The authors report results of an analysis of nasal and throat swabs from 17 patients in Zhuhai, China, who had received a diagnosis of Covid-19. Higher viral loads soon after symptom onset indicate ...the need for isolation strategies different from those used for the earlier SARS epidemic.
Objective
To investigate the brain mechanism of non-correspondence between diseases severity and compression degree of the spinal cord in cervical spondylotic myelopathy (CSM) patients and to test ...the utility of brain imaging biomarkers for predicting prognosis of CSM.
Methods
We calculated voxel-wise zALFF from 54 CSM patients and 50 healthy controls using resting-state fMRI data. In analysis 1, we identified the brain regions exhibited significant differences of zALFF between CSM patients and healthy controls. In analyses 2 through 3, we investigated the zALFF differences between light-symptom CSM patients and severe-symptom CSM patients while carefully matching the degree of compression between these two groups. In analysis 4, we tested the utility of zALFF within the primary motor cortex (M1) for predicting the prognosis of CSM.
Results
We found that (1) compared with the healthy controls, CSM patients exhibited higher ALFF within left M1, bilateral superior frontal gyrus, and lower zALFF within right precuneus and calcarine, suggesting altered brain neural activity in CSM patients; (2) after matching the compression degree, the CSM patients with more severe clinical symptoms exhibited higher zALFF within M1, indicating cortical function contributes to disease’s severity of CSM; (3) taking the M1 zALFF as features in the prognosis prediction model improves the prediction accuracy, indicating that the M1 zALFF provide additional value for predicting the prognosis of CSM patients following decompression surgery.
Conclusion
The functional state of M1 contributes to the disease’s severity of CSM and can provide complementary information for predicting the prognosis of CSM following decompression surgery.
Key Points
•
Cervical spondylotic myelopathy (CSM) patients exhibited increased zALFF within the primary motor cortex (M1), bilateral superior frontal gyrus, and decreased zALFF within the right precuneus and calcarine.
•
After matching the compression degree, the CSM patients with more severe clinical symptoms exhibited higher zALFF within M1, indicating cortical function contributes to disease severity of CSM.
•
zALFF within M1 provided additional value for predicting the prognosis of CSM patients.
It is remarkably desirable and challenging to design a stretchable conductive material with tunable electromagnetic‐interference (EMI) shielding and heat transfer for applications in flexible ...electronics. However, the existing materials sustained a severe attenuation of performances when largely stretched. Here, a super‐stretchable (800% strain) liquid metal foamed elastomer composite (LMF‐EC) is reported, achieving super‐high electrical (≈104 S cm−1) and thermal (17.6 W mK−1) conductivities under a large strain of 400%, which also exhibits unexpected stretching‐enhanced EMI shielding effectiveness of 85 dB due to the conductive network elongation and reorientation. By varying the liquid and solid states of LMF, the stretching can enable a multifunctional reversible switch that simultaneously regulates the thermal, electrical, and electromagnetic wave transport. Novel flexible temperature control and a thermoelectric system based on LMF‐EC is furthermore developed. This work is a significant step toward the development of smart electromagnetic and thermal regulator for stretchable electronics.
This work reports a super‐stretchable liquid metal foamed elastomer composite with both high electrical and thermal conductivities, and electromagnetic interference shielding effectiveness of 85 dB even when strained at 400% due to the stretching enabled conductive network elongation and reorientation, which also exhibits unique multifunctional reversible switch responses for application to tunable control of electromagnetic waves and thermal transport.
The cecal microbiota plays important roles in host food digestion and nutrient absorption, which may in part affect feed efficiency (FE). To investigate the composition and functional differences of ...cecal microbiota between high (n = 30) and low (n = 29) feed conversion ratio (FCR; metric for FE) groups, we performed 16S rRNA gene sequencing and predicted the metagenome function using Phylogenetic Investigation of Communities by Reconstruction of Unobserved Species in yellow broilers. The results showed that the 2 groups had the same prominent microbes but with differing abundance. Firmicutes, Bacteroidetes, and Actinobacteria were 3 prominent bacterial phyla in the cecal microbial community. Although there were no differences in microbial diversity, compositional differences related to FCR were found via linear discriminant analysis (LDA) effect size; the genus Bacteroides had a significantly higher abundance (LDA >2) in the high FE (HFE) group than in the low FE group. Furthermore, genus Bacteroides had a negative FCR-associated correlation (P < 0.05). Oscillospira was positively correlated with Bacteroides in both groups, whereas Dorea was negatively correlated with Bacteroides in the HFE group. Predictive functional analysis revealed that metabolic pathways such as “starch and sucrose metabolism,” “phenylalanine, tyrosine and tryptophan biosynthesis,” and “carbohydrate metabolism” were significantly enriched in the HFE group. The relatively subtle differences in FE-associated cecal microbiota composition suggest a possible link between cecal microbiota and FE. Moreover, Bacteroides may potentially be used as biomarkers for FE to improve growth performance in yellow broilers.
Nociceptive and tactile information is processed in the somatosensory system via reciprocal (i.e., feedforward and feedback) projections between the thalamus, the primary (S1) and secondary (S2) ...somatosensory cortices. The exact hierarchy of nociceptive and tactile information processing within this ‘thalamus-S1-S2’ network and whether the processing hierarchy differs between the two somatosensory submodalities remains unclear. In particular, two questions related to the ascending and descending pathways have not been addressed. For the ascending pathways, whether tactile or nociceptive information is processed in parallel (i.e., 'thalamus-S1′ and 'thalamus-S2′) or in serial (i.e., 'thalamus-S1-S2′) remains controversial. For the descending pathways, how corticothalamic feedback regulates nociceptive and tactile processing also remains elusive. Here, we aimed to investigate the hierarchical organization for the processing of nociceptive and tactile information in the ‘thalamus-S1-S2’ network using dynamic causal modeling (DCM) combined with high-temporal-resolution fMRI. We found that, for both nociceptive and tactile information processing, both S1 and S2 received inputs from thalamus, indicating a parallel structure of ascending pathways for nociceptive and tactile information processing. Furthermore, we observed distinct corticothalamic feedback regulations from S1 and S2, showing that S1 generally exerts inhibitory feedback regulation independent of external stimulation whereas S2 provides additional inhibition to the thalamic activity during nociceptive and tactile information processing in humans. These findings revealed that nociceptive and tactile information processing have similar hierarchical organization within the somatosensory system in the human brain.
We compared the detection frequency of avian influenza H7 subtypes at live poultry markets in Guangdong Province, China, before and after the introduction of a bivalent H5/H7 vaccine in poultry. The ...vaccine was associated with a 92% reduction in H7 positivity rates among poultry and a 98% reduction in human H7N9 cases.
Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we ...used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as “fingerprints” to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.
Objective
The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of
18
F-fluorodeoxyglucose (FDG) positron ...emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).
Method
Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.
Results
The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5–3.0, metastasis was suspected.
Conclusion
An SVM model based on
18
F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.
Key Points
•
The SVM model based on
18
F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC.
•
The SUR
blood
plays a major role in the SVM model.
•
The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.
Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable ...conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.
Objective
The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in
18
F-fluorodeoxyglucose ...(FDG) positron emission tomography/computed tomography (PET/CT).
Methods
Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SE
CT
and SE
PET
) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SE
CT
and SE
PET
.
Results
The AUCs of the SE
CT
and SE
PET
were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SE
CT
and SE
PET
with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SE
CT
(p<0.05).
Conclusion
The stacking model based on
18
F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.
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