PATTERNFINDER: combined analysis of DNA regulatory sequences and double-helix stability

BACKGROUND: Regulatory regions that function in DNA replication and gene transcription contain specific sequences that bind proteins as well as less-specific sequences in which the double helix is often easy to unwind. Progress towards predicting and characterizing regulatory regions could be accelerated by computer programs that perform a combined analysis of specific sequences and DNA unwinding properties. RESULTS: Here we present PATTERNFINDER, a web server that searches DNA sequences for matches to specific or flexible patterns, and analyzes DNA helical stability. A batch mode of the program generates a tabular map of matches to multiple, different patterns. Regions flanking pattern matches can be targeted for helical stability analysis to identify sequences with a minimum free energy for DNA unwinding. As an example application, we analyzed a regulatory region of the human c-myc proto-oncogene consisting of a single-strand-specific protein binding site within a DNA region that unwindsin vivo. The predicted region of minimal helical stability overlapped both the protein binding site and the unwound DNA region identified experimentally. CONCLUSIONS: The PATTERNFINDER web server permits localization of known functional elements or landmarks in DNA sequences as well as prediction of potential new elements. Batch analysis of multiple patterns facilitates the annotation of DNA regulatory regions. Identifying specific pattern matches linked to DNA with low helical stability is useful in characterizing regulatory regions for transcription, replication and other processes and may predict functional DNA unwinding elements. PATTERNFINDER can be accessed freely at

Global Control of Tuberculosis: Current Status and Future Prospects

Tuberculosis is a zoonotic disease that is caused by mycobacterium tuberculosis complex and can infect humans, livestock, and wildlife. It spreads primarily through the respiratory tract and was the leading cause of death due to a single infectious disease before the COVID-19 pandemic. TB is a global public health emergency that has reemerged over the past few decades. Substantial efforts are needed to achieve the goals of the End TB Strategy. The World Health Organization has estimated that approximately 9.9 million people worldwide contracted TB in 2020 and that approximately 140,000 of the 10 million new cases of active TB in 2019 were zoonotic TB. During the COVID-19 pandemic, the number of new TB diagnoses and reports decreased sharply, from 7.1 million in 2019 to 5.8 million in 2020, returning to 2012 levels far below the approximately 10 million TB cases in 2020. Simultaneously, the global decrease in the absolute number of TB deaths until 2019 was followed by an increase in 2020 in four of the six WHO regions and most of the 30 high-TB-burden countries. Therefore, extensive immediate actions worldwide are required to restore the health system, and innovations are needed to accelerate progress toward a tuberculosis-free world

SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization,file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism.Comment: To appear in SANER'2

Sustainable high-strength alkali-activated slag concrete is achieved by recycling emulsified waste cooking oil

To mitigate the shrinkage of high-strength alkali-activated slag concrete (AASC), this paper introduces emulsified cooking oil (ECO) and emulsified waste cooking oil (EWCO) into the AASC system. The effects of admixing ECO and EWCO on the compressive strength, drying shrinkage, autogenous shrinkage, carbonation, and sulfuric acid resistance of the AASC are systematically explored. The optimization mechanism is also proposed based on the surface tension and microstructural analysis. The experimental results show that the admixing ECO and EWCO slightly reduce the compressive strength of the AASC by 7.8%. Interestingly, the admixing ECO and EWCO significantly reduce the drying shrinkage and autogenous shrinkage, simultaneously improving the resistance to carbonation and sulfuric acid of the AASC. Specifically, the introduction of 2 wt.% ECO and EWCO can reduce the autogenous shrinkage of the AASC by 66.7% and 41.0%, respectively. Microstructural observations reveal that the addition of ECO and EWCO can reduce the internal surface tension of the AASC, improve the transport and diffusion of the pore solution, and increase the absorbable free water of the slag, which in turn reduces the shrinkage of the composites. It also increases the ionic concentration in the pore solution, resulting in a more complete reaction of the AASC, which can optimize the pore structure and thus improve the durability of the AASC. This study proposes a promising way to develop sustainable alkali-activated slag concrete achieved by recycling waste materials

Effect of mycotoxins contaminated corn on growth nutrient digestibility and in vitro rumen fermentation in goats

Two trials (in vivo and in vitro) were conducted to evaluate corn naturally contaminated with mycotoxins, majority being aflatoxin B1 (AFB1) on the performance, nutrient digestion and rumen fermentation in growing goats. China Lezhi black goats (12), weighing 16.39 to 16.45 kg, were fed with the diet of 40% concentrate (the mycotoxin naturally contaminated diet containing 74.49 μg/kg AFB1, 2.08 μg/kg AFB2, 59.71 μg/kg DON and 36.51 μg/kg ZEN) for 28 days. The results showed that the contaminated corn had no significant effect on feed intake but decreased the average daily gain (ADG) and feed conversion ratio (FCR) in growing goats. Digestibility of crude protein (CP) in the trial group was significantly lower than the control group and the digestibilities of acid detergent fibre (ADF) and neutral detergent fibre (NDF) decreased too, but not significantly. Neither volatile fatty acid (VFA) nor pH was significantly different between the 2 groups. The ammonia nitrogen (NH3-N) in the trial group was lower in both in vivo trial and in vitro trial (0 h to 3 h). In in vitro experiment, ruminal fluids were collected from 4 China Lezhi goats and incubated at 39°C for 48 h with control corn or AFB1 contaminated corn. The total gas production and gas production rate in the trial group were significantly lower than the control group. These reductions showed the negative effects of the naturally contaminated AFB1 corn on nutrient digestibility and rumen function in growing goats

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection

The utilization of computer technology to solve problems in medical scenarios has attracted considerable attention in recent years, which still has great potential and space for exploration. Among them, machine learning has been widely used in the prediction, diagnosis and even treatment of Sepsis. However, state-of-the-art methods require large amounts of labeled medical data for supervised learning. In real-world applications, the lack of labeled data will cause enormous obstacles if one hospital wants to deploy a new Sepsis detection system. Different from the supervised learning setting, we need to use known information (e.g., from another hospital with rich labeled data) to help build a model with acceptable performance, i.e., transfer learning. In this paper, we propose a semi-supervised optimal transport with self-paced ensemble framework for Sepsis early detection, called SPSSOT, to transfer knowledge from the other that has rich labeled data. In SPSSOT, we first extract the same clinical indicators from the source domain (e.g., hospital with rich labeled data) and the target domain (e.g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end transfer learning method for Sepsis early detection which can automatically select suitable samples from two domains respectively according to the number of iterations and align feature space of two domains. Extensive experiments on two open clinical datasets demonstrate that comparing with other methods, our proposed SPSSOT, can significantly improve the AUC values with only 1% labeled data in the target domain in two transfer learning scenarios, MIMIC $rightarrow$ Challenge and Challenge $rightarrow$ MIMIC.Comment: 14 pages, 9 figure

Effects of geographical, soil and climatic factors on the two marker secondary metabolites contents in the roots of Rubia cordifolia L.

The growth and quality of medicinal plants depend heavily on environmental variables. The quality of Rubia cordifolia, an important medicinal plant, is determined by the two main secondary metabolites of the root, purpurin and mollugin. However, their relationship with environmental factors has not been studied. In this study, the purpurin and mollugin contents of R. cordifolia roots from different sampling sites in China were measured using ultra-high-performance liquid chromatography, and the correlations between the two secondary metabolites and environmental variables were analyzed. The results showed that there were significant differences in the contents of purpurin and mollugin in the roots of R. cordifolia at different sampling points. The content of purpurin ranged from 0.00 to 3.03 mg g-1, while the content of mollugin ranged from 0.03 to 10.09 mg g-1. The quality of R. cordifolia in Shanxi, Shaanxi and Henan border areas and southeastern Liaoning was higher. Liaoning is expected to become a R. cordifolia planting area in Northeast China. Correlation and regression analysis revealed that the two secondary metabolites were affected by different environmental factors, the two secondary metabolites contents were positively correlated with longitude and latitude, and negatively correlated with soil nutrients. In addition, higher temperature and shorter sunshine duration facilitated the synthesis of purpurin. Annual precipitation might be the main factor limiting the quality of R. cordifolia because it had opposite effects on the synthesis of two major secondary metabolites. Therefore, this study is of great significance for the selection of R. cordifolia planting areas and the improvement of field planting quality