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This study aimed to examine the satisfaction with, and feasibility and acceptability of, a virtual home safety assessment implemented for people living with dementia and their caregivers by a trained Occupational Therapist (OT). Seventeen assessments were conducted. Nine caregivers and one patient completed a satisfaction survey. Ten caregivers and the OT assessor completed semi-structured interviews, which were analyzed using qualitative content analysis. Survey respondents reported high levels of satisfaction with the virtual assessment; the majority (90%) reported being comfortable with the virtual modality. Caregivers perceived that their assistance was required to conduct the assessment virtually, and care recipients responded well to technology. In comparison to in-person visits, the virtual assessment was considered easier for people living with dementia, easier to access, and equivalent to an in-person visit. Virtual home assessments are easy to implement, feasible, acceptable, and an effective way to identify and manage safety risks. Given the health system pressures that have led to long wait times for in-home safety assessments, virtual administration can build capacity for home safety assessments, allowing more patients to be assessed sooner, particularly in rural and underserviced areas where travel distances impede timely access to assessment.

Gay, bisexual, and other men who have sex with men (MSM) remain disproportionately affected by sexually transmitted infections (STIs), including HIV, in the United States. Patient-facing digital tools, such as patient portals, can enhance STI prevention and care if designed to align with user needs and preferences. Thus, this study examined interest in specific portal features across various segments of MSM communities. Data were drawn from the 2022-2023 cycle of the American Men's Internet Survey. A randomized subset of participants was asked about interest in four portal features: (A) easier interpretation of lab results, (B) behavioral guidance after positive STI results, (C) ability to order HIV/STI home test kits, and (D) education on undetectable HIV viral load and transmission. To measure associations of sociodemographic, behavioral, and psychosocial variables with these multivariate feature interest outcomes we estimated adjusted prevalence ratios (aPRs) using multivariable Poisson regression models with robust error variance. The final analytic sample included 3,495 MSM (median age = 44 years; 70% White; 13% self-reported as living with HIV). Interest was highest for easier lab interpretation (64%), followed by behavioral guidance (43%), home test kit ordering (36%), and education on undetectable viral load (36%). Adjusted prevalence ratios for endorsing the integration of Undetectable=Untransmittable (U = U) education into portals was 24% (95%CI = 1.07, 1.45) and 37% (95%CI = 1.18, 1.60) higher among Black and Hispanic/Latino participants compared to White participants. Reporting anticipated healthcare stigma (aPR = 1.53; 95%CI = 1.33, 1.76) and socio-sexual networking app use (aPR = 1.17; 95%CI = 1.03, 1.32) were associated with greater interest in home test kits. People living with HIV (PLHIV) expressed lower interest in home test kits (aPR = 0.64; 95%CI = 0.53, 0.77) but higher interest in U = U education (aPR = 1.72; 95%CI = 1.96). Similarly, reporting suicidal thoughts was associated with a 19% higher aPR for interest in the inclusion of U = U education. MSM broadly support patient portal features that enhance understanding of health information, provide actionable guidance, and facilitate testing and education. The variability in preferences by HIV status, age, race/ethnicity, stigma experiences, and mental health underscore the need for tailored design and implementation of digital health tools among MSM communities.

With the rise of next-generation sequencing technologies, gaining new insights into the molecular profiles of cells and tissues has become easier than ever. These technologies enable the investigation of the complete transcriptome, encompassing both known and unknown RNA molecules at single-base pair resolution and at much lower costs. Since comprehensive proteome measurements for biological samples remain challenging, transcriptome sequencing offers valuable insights and new avenues for exploring biological samples. These RNA-sequencing (RNA-seq) experiments generate high-throughput data, making them useful for examining unique molecular profiles alongside biological pathways and networks in various tissues and cellular subsets. Moreover, with the increasing ease of conducting single-cell transcriptomics studies, there has been a surge in large datasets, which greatly enhance our understanding of biological systems in human health and disease. In this chapter, fundamental and advanced analysis methods and concepts for both bulk and single-cell transcriptomics are discussed.

Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.

Percutaneous left atrial appendage occlusion (pLAAO) is increasingly being adopted as an alternative to non-vitamin K antagonist oral anticoagulants (NOACs) for patients with atrial fibrillation. However, the current evidence does not justify this enthusiasm. Key limitations include wide non-inferiority margins in some of the trials, inclusion of components in the primary efficacy endpoint that are not directly influenced by pLAAO, making non-inferiority easier to achieve, limited statistical power to detect differences in ischemic stroke or systemic embolism, and an overstated bleeding advantage.

To evaluate whether an institutional shift from anticoagulation with heparin to bivalirudin in Impella 5.5 patients altered bleeding or thrombotic rates, transfusion dependency, or device biocompatibility. All patients supported by an Impella 5.5 between August 2022 and December 2024 were reviewed retrospectively. Heparin was used before December 2023, at which point the switch was made to bivalirudin. Groups were defined by anticoagulant initiated by postimplantation day 2, and patients with crossover or anticoagulation pauses for >2 days were excluded from the laboratory analyses. Baseline characteristics, anticoagulation details, complications, and clinical outcomes were compared between the heparin and bivalirudin groups. Biocompatibility was assessed via laboratory values recorded over the first 14 days of Impella support or until device explantation in patients supported for <14 days. Among the 168 patients, 83 (49%) received heparin and 85 (51%) received bivalirudin. Baseline characteristics were similar in the 2 groups (P > .05), as were rates of thrombotic events (13% vs 14%; P = .871), total bleeding events (37% vs 30%; P = .275), and transfusion requirements (57% vs 51%; P = .433). However, bivalirudin patients demonstrated significantly faster rates of platelet recovery and lactate dehydrogenase reduction after Impella implantation (all P < .05), had fewer insertion site bleeds that required reoperation (10% vs 1%; P = .015), and were more often within their partial thromboplastin time goal (on 24% vs 41% of device-supported days; P &#x2264; .001). Systemic anticoagulation with bivalirudin in Impella 5.5 patients is associated with clinically meaningful improvements in postoperative thrombocytopenia and biocompatibility, with easier management, fewer insertion site bleeds, and more time within the target anticoagulation range.

Ubiquitination, a post-translational modification, is a critical regulator of intracellular protein function. Ubiquitination modulates protein functions by promoting proteasomal degradation or altering subcellular localization through ubiquitin chain-dependent signaling. Here, we describe two cell lysis methods for detecting SMAD2 ubiquitination levels in HEK293T cells and compare their effectiveness in analyzing protein ubiquitination levels. Protein overexpression in cells was induced by transient transfection. The plasmids (HA-Ub, FLAG-SMAD2, and MYC-SMURF2) and transfection reagents were separately added to basal medium, mixed, and the mixture was added to the cells. Prior to harvest, MG132 was added to inhibit proteasomal degradation and enhance ubiquitinated protein accumulation. The primary divergence between the two experimental approaches is their cell lysis methods-the ice-bath method (performed at 4 &#xb0;C) and the heat-treatment method (involving incubation at 95 &#xb0;C), which substantially affects the efficiency of protein lysis. After cell lysis was completed, the cell lysate, agarose beads, and FLAG antibody were mixed and incubated at 4 &#xb0;C overnight. Ubiquitinated proteins were then detected by western blot analysis. Before detecting ubiquitinated proteins, a light-chain antibody was used for secondary antibody incubation. Then, ubiquitination bands were detected. The results show that both the ice-bath method and the heat-treatment method can be used to detect ubiquitination levels, while the heat-treatment method may make it easier to detect ubiquitination of SMAD2. This study delineates and compares two cell lysis methods for measuring ubiquitination levels in mammalian cells, using SMURF2/SMAD2 as a model, to assist researchers in selecting more appropriate methods for detecting the ubiquitination levels of substrate proteins.

Accurately mapping social and risk networks is critical for understanding and controlling infectious disease transmission, especially among hard-to-reach populations, such as people who inject drugs (PWID). Yet, empirical sociometric network ascertainment remains challenging and resource-intensive. Geometric deep learning may provide a scalable approach to inferring network structure from individual-level data, but its real-world performance and translation to epidemic modeling remain undercharacterized. We trained a graph neural network (GNN) to predict injection partnerships from a longitudinal network study of 2512 PWID in New Delhi, India, using demographic, behavioral, and spatial injection-venue features. We compared the GNN with exponential random graph models (ERGMs), evaluated structural similarity between empirical and imputed networks, and assessed validity in an independent PWID network. To examine translational utility, we calibrated a network-based HIV transmission model on either the empirical or GNN-imputed injection network and compared HIV incidence two years after scaling interventions across venues. The GNN achieves balanced predictive performance (accuracy 60.8%, precision 59.4%, recall 67.9%, F1 63.4%), outperforming ERGMs, and yields an imputed network with structural concordance to the empirical network (spectral similarity 0.87). Incorporating venue data increases accuracy from 51.0% to 60.8%. In the external cohort, the GNN maintains performance (F1: 61.3%) and captures structural changes. In the HIV model, calibrating on the GNN-imputed versus empirical network produces incidence curves that differ by at most 0.4 infections per 100 person-years. GNN-based network imputation can recover sufficient epidemiologically relevant network structure to preserve conclusions about HIV interventions, illustrating how geometric deep learning can support network-informed epidemic modeling when full sociometric ascertainment is infeasible. Many infections, including HIV, spread through patterns of contact between people. For people who inject drugs (PWID), this includes who shares injection equipment with whom. In our study, we used a cohort of PWID in New Delhi, India, in which injection partnerships were directly observed and recorded. These network maps are highly informative for understanding HIV transmission and planning prevention services, but collecting such data is time- and resource-intensive. We investigated whether machine learning could infer these network connections from information that is easier to collect, such as demographics, drug-use patterns, and injection venues. Using machine learning, we were able to closely replicate the real-world network. This suggests that machine learning methods could help plan healthcare interventions when full network data are unavailable.

Plants utilize receptor-like proteins and receptor-like kinases (RLPs/RLKs) to perceive and respond to a wide variety of invading pathogens and insect herbivores. While the strategies employed by microbial pathogens to suppress plant immunity have been well characterized, it remains unclear how herbivorous insects counteract receptor-mediated defenses. Here, we show that salivary effectors evolve independently in whiteflies and planthoppers to dampen RLP4-mediated plant immunity. RLP4, as a leucine-rich repeat RLP (LRR-RLP), confers plant resistance against herbivorous insects by forming the RLP4/SOBIR1 complexes. In the whitefly Bemisia tabaci, BtRDP, the Aleyrodidae-specific salivary sheath protein, interacts with RLP4 from multiple plant species and promotes its ubiquitin-dependent degradation. Overexpression of NtRLP4 in transgenic plants exerts a detrimental effect on B. tabaci by exploiting the crosstalk between the salicylic acid and jasmonic acid pathways. Conversely, overexpression of BtRDP or silencing of NtRLP4 effectively alleviates such negative effects. In planthopper Nilaparvata lugens, the Delphacidae-restricted salivary protein NlSP104 also targets and promotes the degradation of OsRLP4 from rice plants. These findings reveal convergent evolution of salivary proteins in insects and underscore the complex interactions between plants and herbivorous insects. Plants cannot escape from insects, so they rely on their own defense systems. One key strategy involves proteins on the cell surface that act as sensors. These sensors detect insect attacks and trigger protective responses within the plant. Scientists have long known that microbes can disable these sensors, thereby weakening plant defences. However, it has been unclear whether plant-eating insects use similar tactics. Many insects feed by inserting needle-like mouthparts (stylets) into plants and releasing saliva, which contains proteins capable of altering plant responses. To investigate this, Wang et al. studied two major crop pests: the whitefly Bemisia tabaci and the brown planthopper Nilaparvata lugens. They focused on a plant sensor called RLP4, a surface protein that helps plants recognize insect attack and activate defenses. The researchers found that both insects produce salivary proteins that bind to RLP4 and trigger its breakdown inside plant cells. This weakens the plant&#x2019;s defenses and makes feeding easier for the insects. Experiments in tobacco and rice plants showed that increasing RLP4 levels improved resistance to these pests. In contrast, reducing RLP4 levels or introducing the insect salivary proteins made plants more susceptible. Although the two insect proteins are unrelated, they perform the same function, suggesting that different insects have independently evolved similar strategies to overcome plant defenses. These findings reveal a shared mechanism used by plant-eating insects and provide new insight into plant&#x2013;insect interactions. In the future, this knowledge could help guide the development of crops with improved resistance to insect pests. However, further research is needed to determine how widespread this mechanism is and how it can be effectively applied in agriculture.

Similarity impacts all aspects of human behavior, from marital satisfaction to visual perception. Memory is no exception, with a large detrimental effect of similarity on order information recall for all studied features but the semantic ones. In fact, the insensitivity of short-term ordered recall performance to semantic similarity is challenging and a benchmark effect for evaluating memory models. Here, in five large-scale experiments, we revisited three key exceptions showing a detrimental effect of semantic similarity on short-term order recall and systematically tested if their findings were due to methodological issues. Despite the implementation of more stringent methodological controls, we consistently reproduced the detrimental effect of semantic similarity on order information across these five experiments. We then systematically reviewed the literature and found a substantial number of overlooked studies showing the presence of a detrimental effect of semantic similarity on order information recall. We identified task difficulty as a potential moderating variable accounting for previous inconsistencies. Across four additional experiments, we manipulated task difficulty by varying presentation speed and list length. As predicted, the detrimental effect of semantic similarity on order recall emerges under more difficult task conditions, and critically, when the task was easier, its effect on order recall vanished. The theoretical implications for contemporary models are reviewed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Biosensors with good sensing performances with regards to high sensitivity, specificity, shorter response time, the ability to be multiplexed, excellent stability and reproducibility, are always in high demand. As modern biosensors are often fabricated using bioreceptors immobilized on nanoparticles to achieve efficient signal transduction or easier handling, the nanoparticle-bioreceptor (nano-bio) interface has a significant impact on the final sensing metrics. However, the role of nano-bio interfaces in sensing performance could be better understood, to facilitate the rational design of high performing nano-bio based devices. Herein, we aim to provide some basic rules and considerations to optimize nano-bio interfaces to achieve better detection performance when fabricating biosensors. The impact of the nano-bio interfaces on sensing characteristics is discussed from the perspective of bioreceptor-analyte interaction. Four interfacial parameters are included in this review: (1) the conformation of bioreceptors, (2) the coverage of the bioreceptors, (3) composition of mixed ligands, such as bioreceptors and other functional molecules and (4) spatial distribution of bioreceptors on nanoparticle surfaces. Methods to tailor these four interfacial factors are systematically investigated. In parallel, how these tailored nano-bio factors improve the sensing performances is emphasized with corresponding biosensor examples. The analytical methods for characterization of nano-bio interfaces are summarized, particularly at the single particle level. Additionally, the integration of artificial intelligence (AI) with nano-bio interfaces is discussed, highlighting how AI can improve nano-bio interfacial design. Finally, future perspectives on the role of nano-bio interfacial design in enhancing sensing capabilities are presented. This review aims to elucidate the relationship between nano-bio interfacial factors and sensing performances, as well as strategies for achieving precisely controlled nano-bio interfaces, which facilitates the rational design of high-performance biosensors.

With the emergence of generative AI models such as ChatGPT, a&#xa0;new phase of scientific work is also beginning in orthopedics and trauma surgery. As a&#xa0;language-based deep learning model (LLM), ChatGPT offers a&#xa0;wide range of possible applications-especially in the creation, translation, and optimization of scientific texts. It supports authors in finding ideas, linguistic elaboration, and can even be used to check for plagiarism. It is a&#xa0;particularly valuable tool for non-native speakers. However, despite all the opportunities, its use involves considerable risks; studies show a&#xa0;high rate of incorrect or invented references. In addition, journals are sometimes flooded due to mass publication as a&#xa0;result of easier text generation. The scientific discourse, therefore, calls for clear rules on the use of LLM-particularly with regard to transparency, authorship, and the integrity of scientific work. Mit dem Aufkommen generativer KI-Modelle wie ChatGPT beginnt auch in der Orthop&#xe4;die und Unfallchirurgie eine neue Phase wissenschaftlichen Arbeitens. Als sprachbasiertes Deep-Learning-Modell (LLM) bietet ChatGPT vielf&#xe4;ltige Anwendungsm&#xf6;glichkeiten&#xa0;&#x2013; insbesondere bei der Erstellung, &#xdc;bersetzung und Optimierung wissenschaftlicher Texte. Es unterst&#xfc;tzt Autoren bei der Ideenfindung, der sprachlichen Ausarbeitung und kann selbst bei der Plagiatspr&#xfc;fung eingesetzt werden. Besonders f&#xfc;r Nichtmuttersprachler stellt es ein wertvolles Hilfsmittel dar. Doch trotz aller Chancen birgt der Einsatz erhebliche Risiken: Studien belegen eine hohe Rate fehlerhafter oder erfundener Quellenangaben. Zudem kommt es teilweise zu einer &#xdc;berflutung der Journals aufgrund von Massenpublikation durch die leichtere Textgenerierung. Der wissenschaftliche Diskurs fordert daher klare Regeln zur Nutzung von LLM&#xa0;&#x2013; insbesondere im Hinblick auf Transparenz, Autorenschaft und Integrit&#xe4;t der wissenschaftlichen Arbeit.

The traditional extraction method of Lycium barbarum pigment has some problems, such as long extraction time, large solvent consumption, low extraction efficiency and serious environmental pollution, so it is necessary to develop a new extraction method with shorter treatment time, lower cost, easier operation and environmental friendliness than the traditional method. In this study, the pigment from Ningxia Lycium barbarum was extracted, purified and identified by deep eutectic solvents assisted by surfactant. The pigments from Lycium barbarum from Ningxia were extracted, purified and identified by surfactant-assisted deep eutectic solvents (DES). The maximal pigments extraction rate was obtained under optimal conditions (3% surfactant Tween 20 (v/v), menthol-lactic acid with water content of 50% (molar ratio of 1:4), 1% material-liquid ratio (m/v), the water bath at 30 &#x2103; for 2.5&#xa0;h). The pigments extraction rate by surfactant-assisted DES was 6-folds higher than traditional extraction agents (ethanol), and reached 32.09&#xa0;mg/g. The HPLC detection results showed that the main component of the pigments was corn lutein dipalmitate with 348.861&#xa0;mg/g. Furthermore, the purified pigments exhibited excellent free radical scavenging activities for DPPH and ABTS+, and &#xb7;OH radicals, indicating its high antioxidant activity.

Achondroplasia is a condition that affects bone growth and causes shorter height. People with achondroplasia face many challenges in their day-to-day lives and have different views on and expectations about treatments. Understanding these experiences, views, and expectations is important for researchers to develop appropriate treatments to meet people&#x2019;s needs. We interviewed 15 people (aged 12&#x2013;20 years) with achondroplasia and 15 caregivers of people with achondroplasia in the United States and South Korea. People with achondroplasia were asked about symptoms, how achondroplasia affects their lives, experiences with treatment, and potential future treatments. Caregivers responded for the person with achondroplasia they cared for. Common health issues included sleep apnea, teeth misalignment, obesity, and ear infections, and the most common symptoms were pain and snoring. Impacts on quality of life mainly included difficulties with physical activities, walking, completing daily routines, and social activities. When asked about treatments, increased height and reduced pain were the most desired potential benefits. While people with achondroplasia and caregivers were generally happy with treatments, many wished for easier ways to take treatment, ideally with fewer injections. These findings may help guide future research and lead to better treatments to meet the needs of people with achondroplasia.

A position paper released by the European Association of Nuclear Medicine emphasised the need for multidisciplinary engagement to establish dosimetry-based personalised treatment in Radionuclide therapy (RNT). The uncertainty analysis results often ignored in routine clinical practice should be incorporated into the dose calculations to improve the efficacy and accuracy of treatment. In this study, patients with haematological malignancies undergoing radioimmunotherapy were evaluated. Our study aimed to calculate the uncertainties associated with each parameter of the single time point (STP) dosimetry chain and compare the with multiple time points (MTP) in the bone marrow and liver results. 28 patients received an intravenous injection of 111In-besilesomab (0.17&#x2009;&#xb1;&#x2009;0.01GBq) for pre-therapeutic dosimetry and were subsequently treated with 90Y-besilesomab(2.43&#x2009;&#xb1;&#x2009;0.53GBq). A dosimetry analysis was performed on bone marrow (BM) and liver with MTP and STP. We investigated the uncertainty in population mean effective half-life, volume, recovery coefficient, counts, measured activity, fitting parameters, time-integrated-activity, S-factors, and absorbed dose (AD) for a group of patients. The mean absorbed dose per unit administered activity (DpA) to BM was 5.8&#x2009;&#xb1;&#x2009;1.7 mGy/MBq with MTP and 5.8&#x2009;&#xb1;&#x2009;1.6 mGy/MBq with STP, and to the liver was 2.9&#x2009;&#xb1;&#x2009;1.9 mGy/MBq with MTP and 3.1&#x2009;&#xb1;&#x2009;2.4 mGy/MBq with STP. The mean fractional uncertainty associated with total absorbed dose to BM was 13.18&#x2009;&#xb1;&#x2009;3.46% with MTP and 18.75&#x2009;&#xb1;&#x2009;3.22% with STP, and to liver was 5.77&#x2009;&#xb1;&#x2009;3.13% with MTP and 49.78&#x2009;&#xb1;&#x2009;25.36% with STP. A moderate positive relationship (R2&#x2009;=&#x2009;0.7) was noted between post-injection acquisition time and AD uncertainty with STP for BM, whereas a strong positive relationship (R2&#x2009;=&#x2009;1) was noted for the liver. The absorbed dose uncertainty in STP was significantly higher compared to the MTP. Incorporating the uncertainty analysis for STP dosimetry parameters in routine clinical practice is strongly recommended. The accuracy in the acquisition time, population-based half-life and fitting function for time activity curve is vital for minimising uncertainty in STP dosimetry, which is less time-consuming and easier to implement in clinical practice than MTP.

The on-orbit calibration for optical parameters of the space camera is the key to guaranteeing the imaging quality and navigation accuracy. The conventional on-orbit calibration methods are generally constructed based on the star angular distance invariance, which has high computational complexity, due to the complex matrix operation process. The computational and storage resources of spacecraft are severely limited, and the method is hard to realize. This paper proposes an efficient calibration method for the space camera. The core step is to solve the trace of the star matrix constructed by the observed star vectors. The computation process is essentially the addition of several scalars, and it is easier to compute than other calibration models. Besides, the traditional extended Kalman filtering algorithm needs to invert the high-order matrix, which is difficult to autonomously realize on spacecraft with severe resource constraints. The sequential extended Kalman filtering algorithm can avoid the problem, which can quickly and efficiently estimate the optical parameters. Simulation results demonstrate that the internal calibration eliminates most imaging distortion and provides an accurate mapping relationship between the imaging points and the observational direction of the target, with high computational efficiency and calibration accuracy.

Kolmogorov-Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications.

Decisions need evidence, and for healthcare decisions, the evidence decision-makers often want is a systematic review. However, reviews often lack clarity about who is represented within the evidence they synthesize, which limits understanding of how findings apply to diverse populations. PRO EDI was developed to help systematic review authors extract and report equity-related participant data to support greater transparency and more informed judgments about applicability. PRO EDI was developed iteratively between August 2022 and March 2024 and was conceptualized as a way of making it easier to use PROGRESS-Plus, a framework to assess equity in reviews. An initial draft was created and then discussed and revised in collaboration with an international advisory group. A relatively mature version of the tool was then presented to a meeting of the Cochrane Health Equity Thematic Group. The modified version that emerged from that meeting was considered v1 of PRO EDI. PRO EDI has two main components: a participant characteristics table and guidance on how to use the extracted characteristics data within reviews. PRO EDI recommends that six participant characteristics should be extracted for all included studies in a review: age, sex, gender, ethnicity, race and ancestry, socioeconomic status, and location. Other characteristics (e.g., disability) may be important for some reviews. PRO EDI is relevant for all systematic reviews, not just those with an equity focus. The tool has been piloted in several reviews and is publicly available via Trial Forge. PRO EDI gives systematic review authors a consistent way of deciding which participant characteristics to extract from included studies to support equity-related judgments in their results and discussion. It also suggests ways in which those judgments can be presented.