Small Number Of Samples Research Articles

Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning

DNA is a valuable tool for classifying expression of genes in detection of breast cancer. Gene expression data are biological data that extract valuable hidden information from gene datasets. Extracting useful features from datasets is a challenging task. Our gene expression dataset had a small number of samples but many features. This paper compared three types of recurrent deep learning models, including recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for classification of breast cancer. The goals of the study were to improve the accuracy of classification and to enhance the effectiveness of feature selection; the basic principle was to select the best features from the original datasets. The bat algorithm assists in selecting the best relevant feature when integrated with recurrent deep learning models, which improves breast cancer classification by leveraging training datasets. Data preprocessing involves removing unnecessary columns and filling out missing values with the median value. The result was a comparative study using recurrent deep learning with the bat algorithm to classify breast cancer. The bat algorithm with LSTM achieved higher accuracy than RNN and GRU, where GRU had the lowest accuracy.

A Bayesian-Based Credible and Reliable Control Method for Active Vibration Suppression of Structures with Uncertain Parameters

As a classical design method for addressing uncertainty in control systems, robust control is designed with the goal of complete reliability of the control system. Nevertheless, the pursuit of robustness can inadvertently give rise to considerable performance degradation, rendering the resultant closed-loop control system excessively conservative in its design. Reliability-based design methods serve as an effective approach for managing uncertainties, which will achieve a balance between the robustness and performance of the active control system by taking advantage of the characteristic of reliability that allows for a certain level of permissible failure probability. The accurate quantification of uncertainty is the key to achieving reliable control, but common uncertainty quantification methods cannot achieve credible quantification of uncertainty in small samples and update the distribution of new samples. In this paper, a nonprobabilistic Bayesian inference based interval uncertainty quantification method is proposed. This method updates the distribution of interval boundaries and radii by introducing a small number of samples, and obtains interval parameters under a given confidence level. On the basis of uncertainty quantification, the dynamic reliability of the vibration system is derived, and nonprobabilistic reliability is introduced into the design of active controllers through optimization strategies. The reliable control strategy aims to select the optimal controller parameters on a reliable path with a given credibility, in order to solve the problem of overly conservative robust control theory. Two numerical examples were used to verify the feasibility and applicability of this method.

The German Tank Problem with Multiple Factories

During the Second World War, estimates of the number of tanks deployed by Germany were critically needed. The Allies adopted a successful statistical approach to estimate this information: assuming that the tanks are sequentially numbered starting from 1, if we observe k tanks from an unknown total of N, then the best linear unbiased estimator for N is M(1+1/k)-1 where M is the maximum observed serial number. However, in many situations, the original German Tank Problem is insufficient, since typically there are l > 1 factories, and tanks produced by different factories may have serial numbers in disjoint ranges that are often far separated. Clark, Gonye and Miller presented an unbiased estimator for N when the minimum serial number is unknown. Provided one identifies which samples correspond to which factory, one can then estimate each factory's range and summing the sizes of these ranges yields an estimate for the rival's total productivity. We construct an efficient procedure to estimate the total productivity and prove that it is effective when log l/log k is sufficiently small. In the final section, we show that given information about the gaps, we can make an estimator that performs orders of magnitude better when we have a small number of samples.

Measurement of platelet thrombus formation in patients following severe thermal injury

Severe thermal injury significantly impacts upon hemostasis and is associated with classical changes to the circulating platelet count with a nadir followed by a rebound thrombocytosis at days ~3 and ~15 post-injury, respectively. To date, few studies have assessed platelet function following thermal injury as platelet tests often require large quantities of blood, are not representative of normal platelet pathophysiology, and are usually dependent on a normal platelet count. The purpose of this study was to measure platelet thrombus formation in vitro using a whole blood flow chip-based system following thermal injury and to study how platelet counts may impact upon the measurement. Adult (≥16 years) patients (N = 10) with ≥ 20% total burn surface area (TBSA) burn were recruited within 24 h of injury. Healthy controls (N = 25) were also recruited. Whole blood counts were measured using a hematology analyzer (Sysmex XN-1000). Platelet function was measured using the Total Thrombus-formation Analyzer System (T-TAS) within chips coated with tissue factor and collagen at shear rates of either 600 sec−1 (AR chips) or 1200 sec−1 (HD chips), the latter test being independent of platelet count. We confirmed the classical nadir in platelet counts following severe thermal injury at days 2, 3, 4 (p < 0.0001) and day 5 (p < 0.01) post-injury compared to healthy controls. Physiological platelet thrombus formation was significantly (p < 0.01) abnormal at day 3 post-injury using the AR chips but was related to the platelet count. However, although platelet dysfunction was not significant using HD chips, some of the results were independent of platelet count. A small number of samples, however, still gave abnormal results suggesting that there can be an underlying acquired platelet functional abnormality. Furthermore, the AR chip Area Under the Curve (AUC) was significantly lower on day 1 post-injury and negatively associated with severity of injury (TBSA, p < 0.05) and higher platelet function (AUC) positively associated with survival (p < 0.05). This study suggests that measuring platelet dysfunction within a more physiological in vitro test may have potential clinical utility. Larger studies are required to fully understand the impact of platelet dysfunction following severe thermal injury.

Semi-supervised comparative learning compensation method for chemical gas sensor drift.

The gradual and unpredictable variation in chemo-sensory signal responses when exposed to the same analyte under identical conditions, commonly referred to as sensor drift, has long been recognized as one of the most serious challenges faced by chemical sensors. The traditional drift compensation method is both labor-intensive and expensive, as it requires frequent collection and labeling of gas samples for recalibration. Introducing a small number of meaningful drift calibration samples can be an attractive strategy to reduce the computational load and improve the performance of the updated classifier. However, under the influence of drift, new challenges arise due to the difference in the distribution of source and target domain data. This paper proposes a novel algorithm framework called semi-supervised contrastive learning drift compensation (SSCLDC). The framework automatically extracts high-level abstract features based on a multilayer perceptron to better represent the structure of the source data. In addition, to address the issue of data distribution differences caused by drift between the source and target domains. We add a small number of reference sample pairs into the training for semi-supervised learning. Combining a contrastive loss function that can represent the matching degree of paired samples effectively overcomes the problem of sensor drift. The Kennard-Stone sequential algorithm is used to select the representative reference sample from the set of candidate reference samples. Experiments conducted on a widely used long-term chemical gas sensor drift dataset demonstrate that the proposed method outperforms several classic drift compensation techniques, highlighting its effectiveness and practical applicability.

Research on the energy absorption performance of lattice filling structures based on homogenization analysis technique

The lattice structure has great application prospects in the field of energy absorption, but the high cost of numerical analysis makes it difficult to carry out related structural designs. Therefore, this article studies the feasibility of the homogenization analysis technique for the analysis of energy absorption processes in lattice-filled structures. In the study, compression experiments and numerical simulations were conducted for four different types of unit cells (including periodic truss-lattice cells and triply periodic minimal surface (TPMS cells). In the experiments, two compression samples with different numbers of periods were designed for the same unit cell structure. The research shows that the homogenized numerical model takes a lot less time to run than the classical beam and shell element models. The results of the simulation are also in line with the data from the experiments. On the other hand, the material curve constructed based on a small number of unit cell samples has excellent analytical accuracy. This means that relevant research can obtain the material experimental data required for homogenized analysis using lattice samples with low manufacturing costs. Understanding these phenomena can provide a basis for the design of energy absorption structures.

Improved MRF rail surface defect segmentation method based on clustering features

Aiming at the characteristics of small number and many types of rail surface defect samples, as well as the problems of unstable transfer learning effect and threshold segmentation being easily affected by environmental factors in real scenes, an improved Markov defect segmentation method with zero samples is proposed. Firstly, the collected data is processed by Gabor function to highlight the defect features and reduce the data dimension to obtain the reduced dimension feature map; Kmeans clustering is performed on the processed feature map to reduce the distribution of data and reduce the influence of reflection and shadow, and the clustering result is used as the pre-classification matrix; an improved Markov random field two-layer graph model is constructed and inferred through the reduced dimension feature map and the pre-classification matrix; the local geometric structure of the defect part is analyzed according to the eigenvalues of the classification matrix inferred by the model; finally, the defect area is marked and the defect segmentation is completed. The experimental part uses a self-sampling data set, and the final conclusion is drawn based on the comparative experiment and ablation experiment. The experimental results show that the pixel accuracy, average pixel accuracy, weighted intersection-over-union ratio, and average intersection-over-union ratio of this method on the self-sampling data set are respectively 93.6%、80.7%、89.4%、68.2% , which exceeds the accuracy of other comparative detection algorithms.

Metagenomic discovery of microbial eukaryotes in stool microbiomes.

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

Physiological regulation of oral saliva ion composition and flow rate are not coupled in healthy humans-Partial revision of our current knowledge required.

Appropriate composition of oral saliva is essential for a healthy milieu that protects mucosa and teeth. Only few studies, with small sample numbers, investigated physiological saliva ion composition in humans. We determined saliva ion composition in a sufficiently large cohort of healthy adults and analyzed the effect of physiological stimulation. We collected saliva from 102 adults under non-stimulated and physiologically stimulated conditions (chewing). Individual flow rates, pH, osmolality, Na+, K+, Cl-, and HCO3- concentrations under both conditions as well as the individual changes due to stimulation (Δvalues) were determined. Non-stimulated saliva was hypoosmolal and acidic. Na+, Cl-, and HCO3- concentrations remained well below physiological plasma values, whereas K+ concentrations exceeded plasma values more than twofold. Stimulation resulted in a doubling of flow rates and substantial increases in pH, HCO3-, and Na+ concentrations. Overall, stimulation did not considerably affect osmolality nor K+ or Cl- concentrations of saliva. An in-depth analysis of stimulation effects, using individual Δvalues, showed no correlation of Δflow rate with Δion concentrations, indicating independent regulation of acinar volume and ductal ion transport. Stimulation-induced Δ[Na+] correlated with Δ[HCO3-] and Δ[Cl-] but not with Δ[K+], indicating common regulation of ductal Na+, Cl-, and HCO3- transport. We present a robust data set of human oral saliva ion composition in healthy adults and functional insights into physiological stimulation. Our data show (i) that flow-dependence exists for Na+ and HCO3- but not for K+ and Cl- concentrations, (ii) osmolality is flow-independent, (iii) regulation of Na+, Cl-, and HCO3- transport is coupled, (iv) regulation of flow rate and ion concentrations are independent and (v) spatially separated between acini and ducts, respectively.

A-108 Re-evaluation of reference intervals for circulating albumin, total calcium, and magnesium levels among subjects in Dallas County

Abstract Background Reference intervals are either directly established from normal volunteers, adopted from assay manufactures and verified as transferable to the normal population, or indirectly derived using patients’ archived laboratory data. This study describes indirect derivation of reference intervals for albumin, total calcium, and magnesium appropriate to the patient population of Dallas County. The tests were selected due to their often combine measurement in the assessment of calcium and bone mineral metabolism Methods Retrospective reported laboratory results during May 2022 for albumin (n=1342), total calcium (n=1945), and magnesium (n=2148) were obtained. Values were subjected to statistical analysis for nature of distribution, mean, and standard deviation as well as stratified for patients’ characteristics (gender, race, and age). Statistical analysis applied were Hoffman regression analysis, one-way ANOVA, and Tukey-Kramer tests. Results Following removal of outliers, statistically suggested reference intervals for albumin, total calcium, and magnesium were 5.5 to 2.4 g/dL, 10.5 to 7.5 mg/dL, and 2.6 to 1.6 mg/dL respectively. Although there was race specific differences in albumin and calcium levels (p=0.032) and (p=0.002) respectively, differences for magnesium were slightly nonsignificant (p=0.06). Similarly, significant differences due to age were observed for albumin, total calcium, and magnesium (P &amp;lt;0.00 for all). The data suggests that albumin, magnesium, and calcium levels are unequivocally dependent on age and race for patients in Dallas County. The upper limits of population derived, and manufacturer verified reference intervals were 5.45 and 5.2 g/dL for albumin, 10.5 and 10.2 mg/dL for calcium, and 4.6, and 2.6 mg/dL for magnesium respectively, the lower limits were 2.4 and 3.5, 7.5 and 8.4, 1.4, and 1.6 mg/dL respectively. Conclusions Reference intervals for albumin, total calcium, and magnesium appropriate to the patient population of Dallas County were derived. Derived values were statistically different from the manufacturer verified values in use. The study although limited by the small number of acceptable samples, highlights the need for laboratories to derive their respective population-based reference intervals. The study will be expanded to other biochemical parameters utilizing much larger sample volume.

Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning.

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.

Fault diagnoses of a nonlinear cracked rotor-bearing system based on vibration energy space and incremental learning approach

Health services of rotor-bearing systems are directly related to safe and stable works of rotating machineries in the industrial production. To address the issues of a low impact on vibration characteristics caused by faults in the higher rotational speed region, narrow and weak excitation signals, high dimensionality related to fault induction mechanisms, and difficulty in diagnosing using artificial intelligence technology due to the small number of fault samples in rotor systems, a novel fault detection method of nonlinear rotor systems through the incremental two-dimensional principal component analysis (I2DPCA) of the machine learning on the vibration energy space is proposed. Unlike mainly analyzing dynamic characteristics of rotor-bearing systems in vibration space, the method applies signals of the vibration energy space such as energy tracks, energy-time histories and energy-frequency spectra to have more advantages in vibration amplitudes caused by cracks in the higher rotational speed region and to overcome narrow and weak signals in the early fault stage. Then, the I2DPCA is applied to extract online low dimensional fault features and to process small vibration features of the rotor system. Simulation and experiment results show that performances of between-class margins and within-class clusters of different health conditions on the vibration energy space are more perfect than that on the vibration space. Based on the I2DPCA algorithm and the energy space method, the recognition rate of crack faults within the high rotational speed region is up to 99.6%, and it has good convergence and online learning ability in the case of small samples. The achieved conclusions of the method could provide a novel reference direction for fault diagnoses of rotor systems.

Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels. Accordingly, a more sensitive methodology must still be identified. It is imperative that new diagnostic methods be non-invasive, cost-effective, rapid, and highly sensitive. Fourier transform infrared spectroscopy (FTIR) is a technique that fulfils all of the aforementioned criteria. Consequently, the present study used FTIR to assess dried serum samples obtained from a cohort of prostate cancer patients (n = 53) and a control group of healthy individuals (n = 40). Furthermore, this study proposes FTIR markers of prostate cancer obtained from serum. For this purpose, FTIR spectra of dried serum were measured and analysed using statistical, chemometric and machine learning (ML) algorithms including decision trees C5.0, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM). The FTIR spectra of serum collected from patients suffering from prostate cancer exhibited a reduced absorbance values of peaks derived from phospholipids, amides, and lipids. However, these differences were not statistically significant. Furthermore, principal component analysis (PCA) demonstrated that it is challenging to distinguish serum samples from healthy and non-healthy patients. The ML algorithms demonstrated that FTIR was capable of differentiating serum collected from both analysed groups of patients with high accuracy (values between 0.74 and 0.93 for the range from 800 cm−1 to 1800 cm−1 and around 0.70 and 1 for the range from 2800 cm−1 to 3000 cm−1), depending on the ML algorithms used. The results demonstrated that the peaks at 1637 cm−1 and 2851 cm−1 could serve as a FTIR marker for prostate cancer in serum samples. Furthermore, the correlation test indicated a clear correlation between these two wavenumbers and four of the five clinical parameters associated with prostate cancer. However, the relatively small number of samples collected only from patients over the age of 60 indicated that the results should be further investigated using a larger number of serum samples collected from a mean age range. In conclusion, this study demonstrated the potential of FTIR for the detection of prostate cancer in serum samples, highlighting the presence of distinctive spectroscopic markers associated with the analysed cancer type.

Seropositivity to louping ill virus in dogs in the UK.

Louping ill virus (LIV) is a tick-borne flavivirus that can cause fatal meningoencephalomyelitis in dogs. Four dogs with confirmed LIV infection and a case series of dogs with suspected flavivirus infection have been reported in the UK. However, underreporting of LIV infection due to lack of testing is suspected. Surplus serum/plasma from 220 dogs was used to determine the seroprevalence of LIV by haemagglutination inhibition (HAI) test. Signalment and environmental factors were investigated for potential correlations with a positive titre (serum dilution of 1:20 or more). Two hundred and two dogs were suitable for inclusion in the study, nine of which (4.5%) were seropositive. Among the dogs investigated for neurological disease (40/202; 19.8%), six (15%) were seropositive. Ectoparasiticide use approached significance (p = 0.055) for being protective against LIV seropositivity. The main limitations were the specificity of the HAI test, the relatively small number of samples, the low number of seropositive dogs, the poor geographical distribution of the samples and the inherent limitations of questionnaire-based research. The seroprevalence of LIV in the UK dog population appears to be low. However, LIV should be considered in dogs presenting with unexplained acute or subacute progressive neurological clinical signs, especially because of the recent reports of several dogs with clinical flavivirus infections.

Prompt-guided and multimodal landscape scenicness assessments with vision-language models.

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of VLMs enable new opportunities for the annotation and analysis of images. We test the potential of VLMs for landscape scenicness prediction, i.e., the aesthetic quality of a landscape, using zero- and few-shot methods. We experiment with few-shot learning by fine-tuning a single linear layer on a pre-trained VLM representation. We find that a model fitted to just a few hundred samples performs favourably compared to a model trained on hundreds of thousands of examples in a fully supervised way. We also explore the zero-shot prediction potential of contrastive prompting using positive and negative landscape aesthetic concepts. Our results show that this method outperforms a linear probe with few-shot learning when using a small number of samples to tune the prompt configuration. We introduce Landscape Prompt Ensembling (LPE), which is an annotation method for acquiring landscape scenicness ratings through rated text descriptions without needing an image dataset during annotation. We demonstrate that LPE can provide landscape scenicness assessments that are concordant with a dataset of image ratings. The success of zero- and few-shot methods combined with their ability to use text-based annotations highlights the potential for VLMs to provide efficient landscape scenicness assessments with greater flexibility.

Exploration of Deep-Learning-Based Approaches for False Fact Identification in Social Judicial Systems

With the many applications of artificial intelligence (AI) in social judicial systems, false fact identification becomes a challenging issue when the system is expected to be more autonomous and intelligent in assisting a judicial review. In particular, private lending disputes often involve false facts that are intentionally concealed and manipulated due to unique and dynamic relationships and their nonconfrontational nature in the judicial system. In this article, we investigate deep learning techniques to identify false facts in loan cases for the purpose of reducing the judicial workload. Specifically, we adapt deep-learning-based natural language processing techniques to a dataset over 100 real-world judicial rules spanning four courts of different levels in China. The BERT (bidirectional encoder representations from transformers)-based classifier and T5 text generation models were trained to classify false litigation claims semantically. The experimental results demonstrate that T5 has a robust learning capability with a small number of legal text samples, outperforms BERT in identifying falsified facts, and provides explainable decisions to judges. This research shows that deep-learning-based false fact identification approaches provide promising solutions for addressing concealed information and manipulation in private lending lawsuits. This highlights the feasibility of deep learning to strengthen fact-finding and reduce labor costs in the judicial field.

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Image aesthetics assessment (IAA) is inherently subjective, with generic image aesthetics assessment (GIAA) often overlooking individual user preferences. Personalized aesthetic assessment (PIAA) addresses this by accounting for user-specific inconsistencies. However, the limited annotated data from individual users and the scarcity of user-jointly annotated image data pose challenges. To cope with these problems, we propose a multi-scale regional attribute-weighting via meta-learning for PIAA, namely MRAM, which aims to simulate how the brain computes aesthetic values from visual stimuli. The human brain is to predict the subjective aesthetic value of the individual to the image by combining the features of the visual scene segmentation through the weighting method. MRAM introduces a multi-scale attention module to capture visual scene regions segmented at different scales, adjusting feature selection based on user attention. Additionally, a regional attribute-weighting module (RAM) is proposed to weigh these features using aesthetic attribute representations. In the meta-training phase, MRAM learns aesthetic prior knowledge from a diverse set of PIAA tasks. Transitioning to the personalization phase, MRAM is fine-tuned with only a small number of specific user samples, enabling it to rapidly adapt to the unique aesthetic preferences of individuals. Experimental results demonstrate the superior performance of the proposed MRAM compared to state-of-the-art PIAA methods.

An improved sequential importance sampling method for structural reliability analysis of high dimensional problems

Reliability analysis for rare events has proven to be a significant challenge, particularly in non-linear and complex scenarios. Despite the effectiveness of Sequential Importance Sampling (SIS) and Subset Simulation (SUS) in addressing high-dimensional small failure probability problems, the computational burden associated with mechanical simulations for complex structures remains substantial. To address this issue, this paper introduces a novel approach referred to as SIS and Kriging Metamodel Integration (SISAK), designed for computing small failure probabilities in high dimensions. This method involves generating a small number of samples from the original distribution to construct an adaptive metamodel, which is then iteratively optimized using a series of intermediate distributions introduced by SIS. By substituting the function for intermediate distributions with a continuously optimized metamodel, the enhanced approach significantly reduces the computational load associated with mechanical model evaluations compared to the direct use of SIS. This approach does not depend on determining the most probable failure point and does not require consideration of the failure domain shape. Additionally, it inherits the advantages of SIS in addressing high-dimensional small failure probabilities, making it well-suited for structural reliability analysis of nonlinear systems, discontinuous failure domains, cascading functions, and high-dimensional problems.

Ultrasensitive mass spectrometric quantitation of apurinic/apyrimidinic sites in genomic DNA of mammalian cell lines exposed to genotoxic reagents

The apurinic/apyrimidinic (AP) site is an important intermediate in the DNA base excision repair (BER) pathway, having the potential of being a biomarker for DNA damage. AP sites could lead to the stalling of polymerases, the misincorporation of bases and DNA strand breaks, which might affect physiological function of cells. However, the abundance of AP sites in genomic DNA is very low (less than 2 AP sites/106 nts), which requires a sensitive and accurate method to meet its detection requirements. Here, we described an ultrasensitive quantification method based on a hydrazine-s-triazine reagent (i-Pr2N) labeling for AP sites combining with high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The limit of detection reached an ultralow level (40 amol), realizing the most sensitive MS-based quantification for the AP site. To guarantee the accuracy of the quantitative results, the labeling reaction was carried out directly on DNA strands instead of labeling after DNA enzymatic digestion to reduce artifacts that might be produced during the enzymatic process of DNA strands. And selective detection was realized by MS to avoid introducing the false-positive signals from other aldehyde species, which could also react with i-Pr2N. Genomic DNA samples from different mammalian cell lines were successfully analyzed using this method. There were 0.4–0.8 AP sites per 106 nucleotides, and the values would increase 16.1 and 2.75 times when cells were treated with genotoxic substances methyl methanesulfonate and 5-fluorouracil, respectively. This method has good potential in the analysis of a small number of cell samples and clinical samples, is expected to be useful for evaluating the damage level of DNA bases, the genotoxicity of compounds and the drug resistance of cancer cells, and provides a new tool for cell function research and clinical precise treatment.

SUB-immunogold-SEM reveals nanoscale distribution of submembranous epitopes

Electron microscopy paired with immunogold labeling is the most precise tool for protein localization. However, these methods are either cumbersome, resulting in small sample numbers and restricted quantification, or limited to identifying protein epitopes external to the membrane. Here, we introduce SUB-immunogold-SEM, a scanning electron microscopy technique that detects intracellular protein epitopes proximal to the membrane. We identify four critical sample preparation factors contributing to the method’s sensitivity. We validate its efficacy through precise localization and high-powered quantification of cytoskeletal and transmembrane protein distribution. We evaluate the capabilities of SUB-immunogold-SEM on cells with highly differentiated apical surfaces: (i) auditory hair cells, revealing the presence of nanoscale MYO15A-L rings at the tip of stereocilia; and (ii) respiratory multiciliate cells, mapping the distribution of the SARS-CoV-2 receptor ACE2 along the motile cilia. SUB-immunogold-SEM extends the application of SEM-based nanoscale protein localization to the detection of intracellular epitopes on the exposed surfaces of any cell.