An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds

Dynamics of a vortex lattice in a non-equilibrium polariton superfluid

If a quantum fluid is put in motion with enough angular momentum, at equilibrium the ground state of the system is given by an array of quantised vortices. In a driven-dissipative polariton fluid, we demonstrate that the reverse process is also possible. Upon initially imprinting a static and regular vortex array, the quantum fluid starts rotating. By tracking on picosecond time scales many quantized vortices, we present the first measure of rigid-body rotation in a polariton condensate. Such many-body motion agrees with the Feynman quantization of superfluid velocity, which we show to be valid even if our system is expanding and equilibrium is never attained

Development and validation of a preoperative "difficulty score" for laparoscopic transabdominal adrenalectomy: a multicenter retrospective study

BACKGROUND: A difficulty score for laparoscopic adrenalectomy (LA) is lacking in the literature. A retrospective cohort study was designed to develop a preoperative “difficulty score” for LA. METHODS: A multicenter study was conducted involving four Italian tertiary centers for adrenal disease. The population was randomly divided into two subsets: training group and validation one. A multicenter study was undertaken, including 964 patients. Patient, adrenal lesion, surgeon’s characteristics, and the type of procedure were studied as potential predictors of target events. The operative time (pOT), conversion rate (cLA), or both were used as indicators of the difficulty in three multivariate models. All models were developed in a training cohort (70% of the sample) and validated using 30% of patients. For all models, the ability to predict complicated postoperative course was reported describing the area under the curve (AUCs). Logistic regression, reporting odds ratio (OR) with p-value, was used. RESULTS: In model A, gender (OR 2.04, p = 0.001), BMI (OR 1.07, p = 0.002), previous surgery (OR 1.29, p = 0.048), site (OR 21.8, p < 0.001) and size of the lesion (OR 1.16, p = 0.002), cumulative sum of procedures (OR 0.99, p < 0.001), extended (OR 26.72, p < 0.001) or associated procedures (OR 4.32, p = 0.015) increased the pOT. In model B, ASA (OR 2.86, p = 0.001), lesion size (OR 1.20, p = 0.005), and extended resection (OR 8.85, p = 0.007) increased the cLA risk. Model C had similar results to model A. All scores obtained predicted the target events in validation cohort (OR 1.99, p < 0.001; OR 1.37, p = 0.007; OR 1.70, p < 0.001, score A, B, and C, respectively). The AUCs in predicting complications were 0.740, 0.686, and 0.763 for model A, B, and C, respectively. CONCLUSION: A difficulty score based on both pOT and cLA (Model C) was developed using 70% of the sample. The score was validated using a second cohort. Finally, the score was tested, and its results are able to predict a complicated postoperative course. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-021-08678-6