KCNQ1/KCNE1 Interaction in the Cardiac IKs Channel and its Physiological Consequences

Dynamic conformational changes of ion channel proteins during activation gating determine their function as carriers of current. The relationship between these molecular movements and channel function over the physiological timescale of the action potential (AP) has not been fully established due to limitations of existing techniques. We constructed a library of possible cardiac IKs protein conformations and applied a combination of protein segmentation and energy linearization to study this relationship computationally. Simulations reproduced the effects of the beta-subunit (KCNE1) on the alpha-subunit (KCNQ1) dynamics and function, observed in experiments. Mechanistically, KCNE1 increased the probability of “visiting” conducting pore conformations on activation trajectories, thereby increasing IKs current. KCNE1 slowed IKs activation by impeding the voltage sensor (VS) movement and reducing its coupling to pore opening. Conformational changes along activation trajectories determined that the S4-S5 linker (S4S5L) plays an important role in these modulatory effects by KCNE1. Integration of these molecular structure-based IKs dynamics into a model of human cardiac ventricular myocyte, revealed that KCNQ1-KCNE1 interaction is essential for normal AP repolarization

Evolution of a Web-Scale Near Duplicate Image Detection System

Detecting near duplicate images is fundamental to the content ecosystem of photo sharing web applications. However, such a task is challenging when involving a web-scale image corpus containing billions of images. In this paper, we present an efficient system for detecting near duplicate images across 8 billion images. Our system consists of three stages: candidate generation, candidate selection, and clustering. We also demonstrate that this system can be used to greatly improve the quality of recommendations and search results across a number of real-world applications. In addition, we include the evolution of the system over the course of six years, bringing out experiences and lessons on how new systems are designed to accommodate organic content growth as well as the latest technology. Finally, we are releasing a human-labeled dataset of ~53,000 pairs of images introduced in this paper

Visible two-dimensional photonic crystal slab laser

The authors describe the fabrication and performance of photonic crystal lasers fabricated within thin membranes of InGaP/InGaAlP quantum well material and emitting in the visible wavelength range. These lasers have ultrasmall mode volumes, emit red light, and exhibit low threshold powers. They can be lithographically tuned from 650 to 690 nm. Their cavity volumes of approximately 0.01 µm3 are ideally suited for use as spectroscopic sources

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach

In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest in Pinner- Former, a user embedding optimized for long-term future actions via a new dense all-action loss, and capture user's short-term intention by directly learning from the real-time action sequences. We conducted both offline and online experiments to validate the performance of the new model architecture, and also address the challenge of serving such a complex model using mixed CPU/GPU setup in production. The proposed system has been deployed in production at Pinterest and has delivered significant online gains across organic and Ads applications

Efficient Er/O‐Doped Silicon Light‐Emitting Diodes at Communication Wavelength by Deep Cooling

A silicon light source at the communication wavelength is the bottleneck for developing monolithically integrated silicon photonics. Doping silicon with erbium and oxygen ions is considered one of the most promising approaches to produce silicon light sources. However, this method suffers from a high concentration of defects in the form of nonradiative recombination centers at the interface between the crystalline silicon and large Er2O3/ErSi1.7 precipitates during the standard rapid thermal treatment. Here, a deep cooling process is applied to suppress the growth of these precipitates by flushing the high‐temperature Er/O‐doped silicon substrates with helium gas cooled in liquid nitrogen. The resultant light‐emitting efficiency at room temperature is enhanced by two orders of magnitude in comparison with that of the sample treated via standard rapid thermal annealing. The deep‐cooling‐processed Si samples are further processed into light‐emitting diodes. Bright electroluminescence with a main spectral peak at 1536 nm is also observed from the silicon‐based diodes with the external quantum efficiency reaching ≈0.8% at room temperature. Based on these results, the development of electrically driven silicon optical amplifiers or even lasers at communication wavelengths is promising for monolithically integrated silicon photonics.A deep cooling technique is developed for silicon light sources by suppressing the growth of Er/O‐related precipitates. The resultant near‐infrared emission shows efficiency enhancement by two orders of magnitude. Bright electroluminescence with a main spectral peak at 1536 nm is also observed. The external quantum efficiency can reach 0.8% at room temperature.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162702/3/adom202000720.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162702/2/adom202000720-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162702/1/adom202000720_am.pd

An effective tool for predicting survival in breast cancer patients with de novo lung metastasis: Nomograms constructed based on SEER

Background & objectivesAn effective tool for forecasting the survival of BCLM is lacking. This study aims to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) in breast cancer patients with de novo lung metastasis, and to help clinicians develop appropriate treatment regimens for breast cancer lung metastasis (BCLM) individuals.MethodsWe gathered clinical data of 2,537 patients with BCLM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic parameters for BCLM, which were integrated to establish nomograms by R software. The discriminative ability and predictive accuracy of the nomograms were assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots. Kaplan–Meier analyses were applied to evaluate the clinical utility of the risk stratification system and investigate the survival benefit of primary site surgery, chemotherapy, and radiotherapy for BCLM patients.ResultsTwo nomograms shared common prognostic indicators including age, marital status, race, laterality, grade, AJCC T stage, subtype, bone metastasis, brain metastasis, liver metastasis, surgery, and chemotherapy. The results of the C-index, ROC curves, and calibration curves demonstrated that the nomograms exhibited an outstanding performance in predicting the prognosis of BCLM patients. Significant differences in the Kaplan–Meier curves of various risk groups corroborated the nomograms' excellent stratification. Primary site surgery and chemotherapy remarkably improved OS and BCSS of BCLM patients whether the patients were at low-risk or high-risk, but radiotherapy did not.ConclusionsWe successfully developed prognostic stratification nomograms to forecast prognosis in BCLM patients, which provide important information for indicating prognosis and facilitating individualized treatment regimens for BCLM patients