Lateral Chirality-sorting Optical Spin Forces in Evanescent Fields

The transverse component of the spin angular momentum of evanescent waves gives rise to lateral optical forces on chiral particles, which have the unusual property of acting in a direction in which there is neither a field gradient nor wave propagation. As their direction and strength depends on the chiral polarizability of the particle, they act as chirality-sorting and may offer a mechanism for passive chirality spectroscopy. The absolute strength of the forces also substantially exceeds that of other recently predicted sideways optical forces, such that they may more readily offer an experimental confirmation of the phenomenon.Comment: 7 pages, 2 Figure

Connected to Give: Faith Communities

This is the third report in the "Connected to Give" series, and compares the relationship between the charitable giving behavior of American's from a variety of backgrounds, including their key demographics; an examination their motivations for giving; and the types of organizations to which they contribute

New Perspective on Passively Quenched Single Photon Avalanche Diodes: Effect of Feedback on Impact Ionization

Single-photon avalanche diodes (SPADs) are primary devices in photon counting systems used in quantum cryptography, time resolved spectroscopy and photon counting optical communication. SPADs convert each photo-generated electron hole pair to a measurable current via an avalanche of impact ionizations. In this paper, a stochastically self-regulating avalanche model for passively quenched SPADs is presented. The model predicts, in qualitative agreement with experiments, three important phenomena that traditional models are unable to predict. These are: (1) an oscillatory behavior of the persistent avalanche current; (2) an exponential (memoryless) decay of the probability density function of the stochastic quenching time of the persistent avalanche current; and (3) a fast collapse of the avalanche current, under strong feedback conditions, preventing the development of a persistent avalanche current. The model specifically captures the effect of the load’s feedback on the stochastic avalanche multiplication, an effect believed to be key in breaking today’s counting rate barrier in the 1.55–μm detection window

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and rising incidence rate in the developed world. Molecular profiling techniques have been used to study the variability between tumours as well as cancer models such as cell lines, but their translational value is incomplete with current methods. Moreover, first generation computational methods for subtype classification do not make use of multi-omics data in full scale. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We present a novel subtype classification method based on deep learning and apply it to classify CRC tumors using multi-omics data, and further to measure the similarity between tumors and disease models such as cancer cell lines. Multi-omics Autoencoder Integration (maui) efficiently leverages data sets containing copy number alterations, gene expression, and point mutations, and learns clinically important patterns (latent factors) across these data types. Using these latent factors, we propose a refinement of the gold-standard CRC subtypes, and propose best-matching cell lines for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for drug discovery pipelines, biomarker discovery, and target identification

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively

Air pollution, physical activity, and markers of acute airway oxidative stress and inflammation in adolescents

Background: The airway inflammatory response is likely the mechanism for adverse health effects related to exposure to air pollution. Increased ventilation rates during physical activity in the presence of air pollution increases the inhaled dose of pollutants. However, physical activity may moderate the relationship between air pollution and the inflammatory response. The present study aimed to characterize, among healthy adolescents, the relationship between dose of inhaled air pollution, physical activity, and markers of lung function, oxidative stress, and airway inflammation. Methods: With a non-probability sample of adolescents, this observational study estimated the association between air pollution dose and outcome measures by use of general linear mixed models with an unstructured covariance structure and a random intercept for subjects to account for repeated measures within subjects. Results: A one interquartile range (IQR) (i.e., 345.64 µg) increase in ozone (O3) inhaled dose was associated with a 29.16% average decrease in the percentage of total oxidized compounds (%Oxidized). A one IQR (i.e., 2.368E+10 particle) increase in total particle number count in the inhaled dose (PNT) was associated with an average decrease in forced expiratory flow (FEF25-75) of 0.168 L/second. Increasing activity levels attenuated the relationship between PNT inhaled dose and exhaled nitric oxide (eNO). The relationship between O3 inhaled dose and percent oxidized exhaled breath condensate cystine (%CYSS) was attenuated by activity level, with increasing activity levels corresponding to smaller changes from baseline for a constant O3 inhaled dose. Conclusions: The moderating effects of activity level suggest that peaks of high concentration doses of air pollution may overwhelm the endogenous redox balance of cells, resulting in increased airway inflammation. Further research that examines the relationships between dose peaks over time and inflammation could help to determine whether a high concentration dose over a short period of time has a different effect than a lower concentration dose over a longer period of time

Gastrointestinal symptoms in acromegaly: A case control study

BACKGROUND Acromegaly is a chronic disease caused by a pituitary somatotroph adenoma resulting in excess secretion of growth hormone, which leads to excess secretion of Insulin like growth factor 1 from the liver, causing abnormal soft tissue growth. There is increasing awareness that diseases affecting connective tissue are associated with an increase in functional gastrointestinal symptoms. Data was collected from patients with a confirmed diagnosis of acromegaly to evaluate the intensity, variety and impact of abdominal symptoms in comparison with a control group who were healthy participants recruited from the local fracture clinic. AIM To evaluate the frequency type and burden of abdominal symptoms in acromegaly in comparison with a control group. METHODS Medical documentation of patients with a diagnosis of acromegaly treated in one tertiary medical centre between 2010 and 2017 has been analysed. Data was collected from patients with confirmed acromegaly, using the Short Form Health Survey (SF36) and Rome IV Diagnostic questionnaire for Functional Gastrointestinal Disorders in Adults (R4DQ) and compared to a sex- and age-matched control group, to assess the burden of abdominal symptoms. Microsoft Excel and IBM SPSS v 25 were used for data analysis. RESULTS Fifty patients with acromegaly (24 male and 26 females; age range 23-64 years, mean 43) and 200 controls (96 male and 104 females; age range 18-84, mean 42.4) were recruited. 92% (46 out of 50) of patients with acromegaly reported abdominal symptoms and 78% (39 out of 50) had at least one functional gastrointestinal disorder according to the Rome IV diagnostic criteria, compared to 16% of controls (OR > 1, P 1, P 1, P < 0.001) as compared to the control group. CONCLUSION Upper and lower functional gastrointestinal tract disorders (defined by Rome IV diagnostic criteria) are significantly more prevalent in patients with acromegaly compared with healthy age and sex matched controls in our study. Functional constipation is the most commonly reported problem. Poorer quality of life may in part be attributable to the increased prevalence of abdominal symptoms

MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images

Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future