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Abstract Background Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives. Method We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation. Results Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability. Conclusion ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.

Abstract Proteolysis‐targeting chimeras (PROTACs) is a targeted protein degradation (TPD) technique effected by hijacking the ubiquitin‐proteasome system (UPS) of the cells. A PROTAC molecule specifically binds to its protein of interest (POI) and recruits an E3 ligase to assemble a ternary complex. The POI was subsequently ubiquitinated, followed by being degraded by proteasomes. After 20 years of development, PROTAC technology has made a significant progress, and quite some candidates have entered clinical trials. Along with the excitement of realizing that PROTAC technology can develop therapeutics toward those traditionally believed “undruggable” targets; there are still unmet demands in PROTAC design, screening, and intracellular availability. PROTAC technology is rapidly advancing from employing traditional medicinal chemistry methodologies to integrating state‐of‐the‐art chemical biology technologies, becoming a typical example of interdisciplinary medicine. This review summarizes the progress made in PROTAC technology in recent years, including expanding new targets, developing dual‐target PROTACs, rational design and screening strategies, regulatory activation, targeted delivery, and developing biological macromolecule‐contained PROTACs.

Sea water intrusion is an important environmental issue that plagues the economic and social development in coastal areas. The implementation of quantitative simulation, prediction and visual management of sea water intrusion is a cost-effective means for monitoring and mechanism analysis of sea water intrusion, which can provide important theoretical and technical supports for the formulation of sea water intrusion control measures. VFT3D was proposed to model the sea water intrusion processes and predict the impacts on groundwater, which can comprehensively consider the effects of surface water and groundwater, simulate variable-density groundwater flow, and solve complex reactive migration problems. This article provides an introduction to the VFT3D model and presents an application of VFT3D to modeling seawater intrusion process. The simulated results are compared with those of the SEAWAT. The results show that the simulated water heads with VFT3D are close to the SEAWAT simulation results. The SEAWAT is unable to simulate complex chemical reactions during the sea water intrusion. The results indicate that geochemical processes (cation adsorption and exchange) have a significant effect on the movement of cations (Na+, K+, Mg2+ and Ca2+). Geochemical process leads to the concentration change in the transition zone, whose influence on the process of sea water intrusion can not be ignored. Therefore, the variable density and complex reaction process should be involved to more accurately describe the sea water intrusion, which can benefit the implementation of the underground saltwater remediation and management.

Background: Nonalcoholic fatty liver disease (NAFLD) is a common component of chronic liver disease. Total bile acid (TBA) may influence the NAFLD progression through its signaling pathways. We attempted to find out if there is a correlation between TBA and NAFLD. Methods: 427,507 subjects were enrolled in health examinations conducted by The First Affiliated Hospital of Wenzhou Medical University. Among them, only 67616 met the inclusion criteria. Demographic, clinical, and laboratory data were gathered from all subjects. We used multivariate logistic regression model to find the correlation between serum TBA and NAFLD after adjusting for acknowledged risk factors for NAFLD. Results: A negative correlation was found between the TBA and NAFLD after adjusting for confounders in the multivariate logistic regression model (OR: 0.80; 95% CI: 0.72, 0.88, P < 0.001). After subgroup analysis, we found the interaction between NAFLD and diabetes was significant (P = 0.043). In patients with NAFLD without diabetes, TBA showed a protective effect in NAFLD (OR: 0.75; 95% CI: 0.67, 0.85). Conclusion: TBA is protective for NAFLD, but not in patients with NAFLD and diabetes. Further studies are urgently required to completely explore the underlying mechanisms of TBA in the pathogenesis of NAFLD.

The study describes the hydrogeological setting of the Pearl River Delta, a sub-tropical area of southern China encompassing the metropolises of Guangzhou, Shenzhen, Hong Kong and Macau. In the last 40 years, a booming economy and a population of about 60 million has increased water demand satisfied by a huge system of dams and reservoirs. Aquifers in the studied area are underutilized and only a few recent studies have addressed hydrogeological characterization at a local scale. Understanding groundwater dynamics of the Pearl River Delta is important for developing additional water supplies, understanding and mitigating groundwater pollution, and for implementing ‘Sponge City' concepts. Via a collection of data from literature and field surveys, the hydrogeological setting of the area is synthetized and represented through thematic maps, cross sections and a hydro-stratigraphic column. Hydrogeological conceptual models are developed that describe the groundwater dynamics in urban and rural areas within the Pearl River Delta.

In order to solve the problem that the total sugar content of the chlortetracycline fermentation tank can not be automatically detected online, a prediction method which combines the output recursive wavelet neural network and the Gauss process regression is proposed in this paper. A soft sensor model between the measurable parameters (inputs) and the total sugar content (output) of the chlortetracycline fermentation tank was established. The soft sensor model was trained by self updating algorithm. Based on field data, the accuracy and generalization ability of the soft sensor model were analyzed. It is shown that the prediction accuracy of the combined model proposed in this paper is better than that of other single models. The results demonstrate the superiority of the method, and MRE and RMSE are used to evaluate the performance of the soft sensor model. It shows that the prediction precision of the soft sensor model based on ORWNN-GPR combination is relatively high in the long period of fermentation, and is suitable for on-line prediction of the total sugar content of the chlortetracycline fermentation tank. The soft sensor method can effectively reduce the labor intensity of the analysts and saves the production cost for enterprise.

GIS is prone to various defects and faults after putting into operation, and the failure rate is high. At present, the inspection methods of GIS in operation include traditional inspection mode, two-dimensional code inspection mode, radio frequency identification technology mode and robot inspection mode. However, the current inspection robot has the disadvantages of weak adaptability to complex environment, insufficient inspection scope, low efficiency and low intelligence. Therefore, this paper designs a GIS monitoring system based on the rail robot. By installing all kinds of sensors on the rail robot, with the help of binocular vision and mechanical arm, the omni-directional live detection of GIS in transit can be realized.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Abstract Background The capsular polysaccharide (CPS) and iron acquisition systems are important determinants of Klebsiella pneumoniae infections, and we have previously reported that the ferric uptake repressor (Fur) can play dual role in iron acquisition and CPS biosynthesis. In many bacteria, Fur negatively controls the transcription of the small non-coding RNA RyhB to modulate cellular functions and virulence. However, in K. pneumoniae, the role played by RyhB in the Fur regulon has not been characterised. This study investigated Fur regulation of ryhB transcription and the functional role of RyhB in K. pneumoniae. Results Deletion of fur from K. pneumoniae increased the transcription of ryhB; the electric mobility shift assay and the Fur-titration assay revealed that Fur could bind to the promoter region of ryhB, suggesting that Fur directly represses ryhB transcription. Additionally, in a Δfur strain with elevated CPS production, deletion of ryhB obviously reduced CPS production. The following promoter-reporter assay and quantitative real-time PCR of cps genes verified that RyhB activated orf1 and orf16 transcription to elevate CPS production. However, deletion of ryhB did not affect the mRNA levels of rcsA, rmpA, or rmpA2. These results imply that Fur represses the transcription of ryhB to mediate the biosynthesis of CPS, which is independent of RcsA, RmpA, and RmpA2. In addition, the Δfur strain’s high level of serum resistance was attenuated by the deletion of ryhB, indicating that RyhB plays a positive role in protecting the bacterium from serum killing. Finally, deletion of ryhB in Δfur reduced the expression of several genes corresponding to 3 iron acquisition systems in K. pneumoniae, and resulted in reduced siderophore production. Conclusions The regulation and functional role of RyhB in K. pneumoniae is characterized in this study. RyhB participates in Fur regulon to modulate the bacterial CPS biosynthesis and iron acquisition systems in K. pneumoniae.

Abstract Background With the completion of the international HapMap project, many studies have been conducted to investigate the association between complex diseases and haplotype variants. Such haplotype-based association studies, however, often face two difficulties; one is the large number of haplotype configurations in the chromosome region under study, and the other is the ambiguity in haplotype phase when only genotype data are observed. The latter complexity may be handled based on an EM algorithm with family data incorporated, whereas the former can be more problematic, especially when haplotypes of rare frequencies are involved. Here based on family data we propose to cluster long haplotypes of linked SNPs in a biological sense, so that the number of haplotypes can be reduced and the power of statistical tests of association can be increased. Results In this paper we employ family genotype data and combine a clustering scheme with a likelihood ratio statistic to test the association between quantitative phenotypes and haplotype variants. Haplotypes are first grouped based on their evolutionary closeness to establish a set containing core haplotypes. Then, we construct for each family the transmission and non-transmission phase in terms of these core haplotypes, taking into account simultaneously the phase ambiguity as weights. The likelihood ratio test (LRT) is next conducted with these weighted and clustered haplotypes to test for association with disease. This combination of evolution-guided haplotype clustering and weighted assignment in LRT is able, via its core-coding system, to incorporate into analysis both haplotype phase ambiguity and transmission uncertainty. Simulation studies show that this proposed procedure is more informative and powerful than three family-based association tests, FAMHAP, FBAT, and an LRT with a group consisting exclusively of rare haplotypes. Conclusions The proposed procedure takes into account the uncertainty in phase determination and in transmission, utilizes the evolutionary information contained in haplotypes, reduces the dimension in haplotype space and the degrees of freedom in tests, and performs better in association studies. This evolution-guided clustering procedure is particularly useful for long haplotypes containing linked SNPs, and is applicable to other haplotype-based association tests. This procedure is now implemented in R and is free for download.

Abstract Background Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process. Methods Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group. Results In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay. Conclusions Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

Abstract Background Insulin-like growth factor binding protein (IGFBP-3) is an antiproliferative, pro-apoptotic and invasion suppressor protein which is transcriptionally regulated by p53. Promoter methylation has been linked to gene silencing and cancer progression. We studied the correlation between IGFBP-3 and p53 expression as well as IGFBP-3 promoter methylation in ovarian endometrioid carcinoma (OEC) by immunohistochemical staining and quantitative methylation-specific PCR (qMSP). Additionally, we assessed the molecular regulatory mechanism of wild type (wt) p53 on IGFBP-3 expression using two subclones of OEC, the OVTW59-P0 (low invasive) and P4 (high invasive) sublines. Results In 60 cases of OEC, 40.0% showed lower IGFBP-3 expression which was significantly correlated with higher IGFBP-3 promoter methylation. p53 overexpression was detected in 35.0% of OEC and was unrelated to clinical outcomes and IGFBP-3. By Kaplan-Meier analysis, patients with lower IGFBP-3, higher IGFBP-3 promoter methylation, and normal p53 were associated most significantly with lower survival rates. In OEC cell line, IGFBP-3 expression was correlated with IGFBP-3 promoter methylation. IGFBP-3 expression was restored after treatment with a DNA methy-transferase inhibitors (5-aza-deoxycytidine) and suppressed by a p53 inhibitor (pifithrin-α). The putative p53 regulatory sites on the promoter of IGFBP-3 were identified at -210, -206, -183 and -179 bases upstream of the transcription start site. Directed mutagenesis at these sites quantitatively reduced the transcription activity of IGFBP-3. Conclusion Our data suggests that IGFBP-3 silencing through IGFBP-3 promoter methylation in the absence of p53 overexpression is associated with cancer progression. These results support a potential role of IGFBP-3 methylation in the carcinogenesis of OEC.

Abstract Background Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes. Results A novel approach to gene selection is proposed based on kernel similarities and kernel weights. We do not assume uniformity for subject contribution. Weights are calculated via regularized least squares support vector regression (RLS-SVR) of class levels on kernel similarities and are used to weight subject contribution. The cumulative sum of weighted expression levels are next ranked to select responsible genes. These procedures also work for multiclass classification. We demonstrate this algorithm on acute leukemia, colon cancer, small, round blue cell tumors of childhood, breast cancer, and lung cancer studies, using kernel Fisher discriminant analysis and support vector machines as classifiers. Other procedures are compared as well. Conclusion This approach is easy to implement and fast in computation for both binary and multiclass problems. The gene set provided by the RLS-SVR weight-based approach contains a less number of genes, and achieves a higher accuracy than other procedures.

This study used a systemic perspective to examine a five-component experiential process of perceptual and developmental growth, and transfer-to-teaching. Nineteen secondary math and science teachers participated in a year-long, engineering immersion and support experience, with university faculty mentors. Teachers identified critical shifts in perceptions of engineering, and recognized appropriateness of engineering as a career option for their students. They transferred content learning and perceptions to students, through experiential narratives and instructional activities. Teachers reported that their secondary math and science students demonstrated observable change in knowledge, skill and beliefs about engineering, subject area score and skill improvement, class engagement, and engineering-related career aspirations.