GIMO: Gaze-Informed Human Motion Prediction in Context

Predicting human motion is critical for assistive robots and AR/VR applications, where the interaction with humans needs to be safe and comfortable. Meanwhile, an accurate prediction depends on understanding both the scene context and human intentions. Even though many works study scene-aware human motion prediction, the latter is largely underexplored due to the lack of ego-centric views that disclose human intent and the limited diversity in motion and scenes. To reduce the gap, we propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, as well as ego-centric views with eye gaze that serves as a surrogate for inferring human intent. By employing inertial sensors for motion capture, our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. We perform an extensive study of the benefits of leveraging eye gaze for ego-centric human motion prediction with various state-of-the-art architectures. Moreover, to realize the full potential of gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches. Our network achieves the top performance in human motion prediction on the proposed dataset, thanks to the intent information from the gaze and the denoised gaze feature modulated by the motion. The proposed dataset and our network implementation will be publicly available

Reliability of foot posture index (FPI-6) for evaluating foot posture in patients with knee osteoarthritis

Objective: To determine the reliability of FPI-6 in the assessment of foot posture in patients with knee osteoarthritis (KOA).Methods: Thirty volunteers with KOA (23 females, 7 males) were included in this study, assessed by two raters and at three different moments. Inter-rater and test-retest reliability were assessed with Cohen’s Weighted Kappa (Kw) and Intraclass Correlation Coefficient (ICC). Bland-Altman plots and respective 95% limits of agreement (LOA) were used to assess both inter-rater and test-retest agreement and identify systematic bias. Moreover, the internal consistency of FPI-6 was assessed by Spearman’s correlation coefficient.Results: FPI-6 total score showed a substantial inter-rater (Kw = .66) and test-retest reliability (Kw = .72). The six items of FPI-6 demonstrated inter-rater and test-retest reliability varying from fair to substantial (Kw = .33 to .76 and Kw = .40 to .78, respectively). Bland-Altman plots and respective 95% LOA indicated that there appeared no systematic bias and the acceptable agreement of FPI-6 total score for inter-rater and test-retest was excellent. There was a statistically significant positive correlation between each item and the total score of FPI-6, which indicated that FPI-6 had good internal consistency.Conclusion: In conclusion, the reliability of FPI-6 total score and the six items of FPI-6 were fair to substantial. The results can provide a reliable way for clinicians and researchers to implement the assessment of foot posture in patients with KOA

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

BackgroundStroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study designA case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets

Tryptophan metabolism and piglet diarrhea: Where we stand and the challenges ahead

The intestinal architecture of piglets is vulnerable to disruption during weaning transition and leads to diarrhea, frequently accompanied by inflammation and metabolic disturbances (including amino acid metabolism). Tryptophan (Trp) plays an essential role in orchestrating intestinal immune tolerance through its metabolism via the kynurenine, 5-hydroxytryptamine, or indole pathways, which could be dictated by the gut microbiota either directly or indirectly. Emerging evidence suggests a strong association between piglet diarrhea and Trp metabolism. Here we aim to summarize the intricate balance of microbiota–host crosstalk by analyzing alterations in both the host and microbial pathways of Trp and discuss how Trp metabolism may affect piglet diarrhea. Overall, this review could provide valuable insights to explore effective strategies for managing piglet diarrhea and the related challenges

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach

Background: Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data. Methods: In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods. Results: The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R2 value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%. Conclusions: Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances

Acupuncture for low back and/or pelvic pain during pregnancy: a systematic review and meta-analysis of randomised controlled trials

Objective Acupuncture is emerging as a potential therapy for relieving pain, but the effectiveness of acupuncture for relieving low back and/or pelvic pain (LBPP) during the pregnancy remains controversial. This meta-analysis aims to investigate the effects of acupuncture on pain, functional status and quality of life for women with LBPP pain during the pregnancy.Design Systematic review and meta-analysis.Data sources The PubMed, EMBASE databases, Web of Science and Cochrane Library were searched for relevant randomised controlled trials (RCTs) from inception to 15 January 2022.Eligibility criteria for selecting studies RCTs evaluating the effects of acupuncture on LBPP during the pregnancy were included.Data extraction and synthesis The data extraction and study quality assessment were independently performed by three reviewers. The mean differences (MDs) with 95% CIs for pooled data were calculated. We assessed the confidence in the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework.Main outcomes and measures The primary outcomes were pain, functional status and quality of life. The secondary outcomes were overall effects (a questionnaire at a post-treatment visit within a week after the last treatment to determine the number of people who received good or excellent help), analgesic consumption, Apgar scores >7 at 5 min, adverse events, gestational age at birth, induction of labour and mode of birth.Results This meta-analysis included 10 studies, reporting on a total of 1040 women. Overall, acupuncture significantly relieved pain during pregnancy (MD=1.70, 95% CI: (0.95 to 2.45), p<0.00001, I2=90%) and improved functional status (MD=12.44, 95% CI: (3.32 to 21.55), p=0.007, I2=94%) and quality of life (MD=−8.89, 95% CI: (−11.90 to –5.88), p<0.00001, I2 = 57%). There was a significant difference for overall effects (OR=0.13, 95% CI: (0.07 to 0.23), p<0.00001, I2 = 7%). However, there was no significant difference for analgesic consumption during the study period (OR=2.49, 95% CI: (0.08 to 80.25), p=0.61, I2=61%) and Apgar scores of newborns (OR=1.02, 95% CI: (0.37 to 2.83), p=0.97, I2 = 0%). Preterm birth from acupuncture during he study period was reported in two studies. Although preterm contractions were reported in two studies, all infants were in good health at birth. In terms of gestational age at birth, induction of labour and mode of birth, only one study reported the gestational age at birth (mean gestation 40 weeks). Thus, prospective randomised clinical studies or clinical follow-up studies were hence desirable to further evaluate these outcomes.Conclusions Acupuncture significantly improved pain, functional status and quality of life in women with LBPP during the pregnancy. Additionally, acupuncture had no observable severe adverse influences on the newborns. More large-scale and well-designed RCTs are still needed to further confirm these results.PROSPERO registration number CRD42021241771