The Effects of Distancing Design Collaboration Necessitated by COVID-19 on Brain Synchrony in Teams Compared to Co-Located Design Collaboration: A Preliminary Study.

Due to the widespread involvement of distributed collaboration triggered by COVID-19, it has become a new trend that has continued into the post-pandemic era. This study investigated collective performance within two collaborative environments (co-located and distancing settings) by assessing inter-brain synchrony patterns (IBS) among design collaborators using functional near-infrared spectroscopy. The preliminary study was conducted with three dyads who possessed 2-3 years of professional product design experience. Each dyad completed two designated design tasks in distinct settings. In the distributed condition, participants interacted through video conferencing in which they were allowed to communicate by verbalization and sketching using a shared digital whiteboard. To prevent the influences of different sketching tools on design outputs, we employed digital sketching for both environments. The interactions between collaborators were identified in three behaviors: verbal only, sketch only, and mixed communication (verbal and sketch). The consequences revealed a higher level of IBS when mixed communication took place in distributed conditions than in co-located conditions. Comparably, the occurrence of IBS increased when participants solely utilized sketching as the interaction approach within the co-located setting. A mixed communication method combining verbalization and sketching might lead to more coordinated cognitive processes when in physical isolation. Design collaborators are inclined to adjust their interaction behaviors in order to adapt to different design environments, strengthen the exchange of ideas, and construct design consensus. Overall, the present paper discussed the performance of virtual collaborative design based on a neurocognitive perspective, contributing valuable insights for the future intervention design that promotes effective virtual teamwork.

Epigenetic Silencing of Tumor Suppressor lncRNA NKILA: Implication on NF-κB Signaling in Non-Hodgkin's Lymphoma.

The long non-coding RNA (lncRNA) NKILA, localized to 20q13.31, is a negative regulator of NF-κB signaling implicated in carcinogenesis. As a CpG island is embedded in the promoter region of NKILA, it is hypothesized as a tumor suppressor lncRNA silenced by promoter DNA methylation in non-Hodgkin's lymphoma (NHL). By pyrosequencing-verified methylation-specific PCR, NKILA methylation was detected in 1/10 (10%) NHL cell lines, but not in normal peripheral blood buffy coats or tonsils. NKILA methylation correlated with the repression of NKILA in cell lines. Hypomethylation treatment with 5-Aza-2'-deoxycytidine resulted in promoter demethylation and the re-expression of NKILA. In 102 NHL primary samples, NKILA was methylated in 29 (51.79%) diffuse large B-cell lymphoma (DLBCL) and 4 (20%) peripheral T-cell lymphoma cases, but unmethylated in all 26 mantle cell lymphoma cases. Mechanistically, the knockdown of NKILA resulted in promoting IkBα phosphorylation, associated with nucleus translocation of total p65 and phosphorylated p65 in SU-DHL-1 cells, hence constitutive NF-κB activation. Functionally, the knockdown of NKILA in SU-DHL-1 cells led to decreased cell death and increased cellular proliferation. Collectively, NKILA was a tumor suppressor lncRNA frequently hypermethylated in DLBCL. Promoter DNA methylation-mediated NKILA silencing resulted in increased cellular proliferation and decreased cell death via the repression of NF-κB signaling in NHL.

Auxiliary diagnosis model of children with autism spectrum disorder based on random forest / 中国学校卫生

Objective@#The random forest algorithm was used to construct a rapid screening diagnostic prediction model for children with autism spectrum disorder, to provide the references for early detection, early diagnosis of ASD children, and to reduce the pressure of ASD clinical diagnosis and assessment.@*Methods@#The random forest algorithm of machine learning was applied to build the auxiliary diagnosis model. Totally 346 ASD children and 90 normal children were evaluated by Social Responsiveness Scale and Vineland Adaptive Behavior Scales. ROC curve, and accuracy was used to evaluate the models.@*Results@#Among the models, the accuracy of 13 feature factors and 7 feature factors were above 0.9, the sensitivity was up to 0.927, the specificity was up to 0.936 and the AUC was up to 0.979. The accuracy, sensitivity, specificity and AUC of the model were 0.943,0.959,0.931 and 0.978 respectively. The fitting and generalization effects of the three models were all satisfactory.@*Conclusion@#A random forest model based on the SRS Scales and Vineland Adaptive Behavior Scales can be used to diagnose ASD accurately and provide scientific basis for the development of rapid screening and diagnosis tools.

Detection of the Progenitors of Be X-ray Binaries.

A recent survey of the far-ultraviolet spectra of 264 B-emission line stars has revealed 16 systems with hot companions that are the stripped down remains of a former mass donor star. Some of these will probably become Be + neutron star X-ray binaries in the future. The actual numbers of such systems may be large, because the detected systems have companions that occupy the brief and bright, He-shell burning stage of evolution.