High Identification Rate Research Articles

An early economic evaluation of WireSafe™ to prevent guidewire retention in central venous catheter procedures

Background Guidewire retention (GWR) poses a patient safety risk during Central Venous Catheter (CVC) insertions and is listed as a ‘Never Event’ by the National Health Service England (NHSE). WireSafe™ is an intervention to prevent GWR, but its cost-effectiveness is uncertain. This study is an early economic evaluation comparing WireSafe™ to standard care from a UK healthcare perspective. Methods We conducted (i) a primary analysis of GWR related NHSE Never Events data in England between 2016 and 2020; and (ii) a cost-utility analysis, including healthcare costs and quality-adjusted life years (QALYS) for populations receiving CVC-insertions. We applied a cost-effectiveness threshold of £30,000 per QALY and considered three WireSafe™ costing scenarios (£18.50, £4.50, and £2.50). Results NHSE Never Events data showed 61 GWR cases, averaging 1 per month. Most incidents (92%) were identified during hospital stays, with one serious adverse outcome reported (peri-arrest). In a population of 200,000, we estimate WireSafe™ would prevent 59.92 wire retentions, 5.61 procedural adverse events, 0.3 cardiac adverse events, and 0.19 deaths, improving QALYs by 4.87. In the base case analysis WireSafe™ was not cost-effective and had an economically justifiable price of £2.44. There were high levels of uncertainty in the lowest cost-scenario (ICER 95% credible interval: Dominant; £793,398). Conclusions The health benefits of WireSafe™ are limited due to low GWR rates and high identification rates, making WireSafe™ viable only at low costs. Future research should prioritise obtaining more precise estimates of these parameter values which are key determinants of cost-effectiveness.

Sparse representation of finger knuckle print images for personal identification

SummaryFraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.

Exploration of the white matter bundles connected to the pineal gland: A DTI study.

Pineal gland (PG) is a structure located in the midline of the brain, and is considered as a main part of the epithalamus. There are reports on the role of this area for brain function by hormone secretion, as well as few reports on its role in brain cognition. However, little knowledge is available on the PG, and in particular on the structural connectivity of this region with the other brain structures. Using diffusion-weighted images collected by a 3T MRI scanner, and using a sample of 61 (29F) mentally and physically healthy young individuals in the age range of 20-30 years old, we tried to extract the white matter bundles connected to the PG. Based on prior knowledge, seven target bundles were suggested to be between the PG body and the PG roots, Pons, Periventricular region, thalamus, caudate, lentiform, suprachiasmatic nuclei, and the supercervical ganglia. Nearly all the target bundles were successfully extracted, with the exception of the lentiform. Rate of identification of the tracts was different, with the bundle between the PG body and roots having the highest identification rate (97%); then it was with the Pons (70%), Periventricular region (57%), SCN (55%), left thalamus (52%), right thalamus (47%), left caudate (27%) and right caudate (22%). This study is an attempt to expand our knowledge on the neuroanatomy of the PG, which might help for identifying further roles for it in brain functionality, and also be a help for the treatment of some disorders in the future.

Domain adversarial transfer fault diagnosis method of an axial piston pump based on a multi-scale attention mechanism

Effective failure diagnosis of axial piston pumps is crucial to guaranteeing the security and steady run of hydraulic equipment systems. However, the current domain-adaptive methods have insufficient feature extraction and domain-adaptive capability for axial piston pumps across conditions and some unlabeled sample conditions. Therefore, this paper proposes a method of domain adversarial transfer fault diagnosis based on multi-scale attention mechanisms. The method enhances the ability of the model to adapt to the differences in various fault information by constructing a multi-scale residual feature extraction network. The domain adaptive capability of the network is improved by constructing a joint loss function to decrease the distributional discrepancies between the source and the target domain data. The experimental consequences demonstrate that the method can differentiate various fault types of axial piston pumps across conditions and can achieve a high fault identification rate without labeled samples in the target domain.

Sensitivity of Fire Indicators on Forest Inventory Plots Is Affected by Fire Severity and Time since Burning

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators.

Melanin-Based Nanoparticles for Lymph Node Tattooing: Experimental, Histopathological and Ultrastructural Study.

In breast cancer, Targeted Axillary Dissection (TAD) allows for the selective excision of the sentinel lymph node (SLN) during primary tumor surgery. TAD consists of the resection of labelled SLNs prior to neoadjuvant chemotherapy (NACT). Numerous clinical and preclinical studies have explored the use of carbon-based colloids for SLN tattooing prior to NACT. However, carbon vectors show varying degrees of inflammatory reactions and, in about one fifth of cases, carbon particles migrate via the lymphatic pathway to other nodes, causing the SLN to mismatch the tattooed node. To overcome these limitations, in this study, we explored the use of melanin as a staining endogenous pigment. We synthesized and characterized melanin-loaded polymeric nanoparticles (Mel-NPs) and used them to tattoo lymph nodes in pig animal models given the similarity in the size of the human and pig nodes. Mel-NPs tattooed lymph nodes showed high identification rates, reaching 83.3% positive identification 16 weeks after tattooing. We did not observe any reduction in the identification as time increased, implying that the colloid is stable in the lymph node tissue. In addition, we performed histological and ultrastructural studies to characterize the biological behavior of the tag. We observed foreign-body-like granulomatous inflammatory responses associated with Mel-NPs, characterized by the formation of multinucleated giant cells. In addition, electron microscopy studies showed that uptake is mainly performed by macrophages, and that macrophages undergo cellular damage associated with particle uptake.

Research on Product Advertising Design Combining Feature Extraction Technology and Web3D Technology

This work, built on the Unity3D development platform, presents a way for merging feature extraction technology and Web3D technology into advertising design to effectively address the issues of poor efficiency and distortion in the field. Using the candidate text layout generating technique of visual salience, we first build the vector function set based on the three main colors, then we produce the visual communication partition model of advertising design. Next, the number of feature parameters of the shape advertising design is obtained via the establishment of coding coefficient constraint features and the use of an upgraded neural network technique to extract local feature parameter information about the product. Finally, the product design model is brought to life using Web3D technology to boost advertising design's productivity and accuracy. The experiments show that this method not only results in a high rate of correct product identification but also offers a fresh viewpoint on the visual communication of product advertising design by merging the two disciplines.

Abstract PO2-23-06: Clinical study of stained region lymph node biopsy (SrLNB) in axillary surgery after neoadjuvant systemic therapy in patients with axillary lymph node positive breast cancer

Abstract Background The probability of axillary lymph node converting from positive to negative after neoadjuvant systemic therapy (NST) for breast cancer patients is approximately 40%. A variety of axillary lymph node biopsy surgeries based on sentinel lymph node biopsy (SLNB) can help to omit axillary lymph node dissection (ALND). The common disadvantages, however, are mainly the restrictions, such as the need for strict regulation of the use of radiotracers, the high number of retrieved lymph nodes required, and the need to purchase ancillary equipment. The more restrictions there are, the more they limit the spread and availability of the technique and, more importantly, reduce the number of patients who will benefit. In fact, for patients with metastatic lymph nodes, the sentinel lymph nodes must be included in the lymph nodes that have metastasized. If these metastatic lymph nodes can be accurately marked and precisely retrieved after NST, it is possible to assess whether the axillary lymph nodes are converted from positive to negative without the need for SLNB. Materials and methods Breast cancer patients with pathologically confirmed axillary lymph node metastases who were receiving NST were recruited for this study. Before NST, carbon nanoparticles suspension injections were injected into the cortex of the pathologically confirmed metastatic lymph node to stain the region where the metastatic lymph node was located. The lymph nodes in the stained region were retrieved by stained region lymph node biopsy (SrLNB) during the axillary surgery, followed by ALND, without the use of SLNB. Finally, the identification rate and false-negative rate (FNR) of SrLNB were statistically analyzed according to pathology data. Results A total of 159 patients were successfully enrolled in this study between September 24, 2020 and December 2, 2022, with an axillary pCR rate of 40.9% (65/159). The identification rate of SrLNB was 100% (159/159), with an overall FNR of 5.3% (5/94). Subgroup analysis revealed that in the subgroups of 1, 2, 3 and ≥4 SrLN, the FNR was 0%, 0%, 0% and 8.9% (5/56), respectively, whereas in the cN1, cN2 and cN3 subgroups the FNR was 7.7% (3/39), 2.6% (1/38) and 5.9% (1/17), respectively. The FNR indexes were within the safety threshold of 10% in all subgroups. Conclusion SrLNB has a high identification rate and a low FNR and is useful for screening patients with positive to negative axillary lymph nodes for omitting ALND. srLNB requires only the use of an affordable and radiation-free carbon nanoparticles suspension injection for marking, making the whole technique simpler and easier to disseminate. The data from this study also suggest that SLNB may not be mandatory for axillary biopsy in patients with positive axillary lymph nodes undergoing NST. Table 1. General characteristics of enrolled patients. Table 2. Analysis of factors affecting axillary pCR rates. Table 3. Analysis for the FNR of SrLNB. Citation Format: Jue Wang, Rui Chen, Cuiying Li, Hailing Zha, Qiannan Zhu, Lifeng Huang, Xiaoming Zha. Clinical study of stained region lymph node biopsy (SrLNB) in axillary surgery after neoadjuvant systemic therapy in patients with axillary lymph node positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-06.

Abstract PO2-23-02: Results of a prospective randomised multicentre study comparing indocyanine green (ICG) fluorescence combined with a standard tracer versus ICG alone for sentinel lymph node biopsy in early breast cancer: The INFLUENCE Trial

Abstract Background: Use of fluorescence mapping for visualization of lymphatic and nodal tissue for sentinel lymph node biopsy (SNB) reveals high rates of identification (>98%). Studies combining ICG with either blue dye or radioisotope (RI) have shown high levels of concordance (>90%) and comparable performance parameters between ICG and standard tracers for SNB localization. ICG combines many advantages of blue dye and RI without the disadvantages of allergic reactions, staining of skin and surgical tissues along with handling and disposal of radioactivity materials. Moreover, magnetic tracers pose challenges of interference relating to surgical instrumentation and magnetic resonance imaging (MRI). The combination of ICG with a conventional tracer may represent a transition phase and this randomized study evaluates ICG as a sole tracer agent for SNB in early-stage breast cancer. Methods: In a prospective randomized study, 100 patients with unilateral clinically node negative tumours scheduled to undergo routine SNB for core-biopsy proven invasive breast cancer (≤5cm) were identified at multidisciplinary meetings [non-invasive tumours excluded]. All patients had pre-operative axillary ultrasound and breast conserving surgery or mastectomy. Patients were recruited in two cohorts (n=50); cohort 1 was assigned to either ICG [2ml 0.5%] alone (n=25) or combined with RI [Technetium99 nanocolloid, 20MBq] (n=25). Cohort 2 received ICG alone (n=25) or combined with blue dye for SNB localization. The number of nodes whether blue, radioactive, fluorescent or a combination thereof were recorded. Lymphatic and nodal tissue was visualized with a fluorescent camera/detection system. Sensitivity of ICG alone and/or in combination with one or another standard tracer was calculated. The main objective was to assess the performance of ICG alone compared with a standard tracer combination in terms of rates of SNB identification along with procedural node positivity rates. Statistical analysis employed Chi-Square test, Fisher’s Exact test, and logistic regression to determine differences between groups. Results: A total of 100 patients were randomized between March and December 2022 with 3 patients excluded from analysis (non-receipt of treatment allocation). Amongst evaluable patients (n=97), the overall SNB identification rate was 96.9% and by tracer category as follows: ICG alone = 97.9% (46/47); ICG + RI = 100% (25/25); ICG + blue dye = 92% (23/25). For cohort 1, the procedural node positivity rates were 17% for ICG alone and 18% for ICG + RI with corresponding figures of 12% for ICG alone and 20% for ICG + blue dye for cohort 2. Mean procedural node retrieval per case was 2.5 in ICG alone Vs ICG + Blue Dye and 2.3 in ICG alone Vs ICG/RI cohorts. There were no significant differences (p>0.05) in performance of ICG alone or combined with a standard tracer, with ICG alone being non-inferior in terms of procedural and nodal detection rates. Similar conclusions were reached from a secondary analysis adjusting for BMI, age and mode of detection (screening/symptomatic). Conclusion: ICG fluorescence imaging permits real-time visualisation of lymphatics and gives an additional dimension to SNB that appears safe versus the competitive alternatives. These results confirm high sensitivity for fluorescence localisation alone for SNB with comparable performance to combined methods with blue dye or RI. The fluorochrome ICG is reliable as a sole tracer and avoids potential drawbacks of blue dye and RI including staining, allergic reactions, availability and costs. Citation Format: Vassilis Pitsinis, Rahul Kanitkar, Alessio Vinci, Emad Elseedawy, Laura Canna, John R Benson. Results of a prospective randomised multicentre study comparing indocyanine green (ICG) fluorescence combined with a standard tracer versus ICG alone for sentinel lymph node biopsy in early breast cancer: The INFLUENCE Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-02.

An acoustic emission identification model for train axle fatigue cracks based on deep belief network

Railway axles are safety-critical components of the railroad rolling stock and the consequences of possible in-service failures can have a huge impact. Axle fatigue cracks are relatively common defects during train operation, but how to intelligently identify axle fatigue cracks in running trains is still a great challenge. In order to identify axle fatigue cracks more intelligently, the problem that needs to be solved is how to overcome the manual extraction of features by manual experience as well as shallow networks. Therefore, in this paper, an acoustic emission signal identification method based on deep belief networks (DBNs) for axle fatigue cracks is proposed. In this method, a DBN model is constructed. The axle fatigue crack acoustic emission signal data were obtained by our designed acquisition experimental setup, and these data were used to verify the accuracy of the constructed DBN network model identification. The experimental results show that the method of identification of axle fatigue cracks based on DBN, compared with the traditional fault diagnosis method, eliminates the operations of data feature extraction, feature screening, feature fusion, etc and makes complete use of all the information contained in the fault data. The method can not only identify fatigue crack signals but also has a high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

Identification of the efficacy of parentage testing based on bi-allelic autosomal single nucleotide polymorphism markers in Taiwanese population.

Parentage testing is crucial for forensic DNA analysis, using short tandem repeats (STRs). Single nucleotide polymorphisms (SNPs) with high minor allele frequency (MAF) are promising for human identification. This study aimed to develop SNP markers for parentage testing in the Taiwanese population and compare their accuracy with STRs. The TPMv1 SNP microarray (714,457 SNPs) was used to screen 180,000 Taiwanese individuals and analyze the SNP data using PLINK. After quality control, allelic distribution, and MAF considerations, a set of SNPs with significant inheritance information was selected. Parentage testing was conducted on 355 single parent-child pairs using both STRs and SNPs, employing three kinship algorithms: identity by descent, kinship-based inference for genome-wide association studies, and the combined paternity index/probability of paternity (CPI/PP). An Affymetrix signature probe for kinship testing (ASP) was also used. Based on the quality control and selection criteria, 176 SNPs with MAF > 0.4995 were selected from the Taiwanese population. The CPI/PP results calculated using SNPs were consistent with the STR results. The accuracy of the SNPs used in the single-parent-child parentage testing was > 99.99%. The set of 176 SNPs had a higher identification rate in the single parent-child parentage test than in the ASP. The CPI/PP value calculated using 176 SNPs was also more accurate than that calculated using ASP. Our findings suggest that these 176 SNPs could be used for single-parent-child parentage identification in the Taiwanese population.

Characteristics of drawing deficits in people with aphasia: Differences between symbolic and realistic drawn objects.

Pictures drawn by people with aphasia (PWA) are often more challenging to understand than those drawn by healthy people. There are two types of objects: those that tend to be drawn symbolically (symbolically drawn objects-SOs) and those that are likely to be drawn realistically (realistically drawn objects-ROs). To compare the identification rate and number of misunderstanding types between SOs and ROs drawn by PWA and healthy controls (HCs). To reveal trends in the misunderstandings of drawings by PWA, and to identify the language or cognitive abilities related to the identification rate of pictures drawn by PWA. We designed a drawing task involving SOs and ROs. A total of 18 PWA and 30 HCs completed the task, and respondents identified the drawings. The identification rate and number of misunderstandings were analysed with two-way analysis of variance (ANOVA) including group (PWA and HCs) and object type (SOs and ROs). The misunderstandings were divided into four categories varying in semantic and morphological similarity; these ratios were examined with a chi-square test. The relationships of language and cognitive abilities with the identification rate were investigated with multiple regression analyses. There was a significant effect of the interaction between group and object type on the identification rate (F(1.1387) = 3.90, Mean Squared Error (MSE) = 4139.67, p = 0.04): the identification rates for ROs were lower in the PWA than in the HCs. For the number of misunderstanding types, an interaction was observed between group and object type (F(1.56) = 8.26, MSE = 26.93, p < 0.01): the number of misunderstanding types for ROs in the PWA was greater than that in the HCs. The misunderstanding patterns differed between ROs and SOs (χ2(3) = 694.30, p < 0.001, V = 0.37). ROs were semantically related, whereas SOs were morphologically related. The identification rates of ROs and SOs were correlated only with Kanji writing scores (ROs: β = 3.66, p = 0.01; SOs: β = 6.57, p < 0.01). In drawings by the PWA, SOs had a higher identification rate, while ROs had a lower identification rate and a greater variety of misunderstandings. SOs may increase drawing motivation. Interventions to improve the identifiability of SOs and ROs should reflect each character. Identification rates were correlated only with Kanji writing scores. The PWA, whose native language was Japanese and had preserved Kanji writing abilities, and their communication abilities may be increased through drawing. What is already known on the subject PWA often have impaired drawing abilities and draw pictures that third parties misinterpret. Some objects tend to be drawn symbolically, and some are drawn realistically. However, it is not clear whether there is a difference between these types of drawings depicted by PWA in identifiability and the tendency to be misunderstood by ordinary people. In addition, the relationships between language or cognitive abilities and the identification rate of drawn pictures are not clear. What this paper adds to the existing knowledge The identification rate differed between SOs and ROs. In drawings by PWA, SOs had a higher identification rate, while ROs had a lower identification rate and the greatest variety of misunderstandings. Approximately half of the misunderstandings were related to the target object. SOs tended to be confused with morphologically related objects, while ROs tended to be confused with semantically related objects. Identification rates were correlated only with Kanji writing scores. What are the potential or actual clinical implications of this work? To motivate PWA's drawing, it is suitable to begin with SOs. Examining drawing ability from the perspective of SOs and ROs increases the chance of identifying drawing ability. PWA whose native language is Japanese and have preserved Kanji writing abilities may be able to increase their communication abilities through drawing.

Dog Breed Classification and Identification Using Convolutional Neural Networks

This study aimed to assess the effectiveness of using pre-trained models to extract biometric information, specifically the dog breed and dog identity, from images of dogs. The study employed pre-trained models to extract feature vectors from the dog images. Multi-Layer Perceptron (MLP) models then used these vectors as input to train dog breed and identity classifiers. The dog breeds used in this study comprised two Thai breeds, Bangkaew and Ridgeback, and 120 foreign breeds. For dog breed classification, the results showed that, among the ImageNet classification models, the pre-trained NasNetLarge model has the highest dog breed classification accuracy (91%). The newly trained MLP model, which used feature vectors obtained by NasNetLarge, achieved higher accuracy at 93%. For dog identification, the results showed that, without data augmentation, the pre-trained ResNet50 model had the highest dog identification accuracy (75%). However, with data augmentation, MobileNetV2 could achieve a higher accuracy of 77%. When evaluating the identification performance of each breed, it is important to note that pugs achieved the lowest identification rate at 57.4%. Conversely, Bangkaew dogs demonstrated outstanding performance, with the highest identification rate at 98.6%.

Radar emitter structure identification based on stacked frequency sparse auto‐encoder network

AbstractIn the current complex situations of electronic intelligence (ELINT), the authors present a radar emitter structure (RES) identification method based on deep learning at a new level to address the issue of incomplete cognitive information. Firstly, due to the fact that existing simulation data cannot fully reflect the structure features of the entire radar emitter, the structure feature‐level RES model is built using direct digital synthesiser (DDS) technology and radio frequency (RF) simulation platform. Afterwards, considering that the structure features are reflected in the frequency domain, a stacked frequency sparse auto encoder (sFSAE) network is constructed by adding a constraint term in frequency domain to the loss function of sparse auto encoder (SAE). Using deep learning to extract structure features with constraints in different domains is instructive for feature extraction techniques under variable operating parameters. Finally, the extracted structure features are input into the Softmax classifier to perform the identification from the radar signal to the RES. The experimental results show that the proposed method has high generalisation ability and robustness under different modulation types, different operating parameters and different signal to noise ratio (SNR). It also has a high identification rate even for untrained modulated signals.

Research on identification of coal and gangue under complex working conditions based on relief feature

ABSTRACT To improve the identification accuracy of coal gangue under the complex working conditions of pulverized coal and moisture adhesion on the surface, this paper studies the distribution difference of characteristic curves under five working conditions of different proportions of moisture and pulverized coal, and finds that moisture has a greater influence on gradient features, while pulverized coal has a greater influence on gray texture features. Then this paper studies the distribution of coarse texture and fine texture in coal and gangue, and finds that there are many fine textures and few coarse textures on the surface of gangue, while coal shows the opposite law. In addition, the ratio of pixels with large gray value to pixels with small gray value can reduce the occurrence of noncompliance with the above laws. Finally, through the combination of coarse texture, fine texture, and gray value difference, the relief feature is put forward. Compared with different features, the average feature overlap ratio proposed in this paper is lower, at 23.83%. Compared with different methods, this method has a higher identification rate under different working conditions, and the average identification accuracy rate is 89.59%, which is at least 13.93% higher than other methods.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

Establishment and application of a Multiple nucleotide polymorphism molecular identification system for grape cultivars

Grapes (Vitis L.) are one of the most important and popular fruits in China. However, efficient identification of grapes is important in the protection of domestic grapes and breeders’ rights. Here, we developed a new method called multiple nucleotide polymorphisms (MNP), which combines the advantages of multiple PCR amplification and high-throughput sequencing to improve the efficiency of grape variety identification. We identified 582 universal MNP markers and screened them based on the whole genome sequencing data of 31 representative cultivars. Subsequently, MNP libraries of 60 common cultivars were constructed using multiple PCR amplification and sequencing. The pairing comparison of 60 grape materials revealed a high identification rate (>99 %) of MNP markers. Furthermore, MNP accurately distinguishes diploid and polyploid, which can assist in molecular identification of grapes. Overall, MNP molecular identification technology provides a new way for grape variety identification. Compared with SNP, MNP reduces the cost of identification because it does not need to make expensive gene chips. In addition, MNP can perform the amplification of thousands of primers in a single tube and uses sequencing instead of polyacrylamide gel electrophoresis, which greatly saves experimental consumables and labor compared with SSR. Collectively, the developed technique has the advantages of low cost and high efficiency, which is more competitive than other identification methods at present. The developed technology can assist in resource research and promote plant breeding.

Characterization of Functional Coatings on Cork Stoppers with Laser-Induced Breakdown Spectroscopy Imaging.

Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.

Detection of hidden cases of familial hypercholesterolemia in the Netherlands: a central laboratory data-driven strategy

Abstract Introduction Familial hypercholesterolemia (FH) is a common genetic disorder in which patients are at high risk for premature coronary artery disease (CAD) due to lifelong exposure to low-density lipoprotein cholesterol (LDL-C). Although early diagnosis and lipid lowering therapy (LLT) are essential to prevent adverse cardiovascular outcomes, FH patients remain globally underdiagnosed and undertreated, warranting novel screening methods to identify these patients. In the present study we aimed to evaluate whether existing central laboratory data can be utilized as a new method to detect undiagnosed FH patients. Additionally, we investigated the extent of underdiagnosis and undertreatment among patients with severe hypercholesterolemia in the Netherlands. Methods In this single-center, cross-sectional study, we selected individuals with at least one previously measured LDL-C plasma level ≥99.5th percentile for age and sex, from a central laboratory database containing lipid profiles of approximately 600.000 primary care patients. First, a blood sample was obtained to get more recent lipid values. Participating patients were subsequently asked to fill out a detailed questionnaire on medical and family history. Using these data, we calculated the Dutch Lipid Clinic Network (DLCN) criteria for each patient for the clinical diagnosis of FH. Results We identified a total of 5,100 patients with severe hypercholesterolemia (at least one LDL-C ≥99.5th percentile for age and sex) from the central laboratory database. Out of these, 1,206 patients (24%) participated in this study. Mean ± SD LDL-C corrected for LLT was 5.72 ± 1.83 mmol/L, 785 patients (65.1%) used LLT, and 150 patients (12.4%) had CVD in their medical history. In total, 131 patients (11%) had a persistent LDL-C ≥99.5th percentile for age and sex. DLCN criteria for phenotypic FH (DLCN score ≥6) were met by 302 patients (25%) of whom 39 patients (12.9%) had undergone prior genetic testing according to clinical guidelines. Of the 302 clinically diagnosed FH patients only 1 (0.3%) met the European Society of Cardiology (ESC) guideline recommending LDL-C targets &amp;lt;1.8 mmol/L in FH patients. Conclusion These data highlight the significant underdiagnosis and undertreatment of FH in the Netherlands, despite the fact that this country has a relatively high FH identification rate. We demonstrate that the use of central laboratory data is an effective strategy for identifying previously undetected cases of FH and eagerly await the genetic confirmation of the newly identified FH patients in this study, which will provide valuable insights into the genetic basis of this disorder and help further improve patient care.

Abstract 17060: Preliminary Assessment of Artificial Intelligence Guided Echocardiographic Screening of Cardiac Amyloidosis

Background: Cardiac amyloidosis (CA) diagnosis is often overlooked. Novel CA treatments underscore the importance of early identification, as untreated CA has a poor prognosis. EchoNet-Screening is an echocardiogram-based artificial intelligence model to identify CA; previously refined by echocardiograms from patients with known CA. The goal of our study is to prospectively evaluate the accuracy of EchoNet-Screening across a large health system. Methods: All echocardiographic studies from our cardiac quaternary care center between 2019-2021 were batch-processed by EchoNet-Screening to produce a CA risk-prediction score (CA-RPS) for each patient based on the highest quality study, which is determined by the model. 336 patients with the highest CA-RPS underwent evaluation by detailed chart review by our CA clinical cardiology team. High risk patients without an alternative diagnosis are subsequently referred for CA specialty evaluation. (ClinicalTrial.gov/NCT05139797) Results: EchoNet-Screening was performed on echocardiographic images from 58,236 patients. Preliminary retrospective assessment of 336 patients with the highest CA-RPS revealed CA in 142 patients; almost half were not known from our advanced heart failure (AHF) registry and newly identified from chart review. 62 patients are pending prospective enrollment for CA subspecialty evaluation to further characterize the performance of EchoNet-Screening. Conclusions: Preliminary results from our trial demonstrate a high accuracy of EchoNet-Screening for detection of patients with known CA, emphasized by high rates of identification of CA patients even on initial chart review. Further work is ongoing to evaluate the performance of EchoNet-Screening to bring high-risk patients to new diagnoses and eventual care.