Using Flashcards and Descriptive Feedback to Teach Industrial Kitchen Equipment to a Student with Intellectual Disabilities in a Postsecondary Education Program.

With postsecondary education opportunities for adult students with intellectual and developmental disabilities (IDD) on the rise, it is important to find socially validated research-based methods that are appropriate for the university or other community-based postsecondary instructional settings. The present research examines the effects of using flashcards with descriptive feedback and opportunities to respond, to teach one student with intellectual disabilities, enrolled in a postsecondary education-training program, commonly used industrial kitchen equipment. Results showed that discrete trail instruction, which included an error correction strategy of descriptive feedback plus opportunities to correctly respond was highly effective in mastery and maintenance of kitchen equipment identification, and generalization when asked to locate those items in the university kitchen lab.

Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario.

Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand. However, this approach faces significant challenges, particularly the scarcity of training data and the complexity of grammar error correction (GEC), especially for low-resource languages such as Indonesian. To address these challenges, we propose InSpelPoS, a confusion method that combines two synthetic data generation methods: the Inverted Spellchecker and Patterns+POS. Furthermore, we introduce an adapted seq2seq framework equipped with a dynamic decoding method and state-of-the-art Transformer-based neural language models to enhance the accuracy and efficiency of GEC. The dynamic decoding method is capable of navigating the complexities of GEC and correcting a wide range of errors, including contextual and grammatical errors. The proposed model leverages the contextual information of words and sentences to generate a corrected output. To assess the effectiveness of our proposed framework, we conducted experiments using synthetic data and compared its performance with existing GEC systems. The results demonstrate a significant improvement in the accuracy of Indonesian GEC compared to existing methods.

Measuring and modeling the dynamics of mitotic error correction.

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome-autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

High-Throughput Polar Code Decoders with Information Bottleneck Quantization.

In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of 10-7 and a polar code of N=1024,R=0.5 compared to state-of-the-art decoders.

NextPolish2: A Repeat-aware Polishing Tool for Genomes Assembled Using HiFi Long Reads.

The high-fidelity (HiFi) long-read sequencing technology developed by PacBio has greatly improved the base-level accuracy of genome assemblies. However, these assemblies still contain base-level errors, particularly within the error-prone regions of HiFi long reads. Existing genome polishing tools usually introduce overcorrections and haplotype switch errors when correcting errors in genomes assembled from HiFi long reads. Here, we describe an upgraded genome polishing tool - NextPolish2, which can fix base errors remaining in those "highly accurate" genomes assembled from HiFi long reads without introducing excessive overcorrections and haplotype switch errors. We believe that NextPolish2 has a great significance to further improve the accuracy of telomere-to-telomere (T2T) genomes. NextPolish2 is freely available at https://github.com/Nextomics/NextPolish2.

otb: an Automated HiC/HiFi Pipeline Assembles the Prosapia bicincta Genome.

The implementation of a new genomic assembly pipeline named only the best (otb) has effectively addressed various challenges associated with data management during the development and storage of genome assemblies. otb, which incorporates a comprehensive pipeline involving a setup layer, quality checks, templating, and the integration of Nextflow and Singularity. The primary objective of otb is to streamline the process of creating a HiFi/HiC genome, aiming to minimize the manual intervention required in the genome assembly process. The Two-lined spittlebug, (Prosapia bicincta, Hemiptera: Cercopidae), a true bug insect herbivore, serves as a practical test case for evaluating otb. The two-lined spittlebug is both a crucial agricultural pest and a genomically understudied insect belonging to the order Hemiptera. This insect is a significant threat to grasslands and pastures, leading to plant wilting and phytotoxemia when infested. Its presence in tropical and subtropical regions around the world poses a long-term threat to the composition of plant communities in grassland landscapes, impacting rangelands, and posing a substantial risk to cattle production.

Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module.

The error correction model's main purpose in heavy hexagonal quantum codes is to improve their reliability for quantum computing applications. Existing challenges include finding the optimal decoder for quantum error correction in heavy hexagonal codes. This research propels the frontier of quantum error correction, with a specific focus on tailoring topological quantum error-correcting codes for the unique challenges posed by superconducting qubits in quantum computers. In response, this research harnesses the power of deep learning, presenting a Humming sparrow optimization based self-adaptive deep CNN (HSO-based SADCNN) model designed for heavy hexagonal codes. This decoder incorporates a Self-adaptive Deep CNN (SADCNN) Noise Correction Module, a sophisticated component to refine error correction. The proposed decoder's efficacy is rigorously evaluated across varying code distances (three, five, and seven) using the Humming Sparrow Optimization (HSO) algorithm. HSO, intricately designed to fine-tune the SADCNN decoder, significantly enhances its error correction capabilities for heavy hexagonal quantum codes. The algorithm seamlessly integrates advantageous characteristics of herding and tracing from Humming Bird optimization and Sparrow search optimization, representing a critical stride in advancing the reliability of quantum computing applications, particularly within the intricate domain of heavy hexagonal quantum codes. Based upon the achievements, the Training Percentage (TP) 90 metrics demonstrate significant progress, boasting a commendable accuracy of 97.35 % , coupled with reduced logical error probability and a diminished bit error rate, marked at 5.51 and 3.72, respectively.

NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.

BACKGROUNDS: The single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long reads algorithmically with the help of the homologous high-precision and low-cost short reads from the Next Generation Sequencing (NGS) technology. METHODS: In this work, a hybrid error correction method (NmTHC) based on a generative neural machine translation model is proposed to automatically capture discrepancies within the aligned regions of long reads and short reads, as well as the contextual relationships within the long reads themselves for error correction. Akin to natural language sequences, the long read can be regarded as a special "genetic language" and be processed with the idea of generative neural networks. The algorithm builds a sequence-to-sequence(seq2seq) framework with Recurrent Neural Network (RNN) as the core layer. The before and post-corrected long reads are regarded as the sentences in the source and target language of translation, and the alignment information of long reads with short reads is used to create the special corpus for training. The well-trained model can be used to predict the corrected long read. RESULTS: NmTHC outperforms the latest mainstream hybrid error correction methods on real-world datasets from two mainstream platforms, including PacBio and Nanopore. Our experimental evaluation results demonstrate that NmTHC can align more bases with the reference genome without any segmenting in the six benchmark datasets, proving that it enhances alignment identity without sacrificing any length advantages of long reads. CONCLUSION: Consequently, NmTHC reasonably adopts the generative Neural Machine Translation (NMT) model to transform hybrid error correction tasks into machine translation problems and provides a novel perspective for solving long-read error correction problems with the ideas of Natural Language Processing (NLP). More remarkably, the proposed methodology is sequencing-technology-independent and can produce more precise reads.

The Inverse of Exact Renormalization Group Flows as Statistical Inference.

We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection-diffusion equation. We provide a new information-theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one-parameter family of probability distributions solving an integro-differential equation derived from Bayes' law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation, which we dub Bayesian Diffusion. By identifying the features that define Bayesian Diffusion and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.

Investigating the crowding effect of FDI on domestic investments: Evidence from Bangladesh.

This study empirically investigates the crowding effect of Foreign Direct Investment (FDI) on domestic investments in Bangladesh, utilizing annual time series data from 1972 to 2022. Initially, unit root tests are conducted with and without considering structural breaks in the dataset. This study employs the Johansen test of cointegration to investigate the enduring association between the variables and utilizes the Vector Error Correction Model (VECM) to accommodate this relationship over the long term. Following the estimation of the VECM, formulas about the magnitude of the crowding effect (CE) are applied to examine the impact of FDI on domestic investment in Bangladesh. Results indicate that FDI positively influences domestic investments in both the short and long run.

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm.

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time-space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal-noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy-Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.

A communication hub for phosphoregulation of kinetochore-microtubule attachment.

The Mps1 and Aurora B kinases regulate and monitor kinetochore attachment to spindle microtubules during cell division, ultimately ensuring accurate chromosome segregation. In yeast, the critical spindle attachment components are the Ndc80 and Dam1 complexes (Ndc80c and DASH/Dam1c, respectively). Ndc80c is a 600-Å-long heterotetramer that binds microtubules through a globular "head" at one end and centromere-proximal kinetochore components through a globular knob at the other end. Dam1c is a heterodecamer that forms a ring of 16-17 protomers around the shaft of the single kinetochore microtubule in point-centromere yeast. The ring coordinates the approximately eight Ndc80c rods per kinetochore. In published work, we showed that a site on the globular "head" of Ndc80c, including residues from both Ndc80 and Nuf2, binds a bipartite segment in the long C-terminal extension of Dam1. Results reported here show, both by in vitro binding experiments and by crystal structure determination, that the same site binds a conserved segment in the long N-terminal extension of Mps1. It also binds, less tightly, a conserved segment in the N-terminal extension of Ipl1 (yeast Aurora B). Together with results from experiments in yeast cells and from biochemical assays reported in two accompanying papers, the structures and graded affinities identify a communication hub for ensuring uniform bipolar attachment and for signaling anaphase onset.

Microtubule end-on attachment maturation regulates Mps1 association with its kinetochore receptor.

Faithful chromosome segregation requires that sister chromatids establish bi-oriented kinetochore-microtubule attachments. The spindle assembly checkpoint (SAC) prevents premature anaphase onset with incomplete attachments. However, how microtubule attachment and checkpoint signaling are coordinated remains unclear. The conserved kinase Mps1 initiates SAC signaling by localizing transiently to kinetochores in prometaphase and is released upon bi-orientation. Using biochemistry, structure predictions, and cellular assays, we shed light on this dynamic behavior in Saccharomyces cerevisiae. A conserved N-terminal segment of Mps1 binds the neck region of Ndc80:Nuf2, the main microtubule receptor of kinetochores. Mutational disruption of this interface, located at the backside of the paired CH domains and opposite the microtubule-binding site, prevents Mps1 localization, eliminates SAC signaling, and impairs growth. The same interface of Ndc80:Nuf2 binds the microtubule-associated Dam1 complex. We demonstrate that the error correction kinase Ipl1/Aurora B controls the competition between Dam1 and Mps1 for the same binding site. Thus, binding of the Dam1 complex to Ndc80:Nuf2 may release Mps1 from the kinetochore to promote anaphase onset.

NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads.

Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.

Exploring the relationship between teachers' beliefs and practices of oral corrective feedback in synchronous computer-mediated communication.

This study investigated the relationship between the EFL teachers' beliefs about oral. corrective feedback (OCF) and their actual teaching practices in the context of. synchronous computer-mediated communication (SCMC). It aimed to understand the. extent of in/consistency between the teachers' OCF practices and beliefs. regarding the significance of OCF, types of OCF strategies and the types of errors. receiving OCF.Data were collected through two instruments: observations for the. purpose of exploring the teachers' actual OCF practices and a questionnaire to. uncover OCF beliefs.It was found that the teachers expressed strong beliefs. about their awareness of the significance of OCF, which, however, was not. reflected in the frequency of OCF provision. The results also indicated that recasts. were the most frequently employed form of feedback, which is in line with the overall. preferences reported by the teachers in the questionnaire. Additionally, the teachers considered. pronunciation errors the most significant target of OCF. Nevertheless, vocabulary errors had a substantially higher frequency of corrections. It was also noted that the. teachers stated to adapt their OCF provision in SCMC contexts compared to F2F. classroom settings. The observed in/consistencies were discussed considering the. impact of various belief systems on classroom behaviors and several contextual factors.

Micro-Satellite Systems Design, Integration, and Flight.

Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.

Measurable (Minimal) Residual Disease in Myelodysplastic Neoplasms (MDS): Current State and Perspectives.

Myelodysplastic Neoplasms (MDS) have been traditionally studied through the assessment of blood counts, cytogenetics, and morphology. In recent years, the introduction of molecular assays has improved our ability to diagnose MDS. The role of Measurable (minimal) Residual Disease (MRD) in MDS is evolving, and molecular and flow cytometry techniques have been used in several studies. In this review, we will highlight the evolving concept of MRD in MDS, outline the various techniques utilized, and provide an overview of the studies reporting MRD and the correlation with outcomes.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.).

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Many purported pseudogenes in bacterial genomes are bona fide genes.

BACKGROUND: Microbial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly. RESULTS: Using a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors. CONCLUSIONS: Collectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.

Advances in meta-analysis: A unifying modelling framework with measurement error correction.

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta-analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance-covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta-analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance-covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta-analysis to enhance the replicability of psychological research.