Background and AimConventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers.MethodsA total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non‐cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board‐certified specialists (experts).ResultsThe sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non‐cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20.4, P < 0.001).ConclusionsOur AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.

These findings revealed altered norepinephrine transmission in patients with major depressive disorder, suggesting that this alteration could be related to attention in this patient population.

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The paper describes a commercially available fly-over beamforming system based on methodologies already published, but using an array that was designed for quick and precise deployment on a concrete runway rather than for minimum sidelobe level. Time domain tracking Delay And Sum (DAS) beamforming is the first processing step, followed by Deconvolution in the frequency domain to reduce sidelobes, enhance resolution, and get absolute scaling of the source maps. The system has been used for a series of fly-over measurements on a Business Jet type MU300 from Mitsubishi Heavy Industries. Results from a couple of these measurements are presented: Contribution spectra from selected areas on the aircraft to the sound pressure level at the array are compared against the total sound pressure spectrum measured by the array. One major aim of the paper is to verify that the system performs well although the array was designed with quick deployment as a main criterion. The results are very encouraging. A second aim is to elaborate on the handling of the array shading function in connection with the calculation of the Point Spread Function (PSF) used in deconvolution. Recent publications have used a simple formula to compensate for Doppler effects for the case of flat broadband spectra. A more correct formula is derived in the present paper, covering also a Doppler correction to be made in the shading function, when that function is used in the PSF calculation. Nomenclature b(t) = DAS beamformed time signal B() = DAS beamformed frequency spectrum B ij () = DAS beamformed spectrum at focus point j due to model source i c = Propagation speed of sound DAS = Delay And Sum Df mi , Df mj = Doppler frequency shift factor at microphone m for signal from point i and j, respectively 2 f = Frequency H ij () = Element of Point Spread Function: From model source i to focus point j i = Index of monopole point source in Deconvolution source model, I = Number of focus/source points in calculation mesh j = Index of focus position, , or imaginary unit √ k = Wavenumber (k =  /c)  = Parameter defining steepness in radial cut-off of array shading filters m = Microphone index, M = Number of microphones M 0 = Mach number p m (t) = Sound pressure time signal from microphone m ̂ = Shaded time signal for microphone m P m () = Frequency spectrum from microphone m P mi () = Frequency spectrum from microphone m due to model source i PSF = Point Spread Function (2D spatial power response to a monopole point source)  model = DAS beamformed pressure power (pressure squared) from the point source model in deconvolution  measured = DAS beamformed pressure power from an actual measurement Q i () = Amplitude spectrum of model point source i r mj (t) = Distance from microphone m to moving focus point j r mj = Distance from microphone m to focus point j at the center of an averaging interval R m = Distance of microphone m from array center R coh () = Frequency dependent radius of active sub-array s mi (t) = Distance from microphone m to moving source po...

Backgroundα-Amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptor is a primary mediator of fast glutamatergic excitatory signaling in the brain and has been implicated in diverse neuropsychiatric diseases. We recently developed a novel positron emission tomography (PET) ligand, 2-(1-(3-([11C]methylamino)phenyl)-2-oxo-5-(pyrimidin-2-yl)-1,2-dihydropyridin-3-yl) benzonitrile ([11C]HMS011). This compound is a radiolabelled derivative of perampanel, an antiepileptic drug acting on AMPA receptors, and was demonstrated to have promising in vivo properties in the rat and monkey brains. In the current study, we performed a human PET study using [11C]HMS011 to evaluate its safety and kinetics.Four healthy male subjects underwent a 120-min PET scan after injection of [11C]HMS011. Arterial blood sampling and metabolite analysis were performed to obtain parent input functions for three of the subjects using high-performance liquid chromatography. Regional distribution volumes (V Ts) were calculated based on kinetic models with and without considering radiometabolite in the brain. The binding was also quantified using a reference tissue model with white matter as reference.ResultsBrain uptake of [11C]HMS011 was observed quickly after the injection, followed by a rapid clearance. Three hydrophilic and one lipophilic radiometabolites appeared in the plasma, with notable individual variability. The kinetics in the brain with apparent radioactivity retention suggested that the lipophilic radiometabolite could enter the brain. A dual-input graphical model, an analytical model designed in consideration of a radiometabolite entering the brain, well described the kinetics of [11C]HMS011. A reference tissue model showed small radioligand binding potential (BP*ND) values in the cortical regions (BP*ND = 0–0.15). These data suggested specific binding component of [11C]HMS011 in the brain.ConclusionsKinetic analyses support some specific binding of [11C]HMS011 in the human cortex. However, this ligand may not be suitable for practical AMPA receptor PET imaging due to the small dynamic range and metabolite in the brain.Electronic supplementary materialThe online version of this article (doi:10.1186/s13550-017-0313-0) contains supplementary material, which is available to authorized users.