Network structure of adolescent social, emotional, and behavioral difficulties and their differential relationships with suicidality.

BACKGROUND: Social, emotional, and behavioral difficulties (SEBDs) tend to develop during adolescence. Their presence and especially co-occurrence induce numerous disrupting consequences, including suicidality. A recently developed network analysis is suitable to investigate the symptom-level structure of comorbid psychopathology. Rather than pairwise comorbidity networks, the current study investigated a comprehensive network of SEBDs at the symptom level and explored the differential relationships between symptoms of SEBDs and suicidality. METHODS: Recruited from four public schools in China, a sample of adolescents (N = 6974, mean age = 15.84, 50.1% boys) were assessed with the Strengths and Difficulties Questionnaire (SDQ) and one suicidality-related item. The cross-sectional network structure of the SEBD symptoms was investigated. The differential associations between individual symptoms of SEBDs and suicidality were also explored with a relative importance analysis. RESULTS: The results showed that constantly fidgeting, worry a lot, unhappy, down-hearted, tearful, and easily scared emerged as the most central symptoms in the network of SEBDs. Worry a lot, constantly fidgeting, lose my temper, and being bullied served as bridge symptoms, connecting various domains of SEBDs. In addition, the centrality of symptoms was positively associated with the variance shared with suicidality, with worry a lot and unhappy, down-hearted, and tearful explaining a large portion of the variance of suicidality. CONCLUSIONS: Taken together, the results were indicative of close connections among emotional, hyperactivity-inattention, peer, and conduct aspects of adolescent mental health difficulties, as well as the central role of emotional difficulties in the SEBDs network.

Correction: Spontaneous mutations and mutational responses to penicillin treatment in the bacterial pathogen Streptococcus pneumoniae D39.

[This corrects the article DOI: 10.1007/s42995-024-00220-6.].

Spontaneous mutations and mutational responses to penicillin treatment in the bacterial pathogen Streptococcus pneumoniae D39.

Bacteria with functional DNA repair systems are expected to have low mutation rates due to strong natural selection for genomic stability. However, our study of the wild-type Streptococcus pneumoniae D39, a pathogen responsible for many common diseases, revealed a high spontaneous mutation rate of 0.02 per genome per cell division in mutation-accumulation (MA) lines. This rate is orders of magnitude higher than that of other non-mutator bacteria and is characterized by a high mutation bias in the A/T direction. The high mutation rate may have resulted from a reduction in the overall efficiency of selection, conferred by the tiny effective population size in nature. In line with this, S. pneumoniae D39 also exhibited the lowest DNA mismatch-repair (MMR) efficiency among bacteria. Treatment with the antibiotic penicillin did not elevate the mutation rate, as penicillin did not induce DNA damage and S. pneumoniae lacks a stress response pathway. Our findings suggested that the MA results are applicable to within-host scenarios and provide insights into pathogen evolution. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-024-00220-6.

Sliding-Mode Control for Perturbed MIMO Systems With Time-Synchronized Convergence.

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized convergence. We explore sufficient Lyapunov conditions using terminal sliding-mode designs and develop adaptive controllers for the input-dimension-dominant case. To handle perturbations, we design a multivariable disturbance observer with a super-twisting structure, which is integrated into the controller. By utilizing the sliding-mode technique and the disturbance observer, the proposed controller ensures simultaneous convergence of all output dimensions. In the state-dimension-dominant case, where a full-rank system matrix is absent, only specific output elements converge to equilibrium simultaneously. We conduct comparative simulations on a practical system to highlight the effectiveness of our proposed method for the input-dimension-dominant case. Statistical results reveal the benefits of shorter output trajectories and reduced energy consumption. For the state-dimension-dominant case, we present numerical examples to validate the semi-time-synchronized property.

Amphioxus adenosine-to-inosine tRNA-editing enzyme that can perform C-to-U and A-to-I deamination of DNA.

Adenosine-to-inosine tRNA-editing enzyme has been identified for more than two decades, but the study on its DNA editing activity is rather scarce. We show that amphioxus (Branchiostoma japonicum) ADAT2 (BjADAT2) contains the active site 'HxE-PCxxC' and the key residues for target-base-binding, and amphioxus ADAT3 (BjADAT3) harbors both the N-terminal positively charged region and the C-terminal pseudo-catalytic domain important for recognition of substrates. The sequencing of BjADAT2-transformed Escherichia coli genome suggests that BjADAT2 has the potential to target E. coli DNA and can deaminate at TCG and GAA sites in the E. coli genome. Biochemical analyses further demonstrate that BjADAT2, in complex with BjADAT3, can perform A-to-I editing of tRNA and convert C-to-U and A-to-I deamination of DNA. We also show that BjADAT2 preferentially deaminates adenosines and cytidines in the loop of DNA hairpin structures of substrates, and BjADAT3 also affects the type of DNA substrate targeted by BjADAT2. Finally, we find that C89, N113, C148 and Y156 play critical roles in the DNA editing activity of BjADAT2. Collectively, our study indicates that BjADAT2/3 is the sole naturally occurring deaminase with both tRNA and DNA editing capacity identified so far in Metazoa.

Micro Flapping-Wing Vehicles Formation Control With Attitude Estimation.

This article addresses the formation control problem of flapping-wing vehicles (FWVs) under the model uncertainty and the measurement inaccuracy. A two-layer formation strategy is adopted, which consists of a formation control layer for the leaders, and a containment control layer for the followers. In both layers, attitudes and positions are required by the formation geometry. A formation state estimation algorithm is designed to achieve the desired formation states from local neighborhoods. In FWVs, attitude angles are usually achieved from angular velocities, whose measurement error accumulates during integration and leads to divergence of the system. In order to solve this problem, we explore the coupling property between the translational motion and the rotational motion of FWVs, and design a coupling-based estimation method for attitude angles. To compensate for the model uncertainty, the measurement error, and the estimation error, adaptive neural networks are developed together with the control algorithm. The stability of both the control algorithm and the estimation algorithm is guaranteed based on the Lyapunov stability theory. Simulations are conducted to validate our method, and the results illustrate its effectiveness.

How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media.

The rise of cyberbullying has been of great concern for the general public. This study aims to explore public attitudes towards cyberbullying on Chinese social media. Cognition and emotion are important components of attitude, and this study innovatively used text analysis to extract the cognition and emotion of the posts. We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software "Text Mind", a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. The topic model analysis identified three common topics about cyberbullying: critiques on cyberbullying and support for its victims, rational expressions of anger and celebrity worship, and calls for further control. In summary, this study quantitatively reveals the negative attitudes of the Chinese public toward cyberbullying and conveys specific public concerns via three common topics. This will help us to better understand the demands of the Chinese public so that targeted support can be proposed to curb cyberbullying.

Detecting changes in attitudes toward depression on Chinese social media: A text analysis.

BACKGROUND & AIMS: Depression is a common and sometimes severe form of mental illness, and public attitudes towards depression can impact the psychological and social functioning of depressed patients. The purpose of the present study was to investigate public attitudes toward depression and three-year trends in these attitudes using big data analysis of social media posts in China. METHODS: A search of publically available Sina Weibo posts from January 2014 to July 2017 identified 20,129 hot posts with the keyword term "depression". We first used a Chinese Linguistic Psychological Text Analysis System (TextMind) to analyze linguistic features of the posts. And, then we used topic models to conduct semantic content analysis to identify specific themes in Weibo users' attitudes toward depression. RESULTS: Linguistic features analysis showed a significant increase over time in the frequency of terms related to affect, positive emotion, anger, cognition (including the subcategory of insight), and conjunctions. Semantic content analysis identified five common themes: severe effects of depression, stigma, combating stigma, appeals for understanding, and providing support. There was a significant increase over time in references to social (as opposed to professional) support, and a significant decrease over time in references to the severe consequences of depression. CONCLUSIONS: Big data analysis of Weibo posts is likely to provide less biased information than other methods about the public's attitudes toward depression. The results suggest that although there is ongoing stigma about depression, there is also an upward trend in mentions of social support for depressed persons. A supervised learning statistical model can be developed in future research to provide an even more precise analysis of specific attitudes.

A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic.

Background and Objective: The coronavirus disease 2019 (COVID-19) outbreak has been suggested as a collective trauma, which presents a continuing crisis. However, the specific post-traumatic implication of this crisis has not been adequately studied yet. The current study was aimed to ascertain the most central symptom and the strong connections between symptoms of post-traumatic stress disorder (PTSD). At the same time, exploring the relationship between covariates and the network of PTSD symptoms, by taking sex, anxiety, depression, suicidal ideation, quality of life, and social support as covariates, may help us to know the arise and maintenance of PTSD symptoms and give specified suggestions to people under the shadow of COVID-19. Method: The Post-traumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), was used to assess the PTSD symptoms extent of 338 healthy participants over the past month. Networks were analyzed using state-of-the-art regularized partial correlation models. In addition, the centrality of the symptoms and the robustness of the results were analyzed. Results: The network analysis revealed that the especially strong connections emerged between avoidance of thoughts and avoidance of reminders, hypervigilance and exaggerated startle response, intrusive thoughts and nightmares, flashbacks and emotional cue reactivity, and detachment and restricted affect. The most central symptoms were self-destructive/reckless behavior. Incorporation of covariates into the network revealed the strong connections path between self-destructive/reckless behavior and loss of interest and depression. Conclusion: Self-destructive/reckless behavior was the most central symptom in the network of PTSD symptoms related to the COVID-19 pandemic, which as an important target of interfere may have great benefits.

Functional characterization of an orexin neuropeptide in amphioxus reveals an ancient origin of orexin/orexin receptor system in chordate.

Amphioxus belongs to the subphylum cephalochordata, an extant representative of the most basal chordates, whose regulation of endocrine system remains ambiguous. Here we clearly demonstrated the existence of a functional orexin neuropeptide in amphioxus, which is able to interact with orexin receptor, activate both PKC and PKA pathways, decrease leptin expression, and stimulate lipogenesis. We also showed the transcription level of amphioxus orexin was affected by fasting or temperature, indicating a role of this gene in the regulation of energy balance. In addition, the expression of the amphioxus orexin was detected at cerebral vesicle, which has been proposed to be a homolog of the vertebrate brain. These data collectively suggest that a functional orexin neuropeptide has already emerged in amphioxus, which provide insights into the evolutionary origin of orexin in chordate and the functional homology between the cerebral vesicle and vertebrate brain.