Direct-detection mode-division multiplexing in modal basis using phase retrieval.

Mode-division multiplexing (MDM) can increase the capacity of direct-detection short-reach systems in proportion to the number of modes employed. MDM requires compensation of modal crosstalk at a transmitter or receiver by the multi-input multi-output (MIMO) signal processing. We show that the channel estimation required for the MIMO processing in a basis of modes can be expressed as a phase retrieval problem. We propose three techniques for the estimation: sparse training sequences, convex optimization (CO) and alternating minimization. We demonstrate the superior performance of the CO technique.

Optical network scaling: roles of spectral and spatial aggregation.

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral and spatial traffic aggregation become essential for optical network scaling. These aggregation techniques reduce network routing complexity by increasing spectral efficiency to decrease the number of fibers, and by increasing switching granularity to decrease the number of switching components. Spectral aggregation yields a modest decrease in the number of fibers but a substantial decrease in the number of switching components. Spatial aggregation yields a substantial decrease in both the number of fibers and the number of switching components. To quantify routing complexity reduction, we analyze the number of multi-cast and wavelength-selective switches required in a colorless, directionless and contentionless reconfigurable optical add-drop multiplexer architecture. Traffic aggregation has two potential drawbacks: reduced routing power and increased switching component size.

High-dimensional modulation for coherent optical communications systems.

In this paper, we examine the performance of several modulation formats in more than four dimensions for coherent optical communications systems. We compare two high-dimensional modulation design methodologies based on spherical cutting of lattices and block coding of a 'base constellation' of binary phase shift keying (BPSK) on each dimension. The performances of modulation formats generated with these methodologies is analyzed in the asymptotic signal-to-noise ratio regime and for an additive white Gaussian noise (AWGN) channel. We then study the application of both types of high-dimensional modulation formats to standard single-mode fiber (SSMF) transmission systems. For modulation with spectral efficiencies comparable to dual-polarization (DP-) BPSK, polarization-switched quaternary phase shift keying (PS-QPSK) and DP-QPSK, we demonstrate SNR gains of up to 3 dB, 0.9 dB and 1 dB respectively, at a BER of 10(-3).

Diversity-multiplexing tradeoff in mode-division multiplexing.

The capacity of mode-division multiplexing (MDM) systems is limited, for a given outage probability, by mode-dependent loss (MDL) and gain. Modal degrees of freedom may be exploited to increase transmission rate (multiplexing gain) or lower outage probability (diversity gain), but there is a fundamental tradeoff between the achievable multiplexing and diversity gains. In this Letter, we present the diversity-multiplexing tradeoff in MDM systems for the first time, studying the impact of signal-to-noise ratio, MDL, and frequency diversity order on the tradeoff in the strong-mode-coupling regime.

EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records.

Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (<3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.

Fundamental structure of Fresnel diffraction: longitudinal uniformity with respect to fractional Fourier order.

Fresnel integrals corresponding to different distances can be interpreted as scaled fractional Fourier transformations observed on spherical reference surfaces. Transverse samples can be taken on these surfaces with separation that increases with propagation distance. Here, we are concerned with the separation of the spherical reference surfaces along the longitudinal direction. We show that these surfaces should be equally spaced with respect to the fractional Fourier transform order, rather than being equally spaced with respect to the distance of propagation along the optical axis. The spacing should be of the order of the reciprocal of the space-bandwidth product of the signals. The space-dependent longitudinal and transverse spacings define a grid that reflects the structure of Fresnel diffraction.

Fundamental structure of Fresnel diffraction: natural sampling grid and the fractional Fourier transform.

Fresnel integrals corresponding to different distances can be interpreted as scaled fractional Fourier transformations observed on spherical reference surfaces. We show that by judiciously choosing sample points on these curved reference surfaces, it is possible to represent the diffracted signals in a nonredundant manner. The change in sample spacing with distance reflects the structure of Fresnel diffraction. This sampling grid also provides a simple and robust basis for accurate and efficient computation, which naturally handles the challenges of sampling chirplike kernels.

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan.

The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks. We present an international, prospective evaluation of our models' performance across all states and counties in the USA and prefectures in Japan. Nationally, incident mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths during prospective deployment remained consistently <8% (US) and <29% (Japan), while cumulative MAPE remained <2% (US) and <10% (Japan). We show that our models perform well even during periods of considerable change in population behavior, and are robust to demographic differences across different geographic locations. We further demonstrate that our framework provides meaningful explanatory insights with the models accurately adapting to local and national policy interventions. Our framework enables counterfactual simulations, which indicate continuing Non-Pharmaceutical Interventions alongside vaccinations is essential for faster recovery from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions.

Fully automated quantitative cephalometry using convolutional neural networks.

Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

Two brothers with a 7.0 kb gene deletion associated with isolated growth hormone deficiency type 1A.

Familial growth hormone deficiency type 1A is an autosomal recessive disease, caused by various homogenous deletions of both alleles of growth hormone gene 1 (hGH1). The hGH1 gene deletion is an event occurring between the 5' and the 3' flanking regions by unequal recombination, which causes a deletion in the hGH1 gene, mostly of 6.7 kb and rarely 7.6 or 7.0 kb in size. Two brothers diagnosed with GH deficiency syndrome were sent to our hospital for further evaluation. DNA samples of the probands and controls were amplified by PCR; restriction endonuclease analysis was done with Sma I enzyme and the patterns were evaluated. Gel electrophoresis results showed that the two brothers had a 7.0 kb deletion. These are the third and fourth cases reported with a 7.0 kb deletion. Both patients responded well to replacement therapy and did not develop antibodies against rGH. No other relatives presented with macro deletions in the hGH1 gene.

A Turkish family with 6.7 Kb deletion associated with isolated growth hormone deficiency type 1A.

Familial growth hormone deficiency type 1A is an autosomal recessive disease caused by homogenous deletions of both alleles of growth hormone gene 1 (hGH1) in various patterns. The hGH1 gene deletion is an event that probably occurs between the 5' and 3' flanking regions by unequal recombination, and results in deletion of the hGH1 gene in different patterns. Deletions are mostly 6.7 kb and rarely 7.0 kb, 7.6 kb and 45 kb in size. A four-year-old girl diagnosed with growth hormone deficiency syndrome was send to us for further evaluation. DNA samples of the patient, her parents and controls were amplified by polymerase chain reaction (PCR); furthermore, restriction endonuclease analysis was done with Sma I enzyme and the patterns were evaluated. Our gel electrophoresis results show that the gene deletion pattern of the patient represents a homogenous 6.7 kb deletion, while her parents had a heterogeneous 6.7 kb deletion pattern.

Cancer stem cell differentiation: TGFß1 and versican may trigger molecules for the organization of tumor spheroids.

Cancer stem cells (CSCs) have the ability to self-renew similar to normal stem cells. This process is linked with metastasis and resistance to chemotherapy and radiotherapy. In the present study, we constructed an in vitro differentiation model for CSCs. CSCs isolated and proliferated for one passage were maintained as monolayers or spheroid-forming cells with serum included media for differentiation process. Differentiation of adhesion molecules and cellular ultrastructural properties were investigated and compared in both monolayer and spheroid cultures. CD133+/CD44+ cancer-initiating cells were isolated from DU-145 human prostate cancer cell line monolayer cultures and propagated as tumor spheroids and compared with the remaining heterogeneous cancer cell bulk population. Microarray-based gene expression analysis was applied to determine genes with differential expression and protein expression levels of candidates were analyzed by immunohistochemistry. Electron microscopy showed detailed analysis of morphology. TGFß1 was found to be significantly upregulated in monolayer CSCs. High expression levels of VCAN, COL7A1, ITGß3, MMP16, RPL13A, COL4A2 and TIMP1 and low expression levels of THBS1, MMP1 and MMP14 were detected when CSCs were maintained as serum-grown prostate CSC spheroids. Immunohistochemistry supported increased immunoreactivity of TGFß1 in monolayer cultures and VCAN in spheroids. CSCs were found to possess multipotential differentiation capabilities through upregulation and/or downregulation of their markers. TGFß1 is a triggering molecule, it stimulates versican, Col7A1, ITGß3 and, most importantly, the upregulation of versican was only detected in CSCs. Our data support a model where CSCs must be engaged by one or more signaling cascades to differentiate and initiate tumor formation. This mechanism occurs with intracellular and extracellular signals and it is possible that CSCc themselves may be a source for extracellular signaling. These molecules functioning in tumor progression and differentiation may help develop targeted therapy.