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This paper studies the exposure and contribution of financial institutions to systemic risks in financial markets. We employ three popular indicators of a financial institution’s exposure to systemic risks: the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (Volatility, correlation and tails for systemic risk measurement, Social Science Research Network, Rochester, NY, 2012) and the conditional Value-at-Risk (CoVaR) of Adrian and Brunnermeier (2011). We use a primary database of Taiwan financial institutions for our empirical study. A panel contains data of stock market returns and balance sheets of 31 Taiwan financial institutions for 2005–2014. We focus on systemic risk analysis so as to understand the dynamics of volatility, interdependency, and risk during the recent financial crisis. We then report the time series dynamics and cross sectional rankings of these systemic risk measures. The main results indicate that although these three measures differ in their definition of the contributions to systemic risk, all are quite similar in identifying systemically important financial institutions (SIFIs). Moreover, we find empirical evidence that systemic risk contributions are closely related to certain institution characteristic factors. The results of the Granger causality tests prove that a systemic risk measure is a great alternative tool for monitoring early warning signals of distress in the real economy.

The mitogen-activated protein kinase (MAPK) pathway, consisting of the Ras-Raf-MEK-ERK signaling cascade, regulates genes that control cellular development, differentiation, proliferation, and apoptosis. Within the cascade, multiple isoforms of Ras and Raf each display differences in functionality, efficiency, and, critically, oncogenic potential. According to the NCI, over 30% of all human cancers are driven by Ras genes. This dysfunctional signaling is implicated in a wide variety of leukemias and solid tumors, both with and without viral etiology. Due to the strong evidence of Ras-Raf involvement in tumorigenesis, many have attempted to target the cascade to treat these malignancies. Decades of unsuccessful experimentation had deemed Ras undruggable, but recently, the approval of Sotorasib as the first ever KRas inhibitor represents a monumental breakthrough. This advancement is not without novel challenges. As a G12C mutant-specific drug, it also represents the issue of drug target specificity within Ras pathway; not only do many drugs only affect single mutational profiles, with few pan-inhibitor exceptions, tumor genetic heterogeneity may give rise to drug-resistant profiles. Furthermore, significant challenges in targeting downstream Raf, especially the BRaf isoform, lie in the paradoxical activation of wild-type BRaf by BRaf mutant inhibitors. This literature review will delineate the mechanisms of Ras signaling in the MAPK pathway and its possible oncogenic mutations, illustrate how specific mutations affect the pathogenesis of specific cancers, and compare available and in-development treatments targeting the Ras pathway.

This paper proposes a bootstrap testing approach based on an approximate maximum likelihood method to discern whether a diffusion process is linear or whether there are threshold effects in the drift, the diffusion term or in both. It complements an alternative method based on the least-squares estimator which focuses on threshold effects in the drift. Monte Carlo simulations illustrate that the proposed testing approach is able to detect the source of the non-linearity. Two empirical applications show the importance of modeling threshold effects in the diffusion instead of the drift.

The growth factor progranulin (PGRN) has been implicated in embryonic development, tissue repair, tumorigenesis, and inflammation, but its receptors remain unidentified. We report that PGRN bound directly to tumor necrosis factor receptors (TNFRs) and disturbed the TNFα-TNFR interaction. PGRN-deficient mice were susceptible to collagen-induced arthritis, and administration of PGRN reversed inflammatory arthritis. Atsttrin, an engineered protein composed of three PGRN fragments, exhibited selective TNFR binding. PGRN and Atsttrin prevented inflammation in multiple arthritis mouse models and inhibited TNFα-activated intracellular signaling. Collectively, these findings demonstrate that PGRN is a ligand of TNFR, an antagonist of TNFα signaling, and plays a critical role in the pathogenesis of inflammatory arthritis in mice. They also suggest new potential therapeutic interventions for various TNFα-mediated pathologies and conditions, including rheumatoid arthritis.

The author invented an airbag helmet to provide effective protection for two-wheeled vehicle riders. This paper illustrates the desion and analysis of this Helmet Piezoelectric Accelerometer. Analysis is based on the finite element model of solid mechanics (e.g. linear elasticity and piezoelectricity) with a carefully selected software of COMSOL Multiphysics. Finite element analysis method helped the author to reduce the need for physical prototypes in design process. With a high degree of accuracy, the author modeled complex material physical deformations with detailed visualization and solved the problem. Equations were used for calculation during the process. This paper provides the design of comsol-based solid mechanics and piezoelectricity simulation and simulates the process of converting the mechanical vibration into voltage signals by using piezoceramics, so as to supply power to the airbag accelerometer and release the airbag in time. The reliability and safety of the design are verified by means of the mechanical and electrical simulation. In case of a traffic accident, the airbag will inflate instantly and form a balloon-shaped helmet wrapping the rider's head to secure the head effectively.

Protein interacting with C kinase (PICK1) is a scaffolding protein that is present in dendritic spines and interacts with a wide array of proteins through its PDZ domain. The best understood function of PICK1 is regulation of trafficking of AMPA receptors at neuronal synapses via its specific interaction with the AMPA GluA2 subunit. Disrupting the PICK1-GluA2 interaction has been shown to alter synaptic plasticity, a molecular mechanism of learning and memory. Lack of potent, selective inhibitors of the PICK1 PDZ domain has hindered efforts at exploring the PICK1-GluA2 interaction as a therapeutic target for neurological diseases. Here, we report the discovery of PICK1 small molecule inhibitors using a structure-based drug design strategy. The inhibitors stabilized surface GluA2, reduced Aβ-induced rise in intracellular calcium concentrations in cultured neurons, and blocked long term depression in brain slices. These findings demonstrate that it is possible to identify potent, selective PICK1-GluA2 inhibitors which may prove useful for treatment of neurodegenerative disorders.

With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.

We consider the Whittle likelihood estimation of seasonal autoregressive fractionally integrated moving-average models in the presence of an additional measurement error and show that the spectral maximum Whittle likelihood estimator is asymptotically normal. We illustrate by simulation that ignoring measurement errors may result in incorrect inference. Hence, it is pertinent to test for the presence of measurement errors, which we do by developing a likelihood ratio (LR) test within the framework of Whittle likelihood. We derive the non-standard asymptotic null distribution of this LR test and the limiting distribution of LR test under a sequence of local alternatives. Because in practice, we do not know the order of the seasonal autoregressive fractionally integrated moving-average model, we consider three modifications of the LR test that takes model uncertainty into account. We study the finite sample properties of the size and the power of the LR test and its modifications. The efficacy of the proposed approach is illustrated by a real-life example.

Total wrist arthroplasty (TWA), first performed in the late 19th Century, is still an infrequently used operation. It is most commonly indicated in patients with rheumatoid arthritis who have pan-carpal wrist involvement. It is an alternative to total wrist arthrodesis in patients who wish to preserve joint motion. Patients must lead a low-demand lifestyle and have failed non-operative measures. Complications are not insignificant and have been reported to be as high as 43%. Modern generation implants most often fail due to dislocation or loosening. Because wrist arthrodesis remains the gold standard treatment, particularly in patients with higher physical demands, it remains to be seen whether TWA will gain greater acceptance as prosthetic designs evolve.

Objectives: Resilience to pain is a protective factor against aversive pain outcomes, such as suicide. Religiosity as a cornerstone of resilience has been found to be associated with reduced risk of suicidality in chronic pain. However, affiliations to different religions have displayed differences in suicide risk. This study focuses on the roles of pain resilience and Dao religion in mitigating suicidal experience in individuals with chronic pain. Methods: This study adopted a mixed‐method approach. A preliminary investigation was conducted regarding the internal consistency and construct validity of the translated version of the pain resilience scale (PRS). Qualitative data were collected through interviews with individuals experiencing chronic pain. Levels of PRS and gender were included in the logistic regression on the probability of suicide attempts. The role of Dao practice was qualitatively analyzed through narrative analysis. Results: Among the 24 participants, 14 were affiliated with the Dao religion; therefore, the transcripts of these 14 interviews were analyzed. Individuals with moderate scores on the PRS were 11.60 times less likely to have attempted suicide than those with low PRS scores. The likelihood further decreased by 38.7 times in those with high PRS scores. Four themes emerged from the qualitative interviews. The participants experienced a burden from pain, made efforts to please the deities in exchange for better pain control, continuously adjusted to pain, and ultimately developed a new perspective on the relationship between their religion and pain. Many individuals have engaged in Dao rituals to try to alleviate their physical and psychological pain. Most participants tended to offer a religious interpretation of enlightening moments after surviving a suicide attempt. Discussion: This study illustrates how pain resilience and Dao religious practices mitigate suicidality in chronic pain. Trial Registration: ClinicalTrials.gov identifier: NCT05148364.