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Primary cilia play critical roles in development and disease. Their assembly and disassembly are tightly coupled to cell cycle progression. [https://www.selleckchem.com/products/glpg0187.html GLPG0187 ic50] Here, we present data identifying KIF14 as a regulator of cilia formation and Hedgehog (HH) signaling. We show that RNAi depletion of KIF14 specifically leads to defects in ciliogenesis and basal body (BB) biogenesis, as its absence hampers the efficiency of primary cilium formation and the dynamics of primary cilium elongation, and disrupts the localization of the distal appendage proteins SCLT1 and FBF1 and components of the IFT-B complex. We identify deregulated Aurora A activity as a mechanism contributing to the primary cilium and BB formation defects seen after KIF14 depletion. In addition, we show that primary cilia in KIF14-depleted cells are defective in response to HH pathway activation, independently of the effects of Aurora A. In sum, our data point to KIF14 as a critical node connecting cell cycle machinery, effective ciliogenesis, and HH signaling. © 2020 Pejskova et al.Mutations in the channel protein PKD2 cause autosomal dominant polycystic kidney disease, but the function of PKD2 in cilia remains unclear. Here, we show that PKD2 targets and anchors mastigonemes, filamentous polymers of the glycoprotein MST1, to the extracellular surface of Chlamydomonas cilia. PKD2-mastigoneme complexes physically connect to the axonemal doublets 4 and 8, positioning them perpendicular to the plane of ciliary beating. pkd2 mutant cilia lack mastigonemes, and mutant cells swim with reduced velocity, indicating a motility-related function of the PKD2-mastigoneme complex. Association with both the axoneme and extracellular structures supports a mechanosensory role of Chlamydomonas PKD2. We propose that PKD2-mastigoneme arrays, on opposing sides of the cilium, could perceive forces during ciliary beating and transfer these signals to locally regulate the response of the axoneme. © 2020 Liu et al.BACKGROUND Frailty is a strong predictor of adverse outcomes. However, longitudinal drivers of frailty are not well understood. This study aimed at investigating the longitudinal trajectories of a frailty index (FI) from adulthood to late life and identifying the factors associated with the level and rate of change in FI. METHODS An age-based latent growth curve analysis was performed in the Swedish Adoption/Twin Study of Aging (N=1,842; aged 29-102 years) using data from up to 15 measurement waves across 27 years. A 42-item FI was used to measure frailty at each wave. RESULTS A bilinear, two-slope model with a turning point at age 65 best described the age-related change in FI, showing that the increase in frailty was more than twice as fast after age 65. Underweight, obesity, female sex, overweight, being separated from one's co-twin during childhood, smoking, poor social support and low physical activity were associated with a higher FI at age 65, with underweight having the largest effect size. When tested as time-varying covariates, underweight and higher social support were associated with a steeper increase in FI before age 65, whereas overweight and obesity were associated with less steep increase in FI after age 65. CONCLUSIONS Factors associated with the level and rate of change in frailty are largely actionable and could provide targets for intervention. As deviations from normal weight showed the strongest associations with frailty, future public health programs could benefit from monitoring of individuals with abnormal BMI, especially those who are underweight. © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America.MOTIVATION Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high quality generic prediction and informative physicochemical properties identification simultaneously. RESULTS In this work, Physicochemical Property based Therapeutic Peptide Predictor (PPTPP), a Random Forest based prediction method was presented to address this issue. A novel feature encoding and learning scheme were initiated to produce and rank physicochemical property related features. Besides being capable of predicting multiple therapeutics peptides with high comparability to established predictors, the presented method is also able to identify peptides' informative physicochemical properties. Results presented in this work not only illustrated the soundness of its working capacity but also demonstrated its potential for investigating other therapeutic peptides. AVAILABILITY https//github.com/YPZ858/PPTPP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Probabilistic Latent Semantic Analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how Linear Poisson Modelling (LPM) advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond mass spectra, using MRI data from colorectal xenograft models. RESULTS Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumors further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null hypothesis thresholding at high levels of confidence.