Modulating π-bridge in donor-π-acceptor covalent organic frameworks for low-energy-light-driven photocatalytic reaction.

Most of the photocatalytic reactions are currently driven by high-energy light (UV, blue light), which inevitably leads to side reactions and co-catalyst deactivation. Therefore, there is an urgent need to prepare novel photocatalysts with low-energy photocatalytic properties. Herein, we report a rational molecular design of covalent organic frameworks (COFs) equipped with donor-π-acceptor systems with different π-bridges (aromatic ring, mono- and bis-alkynyl).

Covalent Organic Frameworks with Tunable Bridge Positions for Photocatalytic CO Reduction to Propylene Under Visible Light Illumination.

The use of sunlight to convert CO into multi-carbon fuels, particularly propylene, is considered a sustainable carbon cycle pathway, but propylene requires a multi-electron-coupled proton reaction process that has not been reported. Herein, two covalent organic frameworks (DA-COF and DP-COF) are prepared by varying the bridging positions of anthraquinone conjugated units. The experimental results show that the neighbouring bridge in DA-COF forms a unique cleavage structure like an enzyme catalyst, which can provide an efficient microenvironment for the reduction reaction to trap protons.

Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging.

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI.

Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning.

Purpose: Artificial intelligence-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiation therapy target volume. Our goal was to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches.

Automated radiotherapy treatment planning guided by GPT-4Vision.

Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in large foundation models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, a fully automated treatment planning framework that harnesses prior radiation oncology knowledge encoded in multi-modal large language models, such as GPT-4Vision (GPT-4V) from OpenAI.

Photoselectively modulating main products by changing the wavelength of visible light over D-π-A-D conjugated polymers.

The diversity of catalytic products determines the difficulty of selective product modulation, which usually relies on adjusting the catalyst and reaction conditions to obtain different main products selectively. Herein, we synthesized D-π-A-D conjugated organic polymers (TH-COP) using cyclotriphosphonitrile, alkyne, 2H-benzimidazole, and sulfur units as electron donors, π bridges, electron acceptors, and electron donors, respectively. TH-COP exhibited excellent photoinduced carrier separation and redox ability under different visible light wavelengths, and the main products of its CO reduction are CH (1000.

Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI).

Purpose: Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality.

Methods: Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits).

Adaptive Region-Specific Loss for Improved Medical Image Segmentation.

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e.

Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy.

Purpose: This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system.

Methods And Materials: For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method.

MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy.

Purpose: Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice.

Methods: Because of the lack of training labels and difficulty of accurate registration from inferior image quality, a new segmentation-based registration framework was proposed for this multi-modality image registration problem.

Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning.

Purpose: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT).

Methods: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy.

Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet.

Purpose: Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to clinical practice.

Methods: Multiparametric magnetic resonance imaging images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4.

One-dimensional sensor learns to sense three-dimensional space.

A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of "noise". The "noise" feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2-order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena.

Glass 3D printing of microfluidic pressure sensor interrogated by fiber-optic refractometry.

This letter reports a novel fused silica microfluidic device with pressure sensing capability that is fabricated by integrated additive and subtractive manufacturing (IASM) method. The sensor consists of a capillary and a 3D printed glass reservoir, where the reservoir volume change under pressure manifests liquid level deviation inside the capillary, thus realizing the conversion between small pressure change into large liquid level variation. Thanks to the design flexibility of this unique IASM method, the proposed microfluidic device is fabricated with liquid-in-glass thermometer configuration, where the reservoir is sealed following a novel 3D printing assisted glass bonding process.

3D printing of all-glass fiber-optic pressure sensor for high temperature applications.

In this paper, we report a fiber-optic pressure sensor fabricated by three-dimensional (3D) printing of glass using direct laser melting method. An all-glass fiber-housing structure is 3D printed on top of a fused silica substrate, which also serves as the pressure sensing diaphragm. And an optical fiber can be inserted inside the fiber housing structure and brought in close proximity to the diaphragm to form a Fabry-Perot interferometer.

Information integrated glass module fabricated by integrated additive and subtractive manufacturing.

In this Letter, we report a novel integrated additive and subtractive manufacturing (IASM) method to fabricate an information integrated glass module. After a certain number of glass layers are 3D printed and sintered by direct ${{\rm CO}_2}$CO laser irradiation, a microchannel will be fabricated on top of the printed glass by integrated picosecond laser, for intrinsic Fabry-Perot interferometer (IFPI) optical fiber sensor embedment. Then, the glass 3D printing process continues for the realization of bonding between optical fiber and printed glass.

The microdosimetric extension in TOPAS: development and comparison with published data.

Microdosimetric energy depositions have been suggested as a key variable for the modeling of the relative biological effectiveness (RBE) in proton and ion radiation therapy. However, microdosimetry has been underutilized in radiation therapy. Recent advances in detector technology allow the design of new mico- and nano-dosimeters.

Continuous flow synthesis of a pharmaceutical intermediate: a computational fluid dynamics approach.

Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space.

Characterization of DNPH-coated microreactor chip for analysis of trace carbonyls with application for breath analysis.

The analysis of trace carbonyls including aldehydes and ketones is important for monitoring environmental air quality, determining toxicity of aerosol of electronic cigarette, and detecting diseases by breath analysis. This work reports investigation of a single microreactor chip with HClO-acidified DNPH coating for capture and analysis of carbonyls in air and exhaled breath. Three aldehydes and three ketones were spiked into one liter synthetic air in Tedlar bags serving as gaseous carbonyl standard for characterization of capture efficiency (CE).

Modelling of Cellular Survival Following Radiation-Induced DNA Double-Strand Breaks.

A mechanistic model of cellular survival following radiation-induced DNA double-strand breaks (DSBs) was proposed in this study. DSBs were assumed as the initial lesions in the DNA of the cell nucleus induced by ionizing radiation. The non-homologous end-joining (NHEJ) pathway was considered as the domain pathway of DSB repair in mammalian cells.

Impact of potentially variable RBE in liver proton therapy.

Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties.

A hollow coaxial cable Fabry-Pérot resonator for liquid dielectric constant measurement.

We report, for the first time, a low-cost and robust homemade hollow coaxial cable Fabry-Pérot resonator (HCC-FPR) for measuring liquid dielectric constant. In the HCC design, the traditional dielectric insulating layer is replaced by air. A metal disk is welded onto the end of the HCC serving as a highly reflective reflector, and an open cavity is engineered on the HCC.

Displacement and Strain Measurement up to 1000 °C Using a Hollow Coaxial Cable Fabry-Perot Resonator.

We present a hollow coaxial cable Fabry-Perot resonator for displacement and strain measurement up to 1000 °C. By employing a novel homemade hollow coaxial cable made of stainless steel as a sensing platform, the high-temperature tolerance of the sensor is dramatically improved. A Fabry-Perot resonator is implemented on this hollow coaxial cable by introducing two highly-reflective reflectors along the cable.

Probing changes in tilt angle with 20 nanoradian resolution using an extrinsic Fabry-Perot interferometer-based optical fiber inclinometer.

In this paper, we introduce and demonstrate a novel optical fiber extrinsic Fabry-Perot interferometer (EFPI) for tilt measurements with 20 nrad resolution. Compared with in-line optical fiber inclinometers, an extrinsic sensing structure is used in the inclinometer reported herein. Our design greatly improves on the tilt angle resolution, the temperature stability, and the mechanical robustness of inclinometers with advanced designs.