Social groups in various social species are organized with hierarchical structures that shape group dynamics and the nature of within-group interactions. In-group social bonding, exemplified by ...grooming behaviors among animals and collective rituals and team-building activities in human societies, is recognized as a practical adaptive strategy to foster group harmony and stabilize hierarchical structures in both human and nonhuman animal groups. However, the neurocognitive mechanisms underlying the effects of social bonding on hierarchical groups remain largely unexplored. Here, we conducted simultaneous neural recordings on human participants engaged in-group communications within small hierarchical groups (n = 528, organized into 176 three-person groups) to investigate how social bonding influenced hierarchical interactions and neural synchronizations. We differentiated interpersonal interactions between individuals of different (inter-status) or same (intra-status) social status and observed distinct effects of social bonding on inter-status and intra-status interactions. Specifically, social bonding selectively increased frequent and rapid information exchange and prefrontal neural synchronization for inter-status dyads but not intra-status dyads. Furthermore, social bonding facilitated unidirectional neural alignment from group leader to followers, enabling group leaders to predictively align their prefrontal activity with that of followers. These findings provide insights into how social bonding influences hierarchical dynamics and neural synchronization while highlighting the role of social status in shaping the strength and nature of social bonding experiences in human groups.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In DNA microarray data, class imbalance problem occurs frequently, causing poor prediction performance for minority classes. Moreover, its other features, such as high-dimension, small sample, high ...noise etc., intensify this damage. In this study, we propose ACOSampling that is a novel undersampling method based on the idea of ant colony optimization (ACO) to address this problem. The algorithm starts with feature selection technology to eliminate noisy genes in data. Then we randomly and repeatedly divided the original training set into two groups: training set and validation set. In each division, one modified ACO algorithm as a variant of our previous work is conducted to filter less informative majority samples and search the corresponding optimal training sample subset. At last, the statistical results from all local optimal training sample subsets are given in the form of frequence list, where each frequence indicates the importance of the corresponding majority sample. We only extracted those high frequency ones and combined them with all minority samples to construct the final balanced training set. We evaluated the method on four benchmark skewed DNA microarray datasets by support vector machine (SVM) classifier, showing that the proposed method outperforms many other sampling approaches, which indicates its superiority.
► ACO algorithm is modified for undersampling skewed DNA microarray data. ► The significance of each majority sample is estimated by ranking frequence list. ► ACOSampling increases classification performance but spends more time. ► Selecting a few feature genes helps to improve classification performance. ► Some classification tasks are harmful and the others are unharmful.
Cu-CeO2/AC catalysts exhibited extraordinary catalytic performance in the upgradation of ethanol to n-butanol. To the best of our knowledge, this is the first report that the highest n-butanol yields ...of 21.6% and nearly 20% could be achieved over heterogeneous catalysts under mild reaction conditions in batch and fixed-bed reactors, respectively. The high catalytic activity, selectivity and stability of these catalysts could be ascribed to the synergy of Cu, CeO2 and the activated carbon support.
A keyhole refilled Friction Stir Spot Welding (FSSW) process is developed, which consists of two steps. Regular FSSW is performed in the first step. After that, the tool travels along a circular path ...to refill the original keyhole. The process is applied for joining aluminum alloy 6061 to Transformation Induced Plasticity (TRIP) steel. It increases the joint strength by 56.33% compared with that of conventional FSSW joints. A ductile failure mode is observed. The process can be implemented in any universal CNC machine and needs only one simple FSSW tool. An additional pure circular path welding process shows the enhanced performance of the keyhole refilled FSSW joints relies on three bonding mechanisms: the refilled original keyhole, the increased bonding area between steel and aluminum as well as the hook structure generated from the regular FSSW process. Three distinct layers of grain structures can be observed on the hook, which reveal the material flow pattern during the process.
Objectives
To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.
Methods
A dataset ...of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models’ performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC).
Results
The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49–83.23%) to 97.02% (95% CI, 95.22–98.16%) and 87.94% (95% CI, 85.08–90.31%) to 98.83% (95% CI, 97.60–99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63–95.23%) and 88.21% (95% CI, 85.12–90.73%) for the two test cohorts, respectively.
Conclusion
Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy.
Trial registration
Clinical trial number: ChiCTR1900027676
Key Points
• Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy.
• Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes.
• Management of patients becomes more precise based on the DCNN model.
Scientists have reported that plant leaf veins grow into an optimized architecture not only to accomplish their biological and physiological functions but also to sustain the environmental loads. ...Researchers showed that the wind blade mimicking the leaf architecture could always have relatively lower internal strain energy, better static strength and stiffness, smaller stress intensity, and higher fatigue life compared with the conventional blade structures. However, the plant leaf-mimetic wind blade has so far remained at the level of simulations. Here, a new paradigm for design and fabrication of wind blades is demonstrated by 4D printing process, which combines several beneficial attributes in one blade. The proposed blade having the plant leaf structure can show reversible bend-twist coupling (BTC). It does not rely on conventional electromechanical systems such as sensors and actuators to determine proper deflection and change its shape. Additionally, the existing blades capable of BTC through passive methods have inherent flutter instability since they need to be flexible. The proposed blade may solve the flutter challenge. Lastly, this multi-functional blade can lead to eco-friendly wind turbines. Wind-tunnel tests, CFD, and performance analysis are performed on the proposed blade to demonstrate its applicability.
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•This study demonstrates the plant leaf-mimetic wind blade structure “in practice”.•We organized various shape-shifting behaviors in wind blades and their advantages.•This blade may solve flutter issue seen in existing passive blades capable of BTC.•Wind tunnel tests, CFD, and performance analysis are done on the proposed blade.•This multi-functional 4D printed blade can lead to eco-friendly wind turbines.
Value-added utilization of lignin waste streams is vital to fully sustainable and economically viable biorefineries. However, deriving substantial value from its main constituents is seriously ...hindered by the constant requirement for expensive coenzymes. Herein, we devised a coenzyme-free biocatalyst that could transform lignin-derived aromatics into various attractive pharmaceutical and polymer building blocks. At the center of our strategy is the integrated use of new mining phenolic acid decarboxylase and aromatic dioxygenase with extremely high catalytic efficiency, which realizes the value-added utilization of lignin in a coenzyme-independent manner. Notably, a new temperature/pH-directed strategy was proposed to eliminate the highly redundant activities of endogenous alcohol dehydrogenases. The major components of lignin were simultaneously converted to vanillin and 4-vinylphenol. Since the versatile biocatalyst could efficiently convert many other renewable lignin-related aromatics to valuable chemicals, this green route paves the way for enhancing the entire efficiency of biorefineries.
This study demonstrates the significant role of recoil pressure in selective laser melting (SLM) process using multi-laser technology. High-speed photography is utilized to observe the formation ...mechanism, and also the behavior of spatter particles during SLM fabrication. A computational image analysis framework is developed to assess the size and the number of induced spatters. The morphology and the composition of spatters and their influence on the surface of the fabricated parts are determined. Unmelted regions, resulting from spatter deposition into the powder or the solidified layer, are found to be a detrimental type of defects on mechanical properties of SLM parts. This is followed by a discussion on demand for developing a meaningful process parameters optimization to enhance the mechanical properties of SLM products.
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•High-speed photography is utilized to observe the formation mechanism of spatters.•An image analysis framework is developed to assess the distribution of induced spatters.•Spatter particles are detrimental type of defects on mechanical properties of SLM parts.
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