Search

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NWO Rubicon (452019308) Amsterdam Cardiovascular Sciences Background Left Ventricular Ejection Fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD) or benefit from an ICD. Machine (ML) and deep (DL) learning provide new opportunities for personalised predictions using complex, multi-modal physiological data. Objective We hypothesise that risk stratification for ICD implantation can be improved by ML and DL models that combine clinical variables with time series features from 12-lead electrocardiograms (ECG). Methods We present a multicentre study of 1010 patients with an ischaemic, dilated or non-ischaemic cardiomyopathy and LVEF≤35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD (64.9 ±10.8 years, 73.2% male) in two academic hospitals. For each patient, raw 12-lead, 10-second ECG-recordings obtained Results At 3-year follow-up, 16.0% of patients had died of whom 72.8% met the criteria for ICD non-benefit. Extreme gradient boosting models identified subjects with ICD non-benefit with an area under the receiver operator curve (AUROC) of 0.897 ±0.05 during internal validation (Figure 1, solid line). In the external cohort, the AUROC was 0.793 (95% CI 0.75-0.84) (Figure 1, dashed line). Survival analysis for low vs. high predicted risk indicated ICD non-benefit rates of 6.0% versus 30.3% at 3-year follow-up, respectively (Figure 2). Conclusion A ML model that combined clinical with ECG features better predicted ICD non-benefit at 3-years in a primary prevention population than currently available risk scores such as the MADIT-ICD score. This approach may provide new tools to support personalized decision making for ICD therapy. Prospective validation is needed to assess the real-world clinical performance of the model.

Drinking water insecurity in small and rural, remote, or otherwise marginalized communities in Canada is pervasive and complex with multiple dimensions and impacts. These communities face challenges such as variable source water quality, lack of resources, inappropriate treatment technologies, lack of access to training, difficulties retaining qualified personnel, and ineffective governance structures. Currently, there is a gap in the academic literature with respect to drinking water security assessment frameworks or tools for small and rural, remote, or otherwise marginalized communities, particularly in high income countries. Thus, the objective of this study is to introduce a framework for assessing drinking water security, from headwater to consumer, in the context of small and rural, remote, or otherwise marginalized communities. An indicator-based framework has been developed to evaluate drinking water security, prioritize actions and investments, and support decision-making. The framework builds on expert knowledge and a critical review of security, sustainability, and performance indices of water supply and treatment processes obtained from the literature. The framework is organised into four dimensions of drinking water security from headwaters to consumer: upstream watershed security; source water security; community needs and engagement; and treatment and distribution infrastructure. A list of relevant indicators for each dimension has been compiled to support framework application in a format that is accessible to decision-makers in small and rural, remote, or otherwise marginalized communities. Supplementary Information The online version contains supplementary material available at 10.1007/s11269-021-02985-2.

The Pauli exclusion principle forbids indistinguishable fermions to occupy the same quantum mechanical state. Its implications are profound and it for example accounts for the electronic shell structure of atoms. Here we perform measurements on the scattering of off-resonant light from ultracold gasses of fermionic atoms. For Fermi gases in the quantum degenerate regime, we observe a marked suppression in light scattering as compared to a similarly prepared thermal Bose gas. We attribute the observed increased transmission of light through the quantum degenerate Fermi gas to Pauli blocking, where Fermi-Dirac statistics causes atoms to occupy a large region of the momentum space limiting the number of accessible states for the scattered atom. Our work confirms a longstanding fundamental result in the theory of the optical response of quantum gases and is an important step towards novel cooling and thermometry mechanisms for degenerate Fermi gases.

Background Atrial fibrillation (AF) can have organized regions, in the form of consistent dominant frequency sites, focal or reentrant sites, but it is unclear how these overlap with or differ from focal atrial tachycardias (AT) or potential drivers. We set out to develop an intuitive method based on fundamental electrogram shape and timing to separate types of AF. Objective To test the hypothesis that spatial regions of electrogram (EGM) in AF that show similar shapes over time based on cross-correlation analysis may separate patients with differing response to ablation. Methods We recruited N=133 patients (63.8±12.1 Y, 32% women), (i) N=10 had AT, (ii) N=122 AF that was or was not terminated by ablation, and (iii) N=1 pacing. All patients had left atrial mapping by 64 pole baskets. We applied repetitive activity (REACT) mapping that correlates EGMs in contiguous 2x2 regions (Fig. 1A) over 4sec. To calibrate REACT, we introduced simulated variations in shape (gaussian noise) and timing (gaussian delay) to pacing EGMs and computed nomograph over 100 random trials (Fig. 1C). Results Fig. 1B shows that REACT in a 71-year-old man with AT is more organized than in a 65 YO man with AF (100% vs 40% mapped field). Overall, REACT was higher in AT than AF (0.63±0.15 vs 0.36±0.22, p Conclusion Basic electrogram properties in AF of similar shapes in spatial areas over time can separate response to ablation and may represent “islands” of AT. Future studies should investigate the mechanisms for such islands and whether they may be targeted for therapy. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Background/Introduction Signals in Electrophysiology cases are often noisy despite laboratory shielding and filtering, and current noise-reduction methods are suboptimal. Template matching can identify a “nearest type” of electrogram, but libraries of signal shapes may be unavailable. Beat averaging can reduce noise but obscures beat-to-beat variations and is not optimal to analyze dynamically changing signals, such as when moving a catheter in the heart. Smoothing reduces noise yet blurs high frequency components. Purpose We set out to test if machine learned autoencoders could reduce noise in single beats without requiring massive training data or beat libraries. Specifically, we hypothesised that noisy electrograms in small datasets of atrial signals could be de-noised using an encoder-decoder neural network (NN) using transfer learning of machines trained to recognize key features in larger datasets of related signals. Methods We applied NN to monophasic action potentials (MAPs), because they have visually verifiable shapes. The NN was first trained to reconstruct 5706 left and right ventricular MAPs in 42 patients (67±13y; Fig. 1A). Transfer learning was then used to apply the NN to a much smaller dataset of 641 atrial MAPs in 21 patients (67±5y, 13 women; Fig. 1B, D, F). Results NN reconstructed atrial MAPs with a Pearson correlation of 0.87±0.11. After fine-tuning, NN reconstruction accuracy improved dramatically (Pearson 0.99±0.01; p Conclusion Machine learned autoencoders are a novel and powerful approach to de-noise electrophysiological signals in a dynamic, beat-to-beat fashion. The ability to learn fundamental signal features from models trained in large datasets, and apply them via transfer learning to small datasets in different heart chambers may have wide ranging applications for automated signal annotation, mapping and ablation. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Background/Introduction Mapping AF is challenging. Monophasic action potentials (MAPs) show that most of the recorded signals are not representing the atrial activity, and arise from far-field or other artifacts. Thus, reducing noise in electrophysiological signals is essential, yet it can be difficult for cross-talk from other chambers and pacing. Strategies to reduce noise include template matching, averaging, and smoothing, but all of them have major limitations. Furthermore, expert interpretation requires knowledge to discriminate signals from noise, but is subjective. Purpose We hypothesised a) that atrial and ventricular electrograms with varying artifact and noise can be denoised using autoencoder neural networks (NNs) without requiring manual annotation and in a reproducible manner, and b) we could train these NNs on a large available dataset ventricular signals, then apply transfer learning to the original smaller atrial dataset. We applied this approach to MAPs, which have interpretable shapes and would help identifying local from far-field signals helping in diagnosis, mapping and ablation. Methods We first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; Fig. 1A), with 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641 from 21 patients, 67±5y, 13 women; Fig. 2B, D, F). The autoencoder was used to eliminate pacing artifacts in ventricular MAPs (Fig. 1B, C) and denoise atrial MAPs (Fig. 2C, E, G) by reconstructing key learned features. The accuracy of the reconstruction was evaluated using Pearson Correlation Coefficient (PCC) and a novel similarity coefficient (SC). No manual annotation was required to identify noisy signals. Results The trained NN encoder learned key features of ventricular MAPs and reconstructed these clean signals with a SC=0.91±0.16 and PCC=0.99±0.01 (Fig. 1A). With this training, the NN was able to denoise ventricular MAPs with pacing artifact (Fig. 1B, C). After fine-tuning, the NN learned key signal features (upstroke, triangular descent, terminus) and thus reduced diverse noise without specific training or manual annotation. Namely, it was able to reconstruct atrial MAPs eliminating ventricular noise, high frequency noise and truncated signals (Fig. 2). Conclusions Machine learned encoder-decoders are powerful tools that can learn essential features of atrial and ventricular signals and hence isolate noise. Transfer learning is effective when large datasets are unavailable for training. This approach can separate atrial beats from far-field ventricular beats and other sources of noise. The ability to eliminate a diverse range of noise improves this approach over existing techniques and may have far-reaching applications in electrophysiology, such as mapping and ablation. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Introduction Segmenting left atrial (LA) substructures, including the LA body, appendage (LAA), and pulmonary veins (PVs), from computed tomography (CT) is central to electroanatomic mapping for ablation and functional studies in patients with atrial fibrillation (AF). However, this process requires manual outlining which needs special training, is subjective, and is difficult to scale. Computer graphics imaging (CGI) has been applied in media, film, and computer-aided design to reliably segment complex structures using their basic geometric representations. Purpose We hypothesized that LA substructures can be “virtually” dissected using CGI to separate geometric contours of the “convex ellipsoid” LA, “tubular” PVs, and “conical” LAA. We further hypothesized that the results of virtual dissection can be used to train a deep learning (DL) model to segment raw CT scans. Methods First, a mathematical method based on CGI techniques – erosion and dilation – was developed to “virtually dissect” the convex LA body from the original concave shell in publicly available digital atria with diverse simulated morphologies (Fig. 1A). The PVs and LAA were then automatically revealed and labeled by a 3D subtraction approach. Second, we refined precise LA/PV/LAA boundaries by tuning hyper-parameters from N=5 patient shells (Fig. 1B). Third, we used virtual dissection to train a DL model to segment CTs in N=20 patient atria (Fig. 1C). Finally, we applied this pipeline to segment raw CTs in a validation cohort of N=105 patients (23.8% women, 63.8±10.3Y; Fig. 1D). Results Virtual dissection accurately identified LA/PV/LAA boundaries in the training set (Dice coefficients 89–98%). In the independent test cohort (N=105), this automated pipeline accurately segmented raw CTs with Dice 81–95% (Fig. 1D) compared to a panel of experts (p Conclusion CGI of basic cardiac geometry combined with deep learning in small datasets can accurately segment raw CT scans in large populations. This computational pipeline may automate and simplify cardiac image processing and ablation procedures, and could be applied to the ventricle or other organ systems for diverse therapeutic strategies or to train machine learning. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health

Background Although atrial tachycardia (AT) may interconvert with fibrillation (AF) in many patients, it is undefined if this represents a pathophysiological spectrum of organization, or whether it indicates that AF will respond better to ablation. Objective To test the hypothesis that the spatial area within which electrograms (EGMs) repeat in synchronized fashion over time indicates a spectrum from AT, in which areas span the entire atria, to AF, in which areas are limited. We further hypothesized that repetitive areas would be larger in AF patients with acute termination than in those with poor response to ablation. Methods We studied N=234 patients (47% women, 64±10Y), of whom (i) N=10 had AT, (ii) N=120 had AF that terminated with ablation (“Term”), (ii) N=104 had AF that did not terminate (“Non-term”). All patients had global left atrial mapping by 64 pole baskets (Abbott, IL). Spatial areas of repetitive activity (REACT) were calculated by correlating unipolar EGMs in 2x2 grids for 4 sec, repeated for the entire atria (Figure 1A, B). We quantified global organization by averaging the REACT map for each patient. Results Figure 1C shows progressively decreasing areas of repetitive EGM from AT to AF Term to AF Non-term (p0.90, a case of AF REACT 0.45 in a 65 YO male with termination by ablation, and a case of AF with REACT 0.19 in an 85 YO male that did not terminate. Further, ROC analysis of REACT analysis in AF cases predicted termination with an AUC of 0.71. Conclusion Spatial areas of repeating electrogram shapes indicates a spectrum from AT to AF with good and AF with poor acute response to ablation. Future studies should investigate whether REACT areas can be identified non-invasively, such as by body surface ECG, to guide ablation or prognosis. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Background Several mapping systems are being introduced to guide atrial fibrillation (AF) ablation to patient-specific regions of interest. However, results have been extremely heterogeneous between studies, ranging from very poor to very promising. It is unknown if this reflects specific patient characteristics or procedural factors because most prior series were middle sized (N∼30–100 patients). Purpose To study 1 year and 3 year very long-term outcomes from map guided AF-driver ablation in a large patient registry with multiple operators, to identify clinical and procedural features influencing outcomes. In real-world AF patients with diverse comorbidities, we applied a consistent patient-tailored AF mapping and ablation strategy, monitored outcomes carefully and applied statistical and unsupervised machine learning approaches to identify features of success and failure. Method We studied 632 consecutive patients (65±10 y, 178 F) undergoing ablation for drug-refractory AF. 59.7% had persistent AF, and 29.9% had prior unsuccessful ablation (median 1 procedure). All patients underwent pulmonary vein isolation (PVI), followed by ablation of AF regions of interest mapped from 64 pole baskets (RhythmView, Abbott, IL), by 11 operators. Patients were followed using ambulatory ECG monitors quarterly for one year, and at the time of symptoms for 3 years. Results Fig. 1A shows overall freedom from AF at 1-year of 77.5% (95% CI: 74.2%, 80.9%) and at 3 years of 55.5% (95% CI: 51.2%, 60.1%). Freedom from AF/AT at 1-year was 70.1% (95% CI: 66.5%, 73.8%), and at 3 years was 48.6% (95% CI: 44.3%, 53.3%). Success was higher in patients with procedural termination, first ablation versus prior unsuccessful procedures, for paroxysmal AF than non-paroxysmal AF (1 year: AT/AF freedom 74.9% versus 66.7%, p=0.006), and smaller left atrium. Three clusters (Fig 1B) were identified comprising CHA2DS2VASc score, enlarged LA, prior failed case, presenting rhythm and termination during the procedure (Table 2). At 1 year, freedom from AT/AF was 77.8% (95% CI: 72.2%, 82.1%) for cluster 3 and 56.2% (95% CI: 48.3%, 65.4%) for cluster 1 (Fig. 1B). Conclusion In our large registry of N=632 patients undergoing AF-map guided ablations, machine learned clusters identified cohorts with success of 56.2 to 77.8% at 1 year. Future studies should identify if lower success represents technical challenges, such as difficulties in mapping very large atria, or more difficult to treat mechanisms. These results may inform patient inclusion and ablation strategy in upcoming AF treatment trials. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National budget only - NIH, R01 HL149134, R01HL83359

Background/Introduction Over the past decade, various studies have demonstrated an association between remote device monitoring (RM) in implantable cardioverter-defibrillator (ICD) patients and all-cause mortality. However, it is unclear which clinical phenotypes account for this survival benefit. Purpose We aimed to identify clinical phenotypes within an ICD population on RM and conventional follow-up (NRM), and evaluate differences in long-term survival per clinical phenotype. Methods This is a single-center, retrospective, observational study of de novo ICD implantations (single- and dual chamber, biventricular and subcutaneous) between 2010 and 2021 in patients ≥18 years old. Data was extracted from electronic health records. Unsupervised two-step cluster analysis (TSC) was performed to identify distinct clinical phenotypes in the RM and NRM cohorts. Multinomial logistic regression analysis was performed to identify cluster characteristics, differences among the different clusters were analysed by Chi-squared test. Variable importance was evaluated for each of the 18 included clinical variables. Survival analysis was performed using the Kaplan-Meier method, log-rank was used to compare survival between groups. Results A total of 1872 ICD patients were analysed using TCS, comprising 1265 RM and 607 NRM patients, respectively. TCS for a mean follow-up duration of 5.4±3.1 years partitioned the RM and NRM groups into six clusters (Table 1). Clusters RM1 (n=444) and NRM3 (n=220) represented a predominantly primary prevention and DCM phenotype. Clusters RM2 (n=300) and NRM2 (n=173) indicated young and secondary prevention phenotypes. Clusters RM3 (n=220) and NRM1 (n=214) comprised an older, male and ischaemic cardiomyopathy (ICM) phenotype. In survival analysis 5-year cumulative incidence of mortality for the subsequent clusters was 7.4%, 4.5%, 11.6%, 41.4%, 29.8%, and 25.1%, respectively (log rank p-value Conclusion We identify novel clinical phenotypes (clusters) of ICD patients on RM and having conventional follow-up with large differences in long-term survival. Future studies should further explore if these differences reflect benefit from RM, or the underlying natural history of each phenotype. These results may accordingly guide monitoring strategies tailored to specific ICD patient profiles. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Public funding

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background Mapping atrial fibrillation (AF) is complicated by signals which may be local or far-field, but which cannot currently be separated. This could be clarified by a knowledge of atrial refractory periods, yet these are difficult to define from monophasic action potentials (MAP) in patients. We hypothesized that transfer learning using an autoencoder neural network (ANN), first trained with less-noisy ventricular signals, can be applied to de-noise and classify atrial MAPs. Methods We first developed an ANN to encode MAPs in 5706 ventricular MAPs from N=42 patients (age 65±13y) during pacing (fig1. A-B). This created a latent feature space. We now tuned the ANN to classify atrial MAPs in a different cohort of patients with AF. We used a statistical loss function based on mathematical optimization to evaluate the accuracy of final representations of the MAP and classify the different signals. Results The autoencoder ANN reconstructed ventricular MAPs with an average similarity of 0.85 (range 0-1) (an example is shown in fig 1.B). We tested on 3000 atrial MAPs in AF patients (N=21; 67±5y, 13 women). Atrial MAPs were accurately represented (fig 1.E-F) with similarity indices that were higher than those obtained by a panel of 3 experts. Fig. 1 shows the reconstruction of different signals: ventricular MAP (fig 1.A-B), ventricular MAP with pacing artifact (fig. 1.C-D), atrial MAP (transfer learning is assumed in here; fig 1.E-F) and noise or signals with morphologies of no interest (fig 1.G-H). Fig. 2 shows the classification of signals according to the similarity metric that allows distinguishing among the different types of signals without manual annotation (p Conclusion Atrial refractory periods can be defined in single beats in AF patients using autoencoder neural networks and transfer learning. This approach can separate atrial beats from far-field ventricular beats and other sources of noise. Future work can study if this approach can be used to improve AF mapping or define novel physiological phenotypes.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background/Introduction Reducing electrophysiological signal noise is essential for diagnosis, mapping and ablation, yet most approaches are suboptimal. Template matching requires libraries of known signal types, that are difficult to obtain. Beat averaging can reduce noise, yet cannot be applied to single beats and obscures beat-to-beat variations. Beat smoothing can lose critical and subtle signal features. We set out to use neural networks (NN) based on encoder-decoders, which are able to extract key signal features and hence reconstruct them without noise and artifact. Purpose We hypothesised that electrograms with varying sources of artifact can be denoised using autoencoder neural networks. We further hypothesised that this could be achieved in a small data set by developing the method in a larger dataset of related signals, then using transfer learning. We tested this approach for atrial monophasic action potentials (MAPs) that have verifiable shapes. Methods The NN was first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; fig 1.A): 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641, 21 patients, 67±5y, 13 women; fig D,F,H). Results The autoencoder was able to learn key features of MAPs, and hence reconstruct them without artifacts. NN learned ventricular MAPs with similarity coefficient 0.91±0.16, Pearson correlation 0.99± 0.01 (fig A) and learned key features (upstroke, triangular descent, terminus) to reduce noise (fig B-C). Applying this trained NN to atrial MAPs, the approach automatically eliminated ventricular artifact (fig E), high frequency noise (fig G), truncation (fig I), saturation and other artifacts. After fine-tuning, the NN reconstructed atrial MAPs with Pearson correlation = 0.99±0.01 (p Conclusions Machine learned encoder-decoders are powerful tools that can automatically eliminate diverse types of noise in single beats by learning essential signal features. Transfer learning makes this possible without large datasets for training, even from signals in a different cardiac chamber. This approach may have far-reaching applications for mapping and ablation.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH) Background Low left ventricular ejection fraction (LVEF) is an imperfect predictor of sudden cardiac death (SCD) in patients with ischemic cardiomyopathy. Novel features from the ECG might provide a readily available tool to better predict risk. Purpose We hypothesized that machine learning (ML) of the ECG can be used to predict SCD, and the ML-learned ECG features could be referenced to interpretable intracardiac signals (monophasic action potentials: MAP) to provide mechanistic insights. Methods We studied 5603 ECG Lead V1 beats in 41 patients (64±10 Y) with coronary disease and LVEF≤40% in steady-state pacing. Patients were randomly allocated to independent training and test cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines were trained to predict mortality at 3Y from the top 20 features derived from these beats. Patient-level predictions were made by computing an ECG score that indicates the proportion of test set beats in that patient computed by the beat-level model to predict death. Explainability analysis was performed using the arithmetic mean of MAP and ECG beats that predicted SCD versus those that predicted survival. Results Fig 1A. shows ECG lead V1 and MAP in a 79 Y man with LVEF 29%. Fig 1B shows the dataflow in the study. Predictive accuracies of ML models were 78 and 70% and optimal with 20 features for both ECG and MAP models respectively (Fig. 1C). Beat-level predictions in the validation (n=1678 Lead I beats) cohorts yielded c-statistics of 0.78 with the ECG (95% CI, 0.62–0.91) and 0.75 with MAPs (95% CI, 0.75-0.76) (data not shown). In multivariable patient-level models, c-statistic was 0.87 with ECGs (95% CI, 0.76-0.98) (Fig 1D) and 0.82 with MAPs. On explainability analysis, ECG beats that predicted SCD (Fig 2; red) had lower amplitude and more notched T-waves in lead V1 than beats that predicted no SCD (Fig 2; blue). MAP that predicted SCD had higher repolarization current at the same time points. Both QT duration (ECG) and action potential duration (MAP) did not differ (Fig 2). Conclusions Machine learning of the ECG reveals novel predictors of SCD risk in patients with ischemic cardiomyopathy analogous to those identified in intracardiac signals. This approach can be used as a point-of-care ECG risk tool to improve risk stratification and allocation for ICD therapy beyond LVEF alone and may shed insights into the pathophysiology of ventricular arrhythmias.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH, R01 HL149134, R01HL83359 Background Both Sleep Apnea (SA) and Atrial Fibrillation/flutter (AF) are known risk factors for stroke, and both are increasing in prevalence. They are both under-diagnosed in young adults Purpose To define the relationship between stroke, SA, and AF in a very large cohort of 2 million young-middle aged adults aged 20-60 Y in Northern California. Methods We probed the Stanford Research Repository of electronic health data from 01/01/2008 to 11/30/2021 for the diagnoses of stroke, transient ischemic attacks, AF and SA using relevant codes (stroke: 433.X, 434.X, 436.X, I63.X, I65.X, I66.X, G45.X, G46.X; AF: I48.X; SA: G47.X, 327.27). Results We identified 2267485 patients aged 20-60Y (55% F; 32% White, 12% Asian, 3% Black), of whom SA was diagnosed in 52730 (2.3%), AF in 10230 (0.4%) and incident stroke in 10385 (0.4%) (Figure 1A) In patients with SA, 1.5% developed incident stroke. Stroke was more common in patients with SA than those without, regardless of co-existing diagnosis of AF; OR with AF: 1.5 [1.3-1.7, p Although AF was more common in patients with SA than without (odds ratio, OR: 10.1 [9.6-10.6, p Finally, 7% of AF patients developed incident stroke. Of these, 73% had CHADS2VASC ≥2, yet 44% were not anticoagulated. Patients with SA comprised a third of all AF patients with stroke and, compared to AF patients with stroke and without SA, had higher CHADS2VASC (median 3 vs 2, p Conclusions In >2 million young individuals, we uncover a novel association between SA and incident stroke, regardless of the diagnosis of AF. Surprisingly, three quarters of patients with SA developed incident stroke in the absence of AF, and were not anticoagulated. These results underscore the need to screen for AF and sleep apnea in young adults.

Glass-nanocomposites are produced when crystallite particles of nanometer dimension are dispersed or embedded within the glass matrix. In this work we have investigated several glass-nanocomposite systems: AgI-Ag2O–SeO2, Ag2S-Ag2O-MoO3, CdI2-Ag2O-P2O5, CdI2-Ag2O-V2O5 and CdI2-Ag2O-B2O3-P2O5. The structure and ion transport properties of these glasses have been analyzed to understand the effect of nanocrystallites formation and growth on the ion transport properties. It has been observed that the average size of the nanocrystallites within the glassy matrix depends on several factors such as type of glass network, dopant content in single former glasses and glass former ratio in mixed former glasses. From transport study it is found that the formation of nanocrystallites influences the ionic conductivity of these glasses. The degree of influence depends on the size and volume fraction of the nanocrystallites indicating a strong microstructure dependence.

One of the best examples of sexual dimorphism is the development and function of the gonads, ovaries and testes, which produce sex-specific gametes, oocytes, and spermatids, respectively. The development of these specialized germ cells requires sex-matched somatic support cells. The sexual identity of somatic gonadal cells is specified during development and must be actively maintained during adulthood. We previously showed that the transcription factor Chinmo is required to ensure the male sexual identity of somatic support cells in the Drosophila melanogaster testis. Loss of chinmo from male somatic gonadal cells results in feminization: they transform from squamous to epithelial-like cells that resemble somatic cells in the female gonad but fail to properly ensheath the male germline, causing infertility. To identify potential target genes of Chinmo, we purified somatic cells deficient for chinmo from the adult Drosophila testis and performed next-generation sequencing to compare their transcriptome to that of control somatic cells. Bioinformatics revealed 304 and 1549 differentially upregulated and downregulated genes, respectively, upon loss of chinmo in early somatic cells. Using a combination of methods, we validated several differentially expressed genes. These data sets will be useful resources to the community.

The data transfer capacity of a communication channel is limited by the Shannon-Hartley theorem and scales as $\text{log}_2(1 + \text{SNR})$ for a single channel with the power signal-to-noise ratio (SNR). We implement an array of atom-optical receivers in a single-input-multi-output (SIMO) configuration by using spatially distributed probe light beams. The data capacity of the distributed receiver configuration is observed to scale as $\text{log}_2(1 + N\times\text{SNR})$ for an array consisting of $N$ receivers. Our result is independent on the modulation frequency, and we show that such enhancement of the bandwidth cannot be obtained by a single receiver with a similar level of combined optical power. We investigate both theoretically and experimentally the origins of the single channel capacity limit for our implementation.

We study resonant dipole-dipole coupling and the associated van der Waals energy shifts in Rydberg excited atomic rubidium and potassium and investigate F\"orster resonances between interspecies pair states. A comprehensive survey over experimentally accessible pair state combinations reveals multiple candidates with small F\"orster defects. We crucially identify the existence of an ultrastrong, "low" electric field K-Rb F\"orster resonance with a extremely large zero-field crossover distance exceeding 100 $\mu$m between the van der Waals regime and the resonant regime. This resonance allows for a strong interaction over a wide range of distances and by investigating its dependence on the strength and orientation of external fields we show this to be largely isotropic. As a result, the resonance offers a highly favorable setting for studying long-range resonant excitation transfer and entanglement generation between atomic ensembles in a flexible geometry. The two-species K-Rb system establishes a unique way of realizing a Rydberg single-photon optical transistor with a high-input photon rate and we specifically investigate an experimental scheme with two separate ensembles.

Ultracold atomic gases have realized numerous paradigms of condensed matter physics, where control over interactions has crucially been afforded by tunable Feshbach resonances. So far, the characterization of these Feshbach resonances has almost exclusively relied on experiments in the threshold regime near zero energy. Here, we use a laser-based collider to probe a narrow magnetic Feshbach resonance of rubidium above threshold. By measuring the overall atomic loss from colliding clouds as a function of magnetic field, we track the energy-dependent resonance position. At higher energy, our collider scheme broadens the loss feature, making the identification of the narrow resonance challenging. However, we observe that the collisions give rise to shifts in the center-of-mass positions of outgoing clouds. The shifts cross zero at the resonance and this allows us to accurately determine its location well above threshold. Our inferred resonance positions are in excellent agreement with theory., Studies on energy-dependent scattering of ultracold atoms were previously carried out near zero collision energies. Here, the authors observe a magnetic Feshbach resonance in ultracold Rb collisions for above-threshold energies and their method can also be used to detect higher partial wave resonances.

We propose a method for transferring atoms to a target quantum state for a multilevel quantum system with sequentially increasing, but otherwise unknown, energy splitting. This is achieved with a feedback algorithm that processes off-resonant optical measurements of state populations during adiabatic rapid passage in real-time. Specifically, we reliably perform the transfer $|F=2,m_F=2\rangle \rightarrow |1,1\rangle \rightarrow |2,1\rangle$ for a sample of ultracold $^{87}$Rb in the presence of a random external magnetic field.

The wavefunction for indistinguishable fermions is anti-symmetric under particle exchange, which directly leads to the Pauli exclusion principle, and hence underlies the structure of atoms and the properties of almost all materials. In the dynamics of collisions between two indistinguishable fermions this requirement strictly prohibits scattering into 90 degree angles. Here we experimentally investigate the collisions of ultracold clouds fermionic $\rm^{40}K$ atoms by directly measuring scattering distributions. With increasing collision energy we identify the Wigner threshold for p-wave scattering with its tell-tale dumb-bell shape and no $90^\circ$ yield. Above this threshold effects of multiple scattering become manifest as deviations from the underlying binary p-wave shape, adding particles either isotropically or axially. A shape resonance for $\rm^{40}K$ facilitates the separate observation of these two processes. The isotropically enhanced multiple scattering mode is a generic p-wave threshold phenomenon, while the axially enhanced mode should occur in any colliding particle system with an elastic scattering resonance., Comment: 7 pages, 4 figures, link to supplementary material

We experimentally and theoretically investigate in-medium propagation effects of off-resonant light in dense, spatially inhomogeneous ultracold atomic gases. Focussing on frequency modulation spectroscopy as the dispersive detection tool of atoms, we observe that the refractive gradient-index lenses presented by localised atomic ensembles can significantly modify the interpretation of the dispersive signal even for large probe detuning, owing to the collective response of the atoms. We identify criteria for distinguishing between thin and thick atomic lenses, leading to either diffraction-dominated and lensing dominated regimes for the outgoing probe beams. Our findings are consistent with experimental data and solutions of paraxial wave equation for light propagation. Our study provides important practical insights for dispersive, minimally intrusive optical detection and imaging schemes of ultracold atoms and will be valuable for choosing optimal parameter regimes in numerous applications.

Magnetically tunable Feshbach resonances in ultracold atomic systems are chiefly identified and characterized through time consuming atom loss spectroscopy. We describe an off-resonant dispersive optical probing technique to rapidly locate Feshbach resonances and demonstrate the method by locating four resonances of $^{87}$Rb, between the $|\rm{F} = 1, \rm{m_F}=1 \rangle$ and $|\rm{F} = 2, \rm{m_F}=0 \rangle$ states. Despite the loss features being $\lesssim0.1$ G wide, we require only 21 experimental runs to explore a magnetic field range >18 G, where $1~\rm{G}=10^{-4}$ T. The resonances consist of two known s-wave features in the vicinity of 9 G and 18 G and two previously unobserved p-wave features near 5 G and 10 G. We further utilize the dispersive approach to directly characterize the two-body loss dynamics for each Feshbach resonance., To appear in Physical Review A

We provide an experimental demonstration of a direct fiber-optic link for RF transmission ("radio-over-fiber") using a sensitive optical antenna based on a rubidium vapor cell. The scheme relies on measuring the transmission of laser light at an electromagnetically-induced transparency resonance that involves highly-excited Rydberg states. By dressing pairs of Rydberg states using microwave fields that act as local oscillators, we encoded RF signals in the optical frequency domain. The light carrying the information is linked via a virtually lossless optical fiber to a photodetector where the signal is retrieved. We demonstrate a signal bandwidth in excess of 1 MHz limited by the available coupling laser power and optical density. Our sensitive, non-metallic and readily scalable optical antenna for microwaves allows extremely low-levels of optical power ($\sim 1\, \mu$W) throughput in the fiber-optic link. It offers a promising future platform for emerging wireless network infrastructures.

We present a three-dimensional steerable optical tweezer system based on two pairs of acousto-optic deflectors. Radio frequencies used to steer the optical tweezers are generated by direct digital synthesis and multiple cross beam dipole traps can be produced through rapid frequency toggling and time averaging. We demonstrate production of arrays of ultracold atomic clouds in both horizontal and vertical planes and use this as an indicator for the three-dimensional nature of this optical tweezer system., Comment: To appear in Review of Scientific Instruments

Quantum mechanical scattering resonances for colliding particles occur when a continuum scattering state couples to a discrete bound state between them. The coupling also causes the bound state to interact with itself via the continuum and leads to a shift in the bound state energy, but, lacking knowledge of the bare bound state energy, measuring this self-energy via the resonance position has remained elusive. Here, we report on the direct observation of self-interaction by using a nano-eV atom collider to track the position of a magnetically-tunable Feshbach resonance through a parameter space spanned by energy and magnetic field. Our system of potassium and rubidium atoms displays a strongly non-monotonic resonance trajectory with an exceptionally large self-interaction energy arising from an interplay between the Feshbach bound state and a different, virtual bound state at a fixed energy near threshold., Self-interaction of a bound state through its coupling to the continuum is a phenomenon that is very difficult to observe. Here, the authors optically collide atomic clouds of rubidium and potassium to observe the self-interaction energy through precise measurements of magnetically tunable Feshbach resonances.

We report on experiments investigating the collisional properties of atoms at ultralow collision energies using an all-optical atom collider. By using a pair of optical tweezers, we can manipulate two ultracold atom clouds and collide them together at energies up to three orders of magnitude larger than their thermal energy. Our experiments measure the scattering of $\rm ^{87}Rb$, $\rm ^{40}K$, and $\rm ^{40}K$-$\rm ^{87}Rb$ collisions. The versatility of our collider allows us to probe both shape resonances and Feshbach resonances in any partial wave. As examples, we present experiments demonstrating p-wave scattering with indistinguishable fermions, inelastic scattering at non-zero energies near a homonuclear Feshbach resonance, and partial wave interference in heteronuclear collisions., Comment: Proceedings of ICPEAC XXX, to appear in J. Phys. Conf. Ser

In this work we have reported the structure and electrical properties of Ag+ ion conducting glasses of composition xAg2S–(1 − x)(0.40Ag2O–0.30MoO3–0.30P2O5) with varying Ag2S content (x = 0.10,0.20, 0.30 and 0.40 mol fraction). An attempt has been made to correlate the microscopic structure of these glasses to the macroscopic ion transport properties. The structural characterization of these samples has been accomplished using scanning electron and transmission electron microscopic studies. The presence of nanocrystallites has been observed in high Ag2S containing glass (x = 0.40). The electrical measurements have been performed in wide frequency and temperature ranges. The dc conductivity depends strongly on the composition and shows a close correlation with the microstructure of the glasses. The ac conductivity shows a power law dependence on frequency. The scaling of the ac conductivity spectra for different temperatures and compositions has been performed to understand the effect of nanocrystallite formation on the relaxation dynamics.

We have investigated ion dynamics in several single and mixed former glasses in wide composition and temperature ranges. The ionic conductivity of these glasses depends strongly on the dopant salt content as well as on the mixed former ratio. The characteristic lengths for ion dynamics, such as mean square displacement and spatial extent of sub-diffusive motion of lithium ions, have been determined from the ac conductivity and dielectric spectra respectively. Ionic conductivity and the characteristic lengths have been correlated with the modification of the glass network structure.

In this work we have studied isothermal and non-isothermal crystallization kinetics in Ag2O modified selenium molybdate glass employing differential scanning calorimetry. Activation energies for crystallization and glass transition were estimated following Ozawa and Kissinger methods. The value of the Avrami exponent was determined from the experimental results and was related to the nature of crystal nucleation and growth. The Matusita model was employed to extract non-isothermal crystallization parameters. The applicability of the Johnson–Mehl–Avrami (JMA) model to both isothermal and non-isothermal cases was tested. We showed that the JMA model gave the best description of isothermal crystallization, whereas the Sestak–Berggren (SB) model described the non-isothermal transformation kinetics adequately.

Resveratrol (RSV), a natural compound present in the skin and seeds of red grapes, is considered a phytoestrogen and has structural similarity to the synthetic estrogen diethylstilbestrol. RSV inhibits tumor cell growth in estrogen receptor-positive (ER+) and negative (ER-) breast cancer cell lines resulting in cell specific regulation of the G1/S and G2/M stages of the cell cycle. However apoptotic cell death was only observed in ER+ MCF-7 cells. In this study, we designed and synthesized boronic acid derivative of RSV and evaluated their biological effects on ER+ MCF-7 breast cancer cells. The trans-4 analog inhibited the growth of MCF-7 cells and is not a substrate for p-glycoprotein. The trans-4 analog induces G1 cell cycle arrest, which coincides with marked inhibition of G1 cell cycle proteins and a greater pro-apoptotic effect. Finally, the trans-4 analog had no effect on the estrogen-stimulated growth of MCF-7 cells. Our results demonstrate that the trans-4 analog inhibits MCF-7 breast cancer cells by a different mechanism of action than that of RSV (S-phase arrest), and provides a new class of novel boronic acids of RSV that inhibit breast cancer cell growth.

Structure and dielectric constant of silver molybdophosphate mixed network former glasses have been reported in this paper. The Fourier transform infrared (FTIR) spectroscopy has been used to investigate the effect of MoO 3 on the glass network structure. The existence of characteristic absorption bands corresponding to the vibration of P O bond, P–O − mode and P–O–P bond and the presence of MoO 4 2− anions have been ascertained from the FTIR spectra. It is observed that the increase of MoO 3 content in the compositions changes the network structure by creating non-bridging oxygen caused by the breaking of long phosphate chains. It is further observed that the dielectric constant and dielectric strength of these glasses increases with the increase of MoO 3 content, which is attributed to the increase of the polarizability of the glasses due to addition of MoO 3 .

This work reports the study of silver ion dynamics in Ag2S-doped silver molybdate–glass nanocomposites of compositions xAg2S–(1 – x)(yAg2O–(1 – y)MoO3). The volume fraction of crystalline phases in these glass nanocomposites increases with the increase of Ag2S content and considerably influences the dc conductivity. It is observed that a significant amount of volume fraction of crystalline phases for x ≥ 0.15 for the y = 0.20 series and for x = 0.20 for the y = 0.30 series causes the conductivity to decrease. The power law exponent has been obtained from ac conductivity spectra using the power law model and is observed to be almost constant at lower volume fraction of the nanocrystalline phases embedded in the glass matrix, but it decreases for samples with high crystalline volume fraction. The electric modulus data have been analyzed on the basis of the Havriliak–Negami (HN) equation. The Kohlrausch–Williams–Watts (KWW) stretched exponent β, obtained from modulus data, indicates a strong nonexponential r...

We have recently described a novel role for pregnancy-upregulated nonubiquitous calmodulin kinase (Pnck) in the induction of ligand-independent epidermal growth factor receptor (EGFR) degradation (Deb TB, Coticchia CM, Barndt R, Zuo H, Dickson RB, and Johnson MD. Am J Physiol Cell Physiol 295: C365–C377, 2008). In the current communication, we explore the probable mechanism by which Pnck induces ligand-independent EGFR degradation. Pnck-induced EGFR degradation is calcium/calmodulin independent and is regulated by cell density, with the highest EGFR degradation observed at low cell density. Pnck is a novel heat shock protein 90 (Hsp90) client protein that can be coimmunoprecipitated with Hsp90. Treatment of Pnck-overexpressing cells with the pharmacologic Hsp90 inhibitor geldanamycin results in enhanced EGFR degradation, and destruction of Pnck. In cells in which Pnck is inducing EGFR degradation, we observed that Hsp90 exhibits reduced electrophoretic mobility, and through mass spectrometric analysis of immunopurified Hsp90 protein we demonstrated enhanced phosphorylation at threonine 89 and 616 (in both Hsp90-α and -β) and serine 391 (in Hsp90-α). Kinase-active Pnck protein is degraded by the proteasome, concurrent with EGFR degradation. A Pnck mutant (T171A) protein with suppressed kinase activity induced EGFR degradation to essentially the same level as wild-type (WT) Pnck, suggesting that Pnck kinase activity is not required for the induction of EGFR degradation. Although EGFR is degraded, overexpression of WT Pnck paradoxically promoted cellular proliferation, whereas cells expressing mutant Pnck (T171A) were growth inhibited. WT Pnck promoted S to G2transition, but cells expressing the mutant exhibited higher residency time in S phase. Basal MAP kinase activity was inhibited by WT Pnck but not by mutant T171A Pnck protein. Cyclin-dependent kinase (Cdk) inhibitor p21/Cip-1/Waf-1 was transcriptionally suppressed downstream to MAP kinase inhibition by WT Pnck, but not the mutant protein. Collectively, these data suggest that 1) Pnck induces ligand-independent EGFR degradation most likely through perturbation of Hsp90 chaperone activity due to Hsp90 phosphorylation, 2) EGFR degradation is coupled to proteasomal degradation of Pnck, and 3) modulation of basal MAP kinase activity, p21/Cip-1/Waf-1 expression, and cellular growth by Pnck is independent of Pnck-induced ligand-independent EGFR degradation.

Glass-nanocomposites in the system Ag 2 S–Ag 2 O–MoO 3 have been synthesized by quenching of melts. Microstructural studies of the samples have been accomplished using X-ray diffraction, high-resolution transmission electron microscopy and field emission scanning electron microscopy. The formation of glass-nanocomposites and the presence of different crystalline phases, namely α-Ag 2 MoO 4 , Ag 2 Mo 2 O 7 and γ-Ag 2 S dispersed throughout in the glassy matrix have been confirmed from these studies. The formation and growth of different nanocrystalline phases have been studied as a function of composition and heat-treatment time. The effects of variation of Ag 2 S content and heat-treatment on the local network structure within the glassy matrix have been explored using FT-IR spectroscopic study.

Glass-nanocomposites of compositions xAgI - (1 - x)(0.40Ag2O-0.60SeO2) for 0.15 < or = x < or = 0.30 were prepared by quenching the melt of the appropriate mixtures of the chemicals AgI, AgNO3 and SeO2. Differential scanning calorimetry (DSC) was employed to determine the glass transition and crystallization temperatures. The as-prepared samples were heat-treated above glass transition temperature to observe the growth of nanoparticles. X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) were used to explore the microstructure of these samples. These studies revealed the presence of beta-AgI and Ag2SeO3 nanocrystals in both the as-prepared and heat-treated samples. The particle size was found to decrease with the increase of AgI content in both cases. The heat-treated samples showed increased tendency of crystallization for the compositions with higher AgI content. The beta-alpha phase transition of AgI crystals was observed at approximately 147-149 degrees C for heat-treated samples. Fourier transform infrared (FTIR) spectra revealed that the basic structure of the glassy network remained invariant to change of AgI content as well as to heat-treatment.