Tool For Identification Research Articles

Development and evaluation of a lateral flow immunochromatographic assay for the rapid detection of donkey meat in beef as a tool for meat adulteration identification

Development and evaluation of a lateral flow immunochromatographic assay for the rapid detection of donkey meat in beef as a tool for meat adulteration identification

Frequency-dependent nonlinear AVO inversion for fluid mobility in viscoelastic media

Reservoir fluid mobility is defined as the ratio of rock permeability to fluid viscosity, which is an important attribute to guide the prediction of high-quality oil and gas reservoirs. Frequency-dependent amplitude variation with offset (AVO) inversion, based on viscoelastic theory, is a useful tool for fluid identification. In conventional pre-stack inversion, linear approximations are usually used to calculate the reflection coefficients, while nonlinear inversions are only occasionally carried out. However, the nonlinear equation for the reflection coefficient, derived from the exact Zoeppritz equation, has higher accuracy and fewer assumptions than the linear approximations. Therefore, we derive a nonlinear frequency-dependent reflection coefficient equation of the PP-wave in terms of P-wave velocity reflectivity, S-wave velocity reflectivity and fluid mobility, based on the relationship among fluid mobility, incident angle, and seismic wave frequency. We consider viscoelastic media and frequency-dependent responses to improve the authenticity of the reflection coefficients. Additionally, we develop a split-step inversion method for fluid mobility based on the Artificial Neural Network Inversion (ANNI) algorithm. Synthetic and field data examples demonstrate that the proposed split-step inversion method outperforms traditional inversion methods that rely on approximate equations for predicting underground reservoirs, improving the stability of fluid mobility parameter inversion.

The tooth-bearing skeletal elements of the Italian urodeles, a comparative tool for osteological identification

Urodele osteology is characterised by simplified skulls, loss of several bones and a specific sequence of cranial and limb ossification. The relatively few studies devoted to the comparative analysis of isolated urodele bones are mostly focused on the vertebrae and occipital complexes, and to a lesser extent humeri. The tooth-bearing skeletal elements (premaxillae, maxillae, dentaries, and vomers) are strongly neglected in this respect, despite being robust and as such sometimes found as fossils. Herein, we provide for the first time a comparative study of dentigerous bones, focusing on the Italian urodeles. Thirteen of the 19 species present in Italy, representing all genera except one, were analysed, for a total of 70 dry-prepared skeletons. The morphology of dentigerous skeletal elements of Italian urodeles is described and pictured, providing diagnostic characters and dichotomous keys for the identification at the genus level in most cases, and species level in some. The diagnostic morphological characters were included in a phylogenetic analysis, the results of which demonstrate that the tooth-bearing elements can have a phylogenetic value useful for assessing the relationships of living taxa

Comparison of Universal mtDNA Primers in Species Identification of Animals in a Sample with Severely Degraded DNA

Analysis of mitochondrial DNA, specifically the cytochrome b gene (cyt b), has become an essential tool for species identification. In the case of degraded samples, in which DNA is fractionated, universal primers, which are highly effective at amplifying the target region, are necessary. The material analysed in this study was a keychain made of bone, which was secured at a border crossing due to the suspicion that it was made of ivory. Due to processing of the bone and the likelihood of DNA degradation, five pairs of universal primers with different product lengths (from 148 to 990 base pairs) were used for species identification. Fragments of mtDNA from the cyt b and the 12S rRNA and 16S rRNA subunits were analysed. The analysis showed that only one pair of primers (L15601/H15748) enabled identification of the species, which is very common in samples with highly degraded DNA. The material was bone tissue belonging to the species Bos taurus (cattle). Species identification by molecular methods is extremely important in analysis of material when the species cannot be identified on the basis of morphological characteristics.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dictionary components (word length, step, context) as well as the encoding length and number of sequences required for algorithm training. The best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV was 99.25%, compared to minimap2's performance in a cross-validation scenario. The next stage focused on exploring the dictionary components and attempting to optimize it, employing statistical techniques as well as those relying on the explainability of the decisions made. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford nanopore reads, a natural language processing-based approach can reliably replace classical mapping when there is a need for fast, reliable and energy and computationally efficient targeted mapping of a pre-defined subset of transcripts. The developed model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences.

FunCoup 6: advancing functional association networks across species with directed links and improved user experience.

FunCoup 6 (https://funcoup.org) represents a significant advancement in global functional association networks, aiming to provide researchers with a comprehensive view of the functional coupling interactome. This update introduces novel methodologies and integrated tools for improved network inference and analysis. Major new developments in FunCoup 6 include vastly expanding the coverage of gene regulatory links, a new framework for bin-free Bayesian training and a new website. FunCoup 6 integrates a new tool for disease and drug target module identification using the TOPAS algorithm. To expand the utility of the resource for biomedical research, it incorporates pathway enrichment analysis using the ANUBIX and EASE algorithms. The unique comparative interactomics analysis in FunCoup provides insights of network conservation, now allowing users to align orthologs only or query each species network independently. Bin-free training was applied to 23 primary species, and in addition, networks were generated for all remaining 618 species in InParanoiDB 9. Accompanying these advancements, FunCoup 6 features a new redesigned website, together with updated API functionalities, and represents a pivotal step forward in functional genomics research, offering unique capabilities for exploring the complex landscape of protein interactions.

Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery.

Tools for rapid identification of novel and/or emerging viruses are urgently needed for clinical diagnosis of unexplained infections and pandemic preparedness. Here we developed and clinically validated a largely automated metagenomic next-generation sequencing (mNGS) assay for agnostic detection of respiratory viral pathogens from upper respiratory swab and bronchoalveolar lavage samples in <24 h. The mNGS assay achieved mean limits of detection of 543 copies/mL, viral load quantification with 100% linearity, and 93.6% sensitivity, 93.8% specificity, and 93.7% accuracy compared to gold-standard clinical multiplex RT-PCR testing. Performance increased to 97.9% overall predictive agreement after discrepancy testing and clinical adjudication, which was superior to that of RT-PCR (95.0% agreement). To enable discovery of novel, sequence-divergent human viruses with pandemic potential, de novo assembly and translated nucleotide algorithms were incorporated into the automated SURPI+ computational pipeline used by the mNGS assay for pathogen detection. Using in silico analysis, we showed that after removal of all human viral sequences from the reference database, 70 (100%) of 70 representative human viral pathogens could still be identified based on homology to related animal or plant viruses. Our assay, which was granted breakthrough device designation from the US Food and Drug Administration (FDA) in August of 2023, demonstrates the feasibility of routine mNGS testing in clinical and public health laboratories, thus facilitating a robust and rapid response to the next viral pandemic.

MALDI-TOF – an amazing tool for microbial identification

The Biochemical Society identifies and celebrates outstanding science communication talent in the molecular biosciences with its annual Science Communication Prize. Anna Partyka won the second prize in the media category for students in further education. Anna’s piece is titled ‘MALDI-TOF – an amazing tool for microbial identification’.

Predictive modeling of gestational weight gain: a machine learning multiclass classification study

BackgroundGestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines.MethodsWe analyzed data from the Araraquara Cohort, Brazil, which comprised 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthew’s correlation coefficient (MCC).ResultsThe outcomes were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The XGBoost presented the best overall model, achieving an AUC-ROC of 0.79 for GWG within, 0.76 for GWG below, and 0.65 for GWG above. The LightGBM also performed well with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.76 for GWG below, and 0.624 for GWG above. The most important predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference.ConclusionMachine learning models can effectively predict GWG categories, offering a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes.

Characterization and Differentiation of Candida auris on Dixon’s Agar Using Raman Spectroscopy

Candida auris, an emerging multidrug-resistant fungal pathogen, poses significant challenges in healthcare settings due to its high misidentification rate and resilience to treatments. Despite advancements in diagnostic tools, a gap remains in rapid, cost-effective identification methods that can differentiate C. auris from other Candida species, particularly on non-standard culture media. We used Raman spectroscopy to characterize C. auris grown on modified Dixon’s agar (mDixon) and differentiated it from Candida albicans and Candida parapsilosis. Key Raman spectral markers at 1171 cm−1 and 1452 cm−1, linked to mannan and β-glucan composition, differentiated C. auris into two subgroups, A and B. Despite the spectral similarities of groups A and B with C. albicans and C. parapsilosis, respectively, all Candida species were distinguishable through principal component analysis (PCA). Additionally, this study is the first to demonstrate the distinct spectral signature of mDixon agar, achieved through spatially offset Raman spectroscopy (SORS), which enables accurate discrimination between the culture medium and fungal samples. The observed inter-individual variability within C. auris, coupled with the spectral overlap between C. auris subgroups and other Candida species, highlights a major challenge in differentiating closely related fungi due to their similar molecular composition. Enhancements in spectral resolution and further fluorescence minimization from the culture medium are needed to reliably detect the subtle biochemical differences within these species. Despite these challenges, the results underscore the potential of Raman spectroscopy as a real-time, non-destructive, and complementary tool for fungal pathogen identification.

Association of METS-IR index with Type 2 Diabetes: A cross-sectional analysis of national health and nutrition examination survey data from 2009 to 2018.

With rising global diabetes prevalence, precise early identification and management of diabetes risk are critical research areas. The metabolic score for insulin resistance (METS-IR), a novel non-insulin-based tool, is gaining attention for quantifying insulin resistance using multiple metabolic parameters. Despite its potential in predicting diabetes and its precursors, evidence on its specific relationship with diabetes is limited, especially in large-scale population validation and mechanistic exploration. This study aims to analyze the association between METS-IR and type 2 diabetes (T2DM) in American adults. We conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) data from 2009 to 2018. Participants aged 20 years and above were included, excluding individuals with missing data on BMI, fasting blood glucose, high-density lipoprotein cholesterol glycated hemoglobin and diabetes status. Logistic regression analysis, subgroup analysis, and restricted cubic spline analysis were used to assess the association between METS-IR and T2DM, controlling for potential confounding factors. After adjusting for age, gender, race, education level, smoking status, drinking habits, depression, physical activity, hypertension, and hyperlipidemia, we found a positive association between METS-IR and the risk of T2DM. Specifically, each unit increase in METS-IR was associated with a 7% increase in the risk of T2DM (OR = 1.07, 95% CI: 1.06, 1.08). Subgroup analysis showed that the association between METS-IR and T2DM incidence was significantly positive in the highest quartile group, particularly among Mexican Americans over 40 years old and those diagnosed with depression, hypertension, or hyperlipidemia. Our study revealed a significant positive association between METS-IR and the prevalence of T2DM, indicating that this relationship persists even after controlling for various confounding factors. Therefore, monitoring METS-IR may provide a valuable tool for the early identification of individuals at risk of glucose metabolism disorders. Further research should focus on the applicability of METS-IR in different populations and its potential impact on clinical practice.

Exploring the role of graphene-metal hybrid nanomaterials as Raman signal enhancers in early stage cancer detection

Molecular diagnosis plays a significant role in detection of biomolecules linked to early stage cancer since it offers greater sensitivity and reliability for identification of biomarker level changes as the disease progresses. The application of vibrational spectroscopy in biomarker detection is defined by the fingerprint spectrum of a molecule originating from single-molecule vibrations. This characteristic makes surface enhanced Raman spectroscopy (SERS) a promising tool for identification of biomarkers. The performance of the SERS technique largely depends on the material being used as the SERS substrate. Graphene, with its large surface area and abundance of aromatic regions, is considered advantageous as SERS substrate. Combining graphene with metal nanomaterials considerably increases SERS signal intensity, thereby enhancing detection sensitivity. Therefore, this review emphasizes the significance of selecting graphene-metal nanohybrids as suitable SERS substrates for signal amplification. The detail understanding of the mechanism of graphene-metal hybrid in SERS based detection of early stage cancer is also presented. Furthermore, several examples demonstrated the application of graphene-metal hybrid nanomaterials in detecting biomarkers and cancer cell differentiation using SERS imaging.

Depression, health-related quality of life and life satisfaction in patients with heart failure

Background: The Patient Health Questionnaire 9 (PHQ9) has been recognized as an effective tool for identification of patients with heart failure (HF) at risk for lower health-related quality of life (HRQoL). We aimed to compare HRQoL levels, overall satisfaction with health and life, disease severity variables, sociodemographic variables and behavioural risk factors between patients with HF with different levels of depressive symptomatology. Methods: In a &gt;55 years-old general population cross-sectional HF prevalence study, 1851 subjects were screened and those with NTproBNP ≥125 pg/mL (n=930) underwent detailed diagnostic visit to confirm or rule out HF as per 2016 European Society of Cardiology guidelines. HRQoL (the ShortForm 12 Health Survey, SF12; EQ5D3L), depressive symptoms (Patient Health Questionnaire, PHQ9) and satisfaction with life (Satisfaction With Life Scale, SWLS) were also assessed. Patient with HF (75±8 years, 54 % male, New York Heart Association (NYHA) functional class IIII, with mean left ventricular ejection fraction (LVEF) 56±13) were divided into three groups based on the severity of depressive symptomatology as per PHQ9 score (none: score 04, mild: score 59, and moderate-to-severe: score 1027). Multiple group comparisons and pairwise post hoc analyses were performed. Results: Results indicated significant between group differences in NYHA status (p&lt;0.001), number of comorbidities (p=0.006), functional capacity (p=0.01), as well as HRQoL variables (p=0.05 to 0.001) and SWLS score (p&lt;0.05), with nondepressed group generally showing better physical and subjective indicators of health and wellbeing compared with the mild and moderate-to-severe group. Conclusions: Results indicate that even patients with HF with clinically non-significant levels of depressive symptomatology show significantly impaired psychosocial status (diminished HRQoL, lower life satisfaction).

A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.

Morphological and phylogenetic insights into Berberis species: a tool for species identification and conservation

Morphological and phylogenetic insights into Berberis species: a tool for species identification and conservation

Phytolith spectra of the monotypic grass Danthonidium gammiei (Bhide) Hubb in Hook (Danthonioideae) from Northern Western Ghats, India: Taxonomic and environmental significance of its distinctive Bilobates

Grass Silica Short Cell Phytoliths (GSSCPs) are regarded as a potential adjunctive tool for grass subfamily identification owing to their definite morphology and affinity towards certain grass subfamilies. In the present study phytolith spectra of the monotypic genus Danthonidium gammiei (Bhide) Hubb in Hook, collected from northern Western Ghats, Maharashtra is studied in detail for the first time. This grass profusely produces Bilobates along with other GSSCPs and Elongate types. A thorough study is undertaken to find out potential of the Danthonidium gammiei Bilobates in distinguishing this grass from other major Bilobate producing grass sub-families like Panicoideae and Aristidoideae. The possible relationship between Bilobates specific for Danthonidium gammiei and the intensity of rainfall in northern Western Ghats is also discussed here. Statistical analyses (PCA, CA, and CCA) also establish the potential of Bilobate phytoliths in identifying Danthonioideae subfamily and discriminating different rainfall zones of the region.

Early Alarm on the First Occurrence of the Southern Giant Hornet Vespa soror du Buysson, 1905 (Vespidae) in Europe.

An eco-monitoring program to assess the biodiversity of insects affected by yellow-legged hornet (Vespa velutina) trapping in the north of the Iberian Peninsula (Spain) revealed the first occurrence of the southern giant hornet Vespa soror (Hymenoptera, Vespidae) on the European continent. We present a detailed characterization, combining morphological characteristics and molecular tools for genetic identification, as well as key information on its identification with respect to other hornets found on the Iberian Peninsula. We discuss the most plausible pathways and vectors of introduction, its potential invasiveness, and subsequent impacts on host localities. Our preliminary results raise concerns about the potential threat of V. soror to human health and ecosystem dynamics, as it is a highly predatory species on other insects and even small vertebrates. Finally, this study confirms once again the usefulness of studying insects trapped in such traps for rapid response and early detection of inland invasive species. We also propose a common Spanish name for the species, "avispón sóror".

Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD).

Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD. In a four-year follow-up period, 1,680 children were later identified with ADHD using case definition. We trained and tested machine learning models to predict ADHD prospectively. The best-performing model using administrative and EDI data could reliably predict ADHD and achieved an Area Under the Curve (AUC) of 0.811 during cross-validation. Key predictive factors included EDI subdomain scores, sex, and socioeconomic status. Our findings suggest that machine learning algorithms that use population-level surveillance data could be a valuable tool for early identification of ADHD.

Wave speed mapping visualizes the cavotricuspid isthmus reconnection area: A case report.

In addition to the reentrant map, the wave speed map can be helpful in accurately identifying the CTI gap during radiofrequency application for atrial flutter(AFL). However, in complex cases involving extensive scarring and multiple low-velocity local areas, this technique may not be useful. A 73-year-old male patient with a history of pulmonary vein isolation and cavotricuspid isthmus ablation underwent a second catheter ablation owing to recurrent atrial flutter (AFL). The AFL was diagnosed as cavotricuspid isthmus-dependent AFL caused by the reconnection of the previous cavotricuspid isthmus ablation. Wave speed mapping was performed at the same site, and results comprehensively revealed a low-velocity local area. The AFL was terminated after the first radiofrequency application, and the block line was easily completed. Therefore, this technique could be an adjunctive tool for cavotricuspid isthmus gap identification and minimal radiofrequency application.