Abstract: The present disclosure relates to a method for treating a cancer and/or cancer metastasis in a subject comprising administering to the subject irinotecan loaded in a mesoporous silica nanoparticle. The present disclosure also provides a conjugate comprising an agent loaded in a mesoporous silica nanoparticle (MSN) defining at least one pore and having at least one functional group on a sidewall of the at least one pore.

Abstract: The disclosure provides an electronic device and a manufacturing method thereof. The electronic device includes a package structure, a circuit structure, a bonding structure and an external element. The circuit structure is disposed on the package structure and is electrically connected to the package structure. The circuit structure has a recess. The bonding structure includes a first bonding pad and a second bonding pad. The second bonding pad is disposed in the recess, and the second bonding pad is disposed on the first bonding pad. The bonding structure is disposed between the circuit structure and the external element. The external element is electrically connected to the circuit structure through the bonding structure. A width of the first bonding pad is smaller than a width of the second bonding pad.

Abstract: A semiconductor device includes a first gate structure extending along a first lateral direction. The semiconductor device includes a first interconnect structure, disposed above the first gate structure, that extends along a second lateral direction perpendicular to the first lateral direction. The first interconnect structure includes a first portion and a second portion electrically isolated from each other by a first dielectric structure. The semiconductor device includes a second interconnect structure, disposed between the first gate structure and the first interconnect structure, that electrically couples the first gate structure to the first portion of the first interconnect structure. The second interconnect structure includes a recessed portion that is substantially aligned with the first gate structure and the dielectric structure along a vertical direction.

Abstract: A circuit protection device includes a first temperature sensitive resistor, a second temperature sensitive resistor, an electrically insulating multilayer, a first and second electrode layer, and at least one external electrode. The first temperature sensitive resistor and the second temperature sensitive resistor are electrically connected in parallel, and have a first upper electrically conductive layer and a second lower electrically conductive layer, respectively. The electrically insulating multilayer includes an upper insulating layer, a middle insulating layer, and a lower insulating layer. The upper insulating layer is between the first upper electrically conductive layer and the first electrode layer. The middle layer is laminated between the first temperature sensitive resistor and the second temperature sensitive resistor. The lower insulating layer is between the second lower electrically conductive layer and the second electrode layer.

Abstract: Systems and methods for using a prediction model jointly with a normalization model to provide prediction results are provided. One example method includes receiving an input image of a tissue sample of a patient and generating a normalized image by applying a normalization model on the input image. The normalization model is configured to generate normalized data using input data for a prediction model, and the prediction model is configured to generate prediction results using normalized data generated by the normalization model. The normalization model and the prediction model are jointly trained. The method further includes generating a prediction of disease severity for the patient by applying the prediction model on the normalized image.

Abstract: Systems and methods for predicting images with enhanced spatial resolution using a neural network are provided herein. According to an aspect of the invention, a method includes accessing an input image of a biological sample, wherein the input image includes a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel; applying a trained artificial neural network to the input image; generating an output image at a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and outputting the output image.

Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: The present disclosure relates to a method of preventing or treating brain cancers or brain metastases with mesoporous silica nanoparticles (MSNs) loaded with taxane-based chemotherapeutic drugs, in particular paclitaxel (PTX), cabazitaxel (CTX) or docetaxel (DTX), and the MSNs loaded with PTX, CTX or DTX.

Abstract: The disclosure provides a method for delivering an agent to posterior segment of an eye comprising administrating a pharmaceutical composition comprising the agent and mesoporous silica nanoparticles to the eye. An eye drop and a method for treating an ocular disease in a subject in need of such treatment are also provided.

Abstract: The present disclosure relates to systems and methods for cellular imaging and identification through the use of a light sheet flow cytometer. In one implementation, a light sheet flow cytometer may include a light source configured to emit light having one or more wavelengths, at least one optical element configured to form a light sheet from the emitted light, a microfluidic channel configured to hold a sample, and an imaging device. The imaging device may be adapted to forming 3-D images of the sample such that identification tags attached to the sample are visible.

Abstract: The present disclosure relates to mesoporous silica nanoparticles (MSNs) with specific modifications as drug delivery systems containing both tumor targeting and blood-brain barrier (BBB) penetration properties suitable for cancer treatment and/or CNS disease treatment. The present disclosure also relates to method of preparing MSNs and the MSNs prepared by the method as described herein.

Abstract: Systems and methods for predicting images with enhanced spatial resolution using a neural network are provided herein. According to an aspect of the invention, a method includes accessing an input image of a biological sample, wherein the input image includes a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel; applying a trained artificial neural network to the input image; generating an output image at a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and outputting the output image.

Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: The present disclosure relates to a field of hollow silica nanospheres. Particularly, the present disclosure relates to silica nanoparticles as adjuvant to induce or enhance immune response or as carrier to deliver antigen to a body.

Abstract: The present disclosure relates to mesoporous silica nanoparticles having modifications on the surface of the (extended) mesopores, which can be further loaded with one or more types of bioactive ingredients within the (extended) mesopores mesopores, processes of preparing the same and applications of the same.

Abstract: The present disclosure relates to mesoporous silica nanoparticles (MSNs) with specific modifications as drug delivery systems containing both tumor targeting and blood-brain barrier (BBB) penetration properties suitable for cancer treatment and/or CNS disease treatment. The present disclosure also relates to method of preparing MSNs and the MSNs prepared by the method as described herein.

Abstract: Systems and methods for generating virtually stained images of unstained samples are provided. According to an aspect of the invention, a method includes accessing an image training dataset including a plurality of image pairs. Each image pair includes a first image of an unstained first tissue sample, and a second image acquired when the first tissue sample is stained. The method also includes accessing a set of parameters for an artificial neural network, wherein the set of parameters includes weights associated with artificial neurons within the artificial neural network; training the artificial neural network by using the image training dataset and the set of parameters to adjust the weights; accessing a third image of a second tissue sample that is unstained; using the trained artificial neural network to generate a virtually stained image of the second tissue sample from the third image; and outputting the virtually stained image.

Abstract: The present disclosure relates to systems and methods for cellular imaging and identification through the use of a light sheet flow cytometer. In one implementation, a light sheet flow cytometer may include a light source configured to emit light having one or more wavelengths, at least one optical element configured to form a light sheet from the emitted light, a microfluidic channel configured to hold a sample, and an imaging device. The imaging device may be adapted to forming 3-D images of the sample such that identification tags attached to the sample are visible.