Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices

Angiopoietin-like 4 based therapeutics for proteinuria and kidney disease

Current drugs used to treat proteinuric disorders of the kidney have been borrowed from other branches of medicine, and are only partially effective. The discovery of a central, mechanistic role played by two different forms of the secreted glycoprotein angiopoietin-like 4 (Angptl4) in human and experimental glomerular disease has opened new treatment avenues. Localized upregulation of a hyposialylated form (lacks sialic acid residues) of Angptl4 secreted by podocytes induces the cardinal morphological and clinical manifestations of human minimal change disease, and is also being increasingly recognized as a significant contributor toward proteinuria in experimental diabetic nephropathy. Oral treatment with low doses of N-acetyl-D-mannosamine, a naturally occurring precursor of sialic acid, improves sialylation of Angptl4 in vivo, and reduces proteinuria by over 40%. By contrast, a sialylated circulating form of Angptl4, mostly secreted from skeletal muscle, heart and adipose tissue in all major primary glomerular diseases, reduces proteinuria while also causing hypertriglyceridemia. Intravenous administration of recombinant human Angptl4 mutated to avoid hypertriglyceridemia and cleavage has remarkable efficacy in reducing proteinuria by as much as 65% for 2 weeks after a single low dose. Both interventions are mechanistically relevant, utilize naturally occurring pathways, and represent new generation therapeutic agents for chronic kidney disease related to glomerular disorders

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization

Iron Deposition following Chronic Myocardial Infarction as a Substrate for Cardiac Electrical Anomalies: Initial Findings in a Canine Model

Purpose: Iron deposition has been shown to occur following myocardial infarction (MI). We investigated whether such focal iron deposition within chronic MI lead to electrical anomalies. Methods: Two groups of dogs (ex-vivo (n = 12) and in-vivo (n = 10)) were studied at 16 weeks post MI. Hearts of animals from ex-vivo group were explanted and sectioned into infarcted and non-infarcted segments. Impedance spectroscopy was used to derive electrical permittivity () and conductivity (). Mass spectrometry was used to classify and characterize tissue sections with (IRON+) and without (IRON-) iron. Animals from in-vivo group underwent cardiac magnetic resonance imaging (CMR) for estimation of scar volume (late-gadolinium enhancement, LGE) and iron deposition (T2*) relative to left-ventricular volume. 24-hour electrocardiogram recordings were obtained and used to examine Heart Rate (HR), QT interval (QT), QT corrected for HR (QTc) and QTc dispersion (QTcd). In a fraction of these animals (n = 5), ultra-high resolution electroanatomical mapping (EAM) was performed, co-registered with LGE and T2* CMR and were used to characterize the spatial locations of isolated late potentials (ILPs). Results: Compared to IRON- sections, IRON+ sections had higher, but no difference in. A linear relationship was found between iron content and (p1.5%)) with similar scar volumes (7.28%±1.02% (Iron (1.5%)), p = 0.51) but markedly different iron volumes (1.12%±0.64% (Iron (1.5%)), p = 0.02), QT and QTc were elevated and QTcd was decreased in the group with the higher iron volume during the day, night and 24-hour period (p<0.05). EAMs co-registered with CMR images showed a greater tendency for ILPs to emerge from scar regions with iron versus without iron. Conclusion: The electrical behavior of infarcted hearts with iron appears to be different from those without iron. Iron within infarcted zones may evolve as an arrhythmogenic substrate in the post MI period

Cardiac structural and functional profile of patients with delayed QRS transition zone and sudden cardiac death

Delayed QRS transition zone in the precordial leads of the 12-lead electrocardiogram (ECG) has been recently associated with increased risk of sudden cardiac death (SCD), but the underlying mechanisms are unknown. We correlated echocardiographic findings with ECG and clinical characteristics to investigate how alterations in cardiac structure and function contribute to this risk marker. From the ongoing population-based Oregon Sudden Unexpected Death Study (catchment population similar to 1 million), SCD cases with prior ECG available (n = 627) were compared with controls (n = 801). Subjects with delayed transition at V-5 or later were identified, and clinical and echocardiographic patterns associated with delayed transition were analysed. Delayed transition was present in 31% of the SCD cases and 17% of the controls. These subjects were older and more likely to have cardiovascular risk factors and history of myocardial infarction. Delayed transition was associated with increased left ventricular (LV) mass (122.7 +/- 40.2 vs. 102.9 +/- 33.7 g/m(2); P <0.001), larger LV diameter (53.3 +/- 10.4 vs. 49.2 +/- 8.0 mm; P <0.001), and lower LV ejection fraction (LVEF) (46.4 +/- 15.7 vs. 55.6 +/- 12.5%; P <0.001). In multivariate analysis, delayed transition was independently associated with myocardial infarction, reduced LVEF, and LV hypertrophy. The association between delayed transition and SCD was independent of the LVEF (OR 1.57; 95% CI 1.04-2.38; P = 0.032). The underpinnings of delayed QRS transition zone extend beyond previous myocardial infarction and reduced LVEF. Since the association with sudden death is independent of these factors, this novel marker of myocardial electrical remodelling should be explored as a potential risk predictor of SCD.Peer reviewe

What is the real impact of acute kidney injury?

Background: Acute kidney injury (AKI) is a common clinical problem. Studies have documented the incidence of AKI in a variety of populations but to date we do not believe the real incidence of AKI has been accurately documented in a district general hospital setting. The aim here was to describe the detected incidence of AKI in a typical general hospital setting in an unselected population, and describe associated short and long-term outcomes. Methods: A retrospective observational database study from secondary care in East Kent (adult catchment population of 582,300). All adult patients (18 years or over) admitted between 1st February 2009 and 31st July 2009, were included. Patients receiving chronic renal replacement therapy (RRT), maternity and day case admissions were excluded. AKI was defined by the acute kidney injury network (AKIN) criteria. A time dependent risk analysis with logistic regression and Cox regression was used for the analysis of in-hospital mortality and survival. Results: The incidence of AKI in the 6 month period was 15,325 pmp/yr (adults) (69% AKIN1, 18% AKIN2 and 13% AKIN3). In-hospital mortality, length of stay and ITU utilisation all increased with severity of AKI. Patients with AKI had an increase in care on discharge and an increase in hospital readmission within 30 days. Conclusions: This data comes closer to the real incidence and outcomes of AKI managed in-hospital than any study published in the literature to date. Fifteen percent of all admissions sustained an episode of AKI with increased subsequent short and long term morbidity and mortality, even in those with AKIN1. This confers an increased burden and cost to the healthcare economy, which can now be quantified. These results will furnish a baseline for quality improvement projects aimed at early identification, improved management, and where possible prevention, of AKI

Rapidity distribution as a probe for elliptical flow at intermediate energies

Interplay between the spectator and participant matter in heavy-ion collisions is investigated within isospin dependent quantum molecular dynamics (IQMD) model in term of rapidity distribution of light charged particles. The effect of different types and size rapidity distributions is studied in elliptical flow. The elliptical flow patterns show important role of the nearby spectator matter on the participant zone. This role is further explained on the basis of passing time of the spectator and expansion time of the participant zone. The transition from the in-plane to out-of-plane is observed only when the mid-rapidity region is included in the rapidity bin, otherwise no transition occurs. The transition energy is found to be highly sensitive towards the size of the rapidity bin, while weakly on the type of the rapidity distribution. The theoretical results are also compared with the experimental findings and are found in good agreement.Comment: 8 figure

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers

Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems