Kidney disease in nail–patella syndrome

Nail–patella syndrome (NPS) is a pleiotropic autosomal-dominant disorder due to mutations in the gene LMX1B. It has traditionally been characterized by a tetrad of dermatologic and musculoskeletal abnormalities. However, one of the most serious manifestations of NPS is kidney disease, which may be present in up to 40% of affected individuals. Although LMX1B is a developmental LIM-homeodomain transcription factor, it is expressed in post-natal life in the glomerular podocyte, suggesting a regulatory role in that cell. Kidney disease in NPS seems to occur more often in some families with NPS, but it does not segregate with any particular mutation type or location. Two patterns of NPS nephropathy may be distinguished. Most affected individuals manifest only an accelerated age-related loss of filtration function in comparison with unaffected individuals. Development of symptomatic kidney failure is rare in this group, and proteinuria (present in approximately one-third) does not appear to be progressive. A small minority (5–10%) of individuals with NPS develop nephrotic-range proteinuria as early as childhood or young adulthood and progress to end-stage kidney failure over variable periods of time. It is proposed that this latter group reflects the effects of more global podocyte dysfunction, possibly due to the combination of a mutation in LMX1B along with an otherwise innocuous polymorphism or mutation involving any of several genes expressed in podocytes (e.g. NPHS2, CD2AP), the transription of which is regulated by LMX1B

A decision model for pre-evacuation time prediction based on fuzzy logic theory

Efficient evacuation is crucial for reducing deaths and injuries caused by disastrous events such as earthquakes. Notably, pre-evacuation time constitutes a large proportion of the total evacuation time; whether and when to initiate the evacuation largely determines the outcome of the evacuation in an emergency. Despite considerable efforts made to elaborate the pre-evacuation process, the evacuees’ vague and imprecise cognitive evaluation on the environment in pre-evacuation decision-making process is underrepresented in these studies. This study aims to enrich behavioral knowledge in the evacuation process during earthquakes and to explore modeling methods for characterization of the pre-evacuation process. As such, we conducted detailed analysis of real earthquake evacuation records to gain some insight into evacuees’ behavioral features. The extracted information from the records, together with the empirical knowledge formed the basis of building a fuzzy logic based decision-making model. The proposed model allowed the prediction of investigating/evacuating decision time with the consideration of individual heterogeneity and changes of cues. The validity of this model was validated against real-case data with reasonable agreement in average pre-evacuation time. A further parametric study was conducted to investigate the influence of features of physical signals and those of instructions on the investigating/evacuating decisions