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Abstract Purpose To investigate the effects of various demographic, structural, radiographic, and clinical factors on the prognosis of patients with medial compartmental knee osteoarthritis with varus deformity undergoing medial opening wedge high tibial osteotomy (HTO) in combination with bone marrow concentrate (BMC) injection. Methods In this prospective study, 20 patients underwent medial opening wedge HTO in combination with BMC injection with 12 months of follow-up. The structural and radiographic outcomes were evaluated by femorotibial angle and posterior tibial slope angle. The clinical outcomes were evaluated by visual analogue scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and The Knee injury and Osteoarthritis Outcome Score (KOOS). Multivariate nonlinear mixed-effects models with asymptotic regressions were used to model the trajectory of symptom improvement. Results Medial opening wedge HTO in combination with BMC corrected the malalignment of the knee and led to significant symptom relief. The improvement of clinical symptoms reached a plateau 6 months after the surgery. Greater symptom severity at baseline and lower Kellgren-Lawrance (KL) grades were correlated with better post-operative clinical outcomes. Body-Mass-Index (BMI), femorotibial angle, age, and sex may also play a role in influencing the extent of symptom relief. Conclusion Symptom severity at baseline is important for prognosis prediction. In clinical practice, we suggest that the evaluation of clinical features and functional status of the patients be more emphasised.

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models.

The technique of active ionospheric sounding by ionosondes requires sophisticated methods for the recovery of experimental data on ionograms. In this work, we applied an advanced algorithm of deep learning for the identification and classification of signals from different ionospheric layers. We collected a dataset of 6131 manually labeled ionograms acquired from low-latitude ionosondes in Taiwan. In the ionograms, we distinguished 11 different classes of the signals according to their ionospheric layers. We developed an artificial neural network, FC-DenseNet24, based on the FC-DenseNet convolutional neural network. We also developed a double-filtering algorithm to reduce incorrectly classified signals. That made it possible to successfully recover the sporadic E layer and the F2 layer from highly noise-contaminated ionograms whose mean signal-to-noise ratio was low, SNR = 1.43. The Intersection over Union (IoU) of the recovery of these two signal classes was greater than 0.6, which was higher than the previous models reported. We also identified three factors that can lower the recovery accuracy: (1) smaller statistics of samples; (2) mixing and overlapping of different signals; (3) the compact shape of signals.

Self-avoidance is a conserved mechanism that prevents crossover between sister dendrites from the same neuron, ensuring proper functioning of the neuronal circuits. Several adhesion molecules are known to be important for dendrite self-avoidance, but the underlying molecular mechanisms are incompletely defined. Here, we show that FMI-1/Flamingo, an atypical cadherin, is required autonomously for self-avoidance in the multidendritic PVD neuron of Caenorhabditis elegans. The fmi-1 mutant shows increased crossover between sister PVD dendrites. Our genetic analysis suggests that FMI-1 promotes transient F-actin assembly at the tips of contacting sister dendrites to facilitate their efficient retraction during self-avoidance events, probably by interacting with WSP-1/N-WASP. Mutations of vang-1, which encodes the planar cell polarity protein Vangl2 previously shown to inhibit F-actin assembly, suppress self-avoidance defects of the fmi-1 mutant. FMI-1 downregulates VANG-1 levels probably through forming protein complexes. Our study identifies molecular links between Flamingo and the F-actin cytoskeleton that facilitate efficient dendrite self-avoidance. [ABSTRACT FROM AUTHOR]