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MotivationThere exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect.ResultsIn this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalised maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions.AvailabilityThe code for SynBa is available athttps://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).Contacthz381@cam.ac.uk

Purpose To report a previously undocumented variant of sternalis. Methods An unusual muscle was observed during routine dissection. Results The sternalis muscle located in the right thoracic region originated from the superior portion of the rectus abdominis sheath and 5-6th costal cartilages, crossed the midline and attached at the sternum. The muscle fibers then ascended with the left sternocleidomastoid muscle as an additional fasciculus, of which the superior ends were finally terminated at the left mastoid process. The sternalis muscle of the thoracic region was innervated by the anterior cutaneous branches of right intercostal nerve, while the additional fasciculus ascended with the left sternocleidomastoid muscle was innervated by the branches of left accessory nerve. Conclusions This study presents a unilateral sternalis muscle with the contralateral sternocleidomastoid variation. It will enhance the exhaustive classification of sternalis, and provide significant information to radiologists, angiologists and surgeons for better interpretation of images and safer interventions.

To report a previously undocumented variant of sternalis.An unusual muscle was observed during routine dissection.The sternalis muscle located in the right thoracic region originated from the superior portion of the rectus abdominis sheath and 5-6th costal cartilages, crossed the midline and attached at the sternum. The muscle fibers then ascended with the left sternocleidomastoid muscle as an additional fasciculus, of which the superior ends were finally terminated at the left mastoid process. The sternalis muscle of the thoracic region was innervated by the anterior cutaneous branches of right intercostal nerve, while the additional fasciculus ascended with the left sternocleidomastoid muscle was innervated by the branches of left accessory nerve.This study presents a unilateral sternalis muscle with the contralateral sternocleidomastoid variation. It will enhance the exhaustive classification of sternalis, and provide significant information to radiologists, angiologists and surgeons for better interpretation of images and safer interventions.

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Using the dimming control system, it is very convenient to control the lighting brightness of the lighting circuit according to the use requirements of different scenes at different times. It not only realizes the automatic control of lighting, but also creates and beautifies the light environment of the space inside and outside the building, and reduces the energy consumption and prolongs the service life of lamps. In view of this, the lighting condition model system of exhibition hall design based on computer aided design is put forward. Based on the optimization strategy of genetic algorithm, the visual comfort condition satisfied by the quantitative design of green lighting and the quantitative method for calculating the illumination intensity of green lighting are obtained. The system adopts ZigBee wireless network to realize data transmission, wireless monitoring, control and management among nodes of intelligent lighting system. As a bridge between the terminal and the server, the system gateway is responsible for monitoring information communication between ZigBee and Ethernet protocols. The server collects the monitoring information of the storage terminal and provides an interface for accessing and controlling the terminal.

In real-time decision-making problems for complicated stochastic systems, a covariate that reflects the state of the system is observed in real time and a state-dependent decision needs to be made immediately to optimize some system performance. Such system performances, for complicated stochastic systems, often are not in closed-form and require time-consuming simulation experiments to evaluate, which can be prohibitive in real-time tasks. We propose two neural network-assisted methods to address this challenge by effectively utilizing simulation experiments that are conducted offline before the real-time tasks. One key step in the proposed methods integrates a classical simulation meta-modelling approach with neural networks to jointly capture the mapping from the covariate and the decision variable to the system performance, which enhances the use of offline simulation data and reduces the risk of model misspecification. A brief numerical experiment is presented to illustrate the performance of the proposed methods.

We study the problem of simulating a class of nonstationary spatio-temporal Poisson processes. The Poisson intensity function is non-stationary and piecewise linear in both the time dimension and the spatial location dimensions. We propose an exact simulation algorithm based on the inversion method. This simulation algorithm adopts three advantages. First, the entire procedure involves only closed-form computation with no need for numerical integration or numerical inversion of any function. Each step in the algorithm only requires exact arithmetic operations. Second, the proposed algorithm is sample efficient, especially compared to the thinning method when the maximum intensity value is much larger than the minimum intensity value. Third, the algorithm generates arrivals sequentially, one at a time in ascending order, so that they can be conveniently fed into real-time or online decision-making tools.

Background Thyroid carcinoma (TC) is malignant tumor of the endocrine gland with a significantly increased incidence in recent years, which related to metabolic diseases and biological clock disorder. The molecular mechanism and risk metabolic genes associated with biological clock of TC require conducting study. Methods We mined differentially expressed genes of TC (TC-DEGs) from TCGA and GEO database. Searching for biorhythm related genes (BRGs) based on core clock genes by STRING analysis. Next, we used a series of bioinformatics methods to analyze the TC-DEGs and BRGs, including chromosome location, GO and KEGG pathway annotation. Then, we extracted the expression of statistically significant TC metabolic genes by Perl program combined with a computational method of GSEA. Furthermore, screen the risk metabolic genes and reveal potential underlying KEGG pathways of the gene signatures. Results Our results identified 474 DEGs (P < 0.05; |log FC| > 1) in patients with TC compared with the normal controls using the available numerical mRNA expression values. The chromosomal assignments of TC-DEGs and BRGs were inhomogeneous. These genes were mainly distributed on Chrom01, 02, 11, 12, 04, 03 and X, none of the associated genes were distributed on sex Y chromosome. ARNTL and BHLHE40 belong to both the TC-DEGs and BRGs. PER2, NCOR1, RXRG, CCND1, SERPINE1 and PLAU were mined which act as the hub of signaling pathways linking “Thyroid hormone signaling pathway”, “Circadian rhythm”, “Transcriptional misregulation in cancer” and so on. One hundred and forty-one metabolic genes were extracted to train the prognostic model, and 11 risk genes were found, among them, 10 risk gene products have indirect interaction with ARNTL and BHLHE40. Such important genes for TC were regulated by many common conserved microRNAs. Conclusions Our study identified 8 key genes and 11 risk metabolic genes of thyroid carcinoma associated with biological clock by bioinformatics analysis. And the coding products of these genes form a clear interaction network, which is conducive to further study the molecular mechanism, reveal the role of circadian clock genes and metabolic genes in thyroid cancer.

With the development of statistical methods and machine learning algorithms, credit scoring is no longer a task merely based on experience. From single base classifiers to ensemble classifiers and hybrid models, researches have been focusing on combining classifiers and hybridizing with artificial intelligence algorithms to improve performance of the models. Ensemble classifiers have been proven to have a better predictive accuracy than single classifiers, but the method of ensemble affects performance and is worth studying. This study is based on the ensemble of five of the most widely recognized base classifiers in credit scoring, i.e. logistic regression, support vector machine, neural network, gradient boosting decision tree and random forest. It proposes a new method of selecting classifiers using Genetic Algorithm after they are trained, considering both the accuracy and diversity of the ensemble. Besides, unsupervised clustering is integrated with a fuzzy assignment procedure in the model, to make more use of the data pattern and improve performance. The proposed CF-GA-Ens model is tested on three credit scoring datasets (Australian, German, Japanese) and three performance measures (accuracy, AUC, F-score), and the results show that our classifier selection and clustering procedures have a positive impact on all performance measures.