Prediction of COVID-19 Using a WOA-BILSTM Model.

The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm-bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures.

ChromosomeNet: A massive dataset enabling benchmarking and building basedlines of clinical chromosome classification.

Chromosome karyotyping analysis is a vital cytogenetics technique for diagnosing genetic and congenital malformations, analyzing gestational and implantation failures, etc. Since the chromosome classification as an essential stage in chromosome karyotype analysis is a highly time-consuming, tedious, and error-prone task, which requires a large amount of manual work of experienced cytogenetics experts. Many deep learning-based methods have been proposed to address the chromosome classification issues. However, two challenges still remain in current chromosome classification methods. First, most existing methods were developed by different private datasets, making these methods difficult to compare with each other on the same base. Second, due to the absence of reproducing details of most existing methods, these methods are difficult to be applied in clinical chromosome classification applications widely. To address the above challenges in the chromosome classification issue, this work builds and publishes a massive clinical dataset. This dataset enables the benchmarking and building chromosome classification baselines suitable for different scenarios. The massive clinical dataset consists of 126,453 privacy preserving G-band chromosome instances from 2763 karyotypes of 408 individuals. To our best knowledge, it is the first work to collect, annotate, and release a publicly available clinical chromosome classification dataset whose data size scale is also over 120,000. Meanwhile, the experimental results show that the proposed dataset can boost performance of existing chromosome classification models at a varied range of degrees, with the highest accuracy improvement by 5.39 % points. Moreover, the best baseline with 99.33 % accuracy reports state-of-the-art classification performance. The clinical dataset and state-of-the-art baselines can be found at https://github.com/CloudDataLab/BenchmarkForChromosomeClassification.

A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation.

BACKGROUND: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. METHOD: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. RESULTS AND CONCLUSIONS: Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with 77% mAP, 97.5% AP50, and 95.5% AP75 segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.

The seroprevalence of the newly identified porcine circovirus type 4 in China investigated by an enzymed-linked immunosorbent assay.

Porcine circovirus 4 (PCV4) is the fourth porcine circovirus newly identified in China, and it could be detected in diseased and healthy pigs. To date, the prevalence of PCV4 DNA in pig herds has been investigated in many provinces from both China and Korea, with positive rates varied from 3.28% to 25.4% in samples from different regions. However, up to now no serological data have been reported to evaluate the prevalence of PCV4 in pig herds. In this study, an indirect anti-PCV4 IgG enzyme-linked immunosorbent assay (ELISA) based on replicase protein (Rep) was developed and utilized to investigate the seroprevalence of PCV4 in pig herds of China. A total of 1790 swine serum samples from 17 provinces of China were tested including samples confirmed positive for PCV4 DNA. There was no cross-reactivity of this ELISA with PCV1, PCV2 and PCV3. PCV4 Rep antibodies have been detected in serum samples from 16 out of 17 provinces in China. The PCV4 overall seroprevalence was 43.97%, with the highest of 67.8% been detected in sows, followed by fattening and suckling pigs with positive rates of 35.0% and 14.56%, respectively, and the lowest of 12.61% been detected in nursery pigs. Moreover, from the present data, the earliest positive sample could be retrieved to at least 2008. The present study provides an overall seroprevalence of PCV4 in China, and is helpful to understand the prevalence of PCV4 in the pig herds since it was discovered.

A novel chromosome cluster types identification method using ResNeXt WSL model.

Chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, due to the complicated morphological characteristics of various types of chromosome clusters, chromosome instance segmentation is the most challenging stage of chromosome karyotyping analysis, leading chromosome karyotyping analysis to highly dependent on skilled clinical analysts. Since most of the chromosome instance segmentation efforts are currently devoted to segmenting chromosome instances from different types of chromosome clusters, type identification of chromosome clusters is a vital anterior task for chromosome instance segmentation. Firstly, this paper proposes an automatic approach for chromosome cluster identification using recent transfer learning techniques. The proposed framework is based on ResNeXt weakly-supervised learning (WSL) pre-trained backbone and a task-specific network header. Secondly, this paper proposes a fast training methodology that tunes our framework from coarse-to-fine gradually. Extensive evaluations on a clinical dataset consisting of 6592 clinical chromosome samples show that the proposed framework achieves 94.09%accuracy, 92.79%sensitivity, and 98.03%specificity. Such performance is superior to the best baseline model that we obtain 92.17%accuracy, 89.1%sensitivity, and 97.42%specificity. To foster research and application in the chromosome cluster type identification, we make our clinical dataset and code available via GitHub.

Thermogravimetric Analysis-Fourier Transform Infrared Spectroscopy Study on the Effect of Extraction Pretreatment on the Pyrolysis Properties of Eucalyptus Wood Waste.

Eucalyptus wood is one of the important hardwood resources with attractive properties of rapid growth and good quality, which are widely used for the manufacture of wood-based boards, furniture, pulp and paper, and so on. In order to explore the potential of sawdust waste from the eucalyptus wood furniture factory as a bioenergy feedstock, its pyrolysis properties after different solvent extractions were examined using thermogravimetric analysis coupled with Fourier transform infrared spectrometry. The mass ratio of extractives in eucalyptus wood sawdust by benzene-alcohol, hot water, and sodium hydroxide solution was 4.25, 9.68, and 16.11%, respectively. After extraction, the thermal decomposition process of eucalyptus wood was promoted with a higher weight loss rate, lower activation energy, and lower residue content compared to the raw sample without pretreatment, and the promotion level was positively correlated to the strength of extracting solvent. CO2, CO, CH4, H2O, acids, aldehydes, aromatics, ethers, and alcohols were identified as the important intermediates in pyrolysis vapors, which can be tuned by different extraction pretreatments. In terms of typical gas products, benzene-alcohol enhanced the release of carbon dioxide, and hot water enhanced the water generation from dehydration reactions and slightly increased the production of carbon monoxide, while sodium hydroxide promoted the formation of methane at the early stage under 280 °C and later stage over 460 °C during the pyrolysis of eucalyptus wood. It is believed that the extraction pretreatment can not only obtain the bioactive extractive products but also benefit the pyrolysis process by lowering the energy barrier and tuning the composition of pyrolysis products.