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Unseen translation forms a central part of assessment and teaching both in the Latin GCSE and A Level. Developing skills for the unseen translation presents several challenges for the classroom teacher; unseens can be introduced using a scaffolded approach, yet pupils must learn to develop independence for the examination. Unseens can often take an entire lesson, or more to translate, precluding the opportunity for meaningful, immediate feedback. Furthermore, classes of mixed- ability students often suffer from staggered completion rates and unequal attention being divided among students. Within the curriculum, unseen classes can additionally suffer from feeling severed from the specification as passages contain unfamiliar material, the content is discrete from the set texts, and lack of an overarching framework for approaching unseens can make them feel irrelevant to pupils. Therefore, striking a balance between productive support for in-class unseen translation practice and nurturing pupil confidence requires a clear strategy. This article investigates the effect of two methods of formative assessment used in preparation for an unseen translation lesson with a year 12 class. Reflecting on the results of the investigation, this article discusses the opportunities different tasks may afford the Latin teacher for developing a vocabulary curriculum that supports long-term retention of vocabulary, increases the efficiency of unseen classes, and allows the unseen passage to be received as part of a wider framework of learning.

ObjectiveTo develop and validate a nomogram for predicting non-retrieval of the short-term retrievable inferior vena cava (IVC) filters.MethodsIn this study, univariate and multivariate logistic regression analyses were performed to identify predictive factors of short-term retrievable filter (Aegisy or OptEase) non-retrieval, and a nomogram was then established based on these factors. The nomogram was created based on data from a training cohort and validated based on data from a validation cohort. The predictive value of the nomogram was estimated using area under the curve (AUC) and calibration curve analysis (Hosmer-Lemeshow test).ResultsA total of 1,321 patients who had undergone placement of short-term retrievable filters (Aegisy or OptEase) were included in the analysis. The overall retrieval rate was 68.7%. Age, proximal and distal deep vein thrombosis (DVT) vs. distal DVT, active cancer, history of long-term immobilization, VTE was detected in the intensive care unit, active/recurrent bleeding, IVC thrombosis, and history of venous thromboembolism were independent predictive risk factors for non-retrieval of filters. Interventional therapy for DVT, acute fracture, and interval of ≥14 days between filter placement and patient discharge were independent protective factors for non-retrieval of filters. The nomogram based on these factors demonstrated good ability to predict the non-retrieval of filters (training cohort AUC = 0.870; validation cohort AUC = 0.813.ConclusionThis nomogram demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians identify patients who are not candidates for short-term retrievable filter placement and help clinicians make timely, individualized decisions in filter choice strategies.

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios.

The use of embolic protection devices (EPD) in peripheral vascular procedures with higher risk of distal embolic shower has gained wider acceptance with evidence mounting in favor of its use. We report our unique experience on the circumstances leading to and management of entrapment of an EPD within peripheral stent during management of a patient with critical limb ischemia.

Abstract Sketch-based cross-domain visual data retrieval is the process of searching for images or 3D models using sketches as input. Achieving feature alignment is a significantly challenging task due to the high heterogeneity of cross-domain data. However, the alignment process faces significant challenges, such as domain gap, semantic gap, and knowledge gap. The existing methods adopt different ideas for sketch-based image and 3D shape retrieval tasks, one is domain alignment, and the other is semantic alignment. Technically, both tasks verify the accuracy of extracted features. Hence, we propose a method based on the global feature correlation and the feature similarity for multiple sketch-based cross-domain retrieval tasks. Specifically, the data from various modalities are fed into separate feature extractors to generate original features. Then, these features are projected to the shared subspace. Finally, domain consistency learning, semantic consistency learning, feature correlation learning and feature similarity learning are performed jointly to make the projected features modality-invariance. We evaluate our method on multiple benchmark datasets. Where the MAP in Sketchy, TU-Berlin, SHREC 2013 and SHREC 2014 are 0.466, 0.473, 0.860 and 0.816. The extensive experimental results demonstrate the superiority and generalization of the proposed method, compared to the state-of-the-art approaches. The in-depth analyses of various design choices are also provided to gain insight into the effectiveness of the proposed method. The outcomes of this research contribute to advancing the field of sketch-based cross-domain visual data retrieval and are expected to be applied to a variety of applications that require efficient retrieval of cross-domain domain data.

Abstract Memory retrieval can become difficult over time, but it is important to note that memories that appear to be forgotten might still be stored in the brain, as shown by their occasional spontaneous retrieval. Histamine in the central nervous system is a promising target for facilitating the recovery of memory retrieval. Our previous study demonstrated that histamine H3 receptor (H3R) inverse agonists/antagonists, activating histamine synthesis and release, enhance activity in the perirhinal cortex and help in retrieving forgotten long-term object recognition memories. However, it is unclear whether enhancing histaminergic activity alone is enough for the recovery of memory retrieval, considering that H3Rs are also located in other neuron types and affect the release of multiple neurotransmitters. In this study, we employed a chemogenetic method to determine whether specifically activating histamine neurons in the tuberomammillary nucleus facilitates memory retrieval. In the novel object recognition test, control mice did not show a preference for objects based on memory 1 week after training, but chemogenetic activation of histamine neurons before testing improved memory retrieval. This selective activation did not affect the locomotor activity or anxiety-related behavior. Administering an H2R antagonist directly into the perirhinal cortex inhibited the recovery of memory retrieval induced by the activation of histamine neurons. Furthermore, we utilized the Barnes maze test to investigate whether chemogenetic activation of histamine neurons influences the retrieval of forgotten spatial memories. Control mice explored all the holes in the maze equally 1 week after training, whereas mice with chemogenetically activated histamine neurons spent more time around the target hole. These findings indicate that chemogenetic activation of histamine neurons in the tuberomammillary nucleus can promote retrieval of seemingly forgotten object recognition and spatial memories.

Abstract Periodontal instrument fractures are rare events in dentistry, with limited literature available on their occurrence and management. This case report highlights an incident involving the fracture of a periodontal sickle scaler blade during manual instrumentation for the removal of calculus. The fracture occurred during instrumentation on the mesial surface of the maxillary right second molar, and the separated blade was subsequently pushed into the sulcus. A radiographic assessment was performed to verify the precise location of the fractured segment. Following confirmation, the broken blade was subsequently retrieved using curved artery forceps. The case report highlights factors contributing to instrument fractures, emphasizing the importance of instrument maintenance, sterilization cycles, and operator technique. Ethical considerations regarding patient disclosure, informed consent, and instrument retrieval methods are well discussed. This case underscores the importance of truthful communication, the proper use of instruments, equipment maintenance in dentistry, and the significance of ongoing professional development to enhance treatment safety, proficiency, and ethical standards in dental care.

The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization. The present HRM systems are centralized in nature and their verifiable credential system is ineffective; this leads to the intentions of internal data sabotage or internal threats. Besides, the organizations' biases also become more prominent.In this paper, we address the above-mentioned problems with a blockchain framework for HRM to utilize the privacy of data access through a Privacy Information Retrieval (PIR) process. To be specific, our proposed framework called Blockchained piR of resOurces as humaN (BRON), is the first blockchain framework to show an effective mechanism to access data from organizations globally without hampering privacy. BRON uses a generalized user registration process to use the services of data access and in the background, it uses Zero-Knowledge Proofs (ZKPs) for global verification and PIR for privacy-based data retrieval. More specifically, credential verification and ZKP-based PIR are the highlights of our proposed BRON. Another interesting aspect of BRON is the use of Proof-of-Authority (PoA) to validate the anonymity and unlinkability of any HR operation. Finally, BRON has also contributed with a smart contract to incentivize the employees. BRON is very generic and easily be customizable as per the HR requirements. We run a set of experiments on BRON and observe that it is successful in providing privacy-assured data access and decentralized human resource data management. Overall, BRON provides 30% reduced latency and 35% better throughput as compared to the existing blockchain solutions in the direction of HRM.

Cerebral protection devices (CPDs) often were applied to improve the safety of carotid artery stenting, but the CPD itself may cause fatal complications. We report a case of a retained CPD that was an Emboshield NAV6 device left in situ, which was retrieved successfully with esophageal biopsy forceps.

Abstract Background The retrieval of inferior vena cava (IVC) filter is essential for preventing complications associated with the device. Advanced techniques have been developed to improve the success rate of retrieving tip-embedded filters. The forceps technique is frequently used to address this issue. Case presentation We present a case study of two patients who underwent a successful tip-embedded IVC filter retrieval using a modified forceps technique, which has not been previously reported. This technique involves using a wire loop under the filter tip and a forceps to grasp the filter shoulder. By pulling the wire loop and pushing the forceps in counterforce, the filter tip is straightened and aligned with the vascular sheath. The vascular sheath can then dissect the filter tip out from the caval wall and get inside the sheath to complete the retrieval. Conclusions The modified forceps technique we present here offers a new solution for the complex retrieval of IVC filters.

A novel framework of feature generation for Content based image retrieval (CBIR) is proposed. This system is realized on Cyclic transform Analysis (CTA). It introduces statistical descriptors in the signals frequency domain. Then the CT of data is computed by Semi supervised algorithm (SSA) which is a simple & efficient algorithm. Presented Features are Norm-1 & energy CTA extracted from different sections of bi-frequency plane. This layout illustrate good characteristic in database. In addition, this manuscript illustrates a novel framework for generating textural and spatial information, and higher retrieval percentages. The textural features extracted with proposed CTA utilizing first & second moments among the image tiles is so effective in data processing. Spatial information is extracted utilizing decent field matrix (DFM). After that, moments are computed from DFM to get spatial features. The composition of the textural features and conjunction with the spatial information leads to a fantastic features matrix for retrieval. The experimental results on database guaranty the method efficiency on all classes of database with more than 10000 image. For measuring the distance of features a simple matching system based on Minkowski & Canberra distances is introduced. The results are compared with previous scholars and retrieval percentage is increased more than 10% in comparison with previous systems.

Background: A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience. Methods: In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses. Results: Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.

Sea surface temperature (SST) is a vital oceanic parameter that significantly influences air–sea heat flux and momentum exchange. SST datasets are crucial for identifying and describing both short-term and long-term climate perturbations in the ocean. This article focuses on cloud detection and SST retrievals in the Western Pacific Ocean, using observations obtained by the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C satellite. To distinguish between clear-sky and overcast regions, reflectance after sun glint correction and brightness temperature are used as inputs for an alternative decision tree (ADTree). The accuracy of cloud detection is 93.85% for daytime and 91.98% for nighttime, respectively. Application of the cloud detection algorithm improves the accuracy and data availability (spatiotemporal coverage) of SST retrievals. We implement a nonlinear algorithm to retrieve the SST and validate these retrieved values against buoy measurements of SST. Comparisons are conducted for measurements within ±1 h and 0.01° × 0.01° of the retrieval. During the day, the bias and standard deviation (SD) are −0.01 °C and 0.63 °C, respectively, while at night, they stand at −0.08 °C and 0.71 °C, respectively. Furthermore, the intercomparison between the SST products derived from the moderate-resolution imaging spectroradiometer (MODIS) onboard Terra and the results are conducted. During the day, the bias and SD are 0.03 °C and 0.42 °C, respectively, whereas at night, they are 0.25 °C and 0.76 °C, respectively. This article improves the accuracy and applicability of the SST retrieved from the COCTS thermal infrared channels.

Video analysis has attracted the attention of many researchers because of the growing need for multimedia information retrieval for computer vision applications. Content based retrieval and object-based video retrieval are challenging because of the poor feature representation of the objects and the low number of multiclass video datasets available. Even though there have been significant advances in generic object detection & information retrieval tasks, performance of object based indexing & retrieval falls due to the low number of representative video dataset and lack of information on the frequency of objects. In order to facilitate the progress of multimedia information retrieval in the field of computer vision related application research, we present the multiclass object detection datasets. This dataset consists of 732 videos from YouTube with 5 object classes and each video is about 2 minutes long and total size of the dataset being approximately 8.5 GB. In this article, we will provide the baseline evaluation results of two of the latest object detection algorithms used to evaluate the newly created multidimensional object detection dataset. The paper presents the performance metrics of object detection methods at various time frames. Accuracy remains consistently high, ranging from 93.98% at the 5th frame to 84.17% at the 30th frame, suggesting a generally strong ability to make correct predictions. The dataset is publicly available at https://drive.google.com/file/d/1BHVsB38vbu9LUY03XlFWY2o_gxEiIKY7/view?usp=sharing.

The commonly used water depth retrieval models do not consider the influence from instantaneous tidal fluctuations during satellite flying, which causes bathymetric errors. For this reason, the wavelet spline instantaneous tidal correction model, which is applied to calculate accurate tidal heights for any moment of satellite flight, is established for correction of water depth retrieval from satellite multispectral images. Two study areas, Saipan Island and Molokai Island in the USA, are selected, and Landsat 8 images are used to verify the accuracy achieved by the proposed model. Fifteen bathymetric check points and three check lines in each of study area are used to further verify the absolute and relative accuracy achievable for the proposed model. The experimental results from our model demonstrated that the mean absolute errors (relative to reference data) in study area, Saipan, and Molokai are 1.42 and 1.5 m, both of which decrease by 33.33% and 27.54%, respectively. The root mean square errors (RMSEs) are 1.67 and 1.83 m, both of which decrease by 31.84% and 27.09%, respectively. Therefore, it can be concluded that the proposed model can largely increase the accuracy of water depth retrieval by satellite multispectral images.

Blockchain networks provide a reliable and secure mode of communication due to their decentralized and distributed nature. The emergence of amalgamated blockchain-based internet-of-things (IoT) systems has generated a huge amount of data to be online. Though blockchains show potential for ensuring transparency, traceability, and immutable records, the privacy of online data in blockchains becomes a question. The privacy of blockchain transactions is at stake as various privacy breaching methods are used by attackers such as linking the transactions, deanonymization, etc. However, the benefits of blockchain make the technology a dominator in the present and future technological paradigms. The other side of the coin deals with privacy information retrieval (PIR), which is necessary to retrieve private information from servers without much revealing. However, the conjunction of blockchain privacy and PIR is very critical and an important aspect of blockchain solutions. In this present survey, we pioneer in analyzing the privacy factors of existing blockchain solutions. We discuss the privacy parameters and important privacy enhancement techniques for blockchains comprehensively. We show the applicability of privacy in various domains including e-commerce, supply chain, healthcare, and IoT. We also discuss PIR-related issues and solutions in the existing literature. We highlight open research problems and discuss the benefits of collaborating with PIR and blockchain systems to improve privacy in blockchains. Our survey is beneficial for academia and industries to be aware of the present status of privacy solutions in blockchains and to address the identified loopholes to make the systems better.

This position statement on the management of separated instruments, endorsed by the Indian Endodontic Society, aims to provide a brief perspective on the challenges faced during shaping of the complex root canal system, and how the inadvertent separation of a broken instrument must be dealt with. The need for referral of such cases to specialist endodontists who are trained to use advanced instrument retrieval systems with the aid of the dental operating microscope is also highlighted.

Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can “dream” by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like “delusion”) occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

Mandarin qián and English forward are semantically equivalent in the domain of Space, but could be semantically opposite in the domain of Time. In other words, equivalent spatial lexical items could convey opposite temporal concepts. What temporal concepts conveyed by qián and forward would be retrieved by Mandarin–English (M–E) bilinguals with different language proficiencies? Drawing a sample from college students in Mainland China, this study examines how L1 and L2 proficiencies would affect M–E bilinguals' retrieval of temporal concepts by examining their interpretation of the Mandarin temporal metaphor of qián and the English temporal metaphors of forward. The results show that L1 temporal concepts would be retrieved more frequently than L2 temporal concepts regardless of the testing languages, that L1 and L2 proficiencies were not predictors for the way of interpretation, and that the higher L2 proficiency group could retrieve temporal concepts in line with the testing languages with higher accuracy than the lower L2 proficiency group. The findings suggest that bilinguals with higher L2 proficiency may be able to represent temporal concepts with language tags or may have an attentional and/or inhibitory control advantage.

Background Contextual cueing refers to the phenomenon in which individuals utilize frequently encountered environmental contexts, comprised of distractors, as cues to expedite a target search. Due to the conflict between the widespread occurrence of contextual cue transfer and the observed impact of changing the identity of distractors on contextual cue learning, the content of contextual cue representations remains contentious. Considering the independent nature of contextual cue learning and expression, our proposition is twofold: (1) Contextual cue representations are stimulus-specific, and (2) their expression is highly flexible. Methods To validate the model, two experiments were conducted. Experiment 1 aimed to confirm the hypothesis that contextual cue representations are stimulus-specific. We manipulated the identity consistency of distractors within repeated scenes during contextual cue learning. Difficulty in contextual cue learning under the identity-changing condition would suggest the necessity of identity within contextual cue representation, indicating the stimulus-specific nature of these representations. Experiment 2 was designed to affirm the conclusion of Experiment 1 and explore the flexibility in the expression of contextual cue representations. This experiment comprised two phases: learning and testing. During the learning phase, participants were exposed to two sets of repeated scenes in different colors under two learning conditions: load and no-load. Working memory load was introduced to interfere with the expression to prevent it from becoming automatic. In the subsequent testing phase, the colors of the two scene sets were interchanged to impede retrieval based on identity. If both load and no-load conditions demonstrate similar levels of contextual cue effects during the testing phase, it implies the flexibility in the expression of contextual cue representations and confirms the conclusion of Experiment 1. Results In Experiment 1, a notable contextual cue learning effect was observed under the identity-consistent condition (p = 0.001). However, this effect was not evident under the identity-changing condition (p = 0.286). This finding strongly supports the stimulus-specific nature of contextual cue representation. In Experiment 2, the contextual cueing effect appeared but did not show a significant difference between the two conditions (t(23) = 0.02, p = 0.987, BF10 = 0.215), indicating the cognitive system’s ability to flexibly redefine retrieval cues. This adaptability aligns with our hypothesis and confirms the high flexibility in the expression process of contextual cue representations and confirms the conclusion of Experiment 1.

BackgroundThe claustrum (CLA), a subcortical area between the insular cortex and striatum, innervates almost all cortical regions of the mammalian brain. There is growing evidence that CLA participates in many brain functions, including memory, cognition, and stress response. It is proposed that dysfunction or malfunction of the CLA might be the pathology of some brain diseases, including stress-induced depression and anxiety. However, the role of the CLA in fear memory and anxiety disorders remains largely understudied.MethodsWe evaluated the influences of neurotoxic lesions of the CLA using auditory-cued fear memory and anxiety-like behaviors in rats.ResultsWe found that lesions of anterior CLA (aCLA) but not posterior CLA (pCLA) before fear conditioning attenuated fear retrieval, facilitated extinction, and reduced freezing levels during the extinction retention test. Post-learning lesions of aCLA but not pCLA facilitated fear extinction and attenuated freezing behavior during the extinction retention test. Lesions of aCLA or pCLA did not affect anxiety-like behaviors evaluated by the open field test and elevated plus-maze test.ConclusionThese data suggested that aCLA but not pCLA was involved in fear memory and extinction. Future studies are needed to further investigate the anatomical and functional connections of aCLA subareas that are involved in fear conditioning, which will deepen our understanding of CLA functions.

In the literature on human action control, it is assumed that features of stimuli (S) and responses (R) are integrated into internal representations (so-called event files) that are involved in the execution of an action. Experimentally, the impact of this integration on action control is typically analyzed via S-R binding effects. Recent theorizing in the BRAC framework (Frings et al., 2020) suggests to disentangle the processes of S-R binding proper from S-R retrieval as two independent components contributing to S-R binding effects. Since the literature on age effects on S-R binding effects is scarce and does not provide information on whether the existing findings about the two processes can be generalized to older age groups, this is the first study addressing the effects of older age separately on S-R binding proper vs. S-R retrieval. In two established variants of S-R binding tasks (cumulative n = 262), we contrasted binding (by using a saliency manipulation at the time of binding proper) versus retrieval processes (by manipulating the onset of the distractor at the time of retrieval), replicating previous results in younger (18–30 years) and also in older healthy controls (50–70 years). We therefore found no evidence for age effects on S-R binding proper or S-R retrieval. We thus conclude that the processes contributing to S-R binding effects are – at least in the age groups analyzed in this study – robust and age-independent. Statement of significance: In human action control, binding proper and retrieval of features in stimulus-response episodes typically lead to so-called S-R binding effects. Against the background of recent theorizing, binding proper and retrieval should be studied independently. In this article, we ran a younger and an older age group and analyzed possible age-related differences in integration or retrieval. Both groups showed the expected pattern for binding and retrieval as expected from the literature.

Background: This study examines the surface changes of stainless steel miniplates and screws after their retrieval from patients who underwent maxillofacial trauma and orthognathic surgery. The assessment focuses on comparing the alterations in these materials, aiming to contribute to our understanding of their durability and performance in clinical settings. Materials and Methods: A total of 60 stainless steel miniplates and screws were collected from 30 patients who had previously undergone either maxillofacial trauma or orthognathic surgery. The retrieved miniplates and screws were carefully removed from the patients and cleaned to remove any organic debris. Each specimen was then examined for surface changes. Surface changes were evaluated using visual inspection, scanning electron microscopy (SEM), and roughness measurements. Visual inspection provided a qualitative assessment, while SEM allowed for a more detailed examination of the surfaces. Roughness measurements were conducted using a profilometer. Results: Visual inspection revealed varying degrees of surface changes in the retrieved miniplates and screws. These changes included scratches, corrosion, and discoloration. SEM analysis confirmed the presence of surface alterations, with some specimens showing more significant damage, such as pitting and cracks. Roughness measurements indicated an increase in surface roughness for both miniplates and screws, suggesting that the surfaces had become less smooth. Conclusion: This comparative study of stainless steel miniplates and screws retrieved from maxillofacial trauma and orthognathic surgery patients demonstrated that these materials undergo surface changes over time.

Abstract Background For critically ill and injured patients, timely access to definitive care is associated with a reduction in avoidable mortality. Access to definitive care is significantly affected by geographic remoteness. To overcome this disparity, a robust critical care transport (CCT) or retrieval system is essential to support the equity of care and overcome the tyranny of distance. While critical care transport or retrieval systems have evolved over the years, there is no universally accepted system or standard, which has led to considerable variation in practices. The objective of this mixed-methods study was to identify and explore the current clinical, operational, and educational practices of government and charity-funded critical care transport and retrieval organizations operating across access- and weather- challenged geography. Methods This study utilized a mixed-methods approach comprising a rapid review of the literature and semi-structured interviews with identified subject matter experts (SME). Results A total of 44 articles and 14 interviews with SMEs from six different countries, 12 different services/systems, and seven operational roles, including clinicians (physician, paramedic, and nurse), educator, quality improvement, clinical governance, clinical informatics and research, operations manager, and medical director were included in the narrative analysis. The study identified several themes including deployment, crew composition, selection and education, clinical governance, quality assurance and quality improvement and research. Conclusion This mixed-methods study underscores the paucity of literature describing current clinical, operational, and educational practices of government or charity-funded CCT or retrieval programs operating across access- and weather- challenged geography. While many common themes were identified including clearly defined mission profiles, use of dedicated or specialized transport teams, central coordination, rigorous selection processes, service-sponsored graduate education, and strong clinical governance, there is little consensus and considerable variation in current practices. Further research is needed to identify and harmonize best practices within the CCT and retrieval environments.

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research.

A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74° N, −74.03° E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014–2023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA’s AURA satellite. COD results are also provided.

Despite the high diversity of elasmobranch fishes in the Balearic Islands, knowledge of their biology and population dynamics is still scarce. A recent mark-and-recapture experiment off the Balearic Islands tagged 3738 individuals of 23 shark and batoid species during MEDITS and CANAL bottom trawl scientific surveys from June 2021 to August 2023. Retrieval was reported for the sharks Scyliorhinus canicula and Mustelus mustelus, revealing relatively small home ranges for these species (0.2–38.5 km and 7.8–15.3 km for S. canicula and M. mustelus, respectively). Recapture efficiency was higher from scientific surveys than from commercial catches, highlighting potential challenges in collaboration with fishermen and recapture reports. Density estimates obtained from the MEDITS bottom trawl survey suggest a much larger population for S. canicula than estimates from the mark–recapture data, indicating MEDITS density estimates for this species may be overestimated due to its scavenger behavior perhaps favoring individuals searching for discards aggregated in the fishing grounds. This study emphasizes the importance of monitoring, collaborative efforts, and improved reporting mechanisms to enhance our understanding of elasmobranch populations and provide support for sustainable management of these vulnerable marine species.

The innovative normal form, named deep learning-couple-physical and statistical methods (DL-C-PS), has been developed for synchronously retrieving land surface temperature and emissivity (LST&E) from Japan's geostationary meteorological satellite Himawari-8 carrying the Advanced Himawari Imager (AHI). First, geophysical logical reasoning (GLR) and expert knowledge were used to formulate radiative transfer equations (RTEs). Then, a hybrid approach integrating physical and statistical methods was employed to derive the solution, with deep learning (DL) optimizing the solution process. Three band combination schemes were designed to assess the effectiveness of the DL-C-PS normal form. Simulation data from the MODerate spectral resolution atmospheric TRANsmittance mode (MODTRAN) yielded promising results during validation. Root mean square error (RMSE) values were below 1 K for LST and below 0.008 for LSE when using band combinations of four thermal infrared (TIR) bands or at least three TIR bands combined with water vapor information. Cross-validation and in situ validation showed consistent findings with simulation validation. Compared to MODIS LST&E products (MYD21), in most cases, the RMSE values for LST&E were approximately 2 K and less than 0.015 during daytime, and below 1.3 K and 0.017 during night, respectively. Validated against in situ observations, nighttime RMSE values for LST were approximately 1.5 K with correlation coefficient (R) values better than 0.93. The higher RMSE observed in daytime compared to nighttime can be attributed to the influence of the sun's illumination angle on satellite scanning imaging. Overall, this research presented a normal form for multi-parameter estimation by leveraging the optimal calculation of DL and incorporating physical interpretations.

The management of flexor tendon injury has seen many iterations over the years, but more substantial innovations in practice have been sadly lacking. The aim of this study was to investigate the current practice of flexor tendon injury management, and variation in practice from the previous reports, most troublesome complications, and whether there was a clinical interest in potential innovative tendon repair technologies. An online survey was distributed via the British Society for Surgery of the Hand (BSSH) and a total of 132 responses were collected anonymously. Results showed that although most surgeons followed the current medical recommendation based on the literature, a significant number of surgeons still employed more conventional treatments in clinic, such as general anesthesia, ineffective tendon retrieval techniques, and passive rehabilitation. Complications including adhesion formation and re-rupture remained persistent. The interest in new approaches such as use of minimally invasive instruments, biodegradable materials and additive manufactured devices was not strong, however the surgeons were potentially open to more effective and economic solutions.

Proust was undoubtedly a pioneer in exploring cognitive processes engaged in memory. The analysis of the episode of the madeleine, as well as the study of Proust’s statements on the goals of his work, clearly reveal the visionary side of this author. Long before several concepts entered into mainstream scientific thought, Proust proposed, among other things, that recall was a reconstruction, that a sensory cue could provoke a memory recall, and that we should distinguish between voluntary and involuntary memory. Through numerous episodes of “involuntary reminiscence” scattered throughout his work, Proust illustrates a particular form of autobiographic memory recall: a recall that does not involve consciousness and whose starting point is an emotion provoked by a specific cue. This recall, which leads, according to Proust, to a more intense revival of the memory than voluntary recall, has only reached prominence in cognitive science more than 80 years later. Additionaly, Proust underlined the determinant role that emotion may have in this particular form of recall. On the other hand, studies on animals have shown that the presentation of a retrieval cue could induce emotional reactions followed by a facilitation of the memory retrieval associated with the cue. The existence of these data, which support Proust’s proposals, should encourage the neuroscience community to further explore, in humans and animals, this form of cue elicited emotion that initiated involuntary recall of autobiographical memory.

The spectral characterization of exoplanetary atmospheres is a relatively new subject. Until recently, space and ground-based observatories allowed only a few planets to be observed with spectroscopic instruments. The arrival of JWST data and the development of new instruments on Earth will drastically modify the situation, giving access to deeper modelling contraints and to a better understanding on planet formation and evolution. This paper is devoted to describe the turning point experienced by the astronomy of exoplanets that will continue in particular with the launch of the ESA/Ariel mission at the end of the decade.

The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO’s COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model’s ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model’s capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance.

Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for the atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, the Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application, provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with LaSRC software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, the LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC provided reliable results: error was well constrained from 0.01% (NIR) to 0.98% (blue).

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.

Objectives: Non-surgical endodontic retreatment is to be a conservative option over endodontic surgery in case of recurrent infections. Root canal retreatment procedures needs to be initiated faster and proficiently with proper root canal retreatment files. The main aim of this study is to evaluate the remaining root dentin thickness post Gutta Percha (GP) retrieval from the root canal after using two different rotary instruments. Materials and methods: A total of sixty extracted single rooted teeth were chosen. Shaping and Cleaning was done Step Back (Telescopic) technique with apical preparation of size 40 k File. Irrigation was carried out using NaOCl (3 %) and Ethyelenediamine tetraacetic acid (EDTA) to clear the smear layer. The canals were dried using paper points and obturation was completed using lateral compaction technique with AH plus resin sealer. Upon completing the obturation, it was categorized into two groups: Group I- ProTaper retreatment files (PTR) and Group II- Solite RS3 (S-RS3) Retreatment files. Cone Beam Computed Tomography (CBCT) was used to assess the remaining dentin thickness post gutta-percha retrieval at 3,5 and 7 mm respectively. To test the significance between groups independent t test was used. Results: It can be noted from the results that more amount of remaining dentin thickness was seen after using Solite RS3 files at 3 mm, 5 mm and 7 mm on the mesial side compared to ProTaper retreatment files (p 0.05). Conclusion: Within the limitations of the study, it can be concluded that Solite RS3 files have the potential to preserve the remaining dentin thickness. However, more studies including various other parameters should be performed to arrive at a definitive conclusion.

Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.

Abstract Background Prolonged dwelling time of inferior vena cava (IVC) filters has been shown to increase the need for the use of complex IVC filter retrieval techniques. In this report, we describe a case of complex retrieval of an IVC filter with prolonged dwelling time, which was temporarily accompanied by severe bradycardia and hypotension. Case presentation Fifty-nine-year-old male patient past medical history of morbid obesity, atrial fibrillation status post-ablation, obstructive sleep apnea, and end-stage renal disease presented for IVC filter retrieval 16 years after placement. When the IVC filter was covered by sheaths, and the IVC was temporarily collapsed and occluded, the patient developed severe bradycardia and hypotension without compensatory tachycardia. Contrast injection through the common femoral vein sheath showed complete occlusion of IVC while the IVC filter was covered by both sheaths, likely due to the embedment of the IVC filter in the wall by extensive fibrinous tissues. IVC filter was successfully retrieved, and the blood pressure and heart rate were improved immediately afterward. A large non-occlusive IVC thrombus was identified on the final venogram, which was aspirated using a mechanical thrombectomy device. Conclusion Complex retrieval of IVC filters with prolonged dwelled time can result in acute severe bradycardia and hypotension due to vasovagal reaction, acute collapse, and occlusion of IVC in the setting of IVC filter embedment in the wall by extensive fibrinous tissues.

The area of multimedia information retrieval (MMIR) faces two major challenges: the enormously growing number of multimedia objects (i.e., images, videos, audio, and text files), and the fast increasing level of detail of these objects (e.g., the number of pixels in images). Both challenges lead to a high demand of scalability, semantic representations, and explainability of MMIR processes. Smart MMIR solves these challenges by employing graph codes as an indexing structure, attaching semantic annotations for explainability, and employing application profiling for scaling, which results in human-understandable, expressive, and interoperable MMIR. The mathematical foundation, the modeling, implementation detail, and experimental results are shown in this paper, which confirm that Smart MMIR improves MMIR in the area of efficiency, effectiveness, and human understandability.

Retrieving previously stored information makes memory traces labile again and can trigger restabilization in a strengthened or weakened form depending on the reactivation condition. Available evidence for long-term performance changes upon reactivation of motor memories and the effect of post-learning sleep on their consolidation remains scarce, and so does the data on the ways in which subsequent reactivation of motor memories interacts with sleep-related consolidation. Eighty young volunteers learned (Day 1) a 12-element Serial Reaction Time Task (SRTT) before a post-training Regular Sleep (RS) or Sleep Deprivation (SD) night, either followed (Day 2) by morning motor reactivation through a short SRTT testing or no motor activity. Consolidation was assessed after three recovery nights (Day 5). A 2 × 2 ANOVA carried on proportional offline gains did not evidence significant Reactivation (Morning Reactivation/No Morning Reactivation; p = 0.098), post-training Sleep (RS/SD; p = 0.301) or Sleep*Reactivation interaction (p = 0.257) effect. Our results are in line with prior studies suggesting a lack of supplementary performance gains upon reactivation, and other studies that failed to disclose post-learning sleep-related effects on performance improvement. However, lack of overt behavioural effects does not detract from the possibility of sleep- or reconsolidation-related covert neurophysiological changes underlying similar behavioural performance levels.

Video surveillance improves public safety by preventing and sensing criminal activity, enhancing quick counteractions, and presenting evidence to investigators. This is effectively performed by firing a natural language query containing soft biometrics to retrieve a person from a video. State-of-the-art (SOTA) approaches focus on improving retrieval results; thus, the building blocks of any person retrieval system are not accorded due attention, putting novice researchers at a disadvantage. This study aims to provide a design methodology by showcasing the block-by-block construction of a person retrieval system using video and natural language. For each subsystem - natural language processing, person detection, attribute recognition, and ranking- we discuss the available design selections, provide empirical evidence, and discuss bottlenecks and solutions. We thereafter select and integrate the best choices to create an end-to-end system. We highlight the integration challenges and demonstrate that the proposed method achieves an average intersection over union and the true positive rate of $\geq 60\%$ . This is the first study to provide practical guidance to researchers for fast prototyping of person retrieval with subsystem-level understanding and achieve SOTA performance.

The principal-component-based radiative transfer model retrieval algorithm (PCRTM-RA) for carbon monoxide (CO) retrieval has been improved for better use of National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality field campaign. One of the explicit purposes of the campaign was to characterize wildfire-induced atmospheric changes. Coincidental measurements from various airborne instruments, including NAST-I infrared hyperspectral measurements from the NASA ER-2 aircraft, provided us an opportunity to test and improve the PCRTM-RA CO retrieval. By relaxing the vertical correlation of CO profiles in the a priori covariance constraint, a significant improvement in the vertical structure of the CO retrieval has been confirmed. The methodology is validated using a synthetic testing dataset that covers observations associated with various CO vertical profiles, including that of an extremely polluted atmosphere. The methodology is also applied to real NAST-I measurements, and the results have successfully demonstrated the capability of using PCRTM-RA retrieval results for CO plume evolution and transport monitoring.

One of the promising ways for the representation learning is contrastive learning. It enforces that positive pairs become close while negative pairs become far. Contrastive learning utilizes the relative proximity or distance between positive and negative pairs. However, contrastive learning might fail to handle the easily distinguished positive-negative pairs because the gradient of easily divided positive-negative pairs comes to vanish. To overcome the problem, we propose a dynamic mixed margin (DMM) loss that generates the augmented hard positive-negative pairs that are not easily clarified. DMM generates hard positive-negative pairs by interpolating the dataset with Mixup. Besides, DMM adopts the dynamic margin incorporating the interpolation ratio, and dynamic adaptation improves representation learning. DMM encourages making close for positive pairs far away, whereas making a little far for strongly nearby positive pairs alleviates overfitting. Our proposed DMM is a plug-and-play module compatible with diverse contrastive learning loss and metric learning. We validate that the DMM is superior to other baselines on various tasks, video-text retrieval, and recommender system task in unimodal and multimodal settings. Besides, representation learned from DMM shows better robustness even if the modality missing occurs that frequently appears on the real-world dataset. Implementation of DMM at downstream tasks is available here: https://github.com/teang1995/DMM

Key Clinical Message Application of central venous catheters become more common however a rare complication, embolization, can occur which lead to high mortality rate. Therefore, cardiovascular foreign bodies should be immediately remove with a safe and effective method and algorithm. So, we investigate the results of cardiovascular foreign body retrieval in our center based on a designed algorithm. In this case series study, patients who had cardiovascular foreign body embolization were managed by our algorithm. Our approach was based on the time duration of foreign body embolization in cardiovascular system and also its location. If the embolization time was less than 48 h, based on the location of foreign body, insertion site would be right femoral or jugular vein. If the time of foreign body embolization was more than 48 h, echocardiography should be done to assess thrombosis around foreign body. Retrieval was done with snare technique in all patients. Over 12 patients, 9 patient had port catheter foreign body in SVC and atrium, 1 patient had port catheter in atrium, 1 patient had port catheter in pulmonary artery and 1 patient had hemodialysis catheter in inferior vena cava (IVC) and atrium. Foreign body retrieval was successful in all 11 patient. The IVC and atrium were the common site of port catheter fragments. According to our experience, we organize a useful algorithm for retrieval of cardiovascular foreign body based on time of embolization and its location.

Inferior vena cava filters are effective for the management of thromboembolic disease but can erode into adjacent organ systems in rare instances. Endovascular retrieval of eroded filters has been the preferred management for this complication. We present a case for which endovascular retrieval was not appropriate because of filter orientation and erosion into the ureter and describe successful management using open retrieval of a permanent filter with erosion into the renal collecting system requiring reconstruction. Although minimally invasive retrieval is preferred over open repair, this approach should be considered when filter erosion is not amenable to endovascular retrieval.