Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

A mutation in the Cdon gene potentiates congenital nevus development mediated by NRASQ61K

Congenital nevi develop before birth and sometimes cover large areas of the body. They are presumed to arise from the acquisition of a gene mutation in an embryonic melanocyte that becomes trapped in the dermis during development. Mice bearing the Cdk4^R24C::Tyr‐NRAS^Q61K transgenes develop congenital nevus‐like lesions by post‐natal day 10, from melanocytes escaping the confines of hair follicles. We interbred these mice with the collaborative cross (CC), a resource that enables identification of modifier genes for complex diseases (those where multiple genes are involved). We examined variation in nevus cell density in 66 CC strains and mapped a large‐effect quantitative trait locus (QTL) controlling nevus cell density to murine chromosome 9. The best candidate for a gene that exacerbates congenital nevus development in the context of an NRAS mutation is Cdon, a positive regulator of sonic hedgehog (Shh) that is expressed mainly in keratinocytes.     

Length variations in the COII-tRNA(Lys) intergenic region of mitochondrial DNA in Indonesian populations.

The prevalence of a 9-base-pair (bp) deletion between the mitochondrial cytochrome oxidase II (MTCOX*2) and lysine tRNA (MTTK) genes (region V) has been used to estimate the genetic relationships among Asian and Pacific populations. Many East Asian and Pacific Island populations have been examined previously, but the mitochondrial DNA (mtDNA) diversity of the intervening Indonesian archipelago has not previously been systematically examined. The 17,500 islands of Indonesia currently contain nearly 213 million people and extensive cultural, linguistic, and, presumably, genetic diversity. This study of 1091 individuals representing 15 ethnic groups is the most extensive mtDNA survey to date of the Indonesian archipelago. Six distinct length polymorphisms in region V were observed within these 15 populations. The 9-bp deletion was found in every population examined at frequencies comparable to those of previously examined East Asian populations and substantially lower than those in most Pacific Island populations. Despite the inclusion of Austronesian-speaking populations and a Papuan-speaking population, there was no statistically significant heterogeneity in the frequency of the 9-bp deletion among the 15 populations (p = 0.09). These data indicate that substantial gene flow occurred among the populations at some time in the past. Our observations of no significant correlations between genetic and geographic distances (r = -0.04, p = 0.53) coupled with the extensive cultural and linguistic differences currently within the archipelago suggest that little gene flow among neighboring populations has occurred recently.     

How to diagnose heart failure with preserved ejection fraction: the value of invasive stress testing

Heart failure with preserved ejection fraction (HFpEF) is a growing healthcare burden worldwide and its prevalence is increasing. Diagnosing HFpEF is challenging and relies upon the presence of symptoms and/or signs of heart failure, preserved left ventricular systolic function, and evidence of diastolic dysfunction. Current diagnostic algorithms mainly rely on echocardiography (E/e’) and biomarkers (NT-proBNP). However, only a minority of patients with HFpEF are identified, and especially HFpEF patients at an early stage of the disease are easily missed. We propose to incorporate invasive stress testing, by means of right heart catheterisation at rest and during exercise, and accurate assessment of right ventricular function, by means of cardiac magnetic resonance imaging. These additions to the current diagnostic work-up will improve diagnostic sensitivity and accurate staging of HFpEF patients.     

SiDCoN: a tool to aid scoring of DNA copy number changes in SNP chip data

The recent application of genome-wide, single nucleotide polymorphism (SNP) microarrays to investigate DNA copy number aberrations in cancer has provided unparalleled sensitivity for identifying genomic changes. In some instances the complexity of these changes makes them difficult to interpret, particularly when tumour samples are contaminated with normal (stromal) tissue. Current automated scoring algorithms require considerable manual data checking and correction, especially when assessing uncultured tumour specimens. To address these limitations we have developed a visual tool to aid in the analysis of DNA copy number data. Simulated DNA Copy Number (SiDCoN) is a spreadsheet-based application designed to simulate the appearance of B-allele and logR plots for all known types of tumour DNA copy number changes, in the presence or absence of stromal contamination. The system allows the user to determine the level of stromal contamination, as well as specify up to 3 different DNA copy number aberrations for up to 5000 data points (representing individual SNPs). This allows users great flexibility to assess simple or complex DNA copy number combinations. We demonstrate how this utility can be used to estimate the level of stromal contamination within tumour samples and its application in deciphering the complex heterogeneous copy number changes we have observed in a series of tumours. We believe this tool will prove useful to others working in the area, both as a training tool, and to aid in the interpretation of complex copy number changes.     

Sodium-glucose cotransporter 2 inhibitors: a practical guide for the Dutch cardiologist based on real-world experience

Sodium-glucose cotransporter 2 (SGLT2) inhibitors include a relatively new class of glucose-lowering drugs that reduce plasma glucose concentrations by inhibiting proximal tubular reabsorption of glucose in the kidney, while increasing its excretion in urine. Recent large randomised controlled trials have demonstrated that many of these agents reduce the occurrence of major adverse cardiovascular events, hospitalisation for heart failure, cardiovascular death and/or chronic kidney disease progression in patients with and without type 2 diabetes mellitus (DM2). Given their unique insulin-independent mode of action and favourable efficacy and adverse-event profile, SGLT2 inhibitors are promising and they offer an interesting therapeutic approach for the cardiologist to incorporate into routine practice. However, despite accumulating data supporting this class of therapy, cardiologists infrequently prescribe SGLT2 inhibitors, potentially due to a lack of familiarity with their use and the reticence to change DM medication. Here, we provide an up-to-date practical guide highlighting important elements of treatment initiation based on real-world evidence and expert opinion. We describe how to change DM medication, including insulin dosing when appropriate, and how to anticipate any adverse events based on real-world experience in patients with DM2 in the Meander Medical Centre in Amersfoort, the Netherlands. This includes a simple algorithm showing how to initiate SGLT2 inhibitor treatment safely, while considering the consequence of the glucosuric effects of these inhibitors for the individual patient.Supplementary InformationThe online version of this article (10.1007/s12471-021-01580-9) contains supplementary material, which is available to authorized users.     

Asymmetric arginine dimethylation of heterogeneous nuclear ribonucleoprotein K by protein-arginine methyltransferase 1 inhibits its interaction with c-Src

Arginine methylation is a post-translational modification found in many RNA-binding proteins. Heterogeneous nuclear ribonucleoprotein K (hnRNP K) from HeLa cells was shown, by mass spectrometry and Edman degradation, to contain asymmetric N(G),N(G)-dimethylarginine at five positions in its amino acid sequence (Arg256, Arg258, Arg268, Arg296, and Arg299). Whereas these five residues were quantitatively modified, Arg303 was asymmetrically dimethylated in <33% of hnRNP K and Arg287 was monomethylated in <10% of the protein. All other arginine residues were unmethylated. Protein-arginine methyltransferase 1 was identified as the only enzyme methylating hnRNP K in vitro and in vivo. An hnRNP K variant in which the five quantitatively modified arginine residues had been substituted was not methylated. Methylation of arginine residues by protein-arginine methyltransferase 1 did not influence the RNA-binding activity, the translation inhibitory function, or the cellular localization of hnRNP K but reduced the interaction of hnRNP K with the tyrosine kinase c-Src. This led to an inhibition of c-Src activation and hnRNP K phosphorylation. These findings support the role of arginine methylation in the regulation of protein-protein interactions.     

CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture

Protein-ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein-ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.