Abstract: Disclosed is a Flammlina filiformis with a fruiting body of a golden cap and a white stipe. The Flammlina filiformis has a strain number of “Jin Jin No. 1” and has been preserved on Jun. 16, 2022 at the China General Microbiological Culture Collection Center with a registration number of CGMCC No. 40222. Colors of the cap and the stipe of the Flammlina filiformis “Jin Jin No. 1” have obvious differences. Compared with a parent white Flammlina filiformis strain F0012, the Flammlina filiformis “Jin Jin No. 1” has an equivalent average yield, an initial harvest period 1-2 days late, and a golden cap.

Abstract: Disclosed are an array substrate, a display panel and a display. The array substrate is provided with a thin film transistor and a gate driving circuit. A trigger signal input terminal of the gate driving circuit corresponds to an output terminal of the thin film transistor, a first insulating layer is provided between a first metal layer corresponding to the output terminal of the thin film transistor and a second metal layer corresponding to the trigger signal input terminal of the gate driving circuit. A projected area of the first metal layer on the first insulating layer is partially overlapped with a projected area of the second metal layer on the first insulating layer.

Abstract: Disclosed herein are MEMS devices and systems and methods of manufacturing or operating the MEMS devices and systems. In some embodiments, the MEMS devices and systems are used in imaging applications.

Abstract: A method of generating a first performance-data-library (for a standard-cell-library) includes: for each standard cell that includes multiple gates, sorting the gates into groups including searching for matched ones amongst the gates (matched gates), grouping corresponding matched gates into corresponding multiple member-gates, and (for unmatched ones of the gates having no other matched gate (unmatched gates)), grouping the unmatched gates into corresponding single-member groups; for each standard cell, generating a corresponding first volume of performance data including, for each group, discretely calculating the first volume of performance data, mapping the volume of performance data to the subject gate in the group, and, for each multimember group, mapping the volume of performance data to non-subject gates; and basing the first performance-data-library at least in part on the first volumes of performance data.

Abstract: A system and method is provided for controlling an acquisition system to obtain, based on a currently acquired set of projection data, a next set of projection data that is to be acquired using a set of learned imaging conditions. The set of learned imaging conditions are, at least in part, based on training data including projection data and dose information. In one embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen at random. In another embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen based on a test scan and a trained neural network for outputting the initial dose and angle using a loss function.

Abstract: Trenches in which to form a back side isolation structure for an array of CMOS image sensors are formed by a cyclic process that allows the trenches to be kept narrow. Each cycle of the process includes etching to add a depth segment to the trenches and coating the depth segment with an etch-resistant coating. The following etch step will break through the etch-resistant coating at the bottom of the trench but the etch-resistant coating will remain in the upper part of the trench to limit lateral etching and substrate damage. The resulting trenches have a series of vertically spaced nodes. The process may result in a 10% increase in photodiode area and a 30-40% increase in full well capacity.

Abstract: Methods, compositions, and kits for adhering polymers and other materials to another material, and in particular to bone or bone-like structures or surfaces. A composition of matter includes a urethane dimethacrylate-methyl methacrylate copolymer with a plurality of first polymer regions based on urethane dimethacrylate and a plurality of second polymer regions based on methyl methacrylate. The method includes placing an orthopedic joint implant having an attachment surface in a joint space, applying a first non-urethane-containing precursor, a second urethane-containing precursor, and a initiator to the attachment surface; contacting the first and second precursors and the initiator with the joint surface; and copolymerizing the first and second precursors and forming an adhesive copolymer and attaching the implant to the joint.

Abstract: A base substrate and a first metal layer laminated on the base substrate are included. A first laminated portion and a second laminated portion are arranged on and directly contact a side of the first metal layer. The first laminated portion includes a first insulating layer, a second metal layer, a second insulating layer and a first conductive layer. The first laminated portion is arranged with a first via. The second laminated portion includes a third insulating layer and a second conductive layer laminated on the third insulating layer. The second laminated portion is arranged with a second via. The first via is connected to the first conductive layer, the second via is connected to the second conductive layer, and the first conductive layer is connected to the second conductive layer. The second laminated portion is extended to reach an edge of the first metal layer.

Abstract: A fingerprint anti-counterfeiting method and an electronic device are provided. The fingerprint anti-counterfeiting method includes: After detecting a fingerprint input action of a user, an electronic device obtains a fingerprint image generated by the fingerprint input action, and obtains a vibration-sound signal generated by the fingerprint input action. The device determines, based on a fingerprint anti-counterfeiting model, whether the fingerprint input action is performed by a true finger. The fingerprint anti-counterfeiting model is a multi-dimensional network model obtained through learning based on fingerprint images for training and corresponding vibration-sound signals. The fingerprint anti-counterfeiting method in embodiments of this application helps improve a protection capability of the electronic device for a fake fingerprint attack.

Abstract: An electronic device detects a first operation of a user; the electronic device obtains a fingerprint image and a touch signal in response to the first operation; and the electronic device determines, based on the fingerprint image and the touch signal, that the fingerprint image is a fingerprint image of a real finger or a fingerprint image of a fake finger.

Abstract: An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.

Abstract: A fingerprint anti-counterfeiting method and an electronic device are provided. The fingerprint anti-counterfeiting method includes: After detecting a fingerprint input action of a user, an electronic device obtains a fingerprint image generated by the fingerprint input action, and obtains a vibration-sound signal generated by the fingerprint input action. The device determines, based on a fingerprint anti-counterfeiting model, whether the fingerprint input action is performed by a true finger. The fingerprint anti-counterfeiting model is a multi-dimensional network model obtained through learning based on fingerprint images for training and corresponding vibration-sound signals. The fingerprint anti-counterfeiting method in embodiments of this application helps improve a protection capability of the electronic device for a fake fingerprint attack.

Abstract: A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

Abstract: An electronic device detects a first operation of a user; the electronic device obtains a fingerprint image and a touch signal in response to the first operation; and the electronic device determines, based on the fingerprint image and the touch signal, that the fingerprint image is a fingerprint image of a real finger or a fingerprint image of a fake finger.

Abstract: An apparatus and method are provided for computed tomography (CT) imaging to reduce artifacts due to objects outside the field of view (FOV) of a reconstructed image. The artifacts are suppressed by using an iterative reconstruction method to minimize a cost function that includes a de-emphasis operator. The de-emphasis operator operates in the data domain, and minimizes the contributions of data inconsistencies arising from attenuation due to objects outside the FOV. This can be achieved by penalizing images that manifest indicia of artifacts due to outside objects especially those outside objects have high-attenuation densities and minimizing components of the data inconsistency likely attributable to the outside object.

Abstract: A method and apparatus is provided to generate a multiresolution image having at least two regions with different pixel pitches. The multiresolution image is reconstructed using projection data having various pixel pitches corresponding to the pixel pitches of the multiresolution image. By using a higher resolution inside regions of interest (ROIs) in both the image and projection domains and lower resolution outside the ROIs, fast image reconstruction can be performed while avoiding truncation artifacts, which result imaging is limited to an ROI excluding attenuation regions. Further, those regions of greater clinical relevance and greater structural variance within the reconstructed images can be selected to be within the ROIs to improve the clinical benefit of the multiresolution image. The multiresolution image can be reconstructed using an iterative reconstruction method in which the high- and low-resolution regions are uniquely evaluated.

Abstract: An apparatus and method are provided for computed tomography (CT) imaging to reduce artifacts due to objects outside the field of view (FOV) of a reconstructed image. The artifacts are suppressed by using an iterative reconstruction method to minimize a cost function that includes a de-emphasis operator. The de-emphasis operator operates in the data domain, and minimizes the contributions of data inconsistencies arising from attenuation due to objects outside the FOV. This can be achieved by penalizing images that manifest indicia of artifacts due to outside objects especially those outside objects have high-attenuation densities and minimizing components of the data inconsistency likely attributable to the outside object.

Abstract: A method and apparatus is provided to generate a multiresolution image having at least two regions with different pixel pitches. The multiresolution image is reconstructed using projection data having various pixel pitches corresponding to the pixel pitches of the multiresolution image. By using a higher resolution inside regions of interest (ROIs) in both the image and projection domains and lower resolution outside the ROIs, fast image reconstruction can be performed while avoiding truncation artifacts, which result imaging is limited to an ROI excluding attenuation regions. Further, those regions of greater clinical relevance and greater structural variance within the reconstructed images can be selected to be within the ROIs to improve the clinical benefit of the multiresolution image. The multiresolution image can be reconstructed using an iterative reconstruction method in which the high- and low-resolution regions are uniquely evaluated.

Abstract: A computed tomography (CT) imaging apparatus that reduces metal artifacts for digital subtraction angiography by, in circuitry, obtaining mask frames and contrast frames generated during scans of an object; performing a first reconstruction of the mask frames to generate metal volume data; identifying metal voxels in the metal volume data; re-projecting the metal voxels into the mask frames to generate metal-mask frames; defining a region of interest in each of the mask frames, each region of interest including metal regions; re-projecting the contrast frames; blending the mask frames with the re-projected contrast frames; registering the contrast frames with respect to the blended mask frames performing a cross-correlation analysis of the mask frames and the contrast frames, frame-pair-by-frame-pair; subtracting the registered contrast image from the corresponding mask image, frame-pair-by-frame-pair, to generate subtracted frames; and performing a second reconstruction on the subtracted frames.