Enhanced Atrous Spatial Pyramid Pooling Feature Fusion for Small Ship Instance Segmentation.

In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in inaccurate ship segmentation.

Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach.

Raw sequencing reads of miRNAs contain machine-made substitution errors, or even insertions and deletions (indels). Although the error rate can be low at 0.1%, precise rectification of these errors is critically important because isoform variation analysis at single-base resolution such as novel isomiR discovery, editing events understanding, differential expression analysis, or tissue-specific isoform identification is very sensitive to base positions and copy counts of the reads.

Instance-based error correction for short reads of disease-associated genes.

Background: Genomic reads from sequencing platforms contain random errors. Global correction algorithms have been developed, aiming to rectify all possible errors in the reads using generic genome-wide patterns. However, the non-uniform sequencing depths hinder the global approach to conduct effective error removal.

Advanced household profiling using digital water meters.

Advanced householder profiling using digital water metering data analytics has been acknowledged as a core strategy for promoting water conservation because of its ability to provide near real-time feedback to customers and instil long-term conservation behaviours. Customer profiling based on household water consumption data collected through digital water meters helps to identify the water consumption patterns and habits of customers. This study employed advanced customer profiling techniques adapted from the machine learning research domain to analyse high-resolution data collected from residential digital water meters.

Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy.

Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers.

Thromboembolism and anticoagulant therapy during the COVID-19 pandemic: interim clinical guidance from the anticoagulation forum.

Coronavirus disease 2019 (COVID-19) is a viral infection that can, in severe cases, result in cytokine storm, systemic inflammatory response and coagulopathy that is prognostic of poor outcomes. While some, but not all, laboratory findings appear similar to sepsis-associated disseminated intravascular coagulopathy (DIC), COVID-19- induced coagulopathy (CIC) appears to be more prothrombotic than hemorrhagic. It has been postulated that CIC may be an uncontrolled immunothrombotic response to COVID-19, and there is growing evidence of venous and arterial thromboembolic events in these critically ill patients.

Surgeon's preference of subcutaneous tissue resection: most important factor for short-term complications in subcutaneous implant placement after mastectomy-results of a cohort study.

Purpose: Little is known about the reason of high short-term complication rates after the subcutaneous placement of breast implants or expanders after mastectomy without biological matrices or synthetic meshes. This study aims to evaluate complications and their risk factors to develop guidelines for decreasing complication rates.

Methods: We included all cases of mastectomy followed by subcutaneous implant or expander placement between 06/2017 and 05/2018 (n = 92).

Efficacy of intraoperative specimen radiography as margin assessment tool in breast conserving surgery.

Purpose: To explore the ability of intraoperative specimen radiography (SR) to correctly identify positive margins in patients receiving breast conserving surgery (BCS). To assess whether the reoperation rate can be reduced by using this method.

Methods: This retrospective study included 470 consecutive cases receiving BCS due to a primarily diagnosed breast cancer.

Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection.

Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function.

Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves.

Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behavior that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape.

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.

Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins.

Bi-level error correction for PacBio long reads.

The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data.

CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling.

Motivation: CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single-guide RNAs (sgRNAs), such that its cleavage efficiency is high meanwhile the off-target effect is low.

Results: This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM).

From feedback loop transitions to biomarkers in the psycho-immune-neuroendocrine network: Detecting the critical transition from health to major depression.

Background: Biological pathways underlying major depressive disorder (MDD) can be viewed as systems biology networks. The psycho-immune-neuroendocrine (PINE) network comprises central nervous, immune, endocrine and autonomic systems, integrating biological mechanisms of MDD. Such networks exhibit recurrent motifs with specific functions, including positive and negative feedback loops, and are subject to critical transitions, influenced by feedback loop transitions (FLTs).

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures.

Contour Restoration of Text Components for Recognition in Video/Scene Images.

Text recognition in video/natural scene images has gained significant attention in the field of image processing in many computer vision applications, which is much more challenging than recognition in plain background images. In this paper, we aim to restore complete character contours in video/scene images from gray values, in contrast to the conventional techniques that consider edge images/binary information as inputs for text detection and recognition. We explore and utilize the strengths of zero crossing points given by the Laplacian to identify stroke candidate pixels (SPC).

Disseminated Tumor Cells in the Bone Marrow of Patients with Operable Primary Breast Cancer: Prognostic Impact in Immunophenotypic Subgroups and Clinical Implication for Bisphosphonate Treatment.

Background: Disseminated tumor cells (DTC) in the bone marrow (BM) of primary breast cancer (BC) patients are a promising surrogate marker of micrometastatic spread and an independent predictor of poor prognosis for disease-free survival (DFS) and overall survival (OS). The present study aims to analyze DTCs as an independent prognostic factor for DFS/OS in tumor biology and bisphosphonate treatment.

Methods: A total of 504 patients with operable primary BC and a median observation time of 72.

Decision Support Systems and Line Performance: Case of Gold Coast University Hospital.

Computer-based decision support information systems have been promoted for their potential to improve physician performance and patient outcomes and support clinical decision making. The current case study reported design and implementation of a high-level decision support system (DSS) which facilitated the flow of data from operational level to top managers and leadership level of hospitals. The results shows that development of a DSS improve data connectivity, timing, and responsiveness issues via centralised sourcing and storing of principal health-related information in the hospital.

Application areas of multi-user virtual environments in the healthcare context.

This study conducts a systematic literature review on the application of the three-dimensional virtual worlds (3DVW) in healthcare context. During the past decade, 3DVWs have emerged as a cutting edge technology that has much to offer to the healthcare sector. Our systematic review began with an initial set of 1088 studies published from 1990 to 2013 which have used 3DVWs for the healthcare specific purposes.

Formation of polybrominated dibenzofurans (PBDFs) after heating of a salmon sample spiked with decabromodiphenyl ether (BDE-209).

Fish is a major source for the intake of polybrominated diphenyl ethers (PBDEs). However, fish is scarcely consumed without being cooked, and previous studies showed that the heating of salmon fillet contaminated with BDE-209 for longer periods of time was accompanied with the partial transformation of this brominated flame retardant. In this study, we heated salmon fillet spiked with BDE-209 and verified that this process was linked with the formation of polybrominated dibenzofurans (PBDFs) in the fish.

Recall management of patients with Rofil Medical breast implants.

Background: Some Rofil Medical breast implants are relabelled Poly Implant Prothèse (PIP) implants, and it is recommended that Rofil implants be managed in the same way as PIP implants. We report the results of a systematic recall of patients who had received Rofil implants.

Method: All patients who received Rofil implants at our centre were identified and invited for specialist consultation.