Shipped Samples Research Articles

Comparison of shipping versus immediate freezer storage of vaginal samples for vaginal microbiota assessment

ObjectivesWe evaluated how storing vaginal samples at room temperature in stabilising solutions versus immediate freezing affects 16S rRNA gene amplicon sequencing-based microbiota studies, aiming to simplify home and field collection.MethodsTwenty...

Preparing research samples for safe arrival at centers and facilities: recipes for successful experiments.

Preparation of biomacromolecules for structural biology studies is a complex and time-consuming process. The goal is to produce a highly concentrated, highly pure product that is often shipped to large facilities with tools to prepare the samples for crystallization trials or for measurements at synchrotrons and cryoEM centers. The aim of this article is to provide guidance and to discuss general considerations for shipping biomacromolecular samples. Details are also provided about shipping samples for specific experiment types, including solution- and cryogenic-based techniques. These guidelines are provided with the hope that the time and energy invested in sample preparation is not lost due to shipping logistics.

Bio-Optical Properties near a Coastal Convergence Zone Derived from Aircraft Remote Sensing Imagery and Modeling

Bio-optical and physical measurements were collected in the Mississippi Sound (Northern Gulf of Mexico) during the spring of 2018 as part of the Integrated Coastal Bio-Optical Dynamics project. The goal was to examine the impact of atmospheric and tidal fronts on fine-scale physical and bio-optical property distributions in a shallow, dynamic, coastal environment. During a 25-day experiment, eight moorings were deployed in the vicinity of a frontal zone. For a one-week period in the middle of the mooring deployment, focused ship sampling was conducted with aircraft and unmanned aerial vehicle overflights, acquiring hyperspectral optical and thermal data. The personnel in the aircraft located visible color fronts indicating the convergence of two water masses and directed the ship to the front. Dye releases were performed on opposite sides of a front, and coincident aircraft and unmanned aerial vehicle overflights were collected to facilitate visualization of advection/mixing/dispersion processes. Radiometric calibration of the optical hyperspectral sensor was performed. Empirical Line Calibration was also performed to atmospherically correct the aircraft imagery using in situ remote sensing reflectance measurements as calibration sources. Bio-optical properties were subsequently derived from the atmospherically corrected aircraft and unmanned aerial vehicle imagery using the Naval Research Laboratory Automated Processing System.

Hull form optimization research based on multi‐precision back‐propagation neural network approximation model

AbstractIn order to shorten the optimization cycle of ship design optimization and solve the time‐consuming problem of computational fluid dynamics (CFD) numerical calculation, this paper proposes a multi‐precision back‐propagation neural network (MP‐BP) approximation technology. Fewer high‐precision ship samples and more low‐precision ship samples were used to construct an approximate model, back‐propagation (BP) neural network was used to train multi‐precision samples. So that the approximate model is as close as possible to the real model, and achieving the effect of high‐precision approximation model. Subsequently, numerical verification and typical hull form verification are given. Based on CFD and Rankine theory, the multi‐objective design optimization framework for ship comprehensive navigation performance is constructed. The multi‐objective approximation model of KCS ship is constructed by MP‐BP approximation technology, and optimized by particle swarm optimization (PSO) algorithm. The results show that the multi‐objective optimization design framework using the MP‐BP approximation model can capture the global optimal solution and improve the efficiency of the entire hull form design optimization. It can provide a certain degree of technical support for green ship and low‐carbon shipping.

Stability of serum placental growth factor (PlGF) and soluble fms-like tyrosine kinase 1 (sFlt-1)

Placental growth factor (PlGF) and soluble fms-like tyrosine kinase 1 (sFlt-1) are biomarkers used for diagnosis and risk estimation of preeclampsia. Stability in room temperature (RT) may affect the usefulness of these analyses, as shipping at ambient temperature is the most practical and suitable way to ship samples. To date, scientific studies of such stability are lacking. We aimed to assess the stability of PlGF and sFlt-1 at RT in serum from pregnant women. In addition, a smaller study of stability at 4 °C was performed. Serum was collected from 69 pregnant women and stored at RT or at 4 °C for up to 192 h. Analytes were considered stable if the mean percent change ± 90 confidence interval of the mean was within the baseline concentration ± allowable bias. Allowable bias was calculated from data on biological variation. In addition, an instability equation was calculated to assess loss of stability, in line with recent European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) recommendations. The mean percent change was <3.5% for PlGF, <1% for sFlt-1 and <4.5% for sFlt-1/PlGF ratio up to 192 h. PlGF was considered stable for 168 h, and sFlt-1 and sFlt-1/PlGF ratios were considered stable for 192 h at RT. At 4 °C, PlGF was considered stable for 120 h, sFlt-1 for 168 h and sFlt-1/PlGF ratio for 120 h. Both PlGF and sFlt-1 as well as sFlt-1/PlGF ratio show sufficient stability (minimum 168 h) for samples to be shipped at RT.

An Improved SAR Ship Classification Method Using Text-to-Image Generation-Based Data Augmentation and Squeeze and Excitation

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods.

Fine-grained ship image classification and detection based on a vision transformer and multi-grain feature vector FPN model

ABSTRACT In naval and civilian domains, meticulous ship classification and detection are paramount. Nevertheless, predominant research has gravitated toward leveraging Convolutional Neural Network (CNN)-centered methodologies, often overlooking the diverse granularity inherent in ship samples. In our pursuit to holistically extract features from ship images across varying granularities, we present a transformative architecture: the Vision Transformer and Multi-Grain Feature Vector Feature Pyramid Network (ViT-MGFV-FPN). This model synergistically melds the merits of MGFV-FPN with an augmented Vision Transformer (ViT) for a comprehensive image feature extraction. To cater to the extraction of broader image features whilst sidestepping the innate quadratic complexity of traditional ViT, we unveil an enhanced version christened the Global Swin Transformer. Concurrently, the MGFV-FPN is orchestrated to harness the prowess of CNNs in distilling intricate ship attributes. Rigorous empirical evaluations underscore our model’s superiority in juxtaposition with extant CNN and transformer-based paradigms for nuanced ship categorization.

Determination of clopidogrel effect in cats using point-of-care Plateletworks ADP and shipped samples for PFA-200 analysis in a clinical practice setting.

Clopidogrel is the recommended first-line antithrombotic in cats for a variety of conditions; however, it is ineffective in 15-20% of cats. The determination of clopidogrel effectiveness with platelet function assays has historically been limited to specialty centers; however, recent work has suggested that in-hospital or shipped analyses of samples may be feasible. The aim of the present study was to investigate the utility of an in-house analysis and shipping of blood samples collected in primary practices for the determination of clopidogrel effectiveness. Citrated blood samples were collected from cats receiving clopidogrel therapy by veterinarians in clinical practices across Canada, a median of 304.4 km from the reference laboratory (range 8-4425). Samples were analyzed in-house using Plateletworks ADP and shipped for remote analysis using PFA-200 P2Y and COL/ADP cartridges. A total of 30 samples were collected from 25 cats. Of these, the percentage of samples analyzable for the presence or absence of the clopidogrel effect was 86% for Plateletworks ADP, 90% for PFA-200 P2Y and 87% for PFA-200 COL/ADP. There was no significant difference in the number of samples unable to be analyzed by each modality (P = 0.689) due to flow obstruction or other sample characteristics. The prevalence of absence of clopidogrel effectiveness on platelet function assays was 8% with the PFA-200 COL/ADP assay, 25% with the PFA-200 P2Y assay and 30% with the Plateletworks ADP assay. The results of this study confirm that samples of feline blood can be collected in clinical practices and shipped to a reference laboratory for PFA-200 analysis with a high rate of success, comparable to point-of-care analysis.

Research on Ship Resistance Prediction Using Machine Learning with Different Samples

Resistance serves as a critical performance metric for ships. Swift and accurate resistance prediction can enhance ship design efficiency. Currently, methods for determining ship resistance encompass model tests, estimation techniques, and computational fluid dynamics (CFDs) simulations. There is a need to improve the prediction speed or accuracy of these methods. Machine learning is gradually emerging as a method applied in the field of ship research. This study aims to investigate ship resistance prediction methods utilizing machine learning across various datasets. This study proposes two methods: employing stacking ensemble learning to enhance resistance prediction accuracy with identical ship samples and utilizing various ship resistance prediction models for accurate resistance prediction through transfer learning. Initially focusing on container ships as the research subject, the stacking ensemble learning model outperforms the basic machine learning model, the Holtrop and Mennen method, and the updated Guldhammer and Harvald method based on comparative prediction results. Subsequently, the container ship resistance prediction model achieves precise resistance prediction for bulk carriers. This study offers dependable guidance for applying machine learning in predicting ship hydrodynamic performance.

Challenges of rabies surveillance in Madagascar based on a mixed method survey amongst veterinary health officers.

IntroductionIn Madagascar, rabies is endemic and a notifiable disease. The objective of this mixed study was to understand the challenges faced by the Veterinary Health Officers (VHOs) in the current rabies surveillance system in Madagascar.MethodsA survey was conducted from mid-April to the end of July 2021 during which all officially-listed VHOs (N = 150) were contacted by phone at least twice. Participants, representative of the 22 regions of Madagascar, were interviewed by phone based on a semi-structured questionnaire on (1) their knowledge of rabies epidemiology in their area of activities, (2) the occurrence of human and animal rabies and the species affected in the region where they work, (3) the factors that might influence rabies surveillance depending on (a) their activities and area of operation, (b) the socio-cultural aspects of local communities, and (c) the general organization of animal rabies surveillance.ResultsThe majority (80/90) of VHOs declared having been informed of at least one suspected or confirmed case of human and/or animal rabies in their area of activity during their work as VHOs: at least once a month for 11 of them, at least once a year for 40 and with undetermined frequency for 29. Several obstacles hinder the surveillance of rabies resulting in under-reporting. The lack of funds to access suspect animals, collect, pack and ship samples in compliance with biosecurity measures and the cold chain, was mentioned as a major obstacle to surveillance. The second barrier is socio-cultural: in many large coastal regions, dogs are taboo and VHOs fear rejection by the community if they treat dogs.Discussion and conclusionWhile the general population requires information on the rabies situation in Madagascar and on how to proceed in the event of a bite, veterinarians and decision-makers need to be fully aware of an evidence-based approach to rabies surveillance, prevention and control. Communication between the human and animal health sectors should be improved. Politicians need to be persuaded of the importance of funding to eliminate rabies in Madagascar. The adoption, in early 2023, of a national strategic plan for rabies control is a first step in this direction.

SHIP DETECTION IN COSMO-SKYMED SAR IMAGERY USING A NOVEL CNN-BASED DETECTOR: A CASE STUDY FROM THE SUEZ CANAL

Abstract. The Suez Canal, strategically located as the shortest international sea route, plays a crucial role in facilitating the transportation of goods between Asia and Europe. However, the occurrence of traffic disruptions within the Canal poses a serious threat to global trade, as evidenced by the recent incident of the container ship Ever Given, which ran aground in the Suez Canal on March 23, 2021. This event led to a complete blockade of the Canal that lasted for six days, resulting in a fleet of ships waiting to pass through the Canal. This highlights the need to monitor the Canal to prevent similar disturbances in the future. In this paper, we propose a CNN-based attention-guided self-learning framework for ship detection from 3m high-resolution COSMO-SkyMed SAR imagery acquired in April 2021 via the Egyptian Suez Canal. We introduce a self-learning augmented segmentation (SLAS) technique to augment the dataset with new ship samples by pseudo-labeling an unlabeled dataset. We also present the Attention-guided Feature Refinement (AFR) module to extract more significant semantic features and contextual information, especially for ships of varying sizes in SAR images. Finally, the AFR module is fed into a Region Proposal Network (RPN) to generate a set of proposal anchors, which are later used in a Deep Detection Network (DDN) for ship classification and localization. Our experimental results demonstrate that the proposed method outperforms current state-of-the-art detection models in terms of detection accuracy, particularly in complex coastal scenes, with an overall accuracy of up to 87% mean average precision (mAP).

Label Smoothing Auxiliary Classifier Generative Adversarial Network with Triplet Loss for SAR Ship Classification

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Outgassing of CO2 dominates in the coastal upwelling off the northwest African coast

The Eastern Boundary Upwelling System off northwest Africa is among the most productive regions of the ocean. In 2019, two merchant ships equipped with an underway CO2 system sampled the region following exactly the same track from 10°N to 36°N. We determine the sources and sinks of CO2 and the seasonal cycle along the track. A weak permanent upwelling (WPU), a permanent upwelling (PU) and an open ocean regions are identified. The WPU (26°N to 33°N) is a source of CO2 in summer and autumn, and a sink of CO2 in winter and spring. Thermodynamic warming and cooling processes mainly drive the CO2 variations in this region. The PU (20°N to 26°N) is a sink of CO2 in spring and a source of CO2 in other seasons. This region is the most productive and exhibits the largest variability of the CO2 flux. The supply of CO2 from subsurface waters dominate over the carbon uptake by biology, which leads to a strong outgassing, especially in winter and autumn. A sink of CO2 occurs in spring only in the PU. Near Cape Blanc (20°N) in July 2019, a source of CO2 is observed around 20°N within ± 1° of latitude and becomes a sink of CO2 a few days later when the ship samples back the same area. South of 18°N, out of the influence of the coastal upwelling, the region is a sink of CO2 in winter only and the region is mainly controlled by physical processes. Using voyages from 2010 to 2022, seawater fCO2 significantly increases at a rate ranging from 1.82 μatm yr−1 to 2.10 μatm yr−1 close to the atmospheric increase. This is associated with a pH decrease between 0.0016 and 0.0022 yr−1. Nevertheless, there is no clear trend of the CO2 flux in any region.

An experimental study on the moisture migration characteristics of liquefiable iron ore

Cargo liquefaction is still the biggest causes of casualties during its sea transportation. To understand the liquefaction mechanism deeply, research of moisture migration characteristics of liquefiable cargo is the priority. Closer to the actual transportation conditions of iron ore, unsaturated undrained, saturated undrained and saturated drained experiments were all carried out. Effects of ship motion accelerations, frequencies and sample densities were also taken into account. Results indicate that the largest drainage happens in the first ten minutes when drained, and accounts for 75.3% of the total volume. The external loads have a little promotion for the drainage. The pore water pressure dissipates quickly and its value goes below zero. When undrained, the water would migrates upward and the middle part have a relative higher water content. The pore water pressure accumulates and increases quickly in the first 50–100 s, and also the middle part has the largest increase of pore water pressure. A larger motion acceleration could enhance the accumulation of the pore water pressure and the water migration. There are critical motion frequencies, under which the accumulation of the pore water pressure and the water migration become obvious. For the sample with a higher density, the accumulation of the pore water pressure and the ability of water migration are all bounded. This study could be provided as an useful reference to reveal the liquefaction mechanism.

FEASIBILITY ANALYSIS OF OPERATION OF PURSE SEINE AS A FISHING VESSEL IN BONE BAY, SOUTH SULAWESI

This study aims to analyze the feasibility of operating purse seine vessels operated in Bone Bay. The sampling method uses purposive sampling. There are 4 ship samples and to facilitate measurement using ships that are docking. The data analyzed are the ratio of the main size of the ship, stability, seaworthiness criteria, and ship operation criteria.&#x0D; The results showed that the feasibility of operating purse seine vessels operating in Bone Bay, feasible to operate based on the results of the analysis of each parameter. The L/B ratio value of the purse seine vessels studied ranged from 3.73 – 4.59; L/D ranges from 10.72 – 12.58; and B/D ranges from 2.34 – 3.16. The stability curve of sample ship 1 shows a Max GZ value of 163.27 cm at a slower angle of 73.6 deg, sample ship 2 shows a Max GZ value of 159.75 cm at a low angle of 58.2 deg, sample ship 3 shows a Max GZ value of 154.75 cm at a low angle of 60.9 deg and sample ship 4 Data shows a Max GZ value of 139.59 cm at a low angle of 60.9 deg. The criteria value of the sample ship has met the minimum value standards set by the IMO (International Maritime Organization).

Research results further support battery separator's enhanced capabilities

X-SEPA, a membrane-based separator material created to improve the performance of existing battery technologies, and accelerate the development of next-generation batteries, was launched in 2022. Japan's 3DOM Alliance Inc, the firm behind the technology, says that recent studies provide further scientific support for the enhanced charge–discharge cycle life of lithium-ion batteries and lithium metal rechargeable batteries equipped with X-SEPA. Its Singapore-based subsidiary, Noco-Noco Pte Ltd, has also started shipping samples to battery manufacturers worldwide.

T Cell Responses to Dystrophin in a Natural History Study of Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is caused by the lack of dystrophin, but many patients have rare revertant fibers that express dystrophin. The skeletal muscle pathology of DMD patients includes immune cell infiltration and inflammatory cascades. There are several strategies to restore dystrophin in skeletal muscles of patients, including exon skipping and gene therapy. There is some evidence that dystrophin restoration leads to a reduction in immune cells, but dystrophin epitopes expressed in revertant fibers or following genome editing, cell therapy, or microdystrophin delivery after adeno-associated viral gene therapy may elicit T cell production in patients. This may affect the efficacy of the therapeutic intervention, and potentially lead to serious adverse events. To confirm and extend previous studies, we performed annual enzyme- linked immunospot interferon-gamma assays on peripheral blood mononuclear cells from 77 pediatric boys with DMD recruited into a natural history study, 69 of whom (89.6%) were treated with corticosteroids. T cell responses to dystrophin were quantified using a total of 368 peptides spanning the entire dystrophin protein, organized into nine peptide pools. Peptide mapping pools were used to further localize the immune response in one positive patient. Six (7.8%) patients had a T cell-mediated immune response to dystrophin at at least one time point. All patients who had a positive result had been treated with corticosteroids, either prednisolone or prednisone. Our results show that ∼8% of DMD individuals in our cohort have a pre-existing T cell-mediated immune response to dystrophin, despite steroid treatment. Although these responses are relatively low level, this information should be considered a useful immunological baseline before undertaking clinical trials and future DMD studies. We further highlight the importance for a robust, reproducible standard operating procedure for collecting, storing, and shipping samples from multiple centers to minimize the number of inconclusive data.

Performance Evaluation of Data Enhancement Methods in SAR Ship Detection

In recent years, researchers have started to apply neural networks to ship detection of synthetic aperture radar (SAR). However, SAR images are difficult to acquire and interpret manually. Sufficient training samples cannot be obtained, which limits the performance of ship detection. Therefore, data enhancement has become an active means to handle the issue of insufficient samples. To evaluate the performance of diverse data enhancement methods, a variety of data enhancement methods is used to expand the ship samples in the SAR ship detection dataset, including rotation, shift, mirror, brightening, etc. Moreover, we used the combination of diverse data enhancement methods for experiments. Experiments were performed using the SSDD dataset. Based on the experimental results, we analyze the characteristics of diverse data enhancement methods.

A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.

Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.