A simplified guide ruler from numeric table method in doing rotational osteotomy

<p>Abstract</p> <p>Background</p> <p>Čobeljić et al. recently reported a numeric table method to provide precise rotational osteotomy which is a well established orthopaedic procedure. The numeric table requires four pages in length that is rather inconvenient during performing an osteotomy operation. </p> <p>Methods</p> <p>We thus develop our own method by summarizing the data of the four-page table into a small ruler, which is easy to carry and use in operation room. An electrical version of this ruler is also available. We also build a computer model to verify Čobeljić et al. method.</p> <p>Results</p> <p>The error of Čobeljić et al. is between -37% to 16% (mean ± SD = -6% ± 9%). We verify our ruler by calculating the absolute difference between our method and that of Čobeljić et al. The difference is less than 0.1 mm.</p> <p>Conclusion</p> <p>Our ruler is convenient for practical use for the rotational osteotomy procedure with equal precision. Further clinical verification is needed to justify its real significance.</p

Coregulation of transcription factors and microRNAs in human transcriptional regulatory network

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level. Recent studies have suggested that miRNAs and transcription factors are primary metazoan gene regulators; however, the crosstalk between them still remains unclear.</p> <p>Methods</p> <p>We proposed a novel model utilizing functional annotation information to identify significant coregulation between transcriptional and post-transcriptional layers. Based on this model, function-enriched coregulation relationships were discovered and combined into different kinds of functional coregulation networks.</p> <p>Results</p> <p>We found that miRNAs may engage in a wider diversity of biological processes by coordinating with transcription factors, and this kind of cross-layer coregulation may have higher specificity than intra-layer coregulation. In addition, the coregulation networks reveal several types of network motifs, including feed-forward loops and massive upstream crosstalk. Finally, the expression patterns of these coregulation pairs in normal and tumour tissues were analyzed. Different coregulation types show unique expression correlation trends. More importantly, the disruption of coregulation may be associated with cancers.</p> <p>Conclusion</p> <p>Our findings elucidate the combinatorial and cooperative properties of transcription factors and miRNAs regulation, and we proposes that the coordinated regulation may play an important role in many biological processes.</p

Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model

This study introduces multi-task pseudo-label (MPL) learning for a non-intrusive speech quality assessment model. MPL consists of two stages which are obtaining pseudo-label scores from a pretrained model and performing multi-task learning. The 3QUEST metrics, namely Speech-MOS (S-MOS), Noise-MOS (N-MOS), and General-MOS (G-MOS) are selected as the primary ground-truth labels. Additionally, the pretrained MOSA-Net model is utilized to estimate three pseudo-labels: perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI). Multi-task learning stage of MPL is then employed to train the MTQ-Net model (multi-target speech quality assessment network). The model is optimized by incorporating Loss supervision (derived from the difference between the estimated score and the real ground-truth labels) and Loss semi-supervision (derived from the difference between the estimated score and pseudo-labels), where Huber loss is employed to calculate the loss function. Experimental results first demonstrate the advantages of MPL compared to training the model from scratch and using knowledge transfer mechanisms. Secondly, the benefits of Huber Loss in improving the prediction model of MTQ-Net are verified. Finally, the MTQ-Net with the MPL approach exhibits higher overall prediction capabilities when compared to other SSL-based speech assessment models

ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data

Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data. However, the limited access to fully annotated training data poses a major challenge to training generalizable models. We propose "ConDistFL", a framework to solve this problem by combining FL with knowledge distillation. Local models can extract the knowledge of unlabeled organs and tumors from partially annotated data from the global model with an adequately designed conditional probability representation. We validate our framework on four distinct partially annotated abdominal CT datasets from the MSD and KiTS19 challenges. The experimental results show that the proposed framework significantly outperforms FedAvg and FedOpt baselines. Moreover, the performance on an external test dataset demonstrates superior generalizability compared to models trained on each dataset separately. Our ablation study suggests that ConDistFL can perform well without frequent aggregation, reducing the communication cost of FL. Our implementation will be available at https://github.com/NVIDIA/NVFlare/tree/dev/research/condist-fl