Dengue Virus Infects Primary Human Hair Follicle Dermal Papilla Cells

During the epidemic of the dengue virus (DENV) infection in Taiwan in 2014 and 2015, we observed an abnormally high frequency of increased scalp hair shedding in infected individuals that could not be explained by telogen effluvium. In this study, the mechanism of hair loss caused by DENV was explored. Human hair follicle dermal papilla cells (HFDPCs) are essential for hair follicle morphogenesis and cycling. Thus, we established an in vitro DENV infection model in HFDPCs. On immunofluorescence analysis, HFDPCs that were susceptible to DENV infection responded to type I interferon (IFN) treatment, and the cells showed antibody-dependent enhancement (ADE) effect. The expression of the pro-inflammatory cytokines, interleukin 6 (IL-6), and tumor necrosis factor-alpha (TNF-α), revealed an inflammatory response in DENV-infected HFDPCs. In particular, DENV infection impaired cell viability, and it activated caspase-associated cell death signaling in HFDPCs. In conclusion, our data indicate that direct infection with DENV causes inflammation and cell death in HFDPCs, which is involved in the mechanisms of hair loss after DENV infection. The knowledge of DENV infection in an immune-privileged tissue, such as hair follicles, may suggest their use for further studies on post-dengue fatigue syndrome (PDFS)

Pain and Suffering Damages in Personal Injury Cases: An Empirical Study

Many jurisdictions award pain and suffering damages, yet it is difficult for judges or juries to quantify pain. Several jurisdictions, such as California, cap pain and suffering damages or other noneconomic damages, and legal scholars have proposed ways to control such damages. Reforms and proposals, however, have been based on limited empirical evidence. It remains an open question whether components of economic damages explain pain and suffering damages. This study employs a unique dataset of Taiwan district court cases and uses detailed information on the components of pecuniary damages. Pain and suffering damages highly correlate with the plaintiff’s medical expenses, level of injury, and the amount requested by the plaintiff. The association with the amount requested by the plaintiff persists when one accounts for the likely quantifiable influences on pain and suffering damages, evidence of a possible anchoring effect. The strong correlation between economic damages and noneconomic damages persists in a large U.S. dataset of judge and jury trials, in which the noneconomic fraction of total damages is no greater than the pain and suffering fraction of total damages in Taiwan. Judges and juries consistently produce coherent patterns of noneconomic damages

Pain and Suffering Damages in Personal Injury Cases: An Empirical Study

Many jurisdictions award pain and suffering damages, yet it is difficult for judges or juries to quantify pain. Several jurisdictions, such as California, cap pain and suffering damages or other noneconomic damages, and legal scholars have proposed ways to control such damages. Reforms and proposals, however, have been based on limited empirical evidence. It remains an open question whether components of economic damages explain pain and suffering damages. This study employs a unique dataset of Taiwan district court cases and uses detailed information on the components of pecuniary damages. Pain and suffering damages highly correlate with the plaintiff’s medical expenses, level of injury, and the amount requested by the plaintiff. The association with the amount requested by the plaintiff persists when one accounts for the likely quantifiable influences on pain and suffering damages, evidence of a possible anchoring effect. The strong correlation between economic damages and noneconomic damages persists in a large U.S. dataset of judge and jury trials, in which the noneconomic fraction of total damages is no greater than the pain and suffering fraction of total damages in Taiwan. Judges and juries consistently produce coherent patterns of noneconomic damages

Influence of electrode thermal conductivity on resistive switching behavior during reset process

Resistive random access memory (RRAM) is the most promising candidate for non-volatile memory (NVM) due to its extremely low operation voltage, extremely fast write/erase speed, and excellent scaling capability. However, an obstacle hindering mass production of RRAM is the non-uniform physical mechanism in its resistance switching process. This study examines the influence of different electrode thermal conductivity on switching behavior during the reset process. Electrical analysis methods and an analysis of current conduction mechanism indicate that better thermal conductivity in the electrode will require larger input power in order to induce more active oxygen ions to take part in the reset process. More active oxygen ions cause a more complete reaction during the reset process, and cause the effective switching gap (dsw) to become thicker. The effect of the electrode thermal conductivity and input power are explained by our model and clarified by electrical analysis methods. Please click Additional Files below to see the full abstract

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications