Organic Bulk Heterojunction Infrared Photodiodes for Imaging Out to 1300 nm

This work studies organic bulk heterojunction photodiodes with a wide spectral range capable of imaging out to 1.3 μm in the shortwave infrared. Adjustment of the donor-to-acceptor (polymer:fullerene) ratio shows how blend composition affects the density of states (DOS) which connects materials composition and optoelectronic properties and provides insight into features relevant to understanding dispersive transport and recombination in the narrow bandgap devices. Capacitance spectroscopy and transient photocurrent measurements indicate the main recombination mechanisms arise from deep traps and poor extraction from accumulated space charges. The amount of space charge is reduced with a decreasing acceptor concentration; however, this reduction is offset by an increasing trap DOS. A device with 1:3 donor-to-acceptor ratio shows the lowest density of deep traps and the highest external quantum efficiency among the different blend compositions. The organic photodiodes are used to demonstrate a single-pixel imaging system that leverages compressive sensing algorithms to enable image reconstruction

Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications

Hazard ratios of second primary malignancy after radioiodine for differentiated thyroid carcinoma: a large-cohort retrospective study

Introduction: The objective of this study is to evaluate the benefits of radioactive iodine (RAI) treatment and the risk of second primary malignancy (SPM) in RAI-treated patients. Material and methods: The cohort for this analysis consisted of individuals diagnosed with a first primary differentiated thyroid carcinoma (DTC), reported by the Surveillance, Epidemiology, and End Results (SEER) database in 1988–2016. Overall survival (OS) difference was estimated by Kaplan-Meier curves and log-rank test, and hazard ratios (HR) were obtained by Cox proportional-hazards model to evaluate the association between RAI and SPM. Results: Among 130,902 patients, 61,210 received RAI and 69,692 did not, and a total of 8604 patients developed SPM. We found that OS was significantly higher in patients who received RAI than in those who did not (p < 0.001). DTC survivors treated with RAI had increased risk of SPM in females (p = 0.043), particularly for SPM occurring in the ovary (p = 0.039) and leukaemia (p < 0.0001). The risk of developing SPM was higher in the RAI group than in the non-RAI group and the general population, and the incidence increased with age. Conclusions: Increased risk of SPM occurs in female DTC survivors treated with RAI, which become more obvious with increasing age. Our research findings were beneficial to the formulation of RAI treatment strategies and the prediction of SPM for patients with thyroid cancer of different genders and different ages

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Thanks to its impressive capabilities in all-round segmentation tasks and its prompt-based interface, SAM has sparked intensive discussion within the community. It is even said by many prestigious experts that image segmentation task has been "finished" by SAM. However, medical image segmentation, although an important branch of the image segmentation family, seems not to be included in the scope of Segmenting "Anything". Many individual experiments and recent studies have shown that SAM performs subpar in medical image segmentation. A natural question is how to find the missing piece of the puzzle to extend the strong segmentation capability of SAM to medical image segmentation. In this paper, instead of fine-tuning the SAM model, we propose Med SAM Adapter, which integrates the medical specific domain knowledge to the segmentation model, by a simple yet effective adaptation technique. Although this work is still one of a few to transfer the popular NLP technique Adapter to computer vision cases, this simple implementation shows surprisingly good performance on medical image segmentation. A medical image adapted SAM, which we have dubbed Medical SAM Adapter (MSA), shows superior performance on 19 medical image segmentation tasks with various image modalities including CT, MRI, ultrasound image, fundus image, and dermoscopic images. MSA outperforms a wide range of state-of-the-art (SOTA) medical image segmentation methods, such as nnUNet, TransUNet, UNetr, MedSegDiff, and also outperforms the fully fine-turned MedSAM with a considerable performance gap. Code will be released at: https://github.com/WuJunde/Medical-SAM-Adapter

Deep Learning Assisted Plasmonic Dark-Field Microscopy for Super-Resolution Label-Free Imaging.

Dark-field microscopy (DFM) is a widely used imaging tool, due to its high-contrast capability in imaging label-free specimens. Traditional DFM requires optical alignment to block the oblique illumination, and the resolution is diffraction-limited to the wavelength scale. In this work, we present deep-learning assisted plasmonic dark-field microscopy (DAPD), which is a single-frame super-resolution method using plasmonic dark-field (PDF) microscopy and deep-learning assisted image reconstruction. Specifically, we fabricated a designed PDF substrate with surface plasmon polaritons (SPPs) illuminating specimens on the substrate. Dark field images formed by scattered light from the specimen are further processed by a pretrained convolutional neural network (CNN) using a simulation dataset based on the designed substrate and parameters of the detection optics. We demonstrated a resolution enhancement of 2.8 times on various label-free objects with a large potential for future improvement. We highlight our technique as a compact alternative to traditional DFM with a significantly enhanced spatial resolution

Effects of different pressure midfoot wraps on balance and proprioception in amateur basketball athletes

IntroductionAnkle sprains are prevalent in basketball. This study sought to determine how midfoot wraps affect postural stability and ankle proprioception.MethodsTwenty-two amateur basketball athletes performed three single-leg balance tests (static, head-elevated static, and unstable foam pad) under four wrap conditions (no wrap, low, medium, and high pressure), and balance measures were taken using a force platform. Standing time, center of pressure dynamics, surface electromyographic of the supporting leg musculature were recorded. Ankle proprioception joint position matching error was assessed by a digital inclinometer.Results and discussionResults indicated that during balance tests on foam padding, participants demonstrated significantly longer standing time when wearing low-pressure midfoot wraps, compared to high-pressure wraps (F (3,63) = 4.32, p = 0.008, η2 = 0.17). Wearing high-pressure wraps reduced anterior-posterior dynamic stability index variability (F (3,63) = 3.89, p = 0.044, η2 = 0.16), suggesting enhanced sagittal-plane control. Intriguingly, high-pressure conditions evidenced convergent activation trends between medial and lateral gastrocnemius (GM/GL ratio shift from 1.3 to 1.0), albeit without statistical significance (p > 0.05). No significant difference was detected in joint position sense in ankle dorsiflexion, plantarflexion, eversion and inversion between different wrap conditions (p > 0.05). These findings suggest that low-pressure midfoot wraps may improve balance through enhanced cutaneous feedback, while high-pressure wraps enhance anterior-posterior dynamic stability, providing biomechanically informed strategies for ankle injury prevention in basketball

Inverse altitude effect disputes the theoretical foundation of stable isotope paleoaltimetry

Stable isotope paleoaltimetry that reconstructs paleoelevation requires stable isotope (δD or δ18O) values to follow the altitude effect. Some studies found that the δD or δ18O values of surface isotopic carriers in some regions increase with increasing altitude, which is defined as an “inverse altitude effect” (IAE). The IAE directly contradicts the basic theory of stable isotope paleoaltimetry. However, the causes of the IAE remain unclear. Here, we explore the mechanisms of the IAE from an atmospheric circulation perspective using δD in water vapor on a global scale. We find that two processes cause the IAE: (1) the supply of moisture with higher isotopic values from distant source regions, and (2) intense lateral mixing between the lower and mid-troposphere along the moisture transport pathway. Therefore, we caution that the influences of those two processes need careful consideration for different mountain uplift stages before using stable isotope palaeoaltimetry