Induction of fibroblast senescence generates a non-fibrogenic myofibroblast phenotype that differentially impacts on cancer prognosis

Cancer-associated fibroblasts (CAF) remain a poorly characterized, heterogeneous cell population. Here we characterized two previously described tumor-promoting CAF sub-types, smooth muscle actin (SMA)-positive myofibroblasts and senescent fibroblasts, identifying a novel link between the two

Anatexis, cooling, and kinematics during orogenesis: Miocene development of the Himalayan metamorphic core, east-central Nepal

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Evaluation of chemical strategies for improving the stability and oral toxicity of insecticidal peptides

© 2018 by the authors. Spider venoms are a rich source of insecticidal peptide toxins. Their development as bioinsecticides has, however, been hampered due to concerns about potential lack of stability and oral bioactivity. We therefore systematically evaluated several synthetic strategies to increase the stability and oral potency of the potent insecticidal spider-venom peptide !-HXTX-Hv1a (Hv1a). Selective chemical replacement of disulfide bridges with diselenide bonds and N- to C-terminal cyclization were anticipated to improve Hv1a resistance to proteolytic digestion, and thereby its activity when delivered orally. We found that native Hv1a is orally active in blowflies, but 91-fold less potent than when administered by injection. Introduction of a single diselenide bond had no effect on the susceptibility to scrambling or the oral activity of Hv1a. N- to C-terminal cyclization of the peptide backbone did not significantly improve the potency of Hv1a when injected into blowflies and it led to a significant decrease in oral activity. We show that this is likely due to a dramatically reduced rate of translocation of cyclic Hv1a across the insect midgut, highlighting the importance of testing bioavailability in addition to toxin stability

Successful oxytocin-assisted nipple aspiration in women at increased risk for breast cancer

The high rate of interval malignancies urges for new screening methods for women at high risk for breast cancer. Nipple aspiration provides direct access to the breast tissue and its DNA, and therefore is a likely candidate, but clinical applications have been limited by the failure to obtain nipple aspiration fluid from most women. We performed oxytocin-assisted nipple aspiration in 90 women at increased risk for breast cancer based on family history or genetic test results (n = 63) and/or previous breast cancer (n = 34). Nipple fluid was obtained from 81/90 women (90%) and bilaterally in 77%. Mean discomfort rating was 0.6 (on a 0–10 scale), which was significantly lower than for mammography or MRI. These findings suggest that a new tool for biomarker detection in oxytocin-assisted nipple fluid of women at high risk for breast cancer is at hand

No association between islet cell antibodies and coxsackie B, mumps, rubella and cytomegalovirus antibodies in non-diabetic individuals aged 7–19 years

Viral antibodies were tested in a cohort of 44 isletcell antibody-positive individuals age 7–19 years, and 44 of their islet cell antibody-negative age and sex-matched classmates selected from a population study of 4208 pupils who had been screened for islet cell antibodies. Anti-coxsackie B1-5 IgM responses were detected in 14 of 44 (32%) of the islet cell antibody-positive subjects and in 7 of 44 (16%) control subjects. This difference did not reach the level of statistical significance. None of the islet cell antibody-positive subjects had specific IgM antibodies to mumps, rubella, or cytomegalovirus. There was also no increase in the prevalence or the mean titres of anti-mumps-IgG or IgA and anti-cytomegalovirus-IgG in islet cell antibody-positive subjects compared to control subjects. These results do not suggest any association between islet cell antibodies, and possibly insulitis, with recent mumps, rubella or cytomegalo virus infection. Further studies are required to clarify the relationship between islet cell antibodies and coxsackie B virus infections

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Biomechanical properties of a buzz-pollinated flower

Approximately half of all bee species use vibrations to remove pollen from plants with diverse floral morphologies. In many buzz-pollinated flowers, these mechanical vibrations generated by bees are transmitted through floral tissues, principally pollen-containing anthers, causing pollen to be ejected from small openings (pores or slits) at the tip of the stamen. Despite the importance of substrate-borne vibrations for both bees and plants, few studies to date have characterised the transmission properties of floral vibrations. In this study, we use contactless laser vibrometry to evaluate the transmission of vibrations in the corolla and anthers of buzz- pollinated flowers of Solanum rostratum, and measured vibrations in three spatial axes. We found that floral vibrations conserve their dominant frequency (300Hz) as they are transmitted throughout the flower. We also found that vibration amplitude at anthers and petals can be up to >400% higher than input amplitude applied at the receptacle at the base of the flowe , and that anthers vibrate with a higher amplitude velocity than petals. Together, these results suggest that vibrations travel differently through floral structures and across different spatial axes. As pollen release is a function of vibration amplitude, we conjecture that bees might benefit from applying vibrations in the axes associated with higher vibration amplification

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur