Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness.

BACKGROUND: Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of loop prediction techniques that have greatly improved their methods for characterizing loop prediction and related aspects. RESULTS: In this study, we categorized 22 loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII. CONCLUSION: This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.

The Extreme Value Machine.

It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function-ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g. , artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier-the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.

Predicting Change in Posttraumatic Distress Through Change in Coping Self-Efficacy After Using the My Trauma Recovery eHealth Intervention: Laboratory Investigation.

BACKGROUND: Technology offers a unique platform for delivering trauma interventions (ie, eHealth) to support trauma-exposed populations. It is important to evaluate mechanisms of therapeutic change in reducing posttraumatic distress in eHealth for trauma survivors. OBJECTIVE: This study evaluated a proactive, scalable, and individually responsive eHealth intervention for trauma survivors called My Trauma Recovery. My Trauma Recovery is an eHealth intervention aiming to support trauma survivors and consisting of 6 modules: relaxation, triggers, self-talk, professional help, unhelpful coping, and social support. It was designed to enhance trauma coping self-efficacy (CSE). We tested 3 hypotheses. First, My Trauma Recovery would decrease posttraumatic stress symptoms (PTSS). Second, My Trauma Recovery would increase CSE. And last, changes in CSE would be negatively correlated with changes in PTSS. METHODS: A total of 92 individuals exposed to trauma (78/92, 85% females, mean age 34.80 years) participated. Our study was part of a larger investigation and consisted of 3 sessions 1 week apart. Participants completed the baseline online survey assessing PTSS and CSE. Each session included completing assigned modules followed by the online survey assessing CSE. PTSS was remeasured at the end of the last module. RESULTS: PTSS significantly declined from T1 to T9 (F1,90=23.63, P<.001, η2p=.21) supporting the clinical utility of My Trauma Recovery. Significant increases in CSE for sessions 1 and 2 (F8,83=7.51, P<.001) were found. No significant change in CSE was found during session 3 (N=92). The residualized scores between PTSS T1 and T9 and between CSE T1 and T9 were calculated. The PTSS residualized score and the CSE residualized score were significantly correlated, r=-.26, P=.01. Results for each analysis with a probable PTSD subsample were consistent. CONCLUSIONS: The findings of our study show that participants working through My Trauma Recovery report clinically lower PTSS after 3 weeks. The results also demonstrate that CSE is an important self-appraisal factor that increased during sessions 1 and 2. These improvements are correlated with reductions in PTSS. Thus, changes in CSE may be an important mechanism for reductions in PTSS when working on a self-help trauma recovery website and may be an important target for eHealth interventions for trauma. These findings have important implications for trauma eHealth interventions.

Probability Models for Open Set Recognition.

Real-world tasks in computer vision often touch upon open set recognition: multi-class recognition with incomplete knowledge of the world and many unknown inputs. Recent work on this problem has proposed a model incorporating an open space risk term to account for the space beyond the reasonable support of known classes. This paper extends the general idea of open space risk limiting classification to accommodate non-linear classifiers in a multiclass setting. We introduce a new open set recognition model called compact abating probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical extreme value theory for score calibration with one-class and binary support vector machines. Our experiments show that the W-SVM is significantly better for open set object detection and OCR problems when compared to the state-of-the-art for the same tasks.

Toward open set recognition.

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel "1-vs-set machine," which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.

Meta-Recognition: The Theory and Practice of Recognition Score Analysis.

In this paper, we define meta-recognition, a performance prediction method for recognition algorithms, and examine the theoretical basis for its postrecognition score analysis form through the use of the statistical extreme value theory (EVT). The ability to predict the performance of a recognition system based on its outputs for each match instance is desirable for a number of important reasons, including automatic threshold selection for determining matches and nonmatches, and automatic algorithm selection or weighting for multi-algorithm fusion. The emerging body of literature on postrecognition score analysis has been largely constrained to biometrics, where the analysis has been shown to successfully complement or replace image quality metrics as a predictor. We develop a new statistical predictor based upon the Weibull distribution, which produces accurate results on a per instance recognition basis across different recognition problems. Experimental results are provided for two different face recognition algorithms, a fingerprint recognition algorithm, a SIFT-based object recognition system, and a content-based image retrieval system.

A novel approach to medical image compression.

As medical/biological imaging facilities move towards complete film-less imaging, compression plays a key role. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximise compression while maintaining clinical relevance and balancing legal risk. This paper proposes a novel model-based compression technique that makes use of clinically relevant regions as defined by radiologists. Lossless compression is used in these clinically relevant regions, and lossy compression is used everywhere else.