Progressive muscle relaxation alleviates anxiety and improves sleep quality among healthcare practitioners in a mobile cabin hospital: a pre-post comparative study in China.

Objective: To investigate the anxiety levels, sleep quality and potential risk factors of healthcare practitioners involved in the management of COVID-19 patients in a mobile cabin hospital, and further to assess the impact of progressive muscle relaxation (PMR) on their anxiety levels and sleep quality. Methods: We conducted a pre-post self-controlled trial. Healthcare practitioners meeting the inclusion criteria underwent daily 30-min PMR sessions for seven consecutive days. The Pittsburgh Sleep Quality Index (PSQI) and Hamilton Anxiety Scale (HAMA) were used to assess the anxiety and sleep quality of subjects pre- and post-intervention. Statistical analysis was performed using the Wilcoxon test, Mann-Whitney U test, Kruskal-Wallis H test, and Spearman rank correlation. Results: A total of 94 participants completed the study. No statistically significant differences in HAMA or PSQI total scores were observed between groups categorized based on demographic variables such as age, sex, and years of education (p > 0.05). The PSQI total score and its components (excluding sleep medication usage) exhibited a positive correlation with the HAMA total score and its psychological anxiety component (p < 0.05), and a correlation was observed between somatic anxiety manifestations and several components of the PSQI. The PSQI total scores before and after intervention were 10.0 (8.0, 13.0) and 8.0 (6.0, 9.0) respectively (p < 0.001); the HAMA total scores were 8.0 (5.0, 13.0) and 6.0 (4.0, 9.5) respectively (p < 0.001). The detection rates of poor sleep and anxiety states, along with their severity, significantly decreased post-intervention (p < 0.001). Conclusion: Healthcare practitioners experience prominent anxiety and sleep issues in the mobile cabin hospital. PMR can be an effective intervention for improving the anxiety and sleep quality of healthcare professionals during support periods in the mobile cabin hospital. However, trials with larger samples are necessitated to further affirm these preliminary findings.

Green credit policy and labor investment efficiency: evidence from China.

Green finance plays a crucial role in driving green development. By leveraging the implementation of the "Green Credit Guidelines" as a quasi-natural experiment in 2012, our study provides compelling evidence that this green credit policy enhances the efficient investment in labor. Our mechanism analysis indicates that the positive impact primarily stems from the upgrading of human capital and the mitigation of agency conflicts. Moreover, we find that the effect of the green credit policy on the efficient investment in labor by green credit-restricted firms is more pronounced when these firms face robust environmental law enforcement and operate with low labor intensity. Additionally, the enhanced investment in labor demonstrates a significant positive influence on future enterprise value. Overall, our findings underscore the significant improvement in corporate labor investment efficiency resulting from the successful implementation of the Green Credit Guidelines.

Wideband Multichannel Nyquist-Spaced Long-Haul Optical Transmission Influenced by Enhanced Equalization Phase Noise.

Enhanced equalization phase noise (EEPN), generated from the uncompensated dispersion experienced by laser phase noises, can cause serious damage to the transmission quality of optical fiber systems. In this work, the performance of a wideband Nyquist-spaced long-haul nonlinear optical fiber communication systems suffering from EEPN is investigated and discussed through split-step numerical simulations and analytical models based on the perturbation analysis, in the cases of digital nonlinearity compensation (NLC) and electronic dispersion compensation (EDC). The efficiency and the accuracy of the analytical models were validated via simulations, considering the different symbol rates and modulation formats. The performance of the C-band transmission was comprehensively studied based on the model. Our results reveal that the growth of symbol rates and transmission distances aggravates the distortions in the C-band system.

MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs.

Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine.

Deep Visual Odometry With Adaptive Memory.

We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive memory module, which progressively and adaptively saves the information from local to global in a neural analogue of memory, enabling our system to process long-term dependency. Benefiting from global information in the memory, previous results are further refined by an additional refining module. With the guidance of previous outputs, we adopt a spatial-temporal attention to select features for each view based on the co-visibility in feature domain. Specifically, our architecture consisting of Tracking, Remembering and Refining modules works beyond tracking. Experiments on the KITTI and TUM-RGBD datasets demonstrate that our approach outperforms state-of-the-art methods by large margins and produces competitive results against classic approaches in regular scenes. Moreover, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic algorithms tend to fail.

Relationship between Classification of Fabellae and the Severity of Knee Osteoarthritis: A Relevant Study in the Chinese Population.

OBJECTIVE: To classify the fabellae and discuss the relationship between the classification of fabellae and the severity of knee osteoarthritis (KOA) in Chinese. METHODS: From February 2019 to February 2020, 136 patients were measured and classified using three-dimensional computed tomography (CT) reconstruction. According to the CT imaging characteristics, the fabellae were divided into five types: type I, a fabella on the lateral femoral condyle; type II, a fabella on the medial femoral condyle; type III, a fabella on the lateral femoral condyle and a fabella on the medial femoral condyle; type IV, two fabellae on the medial femoral condyle; and type V, two fabellae on the lateral femoral condyle. The severity of KOA was assessed on the Recht grade by magnetic resonance imaging (MRI). The data were analyzed with SPSS 24.0. RESULTS: The classification of fabellae were correlated with KOA grades (χ2  = 35.026, P < 0.05). In terms of KOA grades, grade I and grade II were occupied most by fabellar type II (32, 72.8%); type II and other types showed significant statistical difference (P < 0.05). Grade I and grade II were also mainly fabellar type IV (four, 100%). Fabellar type V's biggest component was grade III and grade IV (six, 75%). Type IV and type V showed significant statistical difference (P < 0.05). CONCLUSION: The classification of fabellae were correlated with KOA grades. The type II may mean the lower KOA grades while type V may mean the higher KOA grades.

Inhaled nitric oxide and acute kidney injury risk: a meta-analysis of randomized controlled trials.

PURPOSE: There are conflicting results as to the effect of inhaled nitric oxide (iNO) therapy on the risk of acute kidney injury (AKI). The aim of this study was to perform a meta-analysis to assess the updated data. METHODS: We systematically searched Web of Science, the Cochrane Library, Wanfang, and PubMed for relevant randomized control trials between database inception and 9/07/2020. Relative risks (RRs) with 95% confidence intervals (CIs) predicting the risk of AKI were extracted to obtain summary estimates using fixed-effects models. The Trim and Fill method was used to evaluate the sensitivity of the results and adjust for publication bias in meta-analysis. RESULTS: 15 randomized controlled studies from 14 articles involving 1853 patients were included in the study. Analyzing the eligible studies we found: (1) iNO therapy significantly increased the risk of AKI in acute respiratory distress syndrome patients (RR 1.55, 95% CI 1.15-2.10, p = 0.004; I 2 for heterogeneity 0%; P het = 0.649). (2) The use of iNO was associated with reduced AKI risk in patients undergoing cardiac surgery (RR 0.80, 95% CI 0.64-0.99, p = 0.037; I 2 for heterogeneity 0%; P het = 0.528). (3) For organ transplantation recipients, there was no effect of iNO administration on the risk of AKI (RR 0.50, 95% CI 0.16-1.56, p = 0.233; I 2 for heterogeneity 0%; P het = 0.842). The Trim and Fill analysis showed that the overall effect of this meta-analysis was stable. CONCLUSIONS: The effect of iNO on AKI risk might be disease-specific. Future RCTs with larger patient populations should aim to validate our findings.

Classification of Hallucal Sesamoid Bone Correlated with Hallux Valgus Severity.

The hallucal sesamoid bones (HSBs), having an important role in reducing load per unit area on the first metatarsal head, can be injured commonly which also affected the first metatarsophalangeal joint and the surrounding structure. Meanwhile, differences among each HSB type may be a major factor affecting the occurrence and development of HV. So far, many researchers had learned that there are three different conditions in hallucal sesamoid bone affecting the choice of clinical surgery corresponding to different solutions in clinic. Thus, it is necessary to study the anatomical morphological characteristics of the HSB which can be helpful in clinical diagnosis and treatment, especially hallux valgus (HV). 150 X-ray and three-dimensional (3D) computed tomographic (CT) images consist of 72 left and 78 right metatarsals were applied in this anatomic study between two variables and showed by a simple scatter plot. The first metatarsophalangeal joint is divided into four different types: type I (no HSB, 1.3%), type II (with one HSB, 0.07%), type IIIa (with two HSBs when THB is bigger, 28%), type IIIb (with two HSBs when FHB is bigger, 65.3%), and type IV (with three HSBs, 4.7%). There was no statistical difference between the left and right sides, except HVA, Meary, and pitch (P < 0.05); all a, b, c, d, and i have statistical difference between male and female (P < 0.05). Meanwhile, HVA and IMA and HVA and type group have a significant correlation. In summary, HVA and IMA and HVA and classification of HSBs have significant correlations. The classification and location of HSBs can be an important basis to choose operation methods and postoperation evaluation.

Morphological parameters of fourth lumbar spinous process palpation: a three-dimensional reconstruction of computed tomography.

BACKGROUND: The localization of lumbar fourth spinous process (L4-SP) is an important anatomical landmark, and identifying its accurate position is essential for the diagnosis and treatment of waist diseases. METHODS: Five hundred participants were scanned with positive and lateral computed tomography (CT), which aimed to clarify anatomic characteristics of L4-SP. Anatomical parameters of the surface localization of L4-SP were measured and recorded through a three-dimensional (3D) reconstruction. RESULTS: Five hundred participants were classified into three types according to the position of BC with the iliac spine. There are just 266 that the line between the highest point of the iliac spine on both sides located on L4-SP (type I, 53.20%), 16 above L4-SP (type II, 3.20%), and 218 below L4-SP (type III, 43.60%). BC in type I (15.92 ± 1.30 mm) is longer than type III (15.56 ± 1.32 mm). While the angle combined with AB and BC is different in the three groups, the angle in type I (173.00 ± 4.83°) is larger than that in type II (164.69 ± 5.50°) and type III (159.45 ± 8.39°). Other measurements were not found any significant differences between above. CONCLUSION: The traditional palpation for L4-SP is not absolutely exact. The accuracy rate is only 53.20%, and the errors may cause serious consequences.

Three-Dimensional (3D) Computed Tomographic (CT) Assessment of the Sustentaculum Tail to Find Distinctive Characteristics: Implications for Surgery.

BACKGROUND With the complexity of calcaneal fracture (CF) increasing, its treatment has changed to include inserting the screw used to secure the facies articular posterior into the sustentaculum tail (ST). Some research progress has been made in this area, but there has been little in-depth research on the anatomical morphology of the sustentaculum tail, which is necessary for clinical surgery, and more information about Chinese anatomic characteristics and improved surgical techniques for CF are needed. MATERIAL AND METHODS This anatomical study, based on a three-dimensional (3D) computed tomographic (CT) reconstruction technique, included 287 dry calcaneus, consisting of 144 left and 143 right calcaneus. The images were reconstructed in 3D after CT scanning. Seven subjects were enrolled (L and R): (1) The vertical distance from inside the sustentaculum tail (IST) to inside the facies articularis talaris posterior; (2) The vertical distance from IST to the outside facies articularis talaris posterior; (3) The thickness of sulcus calcaneal nadir; (4) The distance from IST to processus medislis tuberis calcaneus; (5) The distance from IST to calcaneal posterosuperior tuber; (6) The angle of the prolate axial intersection between ST and calcaneus on the normal superior as ˂α; and (7) The angle of the prolate axial intersection between ST and calcaneus on the normal posterior as ˂ß. All measurement results were analyzed by SPSS 22.0. RESULTS Based on morphological classification, the average length of AB, AC, AE, and AF on left ST were 16.956±1.391 mm, 37.803±2.525 mm, 43.244±3.617 mm, and 51.113±4.455 mm, respectively. Among the others, Ë‚ß was 81.227±6.317 mm on the left and 74.581±9.008 mm on the right (P<0.05). CONCLUSIONS These results suggest better ways to treat the special characteristics and to reduce the risk of CF surgery.

Many-to-many superpixel matching for robust tracking.

We present a robust tracking method based on many-to-many image superpixel matching (MMM). Our MMM tracker represents a target and its background using two sets of superpixels. Multiple hypotheses for superpixel matching are considered for better tracking performance. For each superpixel in an input image, k matching candidates are searched in the representative sets using approximate k -NN searching. The degree of matching is measured using foreground likelihood and matching probability assignment. The superpixel matching results are projected onto a displacement confidence map that depicts the motion probabilities of all the superpixels. During the projection, the displacements confidence of the superpixels are regularized by kernel methods. We estimate the target position by searching for the maximum probability on the displacement confidence map. The experimental results confirm that our superpixel matching achieves better performance than other trackers.

Adaptive mean-shift tracking with auxiliary particles.

We present a new approach for robust and efficient tracking by incorporating the efficiency of the mean-shift algorithm with the multihypothesis characteristics of particle filtering in an adaptive manner. The aim of the proposed algorithm is to cope with problems that were brought about by sudden motions and distractions. The mean-shift tracking algorithm is robust and effective when the representation of a target is sufficiently discriminative, the target does not jump beyond the bandwidth, and no serious distractions exist. We propose a novel two-stage motion estimation method that is efficient and reliable. If a sudden motion is detected by the motion estimator, some particle-filtering-based trackers can be used to outperform the mean-shift algorithm, at the expense of using a large particle set. In our approach, the mean-shift algorithm is used, as long as it provides reasonable performance. Auxiliary particles are introduced to cope with distractions and sudden motions when such threats are detected. Moreover, discriminative features are selected according to the separation of the foreground and background distributions when threats do not exist. This strategy is important, because it is dangerous to update the target model when the tracking is in an unsteady state. We demonstrate the performance of our approach by comparing it with other trackers in tracking several challenging image sequences.

Integrating color and shape-texture features for adaptive real-time object tracking.

We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability. The target model is updated according to the similarity between the initial and current models, and this makes the tracker more robust. The proposed algorithm has been compared with other trackers using challenging image sequences, and it provides better performance.

Coarse-to-Fine vision-based localization by indexing scale-invariant features.

This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable.