eccentricity. This paper studies the partial factors of different types of components in different ratios of live load effect to dead load effect, and some recommendations are proposed to obtain safer designs. Finally, Monte Carlo simulation method is used to analyze the reliability of the eccentric member. The research results show that the value of partial factors of structure proposed in this paper is reasonable

Prediction method of pipeline corrosion depth based on the correlation and Bayesian inference

The number of samples for detecting corrosion characteristic value is difficult to reach a large enough size in practical engineering, which leads to the pipeline corrosion evaluation results tend to be aggressive. For this reason, the influence of sample size on the inference results was analyzed, and based on the Bayesian theory and the uncertainty of measurement, the Bayesian inference method for the pipeline corrosion depth under the condition of small sample size was proposed. Then, the correlation between the corrosion depth and the length was considered, and the prediction method of corrosion depth based on the correlation and Bayesian inference was developed. Thereby, the corrosion depths under different defect lengths was inferred with the pipeline corrosion detection data, and further the effectiveness of the method was verified. The results indicate that: the new method could better reflect the influence of sample size on the inference results, the prediction results are more conservative and consistent with the engineering experience, and so it is safer and more favorable to the engineering application. The research results could provide more accurate information to the prediction of pipeline corrosion depth, as well as theoretical reference to the prediction of the characteristic value of other corroded pipelines with consideration given to the correlation of random variables

Study on regeneration performance of carbon fluoride adsorbent in SF

To study the regeneration of CF-100 type microcrystalline material after adsorbing carbon fluorides in SF6, an experimental device was built that can achieve both vacuum desorption of carbon fluorides and auxiliary heating to promote vacuum desorption of carbon fluorides. Thermogravimetric analyzer and Fourier transform infrared spectrometer were used to analyze the optimal treatment temperature and time of regeneration technology for promoting vacuum desorption of fluorocarbon by auxiliary heating. A comparative study was conducted on the two regeneration techniques mentioned above by analyzing the relationship between adsorption performance and the number of regenerations. The regeneration effects were also validated by using a specific surface area tester to examine the test data. The results demonstrate that desorption of fluorocarbons from CF-100 microcrystalline material is more efficient using a vacuum desorption method aided by auxiliary heating, as opposed to pure vacuum desorption. The CF-100 type of microcrystalline material achieves the best desorption effect when heated to 300°C with an auxiliary heating duration of 2 h, with an activation energy of 293.933 KJ/mol. After activation, CF-100 microcrystalline materials exhibit stable adsorption and desorption performance for decarbonization and possess excellent recyclability

Prompt-Guided Adaptive Model Transformation for Whole Slide Image Classification

Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). Existing approaches typically rely on frozen pre-trained models to extract instance features, neglecting the substantial domain shift between pre-training natural and histopathological images. To address this issue, we propose PAMT, a novel Prompt-guided Adaptive Model Transformation framework that enhances MIL classification performance by seamlessly adapting pre-trained models to the specific characteristics of histopathology data. To capture the intricate histopathology distribution, we introduce Representative Patch Sampling (RPS) and Prototypical Visual Prompt (PVP) to reform the input data, building a compact while informative representation. Furthermore, to narrow the domain gap, we introduce Adaptive Model Transformation (AMT) that integrates adapter blocks within the feature extraction pipeline, enabling the pre-trained models to learn domain-specific features. We rigorously evaluate our approach on two publicly available datasets, Camelyon16 and TCGA-NSCLC, showcasing substantial improvements across various MIL models. Our findings affirm the potential of PAMT to set a new benchmark in WSI classification, underscoring the value of a targeted reprogramming approach