Roles of TGFβ and FGF signals during growth and differentiation of mouse lens epithelial cell in vitro.

Transforming growth factor β (TGFβ) and fibroblast growth factor (FGF) signaling pathways play important roles in the proliferation and differentiation of lens epithelial cells (LECs) during development. Low dosage bFGF promotes cell proliferation while high dosage induces differentiation. TGFβ signaling regulates LEC proliferation and differentiation as well, but also promotes epithelial-mesenchymal transitions that lead to cataracts. Thus far, it has been difficult to recapitulate the features of germinative LECs in vitro. Here, we have established a LEC culture protocol that uses SB431542 (SB) compound to inhibit TGFβ/Smad activation, and found that SB treatment promoted mouse LEC proliferation, maintained LECs' morphology and distinct markers including N-cadherin, c-Maf, Prox1, and αA-, αB-, and β-crystallins. In contrast, low-dosage bFGF was unable to sustain those markers and, combined with SB, altered LECs' morphology and β-crystallin expression. We further found that Matrigel substrate coatings greatly increased cell proliferation and uniquely affected β-crystallin expression. Cultured LECs retained the ability to differentiate into γ-crystallin-positive lentoids by high-dosage bFGF treatment. Thus, a suppression of TGFβ/Smad signaling in vitro is critical to maintaining characteristic features of mouse LECs, especially expression of the key transcription factors c-Maf and Prox1

Physical Biology of the Materials-Microorganism Interface.

Future solar-to-chemical production will rely upon a deep understanding of the material-microorganism interface. Hybrid technologies, which combine inorganic semiconductor light harvesters with biological catalysis to transform light, air, and water into chemicals, already demonstrate a wide product scope and energy efficiencies surpassing that of natural photosynthesis. But optimization to economic competitiveness and fundamental curiosity beg for answers to two basic questions: (1) how do materials transfer energy and charge to microorganisms, and (2) how do we design for bio- and chemocompatibility between these seemingly unnatural partners? This Perspective highlights the state-of-the-art and outlines future research paths to inform the cadre of spectroscopists, electrochemists, bioinorganic chemists, material scientists, and biologists who will ultimately solve these mysteries

Bacteria photosensitized by intracellular gold nanoclusters for solar fuel production.

The demand for renewable and sustainable fuel has prompted the rapid development of advanced nanotechnologies to effectively harness solar power. The construction of photosynthetic biohybrid systems (PBSs) aims to link preassembled biosynthetic pathways with inorganic light absorbers. This strategy inherits both the high light-harvesting efficiency of solid-state semiconductors and the superior catalytic performance of whole-cell microorganisms. Here, we introduce an intracellular, biocompatible light absorber, in the form of gold nanoclusters (AuNCs), to circumvent the sluggish kinetics of electron transfer for existing PBSs. Translocation of these AuNCs into non-photosynthetic bacteria enables photosynthesis of acetic acid from CO2. The AuNCs also serve as inhibitors of reactive oxygen species (ROS) to maintain high bacterium viability. With the dual advantages of light absorption and biocompatibility, this new generation of PBS can efficiently harvest sunlight and transfer photogenerated electrons to cellular metabolism, realizing CO2 fixation continuously over several days

Dolosigranulum pigrum Primer Design for Isolation from Nasal Samples

We aimed to design species specific primers for confirmation of Dolosigranulum pigrum isolates and detection of D. pigrum in nasal samples. Preliminary testing using DNA extracted from nasal swabs indicated that the murJ primer could detect D. pigrum from a nasal bacterial community

Setting a Foundation for Innovation: A Good Health and Wellness in Indian Country Progress Report

In 2014, the Centers for Disease Control and Prevention launched the Good Health and Wellness in Indian Country (GHWIC) program, a five-year project that funds tribes, tribal-serving health organizations, and Tribal Epidemiology Centers (TECs) to promote chronic disease prevention amongst American Indian and Alaska Native (AI/AN) people. This report examines the strategic assessment and planning work grantees performed in the first two years of GHWIC

Stable isotopic evidence of nitrogen sources and C4 metabolism driving the world’s largest macroalgal green tides in the Yellow Sea

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 17437, doi:10.1038/s41598-018-35309-3.During recent years, rapid seasonal growth of macroalgae covered extensive areas within the Yellow Sea, developing the world’s most spatially extensive “green tide”. The remarkably fast accumulation of macroalgal biomass is the joint result of high nitrogen supplies in Yellow Sea waters, plus ability of the macroalgae to optionally use C4 photosynthetic pathways that facilitate rapid growth. Stable isotopic evidence shows that the high nitrogen supply is derived from anthropogenic sources, conveyed from watersheds via river discharges, and by direct atmospheric deposition. Wastewater and manures supply about half the nitrogen used by the macroalgae, fertiliser and atmospheric deposition each furnish about a quarter of the nitrogen in macroalgae. The massive green tides affecting the Yellow Sea are likely to increase, with significant current and future environmental and human consequences. Addressing these changing trajectories will demand concerted investment in new basic and applied research as the basis for developing management policies.This work was supported by the State Key Project of Research and Development Plan (2016YFC1402106)

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment

We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken on the ground as an intermediary for connecting remote-sensing images and language. Specifically, we train an image encoder for remote sensing images to align with the image encoder of CLIP using a large amount of paired internet and satellite images. Our unsupervised approach enables the training of a first-of-its-kind large-scale vision language model (VLM) for remote sensing images at two different resolutions. We show that these VLMs enable zero-shot, open-vocabulary image classification, retrieval, segmentation and visual question answering for satellite images. On each of these tasks, our VLM trained without textual annotations outperforms existing VLMs trained with supervision, with gains of up to 20% for classification and 80% for segmentation

Radon in Schools: A Brief Review of State Laws and Regulations in the United States

Exposure to Radon, a colorless, naturally occurring radioactive gas, is one of leading causes of lung cancer, and may pose a significant long-term risk for school age children. We examined the regulations and statutes in each US state related to radon in schools to delineate key features of policies and discrepancies among states that may have public health implications. Search terms such as “radon”, “school”, “mitigation”, “certification”, “licensing”, and “radon resistant new construction” were used to scan current statutes from each state legislature’s website and regulations from official state government websites for relevant regulatory and statutory requirements concerning radon in schools. State regulations related to the testing, mitigation, and public dissemination of radon levels in schools are inconsistent and the lack of nationwide indoor radon policy for schools may result in unacceptably high radon exposure levels in some US schools. We highlight the features and discrepancies of state laws and regulations concerning radon in schools, and offer several constructive means to reduce risks associated with radon exposure in school children