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Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is pivotal for timely detection and removal, contributing significantly to CRC prevention. The manual analysis of images generated by gastrointestinal screening technologies poses a tedious task for doctors. Therefore, computer vision-assisted cancer detection could serve as an efficient tool for polyp segmentation. Numerous efforts have been dedicated to automating polyp localization, with the majority of studies relying on convolutional neural networks (CNNs) to learn features from polyp images. Despite their success in polyp segmentation tasks, CNNs exhibit significant limitations in precisely determining polyp location and shape due to their sole reliance on learning local features from images. While gastrointestinal images manifest significant variation in their features, encompassing both high- and low-level ones, a framework that combines the ability to learn both features of polyps is desired. This paper introduces UViT-Seg, a framework designed for polyp segmentation in gastrointestinal images. Operating on an encoder-decoder architecture, UViT-Seg employs two distinct feature extraction methods. A vision transformer in the encoder section captures long-range semantic information, while a CNN module, integrating squeeze-excitation and dual attention mechanisms, captures low-level features, focusing on critical image regions. Experimental evaluations conducted on five public datasets, including CVC clinic, ColonDB, Kvasir-SEG, ETIS LaribDB, and Kvasir Capsule-SEG, demonstrate UViT-Seg's effectiveness in polyp localization. To confirm its generalization performance, the model is tested on datasets not used in training. Benchmarking against common segmentation methods and state-of-the-art polyp segmentation approaches, the proposed model yields promising results. For instance, it achieves a mean Dice coefficient of 0.915 and a mean intersection over union of 0.902 on the CVC Colon dataset. Furthermore, UViT-Seg has the advantage of being efficient, requiring fewer computational resources for both training and testing. This feature positions it as an optimal choice for real-world deployment scenarios., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Halogen-powered static conversion batteries (HSCBs) thrive in energy storage applications. They fall into the category of secondary non-flow batteries and operate by reversibly changing the chemical valence of halogens in the electrodes or/and electrolytes to transfer electrons, distinguishing them from the classic rocking-chair batteries. The active halide chemicals developed for these purposes include organic halides, halide salts, halogenated inorganics, organic-inorganic halides and the most widely studied elemental halogens. Aside from this, various redox mechanisms have been discovered based on multi-electron transfer and effective reaction pathways, contributing to improved electrochemical performances and stabilities of HSCBs. In this Review, we discuss the status of HSCBs and their electrochemical mechanism-performance correlations. We first provide a detailed exposition of the fundamental redox mechanisms, thermodynamics, conversion and catalysis chemistry, and mass or electron transfer modes involved in HSCBs. We conclude with a perspective on the challenges faced by the community and opportunities towards practical applications of high-energy halogen cathodes in energy-storage devices., (© 2024. Springer Nature Limited.)

Integrated switches play a crucial role in the development of reconfigurable optical add-drop multiplexers (ROADMs) that have greater flexibility and compactness, ultimately leading to robust single-chip solutions. Despite decades of research on switches with various structures and platforms, achieving a balance between dense integration, low insertion loss (IL), and polarization-dependent loss (PDL) remains a significant challenge. In this paper, we propose and demonstrate a 32 × 4 optical switch using high-index doped silica glass (HDSG) for ROADM applications. This switch is designed to route any of the 32 inputs to the express ports or drop any channels from 32 inputs to the target 4 drop ports or add any of the 4 ports to any of the 32 express channels. The switch comprises 188 Mach-Zehnder Interferometer (MZI) type switch elements, 88 optical vias for the 44 optical bridges, and 618 waveguide-waveguide crossings with three-dimensional (3D) structures. At 1550 nm, the fiber-to-fiber loss for each express channel is below 2 dB, and across the C and L bands, below 3 dB. For each input channel to all 4 drop/add channels at 1550 nm, the loss is less than 3.5 dB and less than 5 dB across the C and L bands. The PDLs for all express and input channels to the 4 drop/add channels are below 0.3 dB over the C band, and the crosstalk is under -50 dB for both the C and L bands., (© 2024. The Author(s).)

Objectives: Indigenous public health theory and the voices of Canadian Indigenous communities remain under-represented in the literature despite the Canadian Truth and Reconciliation Calls to Action, and the perspectives of Inuit are further under-represented in this literature. The goal of this paper is to explore the perspectives of Iqalungmiut (people of Iqaluit), frontline staff, and decision-makers on the management of the COVID-19 outbreak in Iqaluit in April to June 2021 and to identify lessons learned and contributions to public health policy and practice specific to Inuit populations in Canada., Methods: This study used the Piliriqatigiinniq Community Health Research Model which was developed by Nunavummiut to guide community-based health and well-being research. Interviews were conducted with 44 individuals: 22 community members and shelter users; 17 frontline workers; and 5 decision-makers representing municipal and territorial government. Participants were asked about their experiences during the outbreak, sources of information, and strengths and challenges during outbreak management., Results: Challenges included overcrowding, physical disconnection from family members, and mental health and trauma. Community-identified strengths included strong interagency cooperation, food hamper and COVID-19 care kit deliveries, and travel restrictions. Several Inuit positive health-protective pathways were identified including Ilaginniq; Silativut; Inuuqatigiittiarniq; Piliriqatigiinniq; Ikajurniq; and Pijitsirniq., Conclusion: Outbreaks of infectious illness are not new to Nunavut communities and Inuit protective pathways have and continue to be critical avenues to adapt to and mitigate such challenges. This exploratory study provides clear direction for Inuit public health policy and practice in Canada, while contributing to the body of literature on Indigenous public health theory., (© 2024. The Author(s).)

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Simulating the quantum dynamics of molecules in the condensed phase represents a longstanding challenge in chemistry. Trapped-ion quantum systems may serve as a platform for the analog-quantum simulation of chemical dynamics that is beyond the reach of current classical-digital simulation. To identify a 'quantum advantage' for these simulations, performance analysis of both analog-quantum simulation on noisy hardware and classical-digital algorithms is needed. In this Review, we make a comparison between a noisy analog trapped-ion simulator and a few choice classical-digital methods on simulating the dynamics of a model molecular Hamiltonian with linear vibronic coupling. We describe several simple Hamiltonians that are commonly used to model molecular systems, which can be simulated with existing or emerging trapped-ion hardware. These Hamiltonians may serve as stepping stones towards the use of trapped-ion simulators for systems beyond the reach of classical-digital methods. Finally, we identify dynamical regimes in which classical-digital simulations seem to have the weakest performance with respect to analog-quantum simulations. These regimes may provide the lowest hanging fruit to make the most of potential quantum advantages., (© 2024. Springer Nature Limited.)

The examination of entanglement across various degrees of freedom has been pivotal in augmenting our understanding of fundamental physics, extending to high dimensional quantum states, and promising the scalability of quantum technologies. In this paper, we demonstrate the photon number path entanglement in the frequency domain by implementing a frequency beam splitter that converts the single-photon frequency to another with 50% probability using Bragg scattering four-wave mixing. The two-photon NOON state in a single-mode fiber is generated in the frequency domain, manifesting the two-photon interference with two-fold enhanced resolution compared to that of single-photon interference, showing the outstanding stability of the interferometer. This successful translation of quantum states in the frequency domain will pave the way toward the discovery of fascinating quantum phenomena and scalable quantum information processing., (© 2024. The Author(s).)

The morphological analysis test item of urine red blood cells is referred to as "extracorporeal renal biopsy," which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilized in medical examinations. Challenges include low image spatial resolution, blurred distinguishing features between cells, difficulty in fine-grained feature extraction, and insufficient data volume. This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine red blood cell morphology examination items., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: The role of robotics in spine surgery remains controversial, especially for scoliosis correction surgery. This study aims to assess the safety and efficacy of robotic-assisted (RA) surgery specifically for scoliosis surgery by comparing RA to both navigation systems (NS) and conventional freehand techniques (CF)., Methods: As per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, a systematic review and meta-analysis were conducted via an electronic search of the following databases: MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL). All papers comparing RA to either NS or CF for posterior spinal fusion in scoliosis were included. Fixed and random effects models of analysis were utilised based on analysis heterogeneity., Results: 10 observational studies were included in total. RA had significantly greater odds of accurate pedicle screw placement relative to both NS (OR = 2.02, CI = 1.52-2.67, p < 0.00001) and CF (OR = 3.06, CI = 1.79-5.23, p < 0.00001). The downside of RA was the significantly greater operation duration relative to NS (MD = 10.74, CI = 3.52-17.97, p = 0.004) and CF (MD = 40.27, CI = 20.90, p < 0.0001). Perioperative outcomes including estimated blood loss, radiation exposure, length of hospital stay, cobb angle correction rate, postoperative SRS score, VAS pain score, JOA score, as well as rates of neurological injury and revision surgery, were comparable between the groups (p > 0.05)., Conclusion: RA offers significantly greater pedicle screw placement accuracy relative to NS and CF, however, surgery can take longer. In terms of perioperative outcomes, all three techniques are comparable., (© 2024. The Author(s).)

Bio-based coating materials have received increased attention because of their low-cost, environmentally friendly, and sustainable properties. In this paper, a novel coating material was developed to coat ureas using bio-based coating material derived from liquefied eggplant branches to form controlled-release ureas (CRUs). Also, the optimum proportion of liquefier was studied. Furthermore, dimethyl siloxane was used to modify liquified eggplant branches to make them hydrophobic, resulting in hydrophobic controlled-release ureas (SCRUs). This hydrophobic-enabled coating is environmentally friendly and highly efficient. The products were characterized by specific scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and differential scanning calorimetry, and the water contact angles of CRUs and SCRUs were determined. The nutrient-release characteristics of the SCRUs in water were determined at 25 °C and compared with those of CRUs. The results showed that the modification with dimethyl siloxane reduced the N release rate and increased the longevity of the fertilizer coated with hydrophobic bio-based coating material. In addition, organosilicon atoms on the SCRU surface also block the micro-holes on the coating and thus reduce the entry of water onto the coating. The results suggest that the new coating technology can create a hydrophobic surface on bio-based coating material and thus improve their controlled-release characteristics., (© 2024. The Author(s).)

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research., (© 2024. The Author(s).)

The US healthcare system is at a crossroads. With an aging population requiring more care and a strained system facing workforce shortages, capacity issues, and fragmentation, innovative solutions and policy reforms are needed. This paper aims to spark dialogue and collaboration among healthcare stakeholders and inspire action to meet the needs of the aging population. Through a comprehensive analysis of the impact of an aging society, this work highlights the urgency of addressing this issue and the importance of restructuring the healthcare system to be more efficient, equitable, and responsive., (© 2024. The Author(s).)

Purpose: Scoliosis can be treated with vertebral body tethering (VBT) as a motion-sparing procedure. However, the knowledge of how growth is affected by a tether spanning multiple levels is unclear in the literature. Three-dimensional true spine length (3D-TSL) is a validated assessment technique that accounts for the shape of the spine in both the coronal and sagittal planes. This study aimed to assess if 3D-TSL increases over a five-year period after VBT implantation in thoracic curves for idiopathic scoliosis., Methods: Prospectively collected radiographic data from an international pediatric spine registry was analyzed. Complete radiographic data over three visits (post-operative, 2 years, and 5 years) was available for 53 patients who underwent VBT., Results: The mean age at instrumentation of this cohort was 12.2 (9-15) years. The average number of vertebrae instrumented was 7.3 (SD 0.7). Maximum Cobb angles were 50° pre-op, which improved to 26° post-op (p < 0.001) and was maintained at 5 years (30°; p = 0.543). Instrumented Cobb angle was 22° at 5 years (p < 0.001 vs 5-year maximum Cobb angle). An accentuation was seen in global kyphosis from 29° pre-operative to 41° at 5 years (p < 0.05). The global spine length (T1-S1 3D-TSL) started at 40.6 cm; measured 42.8 cm at 2 years; and 44.0 cm at the final visit (all p < 0.05). At 5 years, patients reached an average T1-S1 length that is comparable to a normal population at maturity. Immediate mean post-operative instrumented 3D-TSL (top of UIV-top of LIV) was 13.8 cm two-year length was 14.3 cm; and five-year length was 14.6 cm (all p < 0.05). The mean growth of 0.09 cm per instrumented level at 2 years was approximately 50% of normal thoracic growth. Patients who grew more than 0.5 cm at 2 years had a significantly lower BMI (17.0 vs 19.0, p < 0.05) and smaller pre-operative scoliosis (48° vs 53°, p < 0.05). Other subgroup analyses were not significant for age, skeletal maturity, Cobb angles or number of spanned vertebras as contributing factors., Conclusions: This series demonstrates that 3D-TSL increased significantly over the thoracic instrumented levels after VBT surgery for idiopathic scoliosis. This represented approximately 50% of expected normal thoracic growth over 2 years., (© 2024. The Author(s), under exclusive licence to Scoliosis Research Society.)

Large language models (LLMs) such as Open AI's GPT-4 (which power ChatGPT) and Google's Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy., (© 2024. The Author(s).)

Objectives: The aim of this study was to explore and learn from the experiences of Métis women, Two-Spirit, and gender-diverse people accessing health and social services in Victoria, British Columbia, during the COVID-19 pandemic., Methods: This paper comes from a larger study exploring Métis women, Two-Spirit, and gender-diverse people's experiences accessing health and social services in Victoria. Using a by-and-for Métis approach that employed a conversational interview method, we conducted interviews with Métis women, Two-Spirit, and gender-diverse people who lived in and/or accessed services in Victoria in December 2020 and January 2021. This paper focuses specifically on data addressing how COVID-19 impacted these participants., Results: A total of 24 Métis women, Two-Spirit, and gender-diverse people participated in the study. Overall, three themes specific to COVID-19 were identified. First, participants described the detrimental impacts of COVID-19 on their ability to connect with their Métis community and practice their culture, as well as their overall feelings of isolation. Second, participants highlighted some of the ways that COVID-19 has exacerbated existing barriers to culturally safe healthcare. Last, participants spoke about the mixed economic impacts that COVID-19 has had for them, sharing insight into the ways in which gender, in particular, has shaped their financial instability., Conclusion: Improving access to culturally safe health and social services by incorporating the experiences and expertise of Métis women, Two-Spirit, and gender-diverse people is crucial to mitigating the disproportional negative impacts of the pandemic and improving overall health outcomes within Métis communities across Canada., (© 2024. The Author(s).)

Peer review plays a crucial role in accreditation and credentialing processes as it can identify outliers and foster a peer learning approach, facilitating error analysis and knowledge sharing. However, traditional peer review methods may fall short in effectively addressing the interpretive variability among reviewing and primary reading radiologists, hindering scalability and effectiveness. Reducing this variability is key to enhancing the reliability of results and instilling confidence in the review process. In this paper, we propose a novel statistical approach called "Bayesian Inter-Reviewer Agreement Rate" (BIRAR) that integrates radiologist variability. By doing so, BIRAR aims to enhance the accuracy and consistency of peer review assessments, providing physicians involved in quality improvement and peer learning programs with valuable and reliable insights. A computer simulation was designed to assign predefined interpretive error rates to hypothetical interpreting and peer-reviewing radiologists. The Monte Carlo simulation then sampled (100 samples per experiment) the data that would be generated by peer reviews. The performances of BIRAR and four other peer review methods for measuring interpretive error rates were then evaluated, including a method that uses a gold standard diagnosis. Application of the BIRAR method resulted in 93% and 79% higher relative accuracy and 43% and 66% lower relative variability, compared to "Single/Standard" and "Majority Panel" peer review methods, respectively. Accuracy was defined by the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. Variability was defined by the 95% CI around the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. BIRAR is a practical and scalable peer review method that produces more accurate and less variable assessments of interpretive quality by accounting for variability within the group's radiologists, implicitly applying a standard derived from the level of consensus within the group across various types of interpretive findings., (© 2024. The Author(s).)

In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks' convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network's understanding of semantic information related to the target. Furthermore, this paper presents a training scheme tailored for the module, aiming to achieve finer segmentation and improved generalization performance. Validation of our approach is carried out using TRUS and chest X-ray datasets. Experimental results demonstrate that our method significantly enhances neural networks' convergence speed and precision. For instance, the convergence speed of UNet and UNET +  + is improved by more than 30%. Moreover, Segformer achieves an increase of over 6% and 3% in mIoU (mean Intersection over Union) on two test datasets without requiring pre-trained parameters. Our approach reduces the time and resource costs associated with training neural networks for organ segmentation tasks while effectively guiding the network to achieve meaningful learning even without pre-trained parameters., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Proximal femur geometry is an important risk factor for diagnosing and predicting hip and femur injuries. Hence, the development of an automated approach for measuring these parameters could help physicians with the early identification of hip and femur ailments. This paper presents a technique that combines the active shape model (ASM) and deep learning methodologies. First, the femur boundary is extracted by a deep learning neural network. Then, the femur's anatomical landmarks are fitted to the extracted border using the ASM method. Finally, the geometric parameters of the proximal femur, including femur neck axis length (FNAL), femur head diameter (FHD), femur neck width (FNW), shaft width (SW), neck shaft angle (NSA), and alpha angle (AA), are calculated by measuring the distances and angles between the landmarks. The dataset of hip radiographic images consisted of 428 images, with 208 men and 220 women. These images were split into training and testing sets for analysis. The deep learning network and ASM were subsequently trained on the training dataset. In the testing dataset, the automatic measurement of FNAL, FHD, FNW, SW, NSA, and AA parameters resulted in mean errors of 1.19%, 1.46%, 2.28%, 2.43%, 1.95%, and 4.53%, respectively., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models., (© 2024. The Author(s).)

Pressure ulcers are a common, painful, costly, and often preventable complication associated with prolonged immobility in bedridden patients. It is a significant health problem worldwide because it is frequently seen in inpatients and has high treatment costs. For the treatment to be effective and to ensure an international standardization for all patients, it is essential that the diagnosis of pressure ulcers is made in the early stages and correctly. Since invasive methods of obtaining information can be painful for patients, different methods are used to make a correct diagnosis. Image-based diagnosis method is one of them. By using images obtained from patients, it will be possible to obtain successful results by keeping patients away from such painful situations. At this stage, disposable wound rulers are used in clinical practice to measure the length, width, and depth of patients' wounds. The information obtained is then entered into tools such as the Braden Scale, the Norton Scale, and the Waterlow Scale to provide a formal assessment of risk for pressure ulcers. This paper presents a novel benchmark dataset containing pressure ulcer images and a semi-two-stream approach that uses the original images and the cropped wound areas together for diagnosing the stage of pressure ulcers. Various state-of-the-art convolutional neural network (CNN) architectures are evaluated on this dataset. Our experimental results (test accuracy of 93%, the precision of 93%, the recall of 92%, and the F1-score of 93%) show that the proposed semi-two-stream method improves recognition results compared to the base CNN architectures., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Strengthening the field of imaging informatics by further defining standards and advocating for continuous education are the cornerstones of the American Board of Imaging Informatics (ABII). ABII is the non-profit organization that governs the Imaging Informatics Professional certification program. ABII is responsible for awarding the Certified Imaging Informatics Professional (CIIP) designation to candidates who meet specified educational and experience-based criteria and pass a qualifying exam (1). For this paper, we analyzed Quality Improvement (QI) projects submitted to ABII for satisfaction of the 10-year requirements in 2017-2021. The project reports demonstrated a variety of interventions undertaken to ultimately improve patient care. A retrospective review of these reports exemplifies the critical role the Certified Imaging Informatics Professionals have in delivery of high quality, safe healthcare and their vital contributions to the healthcare industry and practice of medicine., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

As a sugar-rich plant with no impact on global warming and food security, sweet sorghum can be exploited as an alternative source of renewable bioenergy. This study aimed to examine the potential of sweet sorghum juice for the generation of bioethanol using yeast isolated from the juice. The °Brix of sweet sorghum juice was measured using a digital refractometer. Additionally, 18 wild yeasts isolated from fermented sweet sorghum juice were subjected to various biochemical tests to describe them to identify potential yeast for ethanol production. The morphological and biochemical analyses of the yeasts revealed that all of the yeast isolates were most likely members of the genus Saccharomyces. The most ethanol-tolerant yeast isolate SJU14 was employed for sweet sorghum juice fermentation. A completely randomized factorial design was used with various fermentation parameters, primarily pH, temperature, and incubation period. Then ethanol content was determined using a potassium dichromate solution. According to the ANOVA, the highest ethanol content (18.765%) was produced at 30/26 °C, pH 4.5, and incubated for 96 h. Sweet sorghum juice was found to be an excellent source of potent yeasts, which have important industrial properties like the capacity to grow at high ethanol and glucose concentrations. Moreover, it can be utilized as a substitute substrate for the manufacturing of bioethanol production to lessen the environmental threat posed by fossil fuels. Further research is, therefore, recommended to develop strategically valuable applications of sweet sorghum for enhancing the food system and mitigating climate change., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Habitat associations underpin species ecologies in high-diversity systems. Within tropical, shallow water coral reefs, the relationship between fishes and corals is arguably the most iconic and highly scrutinized. A strong relationship between fishes and reef-building hard corals is often assumed, a belief supported by studies that document the decline of reef fishes following coral loss. However, the extent of this relationship is often unclear, as evidenced by conflicting reports. Here we assess the strength of this ecological association by relying on literature that has surveyed both fishes and corals synchronously. We quantitatively synthesize 723 bivariate correlation coefficients (from 66 papers), published over 38 years, that relate fish metrics (abundance, biomass and species richness) with the percentage of hard coral cover. Remarkably, despite extensive variation, the pattern of association on a global scale reveals a predominantly positive, albeit weak (|r| < 0.4), correlation. Even for commonly hypothesized drivers of fish-coral associations, fish family and trophic group, associations were consistently weak. These findings question our assumptions regarding the strength and ubiquity of fish-coral associations, and caution against assuming a direct and omnipresent relationship between these two iconic animal groups., (© 2024. Crown.)

Megafauna (animals ≥45 kg) have probably shaped the Earth's terrestrial ecosystems for millions of years with pronounced impacts on biogeochemistry, vegetation, ecological communities and evolutionary processes. However, a quantitative global synthesis on the generality of megafauna effects on ecosystems is lacking. Here we conducted a meta-analysis of 297 studies and 5,990 individual observations across six continents to determine how wild herbivorous megafauna influence ecosystem structure, ecological processes and spatial heterogeneity, and whether these impacts depend on body size and environmental factors. Despite large variability in megafauna effects, we show that megafauna significantly alter soil nutrient availability, promote open vegetation structure and reduce the abundance of smaller animals. Other responses (14 out of 26), including, for example, soil carbon, were not significantly affected. Further, megafauna significantly increase ecosystem heterogeneity by affecting spatial heterogeneity in vegetation structure and the abundance and diversity of smaller animals. Given that spatial heterogeneity is considered an important driver of biodiversity across taxonomic groups and scales, these results support the hypothesis that megafauna may promote biodiversity at large scales. Megafauna declined precipitously in diversity and abundance since the late Pleistocene, and our results indicate that their restoration would substantially influence Earth's terrestrial ecosystems., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Anion recognition is pertinent to a range of environmental, medicinal and industrial applications. Recent progress in the field has relied on advances in synthetic host design to afford a broad range of potent recognition motifs and novel supramolecular structures capable of effective binding both in solution and at derived molecular films. However, performance in aqueous media remains a critical challenge. Understanding the effects of bulk and local solvent on anion recognition by host scaffolds is imperative if effective and selective detection in real-world media is to be viable. This Review seeks to provide a framework within which these effects can be considered both experimentally and theoretically. We highlight proposed models for solvation effects on anion binding and discuss approaches to retain strong anion binding in highly competitive (polar) solvents. The synthetic design principles for exploiting the aforementioned solvent effects are explored., (© 2024. Springer Nature Limited.)

Cooperation is key to prosperity in human societies. Population structure is well understood as a catalyst for cooperation, where research has focused on pairwise interactions. But cooperative behaviors are not simply dyadic, and they often involve coordinated behavior in larger groups. Here we develop a framework to study the evolution of behavioral strategies in higher-order population structures, which include pairwise and multi-way interactions. We provide an analytical treatment of when cooperation will be favored by higher-order interactions, accounting for arbitrary spatial heterogeneity and nonlinear rewards for cooperation in larger groups. Our results indicate that higher-order interactions can act to promote the evolution of cooperation across a broad range of networks, in public goods games. Higher-order interactions consistently provide an advantage for cooperation when interaction hyper-networks feature multiple conjoined communities. Our analysis provides a systematic account of how higher-order interactions modulate the evolution of prosocial traits., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

As the International Space Station comes to the end of a transformative era of in-space research, NASA's Commercial Low Earth Orbit (LEO) Destinations (CLD) Program aims to catalyze a new generation of platforms with co-investment from the private sector, preventing a potential gap in research performed in LEO, while building a robust LEO economy. In this paper, we provide insight into the CLD Program focusing on Orbital Reef, describing its operational and technical characteristics as well as new opportunities it may enable. Achieving about a third of the pressurized volume of the ISS with the launch of a single pressurized module and growing to support hundreds of Middeck Locker Equivalents (MLE) in passive and active payloads internally and externally, Orbital Reef will enable government, academic, and commercial institutions to continue and expand upon research and development (R&D) efforts currently performed on ISS. Additionally, it will enable nascent markets to establish their operations in space, by initiating new lines of research and technology development and the implementation of new ventures and visions. Using Blue Origin's New Glenn heavy launch system, Sierra Space's cargo and crew Dream Chaser® vehicles, and Boeing's Starliner crew vehicle, and expertise from Amazon/Amazon Supply Chain, Arizona State University, Genesis Engineering, and Redwire, Orbital Reef is being designed to address ISS-era transportation logistics challenges. Finally, this manuscript describes some of the expected challenges from the ISS-to-CLD transition, and provides guidance on how researchers in academia and industry can shape the future of commercial destinations and work performed in LEO., (© 2024. The Author(s).)

Background: Early oral feeding has been previously postulated to contribute to developing postoperative complications following head and neck reconstructive surgeries using free flaps. This study assessed the association between the timing of oral feeding (early vs. late) and postoperative complications and length of hospital stay among these patients., Method: PubMed, Scopus, Cochrane, and Web of Science were searched using terms such as "oral feeding" and "head or neck cancer." We utilized RevMan software version 5.4 for the analysis. The study defined early oral feeding as feeding within 5-day post-operation, while late oral feeding was defined as feeding after the fifth postoperative day. Five papers that met the inclusion criteria were included in the analysis, with 1097 patients., Results: The results showed that early feeding was not significantly associated with postoperative fistulas (RR 0.49, 95% CI 0.23 to 1.05, p-value = 0.07), hematoma/seroma (RR 0.71, 95% CI 0.33 to 1.51, p-value = 0.38), or flap failure (RR 0.84, 95% CI = 0.38 to 1.87, p-value = 0.67). However, early oral feeding was significantly associated with shorter hospital stays than late oral feeding (MD -3.18, 95% CI -4.90 to -1.46, p-value = 0.0003)., Conclusion: No significant difference exists between early and late oral feeding regarding the risk of postoperative complications in head and neck cancer (HNC) patients who underwent free flap reconstruction surgery. However, early oral feeding is significantly associated with a shorter hospital stay than late oral feeding. Thus, surgeons should consider implementing early oral feeding after free flap reconstruction in HNC patients., (© 2024. The Author(s).)

In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods. Qualitative analysis shows the improvement in the contrast between the bone and the soft tissue using the proposed method over other state-of-the-art methods without introducing any artifacts or distortions. Classical and gradient-based quantitative analysis also show significant improvement in the fused image obtained using the proposed method over the five state-of-the-art methods. The percentage improvement in the standard deviation, average gradient, entropy, spatial frequency, QABF, and LABF of the proposed method over the best value obtained by the five state-of-the-art methods is 27.11%, 12.06%, 23.64%, 11.30%, 5.59%, and 13.70% respectively., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Despite progress in the prevention and diagnosis of cancer, current technologies for tumor detection present several limitations including invasiveness, toxicity, inaccuracy, lengthy testing duration and high cost. Therefore, innovative diagnostic techniques that integrate knowledge from biology, oncology, medicinal and analytical chemistry are now quickly emerging in the attempt to address these issues. Following this approach, here we developed a paper-based electrochemical device for detecting cancer-derived Small Extracellular Vesicles (S-EVs) in fluids. S-EVs were obtained from cancer cell lines known to express, at a different level, the αvβ6 integrin receptor, a well-established hallmark of numerous epithelial cancer types. The resulting biosensor turned out to recognize αvβ6-containing S-EVs down to a limit of 0.7*10 3 S-EVs/mL with a linear range up to 10 5 S-EVs /mL, and a relative standard deviation of 11%, thus it may represent a novel opportunity for αvβ6 expressing cancers detection., (© 2024. The Author(s).)

NASA uses a continuous risk management process to seek out new knowledge of spaceflight-induced risk to human health and performance. The evidence base that informs the risk assessments in this domain is constantly changing as more information is gleaned from a continuous human presence in space and from ongoing research. However, the limitations of this evidence are difficult to characterize because fewer than 700 humans have ever flown in space, and information comes from a variety of sources that span disciplines, including engineering, medicine, food and nutrition, and many other life sciences. The Human System Risk Board (HSRB) at NASA is responsible for assessing risk to astronauts and communicating this risk to agency decision-makers. A critical part of that communication is conveying the uncertainty regarding the understanding of the changes that spaceflight induces in human processes and the complex interactions between humans and the spacecraft. Although the strength of evidence grades is common in the academic literature, these scores are often not useful for the problems of human spaceflight. The HSRB continues to update the processes used to report the levels of evidence. This paper describes recent updates to the methods used to assign the level of evidence scores to the official risk postures and to the causal diagrams used by the HSRB., (© 2024. The Author(s).)

This paper presents open challenges and perspectives of propellant management for crewed deep space exploration. The most promising propellants are liquid hydrogen and liquid methane, together with liquid oxygen as an oxidizer. These fluids remain liquid only at cryogenic conditions, that is, at temperatures lower than 120 K. To extend the duration of space exploration missions, or even to enable them, the storage and refueling from a cryogenic on-orbit depot is necessary. We review reference missions, architectures, and technology demonstrators and explain the main operations that are considered as enablers for cryogenic storage and transfer. We summarize the state of the art for each of them, showing that many gaps in physical knowledge still need to be filled. This paper is based on recommendations originally proposed in a White Paper for ESA's SciSpacE strategy., (© 2024. The Author(s).)

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks that affect astronaut health and performance before, during and after spaceflight. The Human System Risk Board (HSRB) at NASA Johnson Space Center is responsible for setting the official risk posture for each of the human system risks and determining-based on evaluation of the available evidence-when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce spaceflight-induced risk to astronauts. The adverse effects of spaceflight begin at launch and continue throughout the duration of the mission, and in some cases, across the lifetime of the astronaut. Historically, research has been conducted in individual risk "silos" to characterize risk, however, astronauts are exposed to all risks simultaneously. In January of 2020, the HSRB at NASA began assessing the potential value of causal diagramming as a tool to facilitate understanding of the complex causes and effects that contribute to spaceflight-induced human system risk. Causal diagrams in the form of directed acyclic graphs (DAGs) are used to provide HSRB stakeholders with a shared mental model of the causal flow of risk. While primarily improving communication among those stakeholders, DAGs also allow a composite risk network to be created that can be tracked and configuration managed. This paper outlines the HSRB's pilot process for this effort, the lessons learned, and future goals for data-driven risk management approaches., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Objectives: Disparities in colorectal cancer (CRC) screening uptake by socioeconomic status have been observed in Canada. We used the OncoSim-Colorectal model to evaluate the health and economic outcomes associated with increasing the participation rates of CRC screening programs to 60% among Canadians in different income quintiles., Methods: Baseline CRC screening participation rates were obtained from the 2017 Canadian Community Health Survey. The survey participants were categorized into income quintiles using their reported household income and 2016 Canadian Census income quintile thresholds. Within each quintile, the participation rate was the proportion of respondents aged 50-74 who reported having had a fecal test in the past two years. Using the OncoSim-Colorectal model, we simulated an increase in CRC screening uptake to 60% across income quintiles to assess the effects on CRC incidence, mortality, and associated economic costs from 2024 to 2073., Results: Increasing CRC screening participation rates to 60% across all income quintiles would prevent 69,100 CRC cases and 36,600 CRC deaths over 50 years. The improvement of clinical outcomes would also translate to increased person-years and health-adjusted person-years. The largest impact was observed in the lowest income group, with 22,200 cases and 11,700 deaths prevented over 50 years. Increased participation could lead to higher screening costs ($121 million CAD more per year) and lower treatments costs ($95 million CAD less per year), averaged over the period 2024-2073., Conclusion: Increased screening participation will improve clinical outcomes across all income groups while alleviating associated treatment costs. The benefits of increased participation will be strongest among the lowest income quintile., (© 2024. The Author(s).)

The valorization of lignin-derived feedstocks by catalytic means enables their defunctionalization and upgrading to valuable products. However, the development of productive, safe, and low-waste processes remains challenging. This paper explores the industrial potential of a chemoenzymatic reaction performing the decarboxylation of bio-based phenolic acids in wet cyclopentyl methyl ether (CPME) by immobilized phenolic acid decarboxylase from Bacillus subtilis, followed by a base-catalyzed acylation. Key-to-success is the continuous control of water activity, which fluctuates along the reaction progress, particularly at high substrate loadings (triggered by different hydrophilicities of substrate and product). A combination of experimentation, thermodynamic equilibrium calculations, and MD simulations revealed the change in water activity which guided the integration of water reservoirs and allowed process intensification of the previously limiting enzymatic step. With this, the highly concentrated sequential two-step cascade (400 g·L -1 ) achieves full conversions and affords products in less than 3 h. The chemical step is versatile, accepting different acyl donors, leading to a range of industrially sound products. Importantly, the finding that water activity changes in intensified processes is an academic insight that might explain other deactivations of enzymes when used in non-conventional media., (© 2024. The Author(s).)

The indiscriminate use of petroleum-based polymers and plastics for single-use food packaging has led to serious environmental problems due the non-biodegradable characteristics. Thus, much attention has been focused on the research of new biobased and biodegradable materials. Yeast and fungal biomass are low-cost and abundant sources of biopolymers with highly promising properties for the development of biodegradable materials. This study aimed to select a preparation method to develop new biodegradable films using the whole biomass of Paecilomyces variotii subjected to successive physical treatments including ultrasonic homogenization (US) and heat treatment. Sterilization process had an important impact on the final filmogenic dispersion and mechanical properties of the films. Longer US treatments produced a reduction in the particle size and the application of an intermediate UT treatment contributed favorably to the breaking of agglomerates allowing the second US treatment to be more effective, achieving an ordered network with a more uniform distribution. Samples that were not filtrated after the sterilization process presented mechanical properties similar to plasticized materials. On the other hand, the filtration process after sterilization eliminated soluble and hydratable compounds, which produced a reduction in the hydration of the films., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes., (© 2024. The Author(s).)

U-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide range of applications. However, these variants primarily focus on enhancing the model's feature extraction capabilities, often resulting in increased parameters and floating point operations (Flops). In this paper, we propose GA-UNet (Ghost and Attention U-Net), a lightweight U-Net for medical image segmentation. GA-UNet consists mainly of lightweight GhostV2 bottlenecks that reduce redundant information and Convolutional Block Attention Modules that capture key features. We evaluate our model on four datasets, including CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, and BraTS 2018 low-grade gliomas (LGG). Experimental results show that GA-UNet outperforms other state-of-the-art (SOTA) models, achieving an F1-score of 0.934 and a mean Intersection over Union (mIoU) of 0.882 on CVC-ClinicDB, an F1-score of 0.922 and a mIoU of 0.860 on the 2018 Data Science Bowl, an F1-score of 0.896 and a mIoU of 0.825 on ISIC-2018, and an F1-score of 0.896 and a mIoU of 0.853 on BraTS 2018 LGG. Additionally, GA-UNet has fewer parameters (2.18M) and lower Flops (4.45G) than other SOTA models, which further demonstrates the superiority of our model., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

A boundary mode localized on one side of a finite-size lattice can tunnel to the opposite side which results in unwanted couplings. Conventional wisdom tells that the tunneling probability decays exponentially with the size of the system which thus requires many lattice sites before eventually becoming negligibly small. Here we show that the tunneling probability for some boundary modes can apparently vanish at specific wavevectors. Thus, similar to bound states in the continuum, a boundary mode can be completely trapped within very few lattice sites where the bulk bandgap is not even well-defined. More intriguingly, the number of trapped states equals the number of lattice sites along the normal direction of the boundary. We provide two configurations and validate the existence of this peculiar finite barrier-bound state experimentally in a dielectric photonic crystal at microwave frequencies. Our work offers extreme flexibility in tuning the coupling between localized states and channels as well as a new mechanism that facilitates unprecedented manipulation of light., (© 2024. The Author(s).)