Low Identification Rate Research Articles

Research on Fault Location Method for Low-Voltage Active Distribution Network Based On Correlation Of Main Frequency Components

In order to solve the problems of poor fault tolerance and low fault identification rate, a fault location method based on the correlation of main frequency components is proposed. Based on the distribution network of main frequency components extracted by Prony algorithm, this paper introduces the fault location model of low-voltage active distribution network built by hierarchical algorithm, and realises the correlation of main frequency components based on the research of fault location method of low-voltage active distribution network based on intelligent algorithm. The experimental results show that the fault tolerance rate of the proposed method based on the correlation of main frequency components is over 76% and the fault recognition rate is over 60%, which appears to be higher than several other methods. It is consequently believed that the proposed method may stand a good chance to offer better fault location performance.

Improved Spike Detection Algorithm Based on Multi-Template Matching and Feature Extraction

Spike detection is of great significance in the detection of epileptic seizures. Many spike detection algorithms have been proposed, but available algorithms often miss spikes during high firing rate epochs or with artifacts. Also, there exists methodological limitations when there are great variations in spike morphology between different patients or the same patient in different EEG epoch. Thus a data-driven spike detection method using multi-template matching, feature extraction and threshold method is proposed in this work to improve the detection performance under different circumstances. First, universal template matching and feature extraction are used to get putative spikes. Then clustering algorithm and adaptive template matching algorithm are applied to maximize the number of detected spikes for these specific candidate single units obtained from the clustering method. In addition, a data-driven approach based on receiver operating characteristic (ROC) is used for low false identification rate detection. The performance is evaluated on real EEG samples and the evaluating results shows that this algorithm has achieved 97.12% average sensitivity and 0.55 average false negative rate per minute. The least sensitivity is 94.32% and the most false negative rate per minute is 1.23.

Improved Combined Scatterers Interferometry With Optimized Point Scatterers (ICOPS) for Interferometric Synthetic Aperture Radar (InSAR) Time-Series Analysis

Despite the development of point-based interferometric synthetic aperture radar (InSAR) time-series methods, such as persistent scatterer interferometry (PSI), it remains difficult to identify reliable measurement points (MPs) in areas under vegetated cover. In this study, we developed a new algorithm, improved combined scatterers interferometry with optimized point scatterers (ICOPS), to monitor changes in surface deformation by combining persistent scatterers (PSs) and distributed scatterers (DSs). The algorithm was subsequently improved through the application of a machine learning process. A generalized likelihood ratio test (GLRT) was applied to identify statistically homogeneous pixels suitable for a small SAR dataset. A machine learning-based optimization process was used to overcome the increase in MPs after the combined scatterers interferometry (CSI) process. The MP optimization process used the support vector regression (SVR) algorithm to find the optimal point from all MPs in the dataset. To improve the reliability of the MP surface deformation mapping after optimization, the optimized hot-spot analysis (OHSA) method was applied to obtain spatially clustered MPs; this was possible despite a low deformation identification rate. To demonstrate the effectiveness of the ICOPS method, we applied this approach to Yellowstone Lake in the USA using 95 SAR images from the Sentinel-1 satellite that was taken during the period of 2017–2020. The CSI method produced an increase in MP density, especially in areas not covered by PSI, which indicated its ability to detect deformations in mountainous areas. Comparisons with GPS data and traditional methods produced promising results with an accuracy of 1 cm/year in terms of the root mean square error (RMSE). The optimization process in CSI also had the advantage of retrieving helpful information in the MP dataset and increased the accuracy of CSI by 12%. Deformation mapping using the optimized results provided new insights regarding the spatial clustering of surface deformation in MPs. This could provide the foundation for developing a posttime-series optimization process based on point scatter as a surface change detection tool.

基于精氨酸酶切的蛋白质C端肽段富集方法的优化及评估

基于聚合物的蛋白质C端反向富集策略是用于研究蛋白质C端最为广泛的策略之一。目前,基于胰蛋白酶(trypsin)切割精氨酸残基C端(ArgC型酶切)的蛋白C端组学方法对蛋白质C端的鉴定深度仍有待提高。为解决这一问题,该研究对此方法进行了优化和评估:建立了基于“V型”过滤装置的“一锅法”富集流程,避免了副反应的干扰,缩短了样本的制备时间;优化了蛋白水平乙酰化反应条件,最大限度地降低了丝氨酸、苏氨酸、酪氨酸残基上的副反应,提高了肽段鉴定的可信性;优化了基于固相萃取枪头膜片过滤柱(StageTip柱)的样品分离过程,使C端肽段的鉴定深度增加至原来的4倍。通过以上优化,按照肽段水平错误发现率(FDR)<0.01、离子分数(ion score)≥20,且C端带有乙醇胺修饰的数据筛选标准,从人HEK 293T细胞中共鉴定出696个蛋白质C端。若仅要求肽段水平FDR<0.01,鉴定数目进一步增加到933个,这是基于聚合物富集策略的蛋白质C端组学方法所得的最大数据集之一。探索了胰蛋白酶镜像酶(LysargiNase)切割精氨酸残基N端(ArgN型酶切)与不同肽段N端衍生化修饰组合对蛋白质C端鉴定数目和种类的影响,“LysargiNase酶切+肽段N端乙酰化”新策略在原有“胰蛋白酶酶切+肽段N端二甲基化”策略的基础上将鉴定蛋白质C端的种类提升了47%。综上,该研究通过对基于Arg型酶切的蛋白C端组学方法的优化,提升了C端肽段的鉴定深度,扩大了C端肽段鉴定的覆盖范围。该方法将有望成为系统性表征蛋白质C端的有力工具。

Evaluation of Serotyping of Environmental and Clinical Isolates of Legionella pneumophila using MALDI-TOF MS.

Legionella pneumophila (L. pneumophila) is responsible for most Legionnaire's disease cases diagnosed worldwide. The species includes 16 serogroups, but most Legionnaire's disease cases (85.7% in Europe, 87.0% in Japan) are caused by L. pneumophila serogroup 1. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) can be used to identify the L. pneumophila serogroup. In this study, we compared three sample preparation methods that are compatible with MALDI-TOF MS: the direct colony transfer method (DCTM), on-target extraction method (OTEM), and in-tube extraction method (ITEM). The aim was to improve the low identification rates for L. pneumophila, and establish and validate a simple, rapid and robust MALDI-TOF MS-based method for routine use in microbiological laboratories for assignment of L. pneumophila isolates to serogroups and identification of reliable peak biomarkers. Using ITEM, 100.0% (29/29) of hot spring water samples and clinical isolates were correctly identified at the species level. Augmented reference spectra correctly identified all 29 strains at the species level and 29 isolates at the serogroup level, displaying sensitivity, specificity and accuracy of 100.0% for serogroup assignment. MALDI-TOF MS is a relatively inexpensive method for assignment of L. pneumophila serogroups that can serve as a first-line tool for rapid prospective typing.

Risk Factors Associated with the Development of Metastases in Patients with Gastroenteropancreatic Neuroendocrine Tumors: A Retrospective Analysis.

Neuroendocrine tumors develop from systemic endocrine and nerve cells, and their occurrence has increased recently. Since these tumors are heterogeneous, pathological classification has been based on the affected organ. In 2019, the World Health Organization introduced a change expected to influence neuroendocrine tumor research, as gastroenteropancreatic neuroendocrine tumors are now included within a unified classification. This retrospective study aimed to investigate the characteristics (e.g., lymph node metastases and all other metastases) of gastroenteropancreatic neuroendocrine tumors using this new classification in 50 cases. Tumor size, depth, MIB-1 index, lymphatic invasion, venous invasion, and neuroendocrine tumor grade were significantly correlated with lymph node metastasis and other metastases. The venous invasion was more strongly correlated with lymph node metastasis and all other types of metastases than with lymphatic invasion. Identification rates for lymphatic invasion were considered lower because of structural problems such as lymphatic vessels being much thinner than veins. However, venous invasion was considered effective in compensating for the low identification rate in cases of lymphatic invasion. In future research, a unified classification and standardized framework for assessment will be important when analyzing the characteristics of neuroendocrine tumors, and large-scale studies are required.

Early identification of persons living with dementia at point‐of‐care hospitalization using the electronic medical record

AbstractBackgroundHospitalization for Alzheimer's disease and related dementias (ADRD) is the single largest expense in the U.S., accounting for two thirds of Medicare costs for ADRD estimated at $155 billion in 2020. Persons living with dementia (PLWD) are twice as likely to be hospitalized, have longer average lengths of stay, higher readmission and mortality rates and are at increased risk for institutionalization. A major challenge is the low rates of identification of PLWD upon admission to the hospital, preventing early institution of person‐centered collaborative care interventions that can improve patient outcomes.MethodTwo trained dementia nurse practitioners performed a prospective daily review of all patients age &gt;70 years admitted to a large academic medical center for a period of 8‐weeks (1/26/2021 – 3/23/2021). The NPs performed a systematic keyword search of the EMR for ADRD diagnoses, which were confirmed through review of past medical history, outpatient primary care and consult notes, problem and medication lists. Hospitalized PLWD were analyzed for quality of care indicators.ResultOf the 249 hospitalized patients (mean age 86.5 years;61% female) with ADRD diagnosis, 47 (18%) had dementia included in the admission diagnosis while 151 (61%) had dementia included in the admission history and/or assessment/plan. 71 patients (29%) had restraints ordered, 120 (48%) were prescribed psychoactive medications and 95 (38%) were put on restraints. Advance directives were completed for 122 (49%) of PLWD and 157 (63%) had a full code status. 149 (60%) had a previous inpatient admission and 57 (23%) had an ED visit in the last 12 months. Ninety‐five (38%) of hospitalized PLWD were discharged to institutional facilities.ConclusionThere are low rates of inclusion of dementia in admission diagnoses and notes of hospitalized PLWD. In contrast, there are high rates of restraint use, psychoactive medication order and discharge to institutional facilities. Most PLWD admitted to the hospital has had a previous hospitalization in the past year. To our knowledge, this is the first study that utilized EMR‐based identification of ADRD diagnosis and tracking of hospital outcomes of PLWD. These findings represent opportunities for improvement in quality and outcomes of hospital care for PLWD.

An End-to-End Identification Algorithm for Smearing Star Image

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

HERMES: a molecular-formula-oriented method to target the metabolome.

Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.

Biostimulant impacts of Glutacetine® and derived formulations (VNT1 and VNT4) on the bread wheat grain proteome

Nitrogen (N) fertilizer is essential to ensure grain yield and quality in bread wheat. Improving N use efficiency is therefore crucial for wheat grain protein quality. In the present work, we analysed the effects on the winter wheat grain proteome of biostimulants containing Glutacetine® or two derived formulations (VNT1 and 4) when mixed with urea-ammonium-nitrate fertilizer. A large-scale quantitative proteomics analysis of two wheat flour fractions produced a dataset of 4369 identified proteins. Quantitative analysis revealed 9, 39 and 96 proteins with a significant change in abundance after Glutacetine®, VNT1 and VNT4 treatments, respectively, with a common set of 11 proteins that were affected by two different biostimulants. The major effects impacted proteins involved in (i) protein synthesis regulation (mainly ribosomal and binding proteins), (ii) defence and responses to stresses (including chitin-binding protein, heat shock 70 kDa protein 1 and glutathione S-transferase proteins), (iii) storage functions related to gluten protein alpha-gliadins and starch synthase and (iv) seed development with proteins implicated in protease activity, energy machinery, and the C and N metabolism pathways. Altogether, our study showed that Glutacetine®, VNT1 and VNT4 biostimulants positively affected protein composition related to grain quality.Data are available via ProteomeXchange with identifier PXD021513. SignificanceWe performed a large-scale quantitative proteomics study of the total protein extracts from flour samples to determine the effect of Glutacetine®-based biostimulants treatment on the protein composition of bread wheat grain. To our knowledge, only a few studies in the literature have applied proteomic approaches to study bread wheat grains and in particular to investigate the effect of biostimulants on the grain proteome of this cereal crop. In addition, most approaches used fractional extraction of proteins to target reserve proteins followed electrophoresis which leads to low identification rate of proteins. We identified and quantified a large protein dataset of 4369 proteins and determined ontological class of proteins affected by biostimulants treatments. Our proteomics investigation revealed the important role of these new biostimulants in achieving significant changes in protein synthesis regulation, storage functions, protease activity, energy machinery, C and N metabolism pathways and responses to biotic and abiotic stresses in grain.

Cascaded fault detection system of error back-propagation network based on node association degree

In view of the problems of low fault identification rate and large global error of network fault system designed by the existing methods, an automatic fault detection system based on node association degree is proposed. Firstly, the hardware design of the system is completed by describing and analyzing the initialization module, detector training module, memory antibody module and fault automatic detection module. Secondly, the system software is designed by network anomaly diagnosis and configuration anomaly detection. Finally, the system can detect the cascade fault of the backward propagation network by the design of software and hardware. The experimental results show that the system designed in this paper has high fault identification rate and small global error of fault detection, and can accurately detect the network cascade faults.

Nonlaboratory-Based Risk Assessment Model For Type 2 Diabetes Mellitus Screening in Chinese Rural Population: A Joint Bagging-Boosting Model.

Diabetes mellitus is one of the major public health problems in the world due to its high prevalence and medical costs. The prevention effort necessitates reliable risk assessment models which can effectively identify high-risk individuals and enable healthcare practitioners to initiate appropriate preventive interventions. However, diabetes risk assessment models based on data analysis face multiple challenges, such as class imbalance and low identification rate. To cope with these challenges, this paper proposed an analytical framework based on data-driven approaches using large population data from the Henan Rural Cohort Study. A joint bagging-boosting model (JBM) was developed and validated. For the convenience of large-scale population screening, our study excluded laboratory variables and collinearity variables using the maximum likelihood ratio method to obtain accessibility variables. Then, we explored the effects of different methods for dealing with the unbalanced nature of the available data, including over-sampling and under-sampling methods. Finally, to improve the overall model performance, a joint model which combined the bagging and boosting algorithms with the stacking algorithm was constructed. The model we built demonstrated good discrimination, with an area under the curve (AUC) value of 0.885, and acceptable calibration (Brier score = 0.072). Compared with the benchmark model, the proposed framework improved the AUC value of the overall model performance by 13.5%, and the recall increased from 0.744 to 0.847. The proposed model contributes to the personalized management of diabetes, especially in medical resource-poor settings.

Contribution of magnetic resonance imaging to the prenatal diagnosis of common congenital vascular anomalies

BackgroundScreening ultrasound (US) has increased the detection of congenital vascular anomalies in utero. Complementary magnetic resonance imaging (MRI) may improve the diagnosis, but its real utility is still not well established.ObjectivesWe aimed to describe the imaging findings on prenatal US and MRI of the most frequent congenital vascular anomalies (lymphatic malformations and congenital hemangiomas) to assess the accuracy of prenatal US and MRI exams for diagnosis and to evaluate the relevance of the additional information obtained by complementary fetal MRI.Materials and methodsAll confirmed postnatal congenital vascular anomalies detected in the last 10 years at 3 university hospitals were retrospectively identified. The prenatal diagnosis was compared with the final diagnosis for both methods and the clinical relevance of additional MRI information was evaluated. A second MRI in advanced pregnancy was performed in fetuses with lesions in a sensitive anatomical location and the clinical relevance of the additional information was evaluated.ResultsTwenty-four cases were included in the study, 20 lymphatic malformations and 4 hemangiomas. MRI slightly improved the diagnosis of lymphatic malformation, 85% vs. 80% at US, especially for abdominal lesions. Both methods had a low identification rate (25%) for tumors. MRI performed late in five fetuses with lymphatic malformation allowed optimized management at birth.ConclusionMRI improves the diagnosis of congenital lymphatic malformations whereas hemangiomas remain difficult to identify in utero. The main role of MRI is to provide high-defined anatomical data to guide management at birth.

A community-supported metaproteomic pipeline for improving peptide identifications in hydrothermal vent microbiota.

Microorganisms in deep-sea hydrothermal vents provide valuable insights into life under extreme conditions. Mass spectrometry-based proteomics has been widely used to identify protein expression and function. However, the metaproteomic studies in deep-sea microbiota have been constrained largely by the low identification rates of protein or peptide. To improve the efficiency of metaproteomics for hydrothermal vent microbiota, we firstly constructed a microbial gene database (HVentDB) based on 117 public metagenomic samples from hydrothermal vents and proposed a metaproteomic analysis strategy, which takes the advantages of not only the sample-matched metagenome, but also the metagenomic information released publicly in the community of hydrothermal vents. A two-stage false discovery rate method was followed up to control the risk of false positive. By applying our community-supported strategy to a hydrothermal vent sediment sample, about twice as many peptides were identified when compared with the ways against the sample-matched metagenome or the public reference database. In addition, more enriched and explainable taxonomic and functional profiles were detected by the HVentDB-based approach exclusively, as well as many important proteins involved in methane, amino acid, sugar, glycan metabolism and DNA repair, etc. The new metaproteomic analysis strategy will enhance our understanding of microbiota, including their lifestyles and metabolic capabilities in extreme environments. The database HVentDB is freely accessible from http://lilab.life.sjtu.edu.cn:8080/HventDB/main.html.

Individual Treatment Progress Predicts Satisfaction With Transition-Related Care for Youth With Gender Dysphoria: A Prospective Clinical Cohort Study

BackgroundThe number of adolescents presenting with gender dysphoria (GD) in healthcare services has increased significantly, yet specialized services offering transition-related care (TRC) for trans youth is lacking. AimTo investigate satisfaction with TRC, regret, and reasons for (dis)satisfaction with transition-related medical interventions (TRMIs) in trans adolescents who had presented to the Hamburg Gender Identity Service for children and adolescents (Hamburg GIS). MethodsData were collected from a clinical cohort sample of 75 adolescents and young adults diagnosed with GD (81% assigned female at birth) aged 11 to 21 years (M = 17.4) at baseline and follow-up (on a spectrum of ongoing care, on average 2 years after initial consultation). To determine progress of the youth's medical transitions, an individual treatment progress score (ITPS) was calculated based on number of desired vs received TRMIs. OutcomesMain outcome measures were satisfaction with TRC at the time of follow-up, ITPS, social support, reasons for regret and termination of TRC, and (dis)satisfaction with TRMIs. ResultsParticipants underwent different stages of TRMIs, such as gender-affirming hormone treatment or surgeries, and showed overall high satisfaction with TRC received at the Hamburg GIS. Regression analysis indicated that a higher ITPS (an advanced transition treatment stage) was predictive of higher satisfaction with TRC. Sex assigned at birth, age, and time since initial consultation at the clinic showed no significant effects for satisfaction with TRC, while degree of social support showed a trend. No adolescents regretted undergoing treatment at follow-up. Additional analysis of free-text answers highlighted satisfaction mostly with the physical results of TRMI. Clinical ImplicationsBecause youth were more satisfied with TRC when their individual transition (ITPS) was more progressed, treatment should start in a timely manner to avoid distress from puberty or long waiting lists. Strengths and LimitationsThis study is one of the first to report on treatment satisfaction among youth with GD from Europe. The ITPS allowed for a more detailed evaluation of TRMI wishes and experiences in relation to satisfaction with TRC and may close a gap in research on these treatments in adolescent populations. However, all participants were from the same clinic, and strict treatment eligibility criteria may have excluded certain trans adolescents from the study. Low identification rates with non-binary identities prevented comparisons between non-binary and binary genders. ConclusionThe study highlights the role of TRMI and individual treatment or transition progress for youth's overall high satisfaction with TRC received at the Hamburg GIS.Nieder TO, Mayer TK, Hinz S, et al. Individual Treatment Progress Predicts Satisfaction With Transition-Related Care for Adolescents With Gender Dysphoria: A Prospective Clinical Cohort Study. J Sex Med 2020;XX:XXX–XXX.

Epidemiological profile of Plasmodium ovale spp. imported from Africa to Anhui Province, China, 2012\u20132019

BackgroundAlthough autochthonous malaria cases are no longer reported in Anhui Province, China, imported malaria has become a major health concern. The proportion of reported malaria cases caused by Plasmodium ovale spp. increased to levels higher than expected during 2012 to 2019, and showed two peaks, 19.69% in 2015 and 19.35% in 2018.MethodsA case-based retrospective study was performed using data collected from the China Information System for Disease Control and Prevention (CISDCP) and Information System for Parasitic Disease Control and Prevention (ISPDCP) from 2012 to 2019 to assess the trends and differences between Plasmodium ovale curtisi (P. o. curtisi) and Plasmodium ovale wallikeri (P. o. wallikeri). Epidemiological characteristics were analyzed using descriptive statistics.ResultsPlasmodium o. curtisi and P. o. wallikeri were found to simultaneously circulate in 14 African countries. Among 128 patients infected with P. ovale spp., the proportion of co-infection cases was 10.16%. Six cases of co-infection with P. ovale spp. and P. falciparum were noted, each presenting with two clinical attacks (the first attack was due to P. falciparum and the second was due to P. ovale spp.) at different intervals. Accurate identification of the infecting species was achieved among only 20.00% of cases of P. ovale spp. infection. At the reporting units, 32.17% and 6.96% of cases of P. ovale spp. infection were misdiagnosed as P. vivax and P. falciparum infections, respectively.ConclusionThe present results indicate that the potential of P. ovale spp. to co-infect with other Plasmodium species has been previously underestimated, as is the incidence of P. ovale spp. in countries where malaria is endemic. P. o. curtisi may have a long latency period of > 3 years and potentially cause residual foci, thus posing challenges to the elimination of malaria in P. ovale spp.-endemic areas. Considering the low rate of species identification, more sensitive point-of-care detection methods need to be developed for P. ovale spp. and introduced in non-endemic areas.

A Survey on Machine Learning Approaches for Developing Intrusion Detection System

Artificial Intelligence is a cutting edge and profoundly modern technological application which has being recently used methodologies in the industry. This approach is inescapable and is broadly utilized for different applications. Machine Learning which is the well-known application of artificial intelligence is considered to be an essential requirement in numerous fields such as Account, Biomedical and Network Security research areas. Intrusion detection system is fundamentally utilized for securing system and data framework from malicious attacks. It screens the activity of host machine or a system. IDS keeps track of the state of hardware and software running in the networks for malicious activities that are planned for stealing data. Applying ML models can bring about low false alarm rate and high identification rate. Machine Learning methods can intelligently identify normal and malicious traffic with high accuracy. This research paper highlights different ML approaches utilized to create IDS. Through the broad study and investigation on current literature, the gap for improving and creating efficient IDS can be determined.

Spatial capture-recapture with random thinning for unidentified encounters.

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain).Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Tyrosine-EDC Conjugation, an Undesirable Side Effect of the EDC-Catalyzed Carboxyl Labeling Approach.

Carbodiimide-catalyzed carboxyl and amine conjugation (amidation) has been widely used to protect carboxyl groups. N-(3-(Dimethylamino)propyl)-N'-ethylcarbodiimide (EDC) is the most common carbodiimide reagent in protein chemistry due to its high catalytic efficiency in aqueous media. The reaction has also been applied in different proteomic studies including protein terminomics, glycosylation, and interaction. Herein, we report that the EDC-catalyzed amidation could cause a +155 Da side modification on the tyrosine residue and severely hamper the identification of Tyr-containing peptides. We revealed the extremely low identification rate of Tyr-containing peptides in different published studies employing the EDC-catalyzed amidation. We discovered a +155 Da side modification occurring specifically on Tyr and decoded it as the addition of EDC. Consideration of the side modification in a database search enabled the identification of 13 times more Tyr-containing peptides. Furthermore, we successfully developed an efficient method to remove the side modification. Our results also imply that chemical reactions in proteomic studies should be carefully evaluated prior to their wide applications. Data are available via ProteomeXchange with identifier PXD020042.

Risk and vulnerabilities of Filipinos in the identification and management of dementia through knowledge, attitudes and practices about dementia in adults aged 18‐65 years old who consult at the outpatient department of a tertiary hospital in Quezon City

AbstractBackgroundDementia is an overwhelming disease that pose a burden in the society. Health information and education about dementia in the Philippines is still limited. Lack of awareness entails low identification and diagnosis rate, limited health care options and under‐utilization of existing programs and interventions. The paper aims to describe the risks and vulnerabilities of Filipinos in the identification and management of Dementia among adults through assessment of their knowledge, attitudes and practices.MethodA descriptive, cross‐sectional study was performed in a government tertiary hospital in Quezon City, Philippines. The primary investigator facilitated interviews guided by an experts reviewed‐interview schedule. A sample of 170 individuals aged 18‐65 years old who seek consult or is accompanying a patient were selected using a random sampling method.ResultThe study showed only 27.65% were knowledgeable of Dementia. Seventy percent of the participants considered it as a normal part of aging. Majority of the respondents (95.29%) showed accepting attitudes towards people with dementia. At least 33% showed a possibility of gearing towards negative practices towards those having the condition. One hundred fifteen out of 170 (67.75%) recognizes that patience is needed in taking care of people with dementia. No significant relationship identified between level of knowledge and attitude (95% CI, Fisher’s p = 0.115). Practices towards people with dementia showed significant relationship with level of knowledge (95% CI, Fisher’s p = &lt;0.001). However, in terms of health seeking behavior, those who showed poor health seeking behavior were not knowledgeable of dementia (72.73%) showing significant correlation (95% CI, Fisher’s p = &lt;0.001).ConclusionDespite the increasing prevalence of dementia in the Philippines, awareness and understanding of the condition remain limited. Though only a few was considered knowledgeable, majority showed accepting attitudes and positive practices. However, the health seeking behavior is influenced by their knowledge on dementia. Limited awareness and understanding, and the fear associated with dementia substantiates the urgency to come up with programs on public education and stigma reduction. These strategies could prevent neglect and social isolation of people living with dementia, help them achieve quality of life by maintaining self‐esteem and improve health‐seeking behaviors.