Spiral-phase-objective for a compact spiral-phase-contrast microscopy.

Spiral-phase-contrast imaging, which utilizes a spiral phase optical element, has proven to be effective in enhancing various aspects of imaging, such as edge contrast and shadow imaging. Typically, the implementation of spiral-phase-contrast imaging requires the formation of a Fourier plane through a 4f optical configuration in addition to an existing optical microscope. In this study, we present what we believe to be a novel single spiral-phase-objective, integrating a spiral phase plate, which can be easily and simply applied to a standard microscope, such as a conventional objective.

Identification of microbial metabolic functional guilds from large genomic datasets.

Heterotrophic microbes play an important role in the Earth System as key drivers of major biogeochemical cycles. Specifically, the consumption rate of organic matter is set by the interaction between diverse microbial communities and the chemical and physical environment in which they reside. Modeling these dynamics requires reducing the complexity of microbial communities and linking directly with biogeochemical functions.

Spectrally encoded dual-mode interferometry with orthogonal scanning.

Spectrally encoded confocal microscopy (SECM) is a high-speed reflectance confocal microscopy technique. Here, we present a method to integrate optical coherence tomography (OCT) and SECM for complementary imaging by adding orthogonal scanning to the SECM configuration. The co-registration of SECM and OCT is automatic, as all system components are shared in the same order, eliminating the need for additional optical alignment.

Label-free photothermal optical coherence microscopy to locate desired regions of interest in multiphoton imaging of volumetric specimens.

Biochip-based research is currently evolving into a three-dimensional and large-scale basis similar to the in vivo microenvironment. For the long-term live and high-resolution imaging in these specimens, nonlinear microscopy capable of label-free and multiscale imaging is becoming increasingly important. Combination with non-destructive contrast imaging will be useful for effectively locating regions of interest (ROI) in large specimens and consequently minimizing photodamage.

Wavelength-tunable spiral-phase-contrast imaging.

Wavelength-tunable spiral-phase-contrast (SPC) imaging was experimentally accomplished in the visible wavelengths spanning a broad bandwidth of ∼200 nm based on a single off-axis spiral phase mirror (OSPM). By the rotation of an OSPM, which was designed with an integer orbital angular momentum (OAM) of l = 1 at a wavelength of 561 nm and incidence angle of 45°, high-quality SPC imaging was obtained at different wavelengths. For the comparison with wavelength-tunable SPC imaging using an OSPM, SPC imaging using a spiral phase plate (manufactured to generate an OAM of l = 1 at 561 nm) was performed at three wavelengths (473, 561, and 660 nm), resulting in clear differences.

Ocean mover's distance: using optimal transport for analysing oceanographic data.

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude.

A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series.

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating division rates of microbial populations by mechanistically describing changes in microbial cell size distributions over time.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends.

Analysis of Autoencoders for Network Intrusion Detection.

As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e.

Post-selection inference for changepoint detection algorithms with application to copy number variation data.

Changepoint detection methods are used in many areas of science and engineering, for example, in the analysis of copy number variation data to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or the presence) of given changepoints post-selection are lacking. Post-selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low-powered hypothesis tests and leaves open several important questions about practical usability.

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico.

Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015-2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score.

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions.

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory.

Results from the second year of a collaborative effort to forecast influenza seasons in the United States.

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance.

A human judgment approach to epidemiological forecasting.

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories.

Comb-referenced laser distance interferometer for industrial nanotechnology.

A prototype laser distance interferometer is demonstrated by incorporating the frequency comb of a femtosecond laser for mass-production of optoelectronic devices such as flat panel displays and solar cell devices. This comb-referenced interferometer uses four different wavelengths simultaneously to enable absolute distance measurement with the capability of comprehensive evaluation of the measurement stability and uncertainty. The measurement result reveals that the stability reaches 3.

Flexible Modeling of Epidemics with an Empirical Bayes Framework.

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the "Predict the Influenza Season Challenge", with the task of predicting key epidemiological measures for the 2013-2014 U.

Absolute positioning by multi-wavelength interferometry referenced to the frequency comb of a femtosecond laser.

A multi-wavelength interferometer utilizing the frequency comb of a femtosecond laser as the wavelength ruler is tested for its capability of ultra-precision positioning for machine axis control. The interferometer uses four different wavelengths phase-locked to the frequency comb and then determines the absolute position through a multi-channel scheme of detecting interference phases in parallel so as to enable fast, precise and stable measurements continuously over a few meters of axis-travel. Test results show that the proposed interferometer proves itself as a potential candidate of absolute-type position transducer needed for next-generation ultra-precision machine axis control, demonstrating linear errors of less than 61.

Risk of dengue for tourists and teams during the World Cup 2014 in Brazil.

Background: This year, Brazil will host about 600,000 foreign visitors during the 2014 FIFA World Cup. The concern of possible dengue transmission during this event has been raised given the high transmission rates reported in the past by this country.

Methodology/principal Findings: We used dengue incidence rates reported by each host city during previous years (2001-2013) to estimate the risk of dengue during the World Cup for tourists and teams.

Frequency-comb-referenced multi-channel fiber laser for DWDM communication.

We propose an all-fiber-based multi-channel optical scheme that enables simultaneous generation of multiple continuous-wave laser wavelengths with stabilization to the frequency comb of a femtosecond laser. The intention is to produce highly stable, accurate wavelength channels with immunity to environmental disturbance so as to enhance the transmission capacity of dense wavelength division multiplexing (DWDM) communications. Generated wavelengths lie over a wide spectral range of 5 THz about 1550 nm, each yielding a narrow linewidth of less than 24 kHz with an absolute position uncertainty of ~2.

Frequency-comb-referenced multi-wavelength profilometry for largely stepped surfaces.

3-D profiles of discontinuous surfaces patterned with high step structures are measured using four wavelengths generated by phase-locking to the frequency comb of an Er-doped fiber femtosecond laser stabilized to the Rb atomic clock. This frequency-comb-referenced method of multi-wavelength interferometry permits extending the phase non-ambiguity range by a factor of 64,500 while maintaining the sub-wavelength measurement precision of single-wavelength interferometry. Experimental results show a repeatability of 3.

All-fiber-based optical frequency generation from an Er-doped fiber femtosecond laser.

Generating precise optical frequencies with a functional power is necessary in many fields of science and technology. Here we demonstrate an all-fiber-based apparatus built to generate near-infrared frequencies directly from an Er-doped fiber femtosecond laser. In our apparatus, only a single resonance mode is extracted at a time on demand via a composite fiber filter comprised of a Fabry-Perot etalon with a Bragg grating.

A wide-range optical frequency generator based on the frequency comb of a femtosecond laser.

A precise way of optical frequency generation is demonstrated with direct use of the frequency comb of a mode-locked femtosecond laser. Only a single mode is extracted at a time on demand from the frequency comb through a composite filtering scheme and then amplified by means of optical injection locking with extremely low background noise. Generated output signals are found to preserve not only the narrow linewidths of the selected individual modes but also the absolute frequency positions of the original comb over a wide spectral range.

TNF-alpha increases tyrosine phosphorylation of vascular endothelial cadherin and opens the paracellular pathway through fyn activation in human lung endothelia.

Tumor necrosis factor (TNF)-alpha is a key mediator of sepsis-associated multiorgan failure, including the acute respiratory distress syndrome. We examined the role of protein tyrosine phosphorylation in TNF-alpha-induced pulmonary vascular permeability. Postconfluent human lung microvascular and pulmonary artery endothelial cell (EC) monolayers exposed to human recombinant TNF-alpha displayed a dose- and time-dependent increase in transendothelial [(14)C]albumin flux in the absence of EC injury.