Placental Vacuolar ATPase Function Is a Key Link between Multiple Causes of Preeclampsia

Preeclampsia, a relatively common pregnancy disorder, is one of the major causes of maternal and fetal morbidity and mortality. Despite numerous research, the etiology of this syndrome remains not well understood as the pathogenesis of preeclampsia is complex, involving interaction between genetic, immunologic, and environmental factors. Preeclampsia, originating in placenta abnormalities, is induced by the circulating factors derived from the abnormal placenta. Recent work has identified various molecular mechanisms related to placenta development, including renin-angiotensin system, 1, 25-dihydroxyvitamin D, and lipoxin A4. Interestingly, advances suggest that vacuolar ATPase, a key molecule in placentation, is closely associated with them. Therefore, this intriguing molecule may represent an important link between various causes of preeclampsia. Here, we review that vacuolar ATPase works as a key link between multiple causes of preeclampsia and discuss the potential molecular mechanisms. The novel findings outlined in this review may provide promising explanations for the causation of preeclampsia and a rationale for future therapeutic interventions for this condition

Fine-Grained Car Detection for Visual Census Estimation

Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

STIM1 Mediates Hypoxia-Driven Hepatocarcinogenesis via Interaction with HIF-1

Hypoxia and intracellular Ca2+ transients are fundamental traits of cancer, whereas the route and regulation of Ca2+ mobilization in hypoxic tumorigenesis are unknown. Here, we show that stromal-interaction molecule 1 (STIM1), an ER Ca2+ sensor, correlates with elevated hypoxia-inducible factor-1 alpha (HIF-1α) in hypoxic hepatocarcinoma cells (HCCs) and is upregulated during hepatocarcinoma growth. HIF-1 directly controls STIM1 transcription and contributes to store-operated Ca2+ entry (SOCE). STIM1-mediated SOCE is also required for HIF-1 accumulation in hypoxic HCCs via activation of Ca2+/calmodulin-dependent protein kinase II and p300. Administration of YC-1, a HIF-1 inhibitor, or knockdown of HIF1A significantly diminishes hypoxia-enhanced STIM1 and suppresses tumorigenesis. Moreover, ectopic expression of STIM1 or HIF-1α partially reverses impaired growth of tumors treated with YC-1. These results suggest a mutual dependency and regulation of STIM1 and HIF-1 in controlling Ca2+ mobilization and hypoxic tumor growth and highlight a potential target for early hypoxia-related intervention

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Preeclampsia induced by cadmium in rats is related to abnormal local glucocorticoid synthesis in placent

Equilateral-triangle-resonator injection lasers with directional emission

Equilateral-triangle-resonator (ETR) lasers with an output waveguide jointed at one vertex of the resonator are fabricated on (100) GaInAsP-InP wafers using photolithography and a two-step inductively coupled plasma (ICP) etching technique. Distinct peaks with the mode spacing of longitudinal mode intervals are observed in the luminescence spectra at room temperature. Furthermore, some minor peaks appear in the middle of the main peaks, which can be attributed to the first-order transverse modes as predicted in the theoretical results. CW directional lasing emissions are achieved for ETR lasers with side lengths ranging from 15 to 30 pm up to 200 K. The temperature dependences of the threshold current and lasing wavelength are measured for an ETR laser with the side length of 20 mu m from 80 to 200 K. The observed threshold current rapidly increases as temperature increases over 170 K