•Exoteric introduction of deep learning and its usage in bioinformatics.•Concrete and representative examples of using deep learning in bioinformatics.•Solutions and suggestions for handling common ...issues when using deep learning.•Thorough survey of the commonly used deep learning models for various data types.
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.
Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such ...as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs’ modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease’ biomarkers.
Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.
A new kind of polysiloxane‐supported ionogel is successfully designed via locking ionic liquids (ILs), 1‐ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIMTf2N), into ...poly(aminopropyl‐methylsiloxane) (PAPMS) grafted with 2‐(methacryloyloxy)ethyl trimethylammonium chloride (METAC) in the presence of tannic acid (TA). The novel ionogel exhibits good mechanical and recovery properties, as well as high ionic conductivity (1.19 mS cm−1) at 25 °C. In addition, the totally physical dual‐crosslinked network based on ionic aggregates among METAC and the hydrogen bonds between PAPMS and TA provides excellent self‐healing ability, which allows the damaged ionogel to almost completely heal (≈83%) in 12 h at room temperature. Interestingly, the obtained ionogel also shows satisfactory adhesive behavior to various solid materials. Moreover, this novel ionogel can maintain its high ionic conductivity and recovery property even at subzero temperatures. Therefore, this polysiloxane‐supported ionogel is anticipated to be advantageous in flexible electronic devices such as sensors and supercapacitors, even at low temperatures.
Healable polysiloxane‐supported ionogel with high ionic conductivity (≈1.19 mS cm−1) and excellent recovery properties is first designed by constructing a totally physical dual‐crosslinked (DC) network based on ionic aggregates among 2‐(methacryloyloxy)ethyl trimethylammonium chloride and the hydrogen bonds between poly(aminopropylmethylsiloxane) and tannic acid. Moreover, this novel ionogel can maintain its high ionic conductivity (0.36 mS cm−1) and good recovery property at subzero temperatures.
Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and ...reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.
•Urban green space can be used for disaster prevention and risk avoidance.•We form four disaster risk maps and analyze the refuge space supply and demand.•The distribution of green space contradicts ...the demand for refuge at three levels.•Nanjing's main urban area can be divided into eight green space layout units.•Matching supply and demand of green space can guide the layout.
Accelerated urbanization has made the disaster situation increasingly severe, especially that related to geological disasters, floods, fires, and other disasters. In response to high-frequency disasters, external disaster prevention spaces, mainly urban green spaces, play a key role in sheltering and housing. While many current studies focus on the balanced layout of green space, which improves their disaster prevention and avoidance capacity to a certain extent, there is insufficient consideration of disaster distribution and population demand, leading to conflicts between green space supply and demand. Therefore, this study further explores the efficiency improvement of disaster-prevention and risk-avoidance green space (DPRAGS) from the perspective of matching supply and demand. Taking the main urban area of Nanjing as an example, we rely on ArcGIS, combined with the maximum capacity limitation coverage model, and conduct green space layout analysis through analysis of urban infrastructure, disaster risk, and the demand and supply of DPRAGS. The research results show that compared with previous green space planning methods involving suitability assessment and accessibility analysis, the unitized planning method, mainly based on supply and demand matching, can better meet the refuge requirements of demand points and improve the rationality of green space layout. This study enriches the theory of urban green space planning and guides the planning of DPRAGS in typical densely populated metropolises, such as Nanjing. Additionally, it can help develop emergency plans and management rules and regulations, ultimately enhancing evacuation efficiency during disasters and improving overall urban disaster prevention and emergency management capabilities.
Metabolically healthy obesity (MHO) and its transition to unhealthy metabolic status have been associated with risk of cardiovascular disease (CVD) in Western populations. However, it is unclear to ...what extent metabolic health changes over time and whether such transition affects risks of subtypes of CVD in Chinese adults. We aimed to examine the association of metabolic health status and its transition with risks of subtypes of vascular disease across body mass index (BMI) categories.
The China Kadoorie Biobank was conducted during 25 June 2004 to 15 July 2008 in 5 urban (Harbin, Qingdao, Suzhou, Liuzhou, and Haikou) and 5 rural (Henan, Gansu, Sichuan, Zhejiang, and Hunan) regions across China. BMI and metabolic health information were collected. We classified participants into BMI categories: normal weight (BMI 18.5-23.9 kg/m²), overweight (BMI 24.0-27.9 kg/m²), and obese (BMI ≥ 28 kg/m²). Metabolic health was defined as meeting less than 2 of the following 4 criteria (elevated waist circumference, hypertension, elevated plasma glucose level, and dyslipidemia). The changes in obesity and metabolic health status were defined from baseline to the second resurvey with combination of overweight and obesity. Among the 458,246 participants with complete information and no history of CVD and cancer, the mean age at baseline was 50.9 (SD 10.4) years, and 40.8% were men, and 29.0% were current smokers. During a median 10.0 years of follow-up, 52,251 major vascular events (MVEs), including 7,326 major coronary events (MCEs), 37,992 ischemic heart disease (IHD), and 42,951 strokes were recorded. Compared with metabolically healthy normal weight (MHN), baseline MHO was associated with higher hazard ratios (HRs) for all types of CVD; however, almost 40% of those participants transitioned to metabolically unhealthy status. Stable metabolically unhealthy overweight or obesity (MUOO) (HR 2.22, 95% confidence interval CI 2.00-2.47, p < 0.001) and transition from metabolically healthy to unhealthy status (HR 1.53, 1.34-1.75, p < 0.001) were associated with higher risk for MVE, compared with stable healthy normal weight. Similar patterns were observed for MCE, IHD, and stroke. Limitations of the analysis included lack of measurement of lipid components, fasting plasma glucose, and visceral fat, and there might be possible misclassification.
Among Chinese adults, MHO individuals have increased risks of MVE. Obesity remains a risk factor for CVD independent of major metabolic factors. Our data further suggest that metabolic health is a transient state for a large proportion of Chinese adults, with the highest vascular risk among those remained MUOO.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The molecular dynamics simulation was used to simulate the influence of the composite wall stacking effect on shale oil occurrence. The kerogen-illite heterogeneous wall pore model was established to ...study the effects of temperature, pore size, and wall component ratio on the adsorption ratio and diffusion capacity of shale oil. The calculation results show that the fluid density distribution in the hybrid nanopore is not uniform. When the pore size increases, the proportion of the first adsorption layer to the total adsorption amount decreases rapidly, and the phenomenon of the "solid-like layer" of shale oil in small pores is more obvious. In addition, increases in temperature have little effect on the density peak of the first adsorption layer. With increases in organic matter content in the shale pore model, the diffusion coefficient of fluid decreases gradually, along with adsorption capacity. The influence of the irregular arrangement of kerogen molecules on the adsorption of shale oil is greater than the influence of surface roughness caused by illite on the adsorption.
The fraily index is a useful proxy measure of accelerated biological ageing and in estimating all-cause and cause-specific mortality in older individuals in European and US populations. However, the ...predictive value of the frailty index in other populations outside of Europe and the USA and in adults younger than 50 years is unknown. We aimed to examine the association between the frailty index and mortality in a population of Chinese adults.
In this prospective cohort study, we used data from the China Kadoorie Biobank. We included adults aged 30–79 years from ten areas (five urban areas and five rural areas) of China who had no missing values for the items that made up the frailty index. We did not exclude participants on the basis of baseline morbidity status. We calculated the follow-up person-years from the baseline date to either the date of death, loss to follow-up, or Dec 31, 2017, whichever came first, through linkage with the registries of China's Disease Surveillance Points system and local residential records. Active follow-up visits to local communities were done annually for participants who were not linked to any established registries. Causes of death from official death certificates were supplemented, if necessary, by reviewing medical records or doing standard verbal autopsy procedures. The frailty index was calculated using 28 baseline variables, all of which were health status deficits measured by use of questionnaires and physical examination. We defined three categories of frailty status: robust (frailty index ≤0·10), prefrail (frailty index >0·10 to <0·25), and frail (frailty index ≥0·25). The primary outcomes were all-cause mortality and cause-specific mortality in Chinese adults aged 30–79 years. We used a Cox proportional hazards model to estimate the associations between the frailty index and all-cause and cause-specific mortality, adjusting for chronological age, education, and lifestyle factors.
512 723 participants, recruited between June 25, 2004, and July 15, 2008, were followed up for a median of 10·8 years (IQR 10·2–13·1; total follow-up 5 551 974 person-years). 291 954 (56·9%) people were categorised as robust, 205 075 (40·0%) people were categorised as prefrail, and 15 694 (3·1%) people were categorised as frail. Women aged between 45 years and 79 years had a higher mean frailty index and a higher prevalence of frailty than did men. During follow-up, 49 371 deaths were recorded. After adjustment for established and potential risk factors for death, each 0·1 increment in the frailty index was associated with a higher risk of all-cause mortality (hazard ratio HR 1·68, 95% CI 1·66–1·71). Such associations were stronger among younger adults than among older adults (pinteraction<0·0001), with HRs per 0·1 increment of the frailty index of 1·95 (95% CI 1·87–2·03) for those younger than 50 years, 1·80 (1·76–1·83) for those aged 50–64 years, and 1·56 (1·53–1·59) for those 65 years and older. After adjustments, there was no difference between the sexes in the association between the frailty index and all-cause mortality (pinteraction=0·75). For each 0·1 increment of the frailty index, the corresponding HRs for risk of death were 1·89 (95% CI 1·83–1·94) from ischaemic heart disease, 1·84 (1·79–1·89) from cerebrovascular disease, 1·19 (1·16–1·22) from cancer, 2·54 (2·45–2·63) from respiratory disease, 1·78 (1·59–2·00) from infection, and 1·78 (1·73–1·83) from all other causes.
The frailty index is associated with all-cause and cause-specific mortality independent of chronological age in younger and older Chinese adults. The identification of younger adults with accelerated ageing by use of surrogate measures could be useful for the prevention of premature death and the extension of healthy active life expectancy.
The National Natural Science Foundation of China, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, the Kadoorie Charitable Foundation, and the Wellcome Trust.
Abstract
Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model ...training. In order to secure patients’ right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients’ private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.
In this study, with the assist of thermal analysis, the stability of fraxinellone was systematically investigated. It is found that the pyrolysis reaction order of fraxinone in nitrogen is 1/4, while ...that in air is 2/3, which indicates that the stability of fraxinone in nitrogen is better than that in air. In addition, the action of fraxinellone in water is an endothermic reaction. Entropy change (Δ
S
m
) are all negative values, and Gibbs free energy (Δ
G
m
) are all positive values, indicating that the fraxinellone solution is relatively stable in pure water system. It is also worth noting that fraxinellone cannot remain stable in the atmosphere, and can be degraded in the natural environment.
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