Evaluation of Error Production in Animal Fluency and Its Relationship to Frontal Tracts in Normal Aging and Mild Alzheimer’s Disease: A Combined LDA and Time-Course Analysis Investigation

Semantic verbal fluency (VF), assessed by animal category, is a task widely used for early detection of dementia. A feature not regularly assessed is the occurrence of errors such as perseverations and intrusions. So far, no investigation has analyzed the how and when of error occurrence during semantic VF in aging populations, together with their possible neural correlates. The present study aims to address the issue using a combined methodology based on latent Dirichlet allocation (LDA) analysis for word classification together with a time-course analysis identifying exact time of errors’ occurrence. LDA is a modeling technique that discloses hidden semantic structures based on a given corpus of documents. We evaluated a sample of 66 participants divided into a healthy young group (n = 24), healthy older adult group (n = 23), and group of patients with mild Alzheimer’s disease (AD) (n = 19). We performed DTI analyses to evaluate the white matter integrity of three frontal tracts purportedly underlying error commission: anterior thalamic radiation, frontal aslant tract, and uncinate fasciculus. Contrasts of DTI metrics were performed on the older groups who were further classified into high-error rate and low-error rate subgroups. Results demonstrated a unique deployment of error commission in the patient group characterized by high incidence of intrusions in the first 15 s and higher rate of perseverations toward the end of the trial. Healthy groups predominantly showed very low incidence of perseverations. The DTI analyses revealed that the patients with AD committing high-error rate presented significantly more degenerated frontal tracts in the left hemisphere. Thus, our findings demonstrated that the appearance of intrusions, together with left hemisphere degeneration of frontal tracts, is a pathognomic trait of mild AD. Furthermore, our data suggest that the error commission of patients with AD arises from executive and working memory impairments related partly to deteriorated left frontal tracts.

Accurate measurement of microsatellite length by disrupting its tandem repeat structure

Replication of tandem repeats of simple sequence motifs, also known as microsatellites, is error prone and variable lengths frequently occur during population expansions. Therefore, microsatellite length variations could serve as markers for cancer. However, accurate error-free quantitation of microsatellite lengths is difficult with current methods because of a high error rate during amplification and sequencing. We have solved this problem by using partial mutagenesis to disrupt enough of the repeat structure so that it can replicate faithfully, yet not so much that the flanking regions cannot be reliably identified. In this work we use bisulfite mutagenesis to convert a C to a U, later read as T. Compared to untreated templates, we achieve three orders of magnitude reduction in the error rate per round of replication. By requiring two independent first copies of an initial template, we reach error rates below one in a million. We discuss potential clinical applications of this method.

Introduction to MYOB Accounting Basics in Accounting Data Processing at SMK 2 BM Swasta Medan Putri

To improve the learning quality of SMK students in the accounting field, various breakthroughs are needed, both in curriculum development, learning innovation, learning media and fulfillment of educational facilities and infrastructure. The use of computers in the learning process is inevitable in today's digital era. The need for computers is increasing rapidly in any field of science, including the field of accounting. Computers can be very helpful in all accounting work which is usually done manually. This can be realized with the help of accounting software, one of which is MYOB Accounting. Today, many students are not very familiar with MYOB as a software that is able to complete work in the accounting field. Students also do not know exactly how to use it. Accounting always contains a count of numbers that are often very large, so they can provide a high error rate when done manually. To find alternative solutions to the problems above, a training on the introduction of MYOB and its benefits in accounting was held. With the aim of training students of SMK 2 BM Swasta Medan Putri to be able to use MYOB and apply it in the accounting field.

An Experience in Enhancing Machine Learning Classifier Against Low-Entropy Packed Malwares

Both benign applications and malwares would take packing for their different purposes to conceal the real part of the program processes. According to recent research reports, existing machine learning (ML) approach-based malware detection engines are difficult to effectively classify the packed malwares, especially when they are in low entropy packed. Recently, we counted and found that the ratio of low-entropy packed ransomware is extremely high. This would cause a high error rate of the result on currently used ML approaches. Thus, we propose a new method to extract entropy-related features and use a stack model to build up an ML malware engine to effectively detect low-entropy packed malwares. We evaluate our method by using over 15,000 malware samples collected from VirusTotal and compare the result to related researches. This experience reports our adopted model and features can significantly lower the error rate of low-entropy packed detection from 11% to 1%.

Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly

A major challenge to long read sequencing data is their high error rate of up to 15%. We present Ratatosk, a method to correct long reads with short read data. We demonstrate on 5 human genome trios that Ratatosk reduces the error rate of long reads 6-fold on average with a median error rate as low as 0.22 %. SNP calls in Ratatosk corrected reads are nearly 99 % accurate and indel calls accuracy is increased by up to 37 %. An assembly of Ratatosk corrected reads from an Ashkenazi individual yields a contig N50 of 45 Mbp and less misassemblies than a PacBio HiFi reads assembly.

Efficient assembly of nanopore reads via highly accurate and intact error correction

Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.

An End-to-end Oxford Nanopore Basecaller Using Convolution-augmented Transformer

Oxford Nanopore sequencing is fastly becoming an active field in genomics, and it’s critical to basecall nucleotide sequences from the complex electrical signals. Many efforts have been devoted to developing new basecalling tools over the years. However, the basecalled reads still suffer from a high error rate and slow speed. Here, we developed an open-source basecalling method, CATCaller, by simultaneously capturing global context through Attention and modeling local dependencies through dynamic convolution. The method was shown to consistently outper-form the ONT default basecaller Albacore, Guppy, and a recently developed attention-based method SACall in read accuracy. More importantly, our method is fast through a heterogeneously computational model to integrate both CPUs and GPUs. When compared to SACall, the method is nearly 4 times faster on a single GPU, and is highly scalable in parallelization with a further speedup of 3.3 on a four-GPU node.

Construction of a highly error-prone DNA polymerase for developing organelle mutation systems

A novel family of DNA polymerases replicates organelle genomes in a wide distribution of taxa encompassing plants and protozoans. Making error-prone mutator versions of gamma DNA polymerases revolutionised our understanding of animal mitochondrial genomes but similar advances have not been made for the organelle DNA polymerases present in plant mitochondria and chloroplasts. We tested the fidelities of error prone tobacco organelle DNA polymerases using a novel positive selection method involving replication of the phage lambda cI repressor gene. Unlike gamma DNA polymerases, ablation of 3′–5′ exonuclease function resulted in a modest 5–8-fold error rate increase. Combining exonuclease deficiency with a polymerisation domain substitution raised the organelle DNA polymerase error rate by 140-fold relative to the wild type enzyme. This high error rate compares favourably with error-rates of mutator versions of animal gamma DNA polymerases. The error prone organelle DNA polymerase introduced mutations at multiple locations ranging from two to seven sites in half of the mutant cI genes studied. Single base substitutions predominated including frequent A:A (template: dNMP) mispairings. High error rate and semi-dominance to the wild type enzyme in vitro make the error prone organelle DNA polymerase suitable for elevating mutation rates in chloroplasts and mitochondria.

Tuned Random Forest Algorithm for Improved Prediction of Cardiovascular Disease

Data mining is becoming more and more popular and essential in the field of medicine. The large amounts of data produced everyday by the medical industry are very complex and voluminous to be processed and analyzed by the usual traditional means. In such cases data mining comes into play. Despite the presence of several prediction algorithms, the efficiency is questionable due to the presence high error rate. Therefore it is necessary to choose a prediction algorithm that gives higher accuracy with fewer errors. The aim of this paper is to create a system for efficient and accurate prediction of cardiovascular disease. The datasets for the process is taken from UCI machine learning repository. The datasets are tested for accuracy using ANOVA technique. The algorithms are investigated using the WEKA tool. The best features for prediction are obtained from feature selection algorithms. Various classification algorithms are applied on the datasets to identify the most efficient algorithm. We observe that random forest gives consistently better accuracy than other algorithms. Tuning is done on the random forest algorithm to further improve the accuracy of prediction system.

Aquatic Microplastic Research—A Critique and Suggestions for the Future

While there are numerous papers on microplastics (mps) being published every week, there is a need for improvement for the field to mature. The papers reporting numbers found in water bodies cannot be compared because there are no standard methods for collection and analysis. It is clear that using nets for sampling misses most of the microfibers, which are the most abundant form when whole water samples are analyzed, and that microscopic identification has a very high error rate compared to chemical analytical equipment which can also identify the polymers. It is clear that most animals studied eat mps; we should learn what attracts the animals to the mps and what proportion pass right through and are defecated vs those that move into the tissues. It is considered that mps are a vector for transfer of toxic chemicals into the food chain. Let us investigate to what degree what proportion of contaminants are removed in the digestive system vs. staying bound tightly to the mps. Experimental studies should also use environmentally relevant doses and the shapes and sizes of mps that are most abundant in the environment.