eQTLHap: a tool for comprehensive eQTL analysis considering haplotypic and genotypic effects.

MOTIVATION: The high accuracy of recent haplotype phasing tools is enabling the integration of haplotype (or phase) information more widely in genetic investigations. One such possibility is phase-aware expression quantitative trait loci (eQTL) analysis, where haplotype-based analysis has the potential to detect associations that may otherwise be missed by standard SNP-based approaches. RESULTS: We present eQTLHap, a novel method to investigate associations between gene expression and genetic variants, considering their haplotypic and genotypic effect. Using multiple simulations based on real data, we demonstrate that phase-aware eQTL analysis significantly outperforms typical SNP-based methods when the causal genetic architecture involves multiple SNPs. We show that phase-aware eQTL analysis is robust to phasing errors, showing only a minor impact ($<4\%$) on sensitivity. Applying eQTLHap to real GEUVADIS and GTEx datasets detects numerous novel eQTLs undetected by a single-SNP approach, with 22 eQTLs replicating across studies or tissue types, highlighting the utility of phase-aware eQTL analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/ziadbkh/eQTLHap. CONTACT: ziad.albkhetan@gmail.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

Enhancing quality of life in progressive multiple sclerosis with powered robotic exoskeleton.

Wearable powered robotic exoskeleton can provide high repetitions and high-intensity gait training. It can promote a sense of well-being when the user is in upright posture to walk around different environment. We present a case of a lady with progressive multiple sclerosis who received 15 sessions of robotic exoskeleton training. Post training, she demonstrated improvement in lower limb strength, sense of well-being and self-esteem that led to improved transfer ability, increased social outings and better quality of life (QOL). Previously, she was depressed and reluctant to go out for social activities. This case suggests the potential of robotic exoskeleton to enhance QOL in people with mobility challenges.

Sensory properties of yellow pea and macadamia honeys from conventional and flow hive extraction methods.

BACKGROUND: The process of harvesting honey is time consuming and labor intensive. A new system, the Flow Frame, has drastically simplified the harvesting process, enabling honey to be extracted directly from the hive with minimal processing. The sensory profile of honey is influenced, first, by botanical origin and subsequently by processing and storage parameters. A reduction in harvest processing may thus influence the sensory profile of honey harvested from FFs compared to that of honey produced from conventional processing. To test this hypothesis, two monofloral honeys (macadamia and yellow pea) were harvested from FFs, or by conventional honey extraction. Sensory profiling using conventional descriptive analysis was carried out for each floral source with an experienced trained panel. RESULTS: The two monofloral honeys harvested using the FF system had significantly (p < 0.05) higher floral and cleaner aftertaste sensory scores than the honey extracted using commercial (C) methods that involve the use of heat and centrifugation. CONCLUSION: The flow system retains honey's natural sensory properties compared to harvesting methods that require heat and centrifugation. © 2019 Society of Chemical Industry.

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity.

Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or "samples") in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.

SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data.

MOTIVATION: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses. RESULTS: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability. AVAILABILITY: Source code and documentation are available for download at http://github.com/ratschlab/spladder Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder CONTACTS: : andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Treatment of Dural Carotid-Cavernous Fistulas via the Medial Ophthalmic Vein.

AIM: To present a novel approach to treatment of dural carotid-cavernous fistulas via the medial ophthalmic vein. DESIGN: Retrospective case series. MATERIAL AND METHODS: In this retrospective case series, we present 2 patients (3 eyes) with Type C dural CCFs, who had failed cannulation via the conventional transfemoral route and the transorbital superior ophthalmic vein approach. They subsequently underwent CCF occlusion via an anterior orbital approach through the medial ophthalmic veins, at the Department of Ophthalmology, National University Hospital Singapore. CCF occlusion was confirmed intraoperatively using angiography. Both patients were evaluated postoperatively for best-corrected visual acuity and resolution of clinical signs and symptoms. RESULTS: Successful occlusion of CCFs via the medial ophthalmic veins were achieved in all three orbits, with excellent visual and cosmetic outcomes postoperatively. CONCLUSION: Dural CCFs may potentially lead to severe visual dysfunction and should be diagnosed and treated promptly. When all venous routes have been exhausted, the transorbital approach via the medial ophthalmic vein remains an excellent and viable alternative to access the fistula. Close cooperation between the orbital, anesthetic and radiological teams is essential in ensuring success of the operation.

Genome-wide analysis of alternative splicing in Volvox carteri.

BACKGROUND: Alternative splicing is an essential mechanism for increasing transcriptome and proteome diversity in eukaryotes. Particularly in multicellular eukaryotes, this mechanism is involved in the regulation of developmental and physiological processes like growth, differentiation and signal transduction. RESULTS: Here we report the genome-wide analysis of alternative splicing in the multicellular green alga Volvox carteri. The bioinformatic analysis of 132,038 expressed sequence tags (ESTs) identified 580 alternative splicing events in a total of 426 genes. The predominant type of alternative splicing in Volvox is intron retention (46.5%) followed by alternative 5' (17.9%) and 3' (21.9%) splice sites and exon skipping (9.5%). Our analysis shows that in Volvox at least ~2.9% of the intron-containing genes are subject to alternative splicing. Considering the total number of sequenced ESTs, the Volvox genome seems to provide more favorable conditions (e.g., regarding length and GC content of introns) for the occurrence of alternative splicing than the genome of its close unicellular relative Chlamydomonas. Moreover, many randomly chosen alternatively spliced genes of Volvox do not show alternative splicing in Chlamydomonas. Since the Volvox genome contains about the same number of protein-coding genes as the Chlamydomonas genome (~14,500 protein-coding genes), we assumed that alternative splicing may play a key role in generation of genomic diversity, which is required to evolve from a simple one-cell ancestor to a multicellular organism with differentiated cell types (Mol Biol Evol 31:1402-1413, 2014). To confirm the alternative splicing events identified by bioinformatic analysis, several genes with different types of alternatively splicing have been selected followed by experimental verification of the predicted splice variants by RT-PCR. CONCLUSIONS: The results show that our approach for prediction of alternative splicing events in Volvox was accurate and reliable. Moreover, quantitative real-time RT-PCR appears to be useful in Volvox for analyses of relationships between the appearance of specific alternative splicing variants and different kinds of physiological, metabolic and developmental processes as well as responses to environmental changes.

Near-optimal experimental design for model selection in systems biology.

MOTIVATION: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. RESULTS: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. AVAILABILITY: Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

Utility of non-contrast-enhanced CT for improved detection of arterial phase hyperenhancement in hepatocellular carcinoma.

PURPOSE: To determine the utility of non-contrast-enhanced CT (NC-CT) for improved detection of hyperenhancement in hepatocellular carcinomas (HCC). METHODS: We performed a retrospective analysis on CT studies of 104 consecutive patients with 124 HCCs between October 2006 and December 2012. The enhancement of HCC was evaluated on quadriphasic CT: non-contrast (NC), arterial phase (AP), portal-venous phase (PVP), and delayed phases (DP) were performed. For determination of enhancement, mean attenuation of the HCC and surrounding non-tumor bearing liver in the four phases were recorded. Hyperenhancement was defined by either HCC (AP-NC) representing absolute lesion enhancement or AP (HCC-liver), representing relative enhancement. An increase of ≥10 Hounsfield units (HU) was considered as enhancement. Two radiologists (R1 and R2) independently performed a conventional qualitative assessment for hyperenhancement of HCC. The accuracy of detection by proposed absolute lesion enhancement HCC (AP-NC) was compared to relative enhancement AP (HCC-liver) and qualitative assessment by readers with the McNemar test. RESULTS: The proposed criteria of absolute lesion enhancement, HCC (AP-NC) identified more hypervascular nodules compared to qualitative analysis (92% vs. 62% for R1 and 69% for R2), equating to an improvement of 23%-31%. In contrast, relative enhancement AP (HCC-liver) was found to be inferior to conventional qualitative analysis (50% vs. 62%-69% for readers). CONCLUSION: NC-CT is useful to improve the detection of hyperenhancement in HCCs for imaging diagnosis.

Machine Learning for Biological Design.

We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives when designing better experiments: to improve the predictive model or to improve the experimental outcome. We survey five different approaches for adaptive experimental design that iteratively search the space of possible experiments while adapting to measured data. The approaches are Bayesian optimization, bandits, reinforcement learning, optimal experimental design, and active learning. These machine learning approaches have shown promise in various areas of biology, and we provide broad guidelines to the practitioner and links to further resources.

The Villain Who Saved the Day: The Paradoxical Value of Amiodarone in Managing Recurrent Ventricular Tachycardia Secondary to Amiodarone-Induced Thyroiditis.

Thyroiditis is a rare and serious complication for patients taking amiodarone. It can manifest with symptoms of hyperthyroidism and serious life-threatening arrhythmias. We present a case of a patient with amiodarone-induced thyrotoxicosis presenting with an electrical storm in which rhythm control was achieved with the utilization of amiodarone.

Multidisciplinary approach in resection of stage IIIA lung cancer with severe aortic stenosis: A case report.

Thoracic surgery in the context of complex multimorbidity and clinical deterioration presents a unique set of challenges when balancing risk and benefit. Advances in anaesthesia, surgical technique, and imaging, have allowed for operative options for patients that were once deemed too high-risk. An effective proactive multi-disciplinary approach is essential for successful outcomes. We report the case of a 65-year-old patient with a background of severe aortic stenosis who underwent lung resection for stage IIIA lung cancer, where pivotal multi-disciplinary team input from the anaesthetic, surgery, critical care and radiology teams, clarified the cause of his clinical deterioration, contributed to decisions over his management and ensured a good clinical outcome.

GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS.

BACKGROUND: It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci. RESULTS: We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives. CONCLUSIONS: We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis. AVAILABILITY: A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.

Case report of culture-negative endocarditis in lupus nephritis.

Background: Cardiovascular involvement is frequent in systemic lupus erythematosus (SLE). Valvular abnormalities are increasingly being recognized with the advent of echocardiography. Case summary: We present a case of a 46-year-old lady who presented to the emergency department with upper limb ischaemia. On examination, she had poor dentition and a short systolic murmur on auscultation. A blood workup revealed a diagnosis of SLE. Further investigations showed vegetations on the mitral valve. Initially, an infective endocarditis (IE) diagnosis was made, which was treated with antibiotics. High-dose steroids and immunosuppressants were initiated due to her clinical deterioration and biopsy-proven lupus nephritis. She improved clinically before being discharged home. Discussion: It can be difficult to distinguish between IE and Libman-Sacks endocarditis (LSE), especially in the setting of risk factors for both. Antibiotics and immunosuppressants might be started simultaneously in these cases. A multidisciplinary team is required to manage challenging cases of culture-negative endocarditis. Procalcitonin may have a role in differentiating bacterial endocarditis and LSE.

Porphyria cutanea tarda: an under-recognised manifestation of haemochromatosis.

We present a case of a woman who presented with a photosensitive skin rash and blisters on her extremities which did not improve with steroids. These were associated with polyarthralgia and a deranged liver function test on her admission. Further workup revealed that the patient has an undiagnosed porphyria cutanea tarda (PCT) and hereditary haemochromatosis. The patient later underwent regular venesections which improved her condition. This case report not only illustrates the challenge in diagnosing PCT but also aims to highlight the association between PCT and hereditary haemochromatosis.

Middle Ear Neuroendocrine Tumor Mimicking As Chronic Otitis Media.

Neuroendocrine neoplasms (NEN) of the head and neck are a rare and diverse group of tumors. Here, we report a case of a 40-year-old man presenting with symptoms resembling chronic left otitis media, including left ear otorrhea, otalgia, and reduced hearing. Otoscopic examination revealed a whitish mass located behind the tympanic membrane. The patient underwent examination under anesthesia and left cortical mastoidectomy, and a histopathological examination of the middle ear biopsy indicated the presence of an epithelial tumor with neuroendocrine differentiation, suggestive of middle ear adenoma. A staging CT scan performed three months after the mastoidectomy showed a hypodensity in the middle ear cavity, with no significant bony erosion, which could potentially indicate a residual or recurrent tumor. Consequently, a radical mastoidectomy was performed. The histopathological examination confirmed the presence of middle ear adenoma with neuroendocrine differentiation.

mGene: accurate SVM-based gene finding with an application to nematode genomes.

We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene's genome-wide predictions revealed that approximately 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of merely 8%. These findings suggest that even the gene catalog of a well-studied organism such as C. elegans can be substantially improved by mGene's predictions. We also provide gene predictions for the four nematodes C. briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting proteomes among these organisms and to the known protein universe, we identified many species-specific gene inventions. In a quality assessment of several available annotations for these genomes, we find that mGene's predictions are most accurate.

Generative embedding for model-based classification of fMRI data.

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups.

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site.

Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.

Overstimulation of the ergoreflex-A possible mechanism to explain symptoms in long COVID.

Long COVID refers to a multitude of symptoms that persist long after SARS-CoV-2 infection. Fatigue and breathlessness are the most common symptoms of long COVID across a range of studies. They are also cardinal symptoms of chronic heart failure (CHF). In this review, we propose that fatigue and breathlessness in patients with long COVID may be explained by skeletal muscle abnormalities, in a manner similar to patients with CHF. The ergoreflex is a cardiorespiratory reflex activated by exercise, which couples ventilation and cardiovascular function to exercise intensity. At least part of the symptomatology of CHF is related to abnormal skeletal muscle and an enhanced ergoreflex, resulting in heightened sympathetic, vasoconstrictor and ventilator drives. Similarly, SARS-CoV-2 infection results in a hyperinflammatory and hypercatabolic state. This leads to reduction in skeletal muscle mass and altered function. We postulate that the ergoreflex is chronically overstimulated, resulting in fatigue and breathlessness. Exercise training preserves muscle mass and function as well as reduces ergoreflex activation; therefore may have a role in improving symptoms associated with long COVID. Should the ergoreflex be proven to be an important pathophysiological mechanism of long COVID, tailored exercise interventions should be trialed with the aim of improving both symptoms and perhaps outcomes in patients with long COVID.