Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees-of-freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV). Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors

MOESM1 of Associations between serum uric acid and the incidence of hypertension: a Chinese senior dynamic cohort study

Additional file 1. Supplement Tables S1 and S2

Amphiphilic Particle-Stabilized Nanoliter Droplet Reactors with a Multimodal Portable Reader for Distributive Biomarker Quantification

Compartmentalization, leveraging microfluidics, enables highly sensitive assays, but the requirement for significant infrastructure for their design, build, and operation limits access. Multimaterial particle-based technologies thermodynamically stabilize monodisperse droplets as individual reaction compartments with simple liquid handling steps, precluding the need for expensive microfluidic equipment. Here, we further improve the accessibility of this lab on a particle technology to resource-limited settings by combining this assay system with a portable multimodal reader, thus enabling nanoliter droplet assays in an accessible platform. We show the utility of this platform in measuring N-terminal propeptide B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, in complex medium and patient samples. We report a limit of detection of ∼0.05 ng/mL and a linear response between 0.2 and 2 ng/mL in spiked plasma samples. We also show that, owing to the plurality of measurements per sample, “swarm” sensing acquires better statistical quantitation with a portable reader. Monte Carlo simulations show the increasing capability of this platform to differentiate between negative and positive samples, i.e., below or above the clinical cutoff for acute heart failure (∼0.1 ng/mL), as a function of the number of particles measured. Our platform measurements correlate with gold standard ELISA measurement in cardiac patient samples, and achieve lower variation in measurement across samples compared to the standard well plate-based ELISA. Thus, we show the capabilities of a cost-effective droplet-reader system in accurately measuring biomarkers in nanoliter droplets for diseases that disproportionately affect underserved communities in resource-limited settings

Increased prevalence of risk factors among pregnant women in Tianjin from 1999 to 2010–2012.

<p>Data are reported in mean ± SD or number (%).</p><p>* Derived from Student’s t- test;</p><p>** Derived from Chi-square test or Fisher’s exact test.</p><p>Abbreviations: BMI, body mass index; BP, blood pressure; GCT, glucose challenge test.</p><p>† To make age between the 1999 study and the current study comparable, age was re-calculated as at pregnancy.</p><p>†† Due to difference in gestational age at GCT between the two studies, adjustment for gestational age at GCT was made for the comparison.</p><p>‡ Only habitual smoking during pregnancy was available in both the study in 1999 and the current study and therefore compared between the two periods.</p><p>Increased prevalence of risk factors among pregnant women in Tianjin from 1999 to 2010–2012.</p

Prevalence of gestational diabetes diagnosed by the 1999 WHO criteria and the IADPSG criteria.

<p>Abbreviations: WHO, World Health Organization; IADPSG, International Association of Diabetes and Pregnancy Study Group; GCT, glucose challenge test; OGTT, oral glucose tolerance test.</p><p>* 40 women with GCT<7.8 mmol/L were referred to have OGTT because they had GCT before the 19th gestational week and meanwhile had a family history of diabetes.</p><p>† The total sample size of 17808 women was used as the denominator in calculation of the prevalence.</p><p>†† The total sample size of 18589 women was used as the denominator in calculation of the prevalence.</p><p>Prevalence of gestational diabetes diagnosed by the 1999 WHO criteria and the IADPSG criteria.</p

Multivariable odds ratios of potential risk factors for gestational diabetes mellitus diagnosed by the IADPSG criteria.

<p>Variables adjusted in the multivariable analysis included the variables listed in the model.</p><p>Abbreviations: IADPSG, International Association of Diabetes and Pregnancy Study Group; BMI, body mass index; BP, blood pressure; GCT, glucose challenge test.</p><p>* P values and 95%CIs of ORs were adjusted for multiple comparisons by Ryan-Holm step-down Bonferroni procedure.</p><p>Multivariable odds ratios of potential risk factors for gestational diabetes mellitus diagnosed by the IADPSG criteria.</p

Table_1_Short Body Height and Pre-pregnancy Overweight for Increased Risk of Gestational Diabetes Mellitus: A Population-Based Cohort Study.DOC

<p>Background: Short height is associated with gestational diabetes mellitus (GDM) but the underlying mechanism remains unknown. This study aims to explore whether short height has a synergistic effect with pre-pregnancy overweight/obesity and undue weight gain on the risk of GDM.</p><p>Methods: We recruited 19,962 singleton pregnant women from their first antenatal care visit in urban Tianjin, China, between October 2010 to August 2012. At 24–28 weeks of gestation, women underwent a 50-g 1-h glucose challenge test (GCT) followed by a 75-g 2-h oral glucose tolerance test (OGTT) if the GCT result was ≥7.8 mmol/L. GDM was defined by the International Association of Diabetes and Pregnancy Study Group's cut-points. Univariable and multivariable logistic regression analyses were performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic spline (RCS) analysis nested in the logistic regression analysis was used to identify a cutoff point of height for GDM. Additive interaction was used to test interactions between short height, pregnancy overweight/obesity and undue weight gain.</p><p>Results: A total of 1,517 (or 7.6%) women developed GDM. The risk of GDM increased rapidly with a decreasing height from 158 cm and downwards. Using height ≥158 cm as the reference group, women with < 158 cm of height were at increased GDM risk (adjusted OR: 1.44, 95%CI: 1.18–1.75). Maternal overweight/obesity at the first antenatal care visit greatly enhanced the OR of short height for GDM (adjusted OR: 3.78, 95%CI: 2.84–5.03) with significant additive interaction (P < 0.05). However, the interaction between short height and undue weight gain was non-significant (P > 0.05).</p><p>Conclusions: In Chinese pregnant women in urban Tianjin, height < 158 cm had a synergistic effect with pre-pregnancy overweight/obesity on the risk of GDM.</p

Clinical and biochemical characteristics of subjects stratified by gestational diabetes mellitus diagnosed by the IADPSG criteria.

<p>Abbreviations: IADPSG, International Association of Diabetes and Pregnancy Study Group; BMI, body mass index; BP, blood pressure; GCT, glucose challenge test.</p><p>Data are reported in mean ± SD or number (%).</p><p>* Derived from Student’s t- test</p><p>** Derived from Chi-square test or Fisher’s exact test.</p><p>† Defined as having continuously smoked one or more cigarettes per day for at least six months before pregnancy.</p><p>†† Defined as having smoked one or more cigarettes per day during pregnancy.</p><p>Clinical and biochemical characteristics of subjects stratified by gestational diabetes mellitus diagnosed by the IADPSG criteria.</p

Impacts of chronic kidney disease and albuminuria on associations between coronary heart disease and its traditional risk factors in type 2 diabetic patients – the Hong Kong diabetes registry-1

<p><b>Copyright information:</b></p><p>Taken from "Impacts of chronic kidney disease and albuminuria on associations between coronary heart disease and its traditional risk factors in type 2 diabetic patients – the Hong Kong diabetes registry"</p><p>http://www.cardiab.com/content/6/1/37</p><p>Cardiovascular Diabetology 2007;6():37-37.</p><p>Published online 2 Dec 2007</p><p>PMCID:PMC2219954.</p><p></p>(p < 0.05). Model one variables include age, sex, and smoking status (current/exit), total cholesterol, HDL-C, Hb, eGFR and use of ACEI/ARB as well as use of insulin at enrolment. b. Black: adjusted curve in patients with eGFR ≥60 ml/min per 1.73 m(p < 0.05); Blue: adjusted curve in patients with eGF

Impacts of chronic kidney disease and albuminuria on associations between coronary heart disease and its traditional risk factors in type 2 diabetic patients – the Hong Kong diabetes registry-3

<p><b>Copyright information:</b></p><p>Taken from "Impacts of chronic kidney disease and albuminuria on associations between coronary heart disease and its traditional risk factors in type 2 diabetic patients – the Hong Kong diabetes registry"</p><p>http://www.cardiab.com/content/6/1/37</p><p>Cardiovascular Diabetology 2007;6():37-37.</p><p>Published online 2 Dec 2007</p><p>PMCID:PMC2219954.</p><p></p>l 1 (p < 0.05). Model one variables include age, sex, and smoking status (current/ex), total cholesterol, HDL-C, Hb, eGFR and use of ACEI/ARB as well as use of insulin at enrolment. The hazard ratio was calculated using the 25percentiles, 75percentiles as the reference level. b. Black: adjusted for model 1 variables (p < 0.05); Blue: further adjusted for eGFR (p < 0.05). Red: further adjusted for eGFR and ACR (p < 0.05); Cyan: limited to eGFR ≥60 ml/min per 1.73 min model 1 (p < 0.05). c. Black: adjusted for model 1 variables (p < 0.05); Blue: further adjusted for eGFR (p < 0.05). Red: further adjusted for eGFR and ACR (p < 0.05); Cyan: limited to eGFR ≥60 ml/min per 1.73 min model 1 (p < 0.05