CONTROLLING ABSOLUTE FREQUENCY OF FEEDBACK IN A SELF-CONTROLLED SITUATION ENHANCES MOTOR LEARNING.

The guidance hypothesis suggested that excessive extrinsic feedback facilitates motor performance but blocks the processing of intrinsic information. The present study tested the tenet of guidance hypothesis in self-controlled feedback by controlling the feedback frequency. The motor learning effect of limiting absolute feedback frequency was examined. Thirty-six participants (25 men, 11 women; M age=25.1 yr., SD=2.2) practiced a hand-grip force control task on a dynamometer by the non-dominant hand with varying amounts of feedback. They were randomly assigned to: (a) Self-controlled, (b) Yoked with self-controlled, and (c) Limited self-controlled conditions. In acquisition, two-way analysis of variance indicated significantly lower absolute error in both the yoked and limited self-controlled groups than the self-controlled group. The effect size of absolute error between trials with feedback and without feedback in the limited self-controlled condition was larger than that of the self-controlled condition. In the retention and transfer tests, the Limited self-controlled feedback group had significantly lower absolute error than the other two groups. The results indicated an increased motor learning effect of limiting absolute frequency of feedback in the self-controlled condition.

Adversarial Attacks on Medical Image Classification.

Due to the growing number of medical images being produced by diverse radiological imaging techniques, radiography examinations with computer-aided diagnoses could greatly assist clinical applications. However, an imaging facility with just a one-pixel inaccuracy will lead to the inaccurate prediction of medical images. Misclassification may lead to the wrong clinical decision. This scenario is similar to the adversarial attacks on deep learning models. Therefore, one-pixel and multi-pixel level attacks on a Deep Neural Network (DNN) model trained on various medical image datasets are investigated in this study. Common multiclass and multi-label datasets are examined for one-pixel type attacks. Moreover, different experiments are conducted in order to determine how changing the number of pixels in the image may affect the classification performance and robustness of diverse DNN models. The experimental results show that it was difficult for the medical images to survive the pixel attacks, raising the issue of the accuracy of medical image classification and the importance of the model's ability to resist these attacks for a computer-aided diagnosis.

Identifying antisense oligonucleotides to disrupt small RNA regulated antibiotic resistance via a cell-free transcription-translation platform.

Bacterial small RNAs (sRNAs) regulate many important physiological processes in cells including antibiotic resistance and virulence genes through base pairing interactions with mRNAs. Antisense oligonucleotides (ASOs) have great potential as therapeutics against bacterial pathogens by targeting sRNAs such as MicF, which regulates outer membrane protein OmpF expression and limits permeability of antibiotics. Here, we devise a cell-free transcription-translation (TX-TL) assay to identify ASO designs that sufficiently sequester MicF. ASOs were then ordered as peptide nucleic acids conjugated to cell-penetrating peptides (CPP-PNA) to allow for effective delivery into bacteria. Subsequent minimum inhibitory concentration (MIC) assays demonstrated that simultaneously targeting the regions of MicF responsible for sequestering the start codon and the Shine-Dalgarno sequence of ompF with two different CPP-PNAs synergistically reduced the MIC for a set of antibiotics. This investigation offers a TX-TL based approach to identify novel therapeutic candidates to combat intrinsic sRNA-mediated antibiotic resistance mechanisms.

Identifying Antisense Oligonucleotides to Disrupt Small RNA Regulated Antibiotic Resistance via a Cell-Free Transcription-Translation Platform.

Bacterial small RNAs (sRNAs) regulate many important physiological processes in cells, including antibiotic resistance and virulence genes, through base-pairing interactions with mRNAs. Antisense oligonucleotides (ASOs) have great potential as therapeutics against bacterial pathogens by targeting sRNAs such as MicF, which regulates outer membrane protein OmpF expression and limits the permeability of antibiotics. Here we devised a cell-free transcription-translation (TX-TL) assay to identify ASO designs that sufficiently sequester MicF. ASOs were then ordered as peptide nucleic acids conjugated to cell-penetrating peptides (CPP-PNA) to allow for effective delivery into bacteria. Subsequent minimum inhibitory concentration (MIC) assays demonstrated that simultaneously targeting the regions of MicF responsible for sequestering the start codon and the Shine-Dalgarno sequence of ompF with two different CPP-PNAs synergistically reduced the MIC for a set of antibiotics. This investigation offers a TX-TL-based approach to identify novel therapeutic candidates to combat intrinsic sRNA-mediated antibiotic resistance mechanisms.

Deep Learning Technology Applied to Medical Image Tissue Classification.

Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models.