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Abstract Background Multidisciplinary cancer care to facilitate the provision of patient centred and evidence-based care is considered best practice internationally. In 2016 multidisciplinary care measures were developed for all local health districts across NSW. The aim of this study was to identify system-level changes and quality improvement activities across the NSW cancer system linked to reporting on these measures. Methods Focus group discussions were used to generate a synergy of ideas from key stakeholders. An exploratory descriptive approach was used within the ontological position of Framework Analysis, the analysis method chosen for this research study, sitting most closely within pragmatism. The use of Framework Analysis in the analytic strategy is because it is well-suited to addressing policy issues and maintaining specific focus within a wider dataset. Results Two focus groups were held with a total of 18 purposively selected participants. Four primary themes emerged: value of electronic documentation; role clarity; relationships; and future development of measures. Key findings included that the reporting of performance measures has expedited the development of electronic documentation and data extraction from the multidisciplinary team meeting (MDT), identified barriers and facilitators to MDT data collection and supported MDT improvement activities across NSW. Conclusions The findings of this study have highlighted that MDTs and their meetings across NSW are harnessing technological advancements to support and further develop their MDTs, as well as the challenges of implementing new processes within the MDTM. This study adds a unique contribution to knowledge of how the reporting of measures can assist in understanding variation in the development and implementation of multidisciplinary teams, as well as highlighting future programs of work to decrease variation in multidisciplinary team meetings and quality improvement activities.

Wave height prediction is hampered by the volatility and unpredictability of ocean data. Traditional single predictors are inadequate in capturing this complexity, and weighted fusion methods fail to consider inter-model correlations, resulting in suboptimal performance. To overcome these challenges, we presented an improved stacking-based model that combined the long short-term memory (LSTM) network with extremely randomized trees (ET) for wave height prediction. Initially, features with weak correlation to wave height were excluded using the Pearson correlation coefficient. Subsequently, a stacking ensemble tailored for time series cross-validation was deployed, employing LSTM and ET as base learners to capture temporal and feature-specific patterns, respectively. Lasso regression was utilized as the meta-learner, harmonizing these insights to improve accuracy by leveraging the strengths of each model across different dimensions of the data. Validation using datasets from four buoy stations demonstrated the superior predictive capability of our proposed model over single predictors such as temporal convolutional networks (TCN) and XGBoost, and fusion methods like LSTM-ET-BP.

Abstract Epilepsies are a group of neurological disorders characterized by abnormal spontaneous brain activity, involving multiscale changes in brain functional organizations. However, it is not clear to what extent the epilepsy-related perturbations of spontaneous brain activity affect macroscale intrinsic dynamics and microcircuit organizations, that supports their pathological relevance. We collect a sample of patients with temporal lobe epilepsy (TLE) and genetic generalized epilepsy with tonic-clonic seizure (GTCS), as well as healthy controls. We extract massive temporal features of fMRI BOLD time-series to characterize macroscale intrinsic dynamics, and simulate microcircuit neuronal dynamics used a large-scale biological model. Here we show whether macroscale intrinsic dynamics and microcircuit dysfunction are differed in epilepsies, and how these changes are linked. Differences in macroscale gradient of time-series features are prominent in the primary network and default mode network in TLE and GTCS. Biophysical simulations indicate reduced recurrent connection within somatomotor microcircuits in both subtypes, and even more reduced in GTCS. We further demonstrate strong spatial correlations between differences in the gradient of macroscale intrinsic dynamics and microcircuit dysfunction in epilepsies. These results emphasize the impact of abnormal neuronal activity on primary network and high-order networks, suggesting a systematic abnormality of brain hierarchical organization.

Abstract Background Pregnant women with gestational diabetes mellitus (GDM) often have an increased risk of adverse pregnancy outcomes. The purpose of this study was to explore the prevalence and characteristics of GDM in Xi’an from 2015 to 2021 since the implementation of China’s “Two-Child policy” and to provide a clinical basis for the management of GDM. Methods We analyzed the oral glucose tolerance test (OGTT) results of 152,836 pregnant women who underwent routine prenatal examination at the Northwest Women and Children’s Hospital from 2015 to 2021. Additionally, we analyzed the GDM prevalence and characteristics. Results The prevalence of GDM in the Xi’an urban area was 24.66% and exhibited an increasing trend annually (χ2 for trend = 43.922, p

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

Spontaneous fluctuations in brain activity exhibit complex spatiotemporal patterns across animal species. Here the authors show that sensory-motor regions and spatial heterogeneity in excitation-inhibition balance might shape multi-stability in brain dynamics.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Abstract Background Assessment of clinical variation has attracted increasing interest in health systems internationally due to growing awareness about better value and appropriate health care as a mechanism for enhancing efficient, effective and timely care. Feedback using administrative databases to provide benchmarking data has been utilised in several countries to explore clinical care variation and to enhance guideline adherent care. Whilst methods for detecting variation are well-established, methods for determining variation that is unwarranted and addressing this are strongly debated. This study aimed to synthesize published evidence of the use of feedback approaches to address unwarranted clinical variation (UCV). Methods A rapid review and narrative evidence synthesis was undertaken as a policy-focused review to understand how feedback approaches have been applied to address UCV specifically. Key words, synonyms and subject headings were used to search the major electronic databases Medline and PubMed between 2000 and 2018. Titles and abstracts of publications were screened by two reviewers and independently checked by a third reviewer. Full text articles were screened against the eligibility criteria. Key findings were extracted and integrated in a narrative synthesis. Results Feedback approaches that occurred over a duration of 1 month to 9 years to address clinical variation emerged from 27 publications with quantitative (20), theoretical/conceptual/descriptive work (4) and mixed or multi-method studies (3). Approaches ranged from presenting evidence to individuals, teams and organisations, to providing facilitated tailored feedback supported by a process of ongoing dialogue to enable change. Feedback approaches identified primarily focused on changing clinician decision-making and behaviour. Providing feedback to clinicians was identified, in a range of a settings, as associated with changes in variation such as reducing overuse of tests and treatments, reducing variations in optimal patient clinical outcomes and increasing guideline or protocol adherence. Conclusions The review findings suggest value in the use of feedback approaches to respond to clinical variation and understand when action is warranted. Evaluation of the effectiveness of particular feedback approaches is now required to determine if there is an optimal approach to create change where needed.

Trust propagation is being increasingly adopted to assist recommendation systems in providing more reliable information, upon which, users can make more accurate decisions. Optimal trust path search integrating trust value and path length plays a critical role in trust propagation, but suffers from insufficient performance regarding search accuracy and time. Generally, the quality of trust propagation is affected by the path length, and the longer the path is, the worse the trust quality is. However, the length of the path is not the unique crucial factor. Some longer paths with greater trust values may be more credible. In addition, the A* algorithm can find an optimal solution, but it expends much time to distinguish some similar paths. The A* algorithm is improved and a dynamic weighted heuristic trust path search algorithm is proposed. According to the six-degree space theory, the paths are extended to six-degree admissible trust paths. Then, according to the depths of the nodes in the search path, it relaxes the evaluation functionf(n) by devising a dynamic weighted factor w, inserts all nodes satisfied specific conditions into the FOCAL list. Furthermore, it sets the secondary heuristic factor and selects the nodes with the minimum heuristic factor value to reach the target node, and outputs the optimal trust path. Experiments on the public Advogato and FilmTrust datasets demonstrated that the proposed algorithm could efficiently identify the reliable trust paths and predict trust value with high accuracy and reduced computational complexity. The proposed algorithm could be applied to recommendation systems in the future.

In this paper, a parallel quadrupedal robot was designed that is capable of versatile dynamic locomotion and perception-less terrain adaptation. Firstly, a quadrupedal robot with a symmetric legs and a powerful actuator was implemented for highly dynamic movement. Then, a fast and reliable method based on generalized least square was proposed for estimating the terrain parameters by fusing the body, leg, and contact information. On the basis of virtual model control (VMC) with the quadratic program (QP) method, the optimal foot force for terrain adaptation was achieved. Finally, the results obtained by simulation and indoor and outdoor experiments demonstrate that the robot can achieve a robust and versatile dynamic locomotion on uneven terrain, and the rejection of disturbances is reliable, which proves the effectiveness and robustness of this proposed method.

An individual’s pattern of resting state brain connectivity, as measured with fMRI, has been shown to predict cognitive and behavioral traits. Here, the authors show that different traits are predicted by different time-scales of resting state activity (dynamic vs. static).

[This corrects the article DOI: 10.1371/journal.pbio.3000602.].

Preeclampsia is a pregnancy disorder, whereas the underlying mechanisms and etiological factors of this complication remain elusive. Studies have reported that decreased invasiveness of trophoblast cells, immunity disorder in the maternal–fetal interface, and oxidative stress may contribute to the development of preeclampsia. In the present study, we firstly co-cultured the smooth muscle cells (SMCs) and endothelial cells (ECs) to mimic the decidua and myometrium interface and examined the effects of osteopontin (OPN) on the invasive potential of trophoblasts in the SMC-EC co-culturing system. Our results showed that HTR-8/SVneo cells after hypoxia treatment showed enhanced invasive potential in the SMC-EC co-culturing system. OPN levels in the culture media from hypoxia-treated HTR-8/SVneo cells were significantly increased. More importantly, OPN treatment upregulated integrin, beta 3 and integrin, beta 5 expression in HTR-8/SVneo cells, and promoted HTR-8/SVneo cell invasion in the transwell invasion assay and SMC-EC co-culturing system. Mechanistically, treatment with integrin αvβ3 inhibitor significantly attenuated the enhanced invasive potential of HTR-8/SVneo cells treated with OPN in the SMC-EC co-culturing system. In conclusion, our study for the first time established the SMC-EC co-culturing system to examine the invasive potential of trophoblasts. Our results indicated that OPN promoted the invasive capacity of trophoblasts via at least targeting αvβ3 in the EC-SMC co-culturing system. Future studies were required to further validate the EC-SMC co-culturing system and to determine the molecular mechanisms of OPN-mediated trophoblast invasion.

The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.

Aiming at highly dynamic locomotion and impact mitigation, this paper proposes the design and implementation of a symmetric legged robot. Based on the analysis of the three-leg topology in terms of force sensitivity, force production, and impact mitigation, the symmetric leg was designed and equipped with a high torque density actuator, which was assembled by a custom motor and two-stage planetary. Under the kinematic and dynamic constraints of the robot system, a nonlinear optimization for high jumping and impact mitigation is proposed with consideration of the peak impact force at landing. Finally, experiments revealed that the robot achieved a jump height of 1.8 m with a robust landing, and the height was equal to approximately three times the leg length.

The jumping motion of legged robots is an effective way to overcome obstacles in the rugged microgravity planetary exploration environment. At the same time, a quadruped robot with a manipulator can achieve operational tasks during movement, which is more practical. However, the additional manipulator will restrict the jumping ability of the quadruped robot due to the increase in the weight of the system, and more active degrees of freedom will increase the control complexity. To improve the jumping height of a quadruped robot with a manipulator, a bio-inspired take-off maneuver based on the coordination of upper and lower limbs is proposed in this paper. The kinetic energy and potential energy of the system are increased by driving the manipulator-end (ME) to swing upward, and the torso driven by the legs will delay reaching the required peak speed due to the additional load caused by the accelerated ME. When the acceleration of ME is less than zero, it will pull the body upward, which reduces the peak power of the leg joints. Therefore, the jumping ability of the system is improved. To realize continuous and stable jumping, a control framework based on whole-body control was established, in which the quadruped robot with a manipulator was a simplified floating seven-link model, and the hierarchical optimization was used to solve the target joint torques. This method greatly simplifies the dynamic model and is convenient for calculation. Finally, the jumping simulations in different gravity environments and a 15° slope were performed. The jump heights have all been improved after adding the arm swing, which verified the superiority of the bio-inspired take-off maneuver proposed in this paper. Furthermore, the stability of the jumping control method was testified by the continuous and stable jumping.

Load capacity is an important index to reflect the practicability of legged robots. Existing research into quadruped robots has not analyzed their load performance in terms of their structural design and control method from a systematic point of view. This paper proposes a structural design method and crawling pattern generator for a planar quadruped robot that can realize high-payload locomotion. First, the functions required to evaluate the leg’s load capacity are established, and quantitative comparative analyses of the candidates are performed to select the leg structure with the best load capacity. We also propose a highly integrated design method for a driver module to improve the robot’s load capacity. Second, in order to realize stable load locomotion, a novel crawling pattern generator based on trunk swaying is proposed which can realize lateral center of mass (CoM) movement by adjusting the leg lengths on both sides to change the CoM projection in the trunk width direction. Finally, loaded crawling simulations and experiments performed with our self-developed quadruped robot show that stable crawling with load ratios exceeding 66% can be realized, thus verifying the effectiveness and superiority of the proposed method.

Epithelial-myoepithelial tumor is extremely rare as a pulmonary neoplasm. Only 20 cases have been reported to date, of which 14 were malignant. We report a case of intrabronchial epithelial-myoepithelial carcinoma in a 73-year-old man with a history of heavy smoking. The tumor was well-circumscribed and caused distal airway obstruction. Histologically, the tumor showed glandular and solid architecture. The glands were composed of an inner layer of epithelial cells and an outer layer of myoepithelial cells. The solid areas consisted of spindle-shaped myoepithelial cells. Immunohistochemical staining was positive for p53 and c-Kit (CD117). Focal atypia and increased mitotic activity were present, but no vascular invasion or nodal metastasis was identified. [ABSTRACT FROM AUTHOR]

HLA-C*07:565 differs from HLA-C*07:02:01:01 (400C->T, exon 3, L110F). [ABSTRACT FROM AUTHOR]

For the inverse proposition design of a three-dimensional centrifugal impeller using the streamline curvature method, the cubic spline curve fitting method is extensively used to solve the velocity gradient equation. Given the deficiency in stability with the cubic spline curve fitting method, a new finite difference method is proposed to solve the velocity gradient equation on the S2m stream surface. In the finite difference scheme, the relative velocity derivative along the streamline direction is decomposed into two terms. One term uses forward difference, and the other uses backward difference. The difference schemes of all other parameter derivatives in the velocity gradient equation adopt forward difference. The method can guarantee the matrix main diagonal elements of the dominant, indicating stable convergence in solving the velocity field. Robustness analysis is performed for both methods, and the new finite difference method shows excellent superiority in stability. Finally, the finite difference method is applied to redesign the Krain impeller. Through computational fluid dynamics, the efficiency of the redesigned impeller at the design operating point is increased by approximately 0.3% and the pressure ratio by approximately 5%. These results show that the difference method is feasible to solve the S2m stream surface velocity gradient equation.