A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

Novel empirical likelihood inference for the mean difference with right-censored data

This paper focuses on comparing two means and finding a confidence interval for the difference of two means with right-censored data using the empirical likelihood method combined with the independent and identically distributed random functions representation. In the literature, some early researchers proposed empirical link-based confidence intervals for the mean difference based on right-censored data using the synthetic data approach. However, their empirical log-likelihood ratio statistic has a scaled chi-squared distribution. To avoid the estimation of the scale parameter in constructing confidence intervals, we propose an empirical likelihood method based on the independent and identically distributed representation of Kaplan–Meier weights involved in the empirical likelihood ratio. We obtain the standard chi-squared distribution. We also apply the adjusted empirical likelihood to improve coverage accuracy for small samples. In addition, we investigate a new empirical likelihood method, the mean empirical likelihood, within the framework of our study. The performances of all the empirical likelihood methods are compared via extensive simulations. The proposed empirical likelihood-based confidence interval has better coverage accuracy than those from existing methods. Finally, our findings are illustrated with a real data set.

Testing coverage criteria for optimized deep belief network with search and rescue

A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.

Constructing decision rules from naive bayes model for robust and low complexity classification

A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naïve Bayes (NB) which has been relayed on probability theory. NB has two major limitations: First, it requires to rescan the dataset and applying a set of equations each time to classify instances, which is an expensive step if a dataset is relatively large. Second, NB may remain challenging for non-statisticians to understand the deep work of a model. On the other hand, Rule-Based classifiers (RBCs) have used IF-THEN rules (henceforth, rule-set), which are more comprehensible and less complex for classification tasks. For elevating NB limitations, this paper presents a method for constructing a rule-set from the NB model, which serves as RBC. Experiments of the constructing rule-set have been conducted on (Iris, WBC, Vote) datasets. Coverage, Accuracy, M-Estimate, and Laplace are crucial evaluation metrics that have been projected to rule-set. In some datasets, the rule-set obtains significant accuracy results that reach 95.33 %, 95.17% for Iris and vote datasets, respectively. The constructed rule-set can mimic the classification capability of NB, provide a visual representation of the model, express rules infidelity with acceptable accuracy; an easier method to interpreting and adjusting from the original model. Hence, the rule-set will provide a comprehensible and lightweight model than NB itself.

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs

ObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.DesignVignettes study.Setting200 primary care vignettes.Intervention/comparatorFor eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes’ gold-standard.Primary outcome measures(1) Proportion of conditions ‘covered’ by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of ‘safe’ urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).ResultsCondition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs—Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs—Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs—Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3).ConclusionsThe utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Family Law Concentrate

Each Concentrate revision guide is packed with essential information, key cases, revision tips, exam Q&As, and more. Concentrates show you what to expect in a law exam, what examiners are looking for, and how to achieve extra marks. Family Law Concentrate is supported by extensive online resources to take your learning further. It has been written by experts and covers all the key topics so that you can approach your exams with confidence. The clear, succinct coverage enables you to quickly grasp the fundamental principles of this area of law and helps you to succeed in exams. This guide has been rigorously reviewed and is endorsed by students and lecturers for level of coverage, accuracy, and exam advice. It is clear, concise, and easy to use, helping you get the most out of your revision. After an introduction, the book covers: families, civil partnerships, and cohabitation; nullity; divorce, dissolution, and judicial separation; domestic abuse; financial provision on divorce or dissolution; Children—private law; Children—public law; adoption; and child abduction. This, the fifth edition, has been fully updated in light of recent developments in the law, including the extension of civil partnerships to heterosexual couples, the Law Commission reviews of the law of surrogacy and marriage and proposals to reform the law of divorce and domestic abuse.

Catalytic prior distributions with application to generalized linear models

A catalytic prior distribution is designed to stabilize a high-dimensional “working model” by shrinking it toward a “simplified model.” The shrinkage is achieved by supplementing the observed data with a small amount of “synthetic data” generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable with or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation. The catalytic priors have simple interpretations and are easy to formulate.

Original research: How accurate are digital symptom assessment apps for suggesting conditions and urgency advice?: a clinical vignettes comparison to GPs

ObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of 8 popular symptom assessment apps with each other and with 7 General Practitioners.DesignClinical vignettes study.Setting200 clinical vignettes representing real-world scenarios in primary care.Intervention/comparatorCondition coverage, suggested condition accuracy, and urgency advice performance was measured against the vignettes’ gold-standard diagnoses and triage level.Primary outcome measuresOutcomes included (i) proportion of conditions “covered” by an app, i.e. not excluded because the patient was too young/old, pregnant, or comorbid, (ii) proportion of vignettes in which the correct primary diagnosis was amongst the top 3 conditions suggested, and, (iii) proportion of “safe” urgency level advice (i.e. at gold standard level, more conservative, or no more than one level less conservative).ResultsCondition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%. The top-3 suggestion accuracy (M3) of GPs was on average 82.i±5.2%. For the apps it was - Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps exclude certain user groups (e.g. younger users) or certain conditions - for these apps condition-suggestion performance is generally greater with exclusion of these vignettes. For safe urgency advice, tested GPs had an average of 97.0±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 S.D. of the GPs (mean) - Ada: 97.0%; Babylon: 95.i%; Symptomate: 97.8%. One app had a safety performance within 2 S.D.s of GPs - Your.MD: 92.6%. Three apps had a safety performance outside 2 S.D.s of GPs - Buoy: 80.0% (p<0.001); K Health: 81.3% (p <0.001); Mediktor: 87.3% (p =1.3×10-3).ConclusionsThe utility of digital symptom assessment apps relies upon coverage, accuracy, and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance

The paper considers the estimation problem of the autoregressive parameter in the first-order autoregressive process with Gaussian noises when the noise variance is unknown. We propose a non-asymptotic technique to compensate the unknown variance, and then, to construct a point estimator with any prescribed mean square accuracy. Also a fixed-width confidence interval with any prescribed coverage accuracy is proposed. The results of Monte-Carlo simulations are given.

A Roadmap for Arctic Observing and Data Systems

&lt;p&gt;A changing Arctic&lt;/p&gt;&lt;p&gt;In recent decades, sustained observations of Arctic environmental and socio-economic systems have revealed a pace, magnitude, and extent of change that is unprecedented by many measures. These changes include rapid depletion of the cryosphere, shifts in ecological communities that threaten biodiversity and increasing challenges to food security and resilience across northern communities.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The Sustaining Arctic Observing Networks (SAON)&lt;/p&gt;&lt;p&gt;SAON is a joint initiative of the Arctic Council and the International Arctic Science Committee (IASC). It was created to strengthen multinational engagement in and coordination of pan-Arctic observing. SAON&amp;#8217;s intent is to unite Arctic and non-Arctic countries and Indigenous Peoples in support of a systematic network of activities through structured facilitation.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;A Roadmap for Arctic Observing and Data Systems (ROADS)&lt;/p&gt;&lt;p&gt;In its recent strategic plan, SAON identified the need for a Roadmap for Arctic Observing and Data Systems (ROADS) to set a course for the needed system and to specify how the various partners and players are going to collectively work towards getting it there. The purpose of ROADS is to stimulate multinational resource mobilization around specific plans with clear value propositions, to serve as a tool for the joint utilization of Indigenous Knowledge and science, to coordinate engagement and to ensure that maximal benefits are delivered. A well-defined assessment process is required to establish a communal view of &amp;#8220;societal benefit&amp;#8221;, and a key tool for such assessment will be The International Arctic Observing Assessment Framework (IAOAF) following the First Arctic Science Ministerial.&lt;/p&gt;&lt;p&gt;Continuing multinational coordination through SAON was endorsed by the Second Arctic Science Ministerial in their Joint Statement with an emphasis on: &amp;#8220;moving from the design to the deployment phase of an integrated Arctic observing system&amp;#8221;.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Essential Arctic Variables&lt;/p&gt;&lt;p&gt;SAON has identified the essential variable strategy as a best practice for supporting network development. The approach is conceptually holistic, yet can proceed step-wise as essential variables achieve readiness. ROADS will be organized around Essential Arctic Variables (EAVs). These are conceptually broad observing categories (e.g. &amp;#8220;sea ice&amp;#8221;) identified for their criticality to achieving Arctic societal benefit. EAVs are defined by their observing system requirements (e.g. spatial resolution, frequency, coverage, accuracy), which are technology-neutral and should transcend specific observing strategies, programs or regions. They are implemented through specific recommendations based on best available technology and practices.&lt;/p&gt;