Cognitive Phenotype Shifts in Risk-Taking: Interplay of Non-Suicidal Self-Injury Behaviors and Intensified Depression.

BACKGROUND: Non-suicidal self-injury (NSSI) behavior is significantly prevalent in both adolescents and psychiatric populations, particularly in individuals with major depressive disorder (MDD). NSSI can be considered a result of risky decision-making in response to negative emotions, where individuals choose self-harm over other less harmful alternatives, suggesting a potential decision-making deficit in those engaging in NSSI. This study delves into the complex relationship between NSSI and depression severity in decision-making and its cognitive underpinnings. METHODS: We assessed decision behaviors in 57 MDD patients with NSSI, 42 MDD patients without NSSI and 142 healthy controls using the Balloon Analogue Risk Task, which involves risk-taking, learning, and exploration in uncertain scenarios. Using computational modeling, we dissected the nuanced cognitive dimensions influencing decision behaviors. A novel statistical method was developed to elucidate the interaction effects between NSSI and depression severity. RESULTS: Contrary to common perceptions, we found that individuals with NSSI behaviors were typically more risk-averse. Meanwhile, there was a complex interaction between NSSI and depression severity in shaping risk-taking behaviors. As depressive symptoms intensified, these individuals with NSSI began to perceive less risk and behave more randomly. CONCLUSIONS: This research provides new insights into the cognitive aspects of NSSI and depression, highlighting the importance of considering the influence of comorbid mental disorders when investigating the cognitive underpinnings of such behaviors, especially in the context of prevalent cross-diagnostic phenomena like NSSI behaviors.

A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning.

The initial field has a crucial influence on numerical weather prediction (NWP). Data assimilation (DA) is a reliable method to obtain the initial field of the forecast model. At the same time, data are the carriers of information. Observational data are a concrete representation of information. DA is also the process of sorting observation data, during which entropy gradually decreases. Four-dimensional variational assimilation (4D-Var) is the most popular approach. However, due to the complexity of the physical model, the tangent linear and adjoint models, and other processes, the realization of a 4D-Var system is complicated, and the computational efficiency is expensive. Machine learning (ML) is a method of gaining simulation results by training a large amount of data. It achieves remarkable success in various applications, and operational NWP and DA are no exception. In this work, we synthesize insights and techniques from previous studies to design a pure data-driven 4D-Var implementation framework named ML-4DVAR based on the bilinear neural network (BNN). The framework replaces the traditional physical model with the BNN model for prediction. Moreover, it directly makes use of the ML model obtained from the simulation data to implement the primary process of 4D-Var, including the realization of the short-term forecast process and the tangent linear and adjoint models. We test a strong-constraint 4D-Var system with the Lorenz-96 model, and we compared the traditional 4D-Var system with ML-4DVAR. The experimental results demonstrate that the ML-4DVAR framework can achieve better assimilation results and significantly improve computational efficiency.

Cross-neutralizing antibody titres against non-vaccine types induced by a recombinant trivalent HPV vaccine (16/18/58) in rhesus macaques.

Human papillomavirus (HPV) causes not only most cervical cancers but also cancers of the vagina, vulva, penis, anus, rectum, and oropharynx. Every year, 200,000 women die of cervical cancer in the world, and China accounts for about 10%. HPV vaccines are effective in preventing HPV infections thus HPV-related cancers worldwide. Studies on the clinical trials of the 2v Cervarix™ and the 4v Gardasil® have suggested that immunization with either of these vaccines provided some level of protection against other HPV types that are closely related to the types contained in the vaccines. Here we conducted a preliminary evaluation on the ability to induce cross-neutralizing antibodies in rhesus monkeys by a 3v HPV vaccine that targets HPV16, 18, and 58 and it is specifically designed for Chinese women. We found that this vaccine is no less than Gardasil® in terms of the ability to induce NAbs against non-vaccine types of HPV in rhesus macaques. These results provided evidence from the immunogenicity point of view that the KLWS 3v HPV vaccine is a strong competitor to the imported 2v and 4v HPV vaccines currently available on the market.

Development and characterization of an HPV18 detection kit using two novel HPV18 type-specific monoclonal antibodies.

BACKGROUND: HPV 18 is one of the most prevalent oncogenic types, only second to HPV 16, and included in the licensed vaccines on the market. In this study, we describe the production and characterization of a panel of monoclonal antibodies (mAb) to HPV18. METHODS: The immunocompetence of 1B1 and 4C2 mAbs for HPV L1 protein was evaluated by SDS-PAGE analysis, neutralization assays, affinity identification, and ELISA. The 1B1 and 4C2 genes were sequenced and analyzed. Finally, the detection kit with the two mAbs was assessed for linearity, repeatability and specificity. RESULTS: Both mAbs specifically recognized HPV18 L1 and virus-like particles (VLPs). These mAbs are conformation-neutralizing antibodies that have high affinity and type specificity. Based on these characteristics of these mAbs, we developed an ELISA kit for specifically detecting HPV 18 antigen. We showed that this kit displayed good linearity, repeatability and sensitivity for detecting HPV18 L1 pentamer and HPV18 VLP. CONCLUSIONS: We characterized two monoclonal neutralizing antibodies for HPV L1 protein, and developed an ELISA kit for specifically detecting HPV 18 antigen. This newly developed kit can be used to monitor the potency of HPV vaccines throughout the entire production process as well as preliminary analysis of HPV18 infections.

Structural basis of substrate specificity in porcine RNase 4.

UNLABELLED: RNase 4, a member of the RNase A superfamily with substrate preference for uridine, has roles in host defence, angiogenesis and neurodegenerative diseases. It also exhibits the highest interspecies amino acid sequence similarity amongst RNase A family members. However, compared to other members of the RNase A family, including eosinophil-derived neurotoxin, eosinophil cationic protein and angiogenin, little is known about the molecular basis of substrate specificity in RNase 4. Here we report high to medium resolution structures of native porcine RNase 4 (PL3), a 'substrate-specificity' determining mutant D80A and their respective complexes with deoxyuridine 5'-monophosphate (dUMP) and deoxycytidine 5'-monophosphate (dCMP). These structures provide insight into the structural basis of the uridine versus cytosine substrate specificity in RNase 4: in the D80A mutant (D80A•dCMP), the side chain of Arg101 is positioned further away from the substrate-binding pocket due to the loss of the Asp80 side chain, reducing the repulsion force on the less favoured dCMP from Arg101 and allowing the ligand to occupy the binding pocket. This can also explain the observation that the ligand in the D80A•dCMP complex is stabilized only by a small number of hydrogen bonds. Compared to the previously reported structure of the human RNase 4•2'-deoxyuridine 3'-phosphate complex, the structure of PL3•dUMP complex shows additional hydrogen bonds between the ligand and the protein. In addition, the interaction between Arg101 and the dUMP ligand is absent. These observed differences are probably the result of the flexibility and different 'positioning' of the phosphate group among the mononucleotide ligands. DATABASE: The atomic coordinates and structure factors for PL3 (5AR6), D80A (5ARJ), PL3∙dUMP (5ARK) and D80A∙dCMP (5ARL) complexes have been deposited with the Protein Data Bank, Research Collaboratory for Structural Bioinformatics, Rutgers University, New Brunswick, NJ, USA (http://www.rcsb.org/).