Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction

The prediction of adaptive radiation therapy (ART) prior to radiation therapy (RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce toxicity and prolong the survival of patients. Currently, due to the complex tumor micro-environment, a single type of high-resolution image can provide only limited information. Meanwhile, the traditional softmax-based loss is insufficient for quantifying the discriminative power of a model. To overcome these challenges, we propose a supervised multi-view contrastive learning method with an additive margin (MMCon). For each patient, four medical images are considered to form multi-view positive pairs, which can provide additional information and enhance the representation of medical images. In addition, the embedding space is learned by means of contrastive learning. NPC samples from the same patient or with similar labels will remain close in the embedding space, while NPC samples with different labels will be far apart. To improve the discriminative ability of the loss function, we incorporate a margin into the contrastive learning. Experimental result show this new learning objective can be used to find an embedding space that exhibits superior discrimination ability for NPC images.Comment: submitted to ICASSP 2023, 5 page

POISE: Efficient Cross-Domain Chinese Named Entity Recognization via Transfer Learning

To improve the performance of deep learning methods in case of a lack of labeled data for entity annotation in entity recognition tasks, this study proposes transfer learning schemes that combine the character to be the word to convert low-resource data symmetry into high-resource data. We combine character embedding, word embedding, and the embedding of the label features using high- and low-resource data based on the BiLSTM-CRF model, and perform the feature-transfer and parameter-sharing tasks in two domains of the BiLSTM network to annotate with zero resources. Before transfer learning, we must first calculate the label similarity between two different domains and select the label features with large similarity for feature transfer mapping. All training parameters of the source domain in the model are shared during the BiLSTM network processing and CRF layer. In addition, we also use the method of combining characters and words to reduce the problem of word segmentation across domains and reduce the error rate in label mapping. The results of experiments show that in terms of the overall F1 score, the proposed model without supervision was superior by 9.76 percentage points to the general parametric shared transfer learning method, and by 9.08 and 12.38 percentage points, respectively, to two recent high–low resource learning methods. The proposed scheme improves performance in terms of transfer learning between the high- and low-resource data and can identify the predicted data in the target domain

An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others

A Survey of Deep Learning for Electronic Health Records

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research

A Survey of Deep Learning for Electronic Health Records

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research

Effects of PSMA1 on the differentiation and lipid deposition of bovine preadipocytes

In this study, our goal was to clarify the role of proteasomal subunit α-1 (PSMA1) in both the differentiation of preadipocytes and the accumulation of lipids in adipocytes. Preadipocytes from healthy one-day-old calves were collected, isolated, and cultured in vitro. The expression pattern of the PSMA1 gene was explored during the differentiation of bovine preadipocytes firstly. Then, the expression of the PSMA1 gene was inhibited by transfection of a chemically synthesized small interfering RNA (siRNA) before differentiation. After induction of differentiation, the mRNA levels of key regulating genes involved in preadipocyte differentiation and the lipid content of mature adipocytes with and without inhibition of PSMA1 were detected by qRT-PCR and oil red O staining, respectively. The data showed that PSMA1 mRNA was differentially expressed during the differentiation of bovine preadipocytes under normal culture conditions in vitro. The expression level of peroxisome proliferator-activated receptor gamma (PPARγ), CCAAT enhancer-binding protein alpha (C/EBPα), and lipoprotein lipase (LPL) were significantly decreased in the transfected PSMA1-siRNA group compared with those in the control group, and the mRNA levels of the preadipocyte factor-1 (Pref-1) were significantly upregulated in the transfected PSMA1-siRNA group compared with those in the control group. In addition, significantly fewer lipid droplets were formed by adipocytes transfected with PSMA1-siRNA than by the negative control group (adipocytes transfected with NC-siRNA). Therefore, PSMA1 plays an important role in differentiation and lipid depositio

High-Performance Photoelectronic Sensor Using Mesostructured ZnO Nanowires

Semiconductor photoelectrodes that simultaneously possess rapid charge transport and high surface area are highly desirable for efficient charge generation and collection in photoelectrochemical devices. Herein, we report mesostructured ZnO nanowires (NWs) that not only demonstrate a surface area as high as 50.7 m²/g, comparable to that of conventional nanoparticles (NPs), but also exhibit a 100 times faster electron transport rate than that in NP films. Moreover, using the comparison between NWs and NPs as an exploratory platform, we show that the synergistic effect between rapid charge transport and high surface area leads to a high performance photoelectronic formaldehyde sensor that exhibits a detection limit of as low as 5 ppb and a response of 1223% (at 10 ppm), which are, respectively, over 100 times lower and 20 times higher than those of conventional NPs-based device. Our work establishes a foundational pathway toward a better photoelectronic system by materials design