Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests.

OBJECTIVE: The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V̇O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linear V̇O2 responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR and V̇O2 data of single ramp incremental running tests. APPROACH: A large-scale open access dataset of 735 ramp incremental running tests is analysed. The dynamics are obtained by means of 1st order ARX models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate. MAIN RESULTS: As the work rate increases, τ-values increase on average from 38 to 132 seconds for HR, and from 27 to 35 seconds for V̇O2. Both increases are statistically significant (p < 0.01). Further, SSG-values decrease on average from 14 to 9 bpm/(km.h-1) for HR, and from 218 to 144 mL.min-1 for V̇O2 (p < 0.01 for decrease parameters of HR and V̇O2). The results of this modelling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures. SIGNIFICANCE: We show that time-variant modelling is able to determine the time-varying dynamics HR and V̇O2 responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Individual identification in acoustic recordings.

Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.

Insights into the genetic diversity of Angiostrongylus spp. causing human angiostrongyliasis and implications for molecular identification and diagnosis.

Angiostrongylus cantonensis and Angiostrongylus costaricensis are known human pathogens responsible for eosinophilic angiostrongyliasis and abdominal angiostrongyliasis, respectively. Humans are accidental hosts, where infection occurs through the consumption of the infective larva stage 3 in intermediate or paratenic hosts. The proven method for abdominal angiostrongyliasis diagnosis is the histological examination through tissue biopsy, while the diagnosis of eosinophilic angiostrongyliasis is the detection of larva in the cerebrospinal fluid. As there is molecular evidence of cryptic species within A. cantonensis and A. costaricensis lineages, along with morphological similarities within both lineages, accurate species identification and disease diagnosis may be challenging. Moreover, species within the lineages share similar intermediate and definitive hosts and geographic distribution. For example, both A. cantonensis and Angiostrongylus malaysiensis (a closely related species in A. cantonensis lineage) overlap in their geographic distribution in Southeast Asia. Additionally, variations in the molecular makeup of A. costaricensis and A. cantonensis lineages may impact the pathogenicity, infectivity, and disease severity of angiostrongyliasis. Understanding of the genetic diversity of both lineages is a cornerstone for improved diagnosis and disease intervention, especially in a changing global environment. To shed light and provide insights into the genetic diversity of the Angiostrongylus lineages causing human angiostrongyliasis, we aim to present an up-to-date review of the studies conducted and genetic markers used for A. costaricensis and A. cantonensis lineages. The implications for accurate molecular identification and diagnosis of human angiostrongyliasis are also discussed.

Dental Anomalies: An Identification Marker in Forensics.

Aim This study aims to evaluate the non-morphological traits of the South Indian population. Introduction Dental morphological traits, also known as non-metric dental traits, exhibit variation in appearance both within and between groups. The study analyzed the non-metric traits among the South Indian population, as few variants can be grouped within the population.  Materials and methods A total of 500 extracted tooth samples were collected. The dental non-metric traits that were evaluated are the cusp of Carabelli (CC), Talon's cusp (TC), shoveled incisor (SI), peg-shaped lateral incisor (PL), protostylid (PR), Dryopithecus pattern groove (DP), hypoconulid (HY), parastyle (PA), multiple parastyle (MPA), paracone (PC), Bushman's canine (BC), interruption groove (IG), tuberculum dentale (TD), tuberculum intermedium (TI), radix entomolaris (RE), fusion (F), radiculous premolar (RP), dilaceration (D), dens evaginatus (DE), and enamel pearl (EP).  Results Out of 20 dental non-metric traits that were evaluated in the study, 14 traits were identified to be common within the population. The prevalence were as follows: cusp of Carabelli (52%), shoveled incisor (8.2%), peg-shaped lateral incisor (7.4%), parastyle (0.8%), multiple parastyle (0.2%), Bushman's canine (0.4%), interruption groove (2.2%), tuberculum intermedium (0.6%), radix entomolaris (39.6%), fusion (2.8%), radiculous premolar (0.2%), dilaceration (58.2%), dens evaginatus (1.2%), and enamel pearl (0.8%) among the South Indian population.  Conclusion The current study was discovered to have more Carabelli traits, shoveled incisors, radix entomolaris, and dilaceration than other non-metric features. This shows that these characteristics are more prevalent in the South Indian population, which could be one of the strategies used to validate human identification in a forensic context.

Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.

Equipment downtime resulting from maintenance in various sectors around the globe has become a major concern. The effectiveness of conventional reactive maintenance methods in addressing interruptions and enhancing operational efficiency has become inadequate. Therefore, acknowledging the constraints associated with reactive maintenance and the growing need for proactive approaches to proactively detect possible breakdowns is necessary. The need for optimisation of asset management and reduction of costly downtime emerges from the demand for industries. The work highlights the use of Internet of Things (IoT)-enabled Predictive Maintenance (PdM) as a revolutionary strategy across many sectors. This article presents a picture of a future in which the use of IoT technology and sophisticated analytics will enable the prediction and proactive mitigation of probable equipment failures. This literature study has great importance as it thoroughly explores the complex steps and techniques necessary for the development and implementation of efficient PdM solutions. The study offers useful insights into the optimisation of maintenance methods and the enhancement of operational efficiency by analysing current information and approaches. The article outlines essential stages in the application of PdM, encompassing underlying design factors, data preparation, feature selection, and decision modelling. Additionally, the study discusses a range of ML models and methodologies for monitoring conditions. In order to enhance maintenance plans, it is necessary to prioritise ongoing study and improvement in the field of PdM. The potential for boosting PdM skills and guaranteeing the competitiveness of companies in the global economy is significant through the incorporation of IoT, Artificial Intelligence (AI), and advanced analytics.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions.

Introduction: The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established. Methods: The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models. Results: The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%. Discussion: This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Taxonomic review of the genus Ponyalis Fairmaire, 1899 (Coleoptera, Lycidae), with descriptions of six new species from China.

The lycid genus Ponyalis Fairmaire, 1899 is reviewed. Six new species are described from China, including P.longicornis sp. nov., P.truncata sp. nov., P.dabieshanensis sp. nov., P.hainanensis sp. nov., P.quadricollimima sp. nov., and P.zhejiangensis sp. nov. Nine previously known species, including P.alternata (Pic, 1927), P.fukiensis (Bocak, 1999), P.gracilis (Bocak, 1999), P.himalejica (Bourgeois, 1885), P.klapperichi (Bocak, 1999), P.laticornis Fairmaire, 1899, P.nigrohumeralis (Pic, 1939), P.quadricollis (Kiesenwetter, 1874), and P.variabilis Li, Bocak & Pang, 2015 are illustrated with images of the habitus and aedeagi to make the comparisons with the new species. In addition, a distribution map and an identification key to all 24 species of Ponyalis are provided.

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification.

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 µW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

Clinical spectrum, phenotypic and molecular characterization, and antifungal susceptibility of an emerging human pathogen, Acrophialophora, from India.

Acrophialophora is implicated in superficial and invasive infections, especially in immunosuppressed individuals. The present study was undertaken to provide clinical, microbiological, phylogenetic, and antifungal susceptibility testing (AFST) profile of Acrophialophora isolated from India. All the isolates identified as Acrophialophora species at National Culture Collection for Pathogenic Fungi, Chandigarh, India were revived. Phenotypic and molecular characterization was performed, followed by temperature studies, scanning electron microscopy (SEM) and AFST. We also performed systematic review of all the cases of Acrophialophora species reported till date. A total of nine isolates identified as Acrophialophora species were identified by molecular method as A. fusispora (n = 8) and A. levis (n = 1), from brain abscess (n = 4), respiratory tract (n = 3) and corneal scraping (n = 2). All patients but two had predisposing factors/co-morbidities. Acrophialophora was identified as mere colonizer in one. Temperature studies and SEM divulged variation between both species. Sequencing of the ITS ribosomal DNA and beta-tubulin loci could distinguish species, while the LSU ribosomal DNA locus could not. AFST showed lowest MICs for triazoles and highest for echinocandins. Systematic literature review revealed 16 cases (11 studies), with ocular infections, pulmonary and central nervous system infections, and A. fusispora was common species. All the patients except three responded well. High MICs were noted for fluconazole, micafungin and caspofungin. This is the first study delineating clinical, phenotypic, and genotypic characteristics of Acrophialophora species from India. The study highlights microscopic differences between both species and emphasizes the role of molecular methods in precise identification. Triazoles appear to be the most effective antifungals for managing patients.

We describe clinical, phenotypic, and genotypic characteristics of Acrophialophora species. This species causes mild infection to fatal infection in immunosuppressed individuals. Triazoles are effective in treating such infections.

Metabolomics efficiently discriminates monozygotic twins in peripheral blood.

Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.

Identification and characterization of the rat in-vivo and in-vitro metabolites of tazemetostat using LC-QTOF-MS.

In drug discovery, metabolite profiling unveils biotransformation pathways and potential toxicant formation, guiding selection of candidates with optimal pharmacokinetics and safety profiles. Tazemetostat (TAZ) is employed in treating locally advanced or metastatic epithelioid sarcoma. Identification of drug metabolites are of significant importance in improving safety, efficacy and reduced toxicity of drugs. The current study aimed to investigate the comprehensive metabolic fate of TAZ using different in vivo (rat) and in vitro (RLM, HLM, HS9) models. For in vivo studies, drug was orally administered to Sprague-Dawley rats with subsequent analysis of plasma, feces and urine samples. A total of 21 new metabolites were detected across various matrices and were separated on Phenomenex kinetex C18 (2.5 µm; 150 × 4.6 mm) column using acetonitrile and 0.1% formic acid in water as mobile phase. LC-QTOF-MS/MS and NMR techniques were employed to identify and characterize the metabolites from extracted samples. The major metabolic routes found in biotransformation of TAZ were hydroxylation, N-dealkylation, N-oxidation, hydrogenation, hydrolysis and N-acetylation. In silico toxicity revealed potential immunotoxicity for TAZ and few of its metabolites. This research article is the first time to discuss the complete metabolite profiling including identification and characterization of TAZ metabolites as well as its biotransformation mechanism.

Site-specific identification and quantitation of endogenous SUMOylation based on SUMO-specific protease and strong anion exchange chromatography.

Small ubiquitin-like modifier (SUMO) modification regulates various eukaryotic cellular processes and plays a pivotal role in interferon (IFN)-mediated antiviral defense. While immunoprecipitation enrichment method is widely used for proteome-wide analysis of endogenous SUMOylation, the inability to target all SUMO forms and high cost of antibodies limited its further application. Herein, we proposed an antibody-free enrichment method based on SUMO-specific protease and strong anion exchange chromatography (SPAX) to globally profile the endogenous SUMOylation. The SUMO1/2/3-modified peptides could be simultaneously enriched by SAX chromatography by utilizing its electrostatic interaction with SUMO1/2/3 remnants, which contained multiple aspartic acids (D) and glutamic acids (E). To remove the co-enriched D/E-containing peptides which might interfere with the detection of low-abundance SUMOylated peptides, SUMO-specific protease was used to cleave the SUMO1/2/3 remnants from enriched SUMOylated peptides. As the deSUMOylated peptides lost SUMO remnants, their interaction with SAX materials became weaker, and the D/E-containing peptides could thus be depleted through the second SAX separation. The SPAX method identified over twice the SUMOylated sites than using SAX method only, greatly improving the identification coverage of endogenous SUMOylated sites. Our strategy was then applied to the site-specific identification and quantification of endogenous SUMOylation in A549 cells stimulated by IFN-γ for the first time. A total of 226 SUMOylated sites on 146 proteins were confidently identified, among which multiple up-regulated sites were involved in IFN-mediated antiviral defense, demonstrating the great promise of SPAX to globally profile and discover endogenous SUMOylation with significant biological functions.

A remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships.

Conventional methods for identifying soil heavy metal (HM) pollution sources are limited to area scale, failing to accurately pinpoint sources at specific sites due to the spatial heterogeneity of HMs in mining areas. Furthermore, these methods primarily focus on existing solid waste polluted dumps, defined as "direct pollution sources", while neglecting existing HM pollution hotspots generated by historical anthropogenic activities (e.g., mineral development, industrial discharges), defined as "potential pollution sources". Addressing this gap, a novel remote sensing analysis method is proposed to identify both direct and potential pollution sources at site scale, considering source-sink relationships. Direct pollution sources are extracted using a supervised classification algorithm on high-resolution multispectral imagery. Potential pollution sources depend on the spatial distribution of HM pollution, mapped using a machine learning model with hyperspectral imagery. Additionally, a source identification algorithm is developed for gridded pollution source analysis. Validated through a case study in a cadmium (Cd)-polluted mine area, the proposed method successfully extracted 21 solid waste polluted dumps with an overall accuracy approaching 90 % and a Kappa coefficient of 0.80. Simultaneously, 4167 HM pollution hotspots were identified, achieving optimal inversion accuracy for Cd (Rv2 = 0.91, RMSEv = 4.27, and RPDv = 3.02). Notably, the spatial distribution patterns of these identified sources exhibited a high degree of similarity. Further analysis employing the identification algorithm indicated that 3 polluted dumps and 258 pollution hotspots were pollution sources for a selected high-value point of Cd content. This innovative method provides a valuable methodological reference for precise prevention and control of soil HM pollution.

Orchidinae-205: A new genome-wide custom bait set for studying the evolution, systematics, and trade of terrestrial orchids.

Terrestrial orchids are a group of genetically understudied, yet culturally and economically important plants. The Orchidinae tribe contains many species that produce edible tubers that are used for the production of traditional delicacies collectively called 'salep'. Overexploitation of wild orchids in the Eastern Mediterranean and Western Asia threatens to drive many of these species to extinction, but cost-effective tools for monitoring their trade are currently lacking. Here we present a custom bait kit for target enrichment and sequencing of 205 novel genetic markers that are tailored to phylogenomic applications in Orchidinae s.l. A subset of 31 markers capture genes putatively involved in the production of glucomannan, a water-soluble polysaccharide that gives salep its distinctive properties. We tested the kit on 73 taxa native to the area, demonstrating universally high locus recovery irrespective of species identity, that exceeds the total sequence length obtained with alternative kits currently available. Phylogenetic inference with concatenation and coalescent approaches was robust and showed high levels of support for most clades, including some which were previously unresolved. Resolution for hybridizing and recently radiated lineages remains difficult, but could be further improved by analysing multiple haplotypes and the non-exonic sequences captured by our kit, with the promise to shed new light on the evolution of enigmatic taxa with a complex speciation history. Offering a step-up from traditional barcoding and universal markers, the genome-wide custom loci targeted by Orchidinae-205 are a valuable new resource to study the evolution, systematics and trade of terrestrial orchids.

Genome-Wide Identification of CHYR Gene Family in Sophora alopecuroides and Functional Analysis of SaCHYR4 in Response to Abiotic Stress.

Sophora alopecuroides has important uses in medicine, wind breaking, and sand fixation. The CHY-zinc-finger and RING-finger (CHYR) proteins are crucial for plant growth, development, and environmental adaptation; however, genetic data regarding the CHYR family remain scarce. We aimed to investigate the CHYR gene family in S. alopecuroides and its response to abiotic stress, and identified 18 new SaCHYR genes from S. alopecuroides whole-genome data, categorized into 3 subclasses through a phylogenetic analysis. Gene structure, protein domains, and conserved motifs analyses revealed an exon-intron structure and conserved domain similarities. A chromosome localization analysis showed distribution across 12 chromosomes. A promoter analysis revealed abiotic stress-, light-, and hormone-responsive elements. An RNA-sequencing expression pattern analysis revealed positive responses of SaCHYR genes to salt, alkali, and drought stress. SaCHYR4 overexpression considerably enhanced alkali and drought tolerance in Arabidopsis thaliana. These findings shed light on SaCHYR's function and the resistance mechanisms of S. alopecuroides, presenting new genetic resources for crop resistance breeding.

Four new species of Marphysa (Annelida, Eunicida, Eunicidae) from the east coast of Peninsular Malaysia.

Four new species of Marphysa are described from Terengganu state on the east coast of Peninsular Malaysia, using morphological and molecular (cytochrome oxidase subunit I (COI) gene) data. These species belong to different groups of Marphysa: Marphysakertehensis sp. nov. belongs to Group A (Mossambica), Marphysamerchangensis sp. nov. and Marphysasetiuense sp. nov. belong to Group B (Sanguinea) and Marphysaibaiensis sp. nov. belongs to Group E (Gravelyi). Marphysakertehensis sp. nov. is characterised by having only limbate chaetae, absence of subacicular hooks, three types of pectinate chaetae including wide, thick isodont with short and slender inner teeth, and pectinate branchiae with up to nine branchial filaments. Marphysamerchangensis sp. nov. is characterised by the presence of eyes, unidentate subacicular hooks, four types of pectinate chaetae including wide, thick anodont pectinate chaetae with five long and thick inner teeth, and pectinate branchiae with up to six branchial filaments. Marphysasetiuense sp. nov. has mostly unidentate subacicular hooks (bidentate on several posterior chaetigers), four types of pectinate chaetae including wide, thick anodont pectinate chaetae with seven thick and long inner teeth, and pectinate branchiae with up to five branchial filaments. Marphysaibaiensis sp. nov. has bidentate subacicular hooks throughout, five types of pectinate chaetae, including a heterodont with 12 short and slender inner teeth, and pectinate branchiae with up to eight branchial filaments. The designation of these new species based on morphology is fully supported by molecular data. Habitat descriptions of each species are also included.

Mycological air contamination level and biodiversity of airborne fungi isolated from the zoological garden air - preliminary research.

The aim of this paper was to evaluate the degree of mycological air contamination and determine the taxonomic diversity of airborne fungi residing in the air of 20 different animal facilities in a zoological garden. The concentrations of fungi in the zoological garden were measured using a MAS-100 air sampler. The collected microorganisms were identified using the combination of molecular and morphological methods. The fungal concentration ranged from 50 to 3.65 × 104 CFU/m3 during the whole study. The quantitative analysis of the fungal aerosol showed that the obtained concentration values were lower than the recommended permissible limits (5 × 104 CFU/m3 for fungi). Environmental factors, including temperature and relative humidity, exerted a varying effect on the presence and concentration of isolated fungi. Relative humidity was shown to correlate positively with the concentration of fungal spores in the air of the facilities studied (rho = 0.57, p < 0.0021). In parallel, no significant correlation was established between temperature and total fungal concentration (rho = - 0.1, p < 0.2263). A total of 112 fungal strains belonging to 50 species and 10 genera were isolated. Penicillium was the dominant genera, including 58.9% of total fungal strains, followed by Aspergillus 25.89%, Cladosporium 3.57%, Talaromyces 3.57%, Mucor 1.78%, Schizophyllum 1.78%, Syncephalastrum 0.89%, Alternaria 0.89%, Absidia 0.89%, and Cunninghamella 0.89%. Our preliminary studies provide basic information about the fungal concentrations, as well as their biodiversity in zoological garden. Further studies are needed to generate additional data from long-term sampling in order to increase our understanding of airborne fungal composition in the zoological garden.

Computer-assisted patient identification tool in inborn errors of metabolism - potential for rare disease patient registry and big data analysis.

BACKGROUND: Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and often not user-friendly. Our goal is to establish Hong Kong's first computer-assisted patient identification tool for rare diseases, starting with inborn errors of metabolism (IEM). METHODS: Patient data from 2010 to 2019 was retrieved from electronic databases. Through big data analytics, patient data were filtered based on specific IEM-related biochemical and genetic tests. Clinical notes were analyzed using a rule-based natural language processing technique called regular expression. The algorithm classified each extracted paragraph as "IEM-related" or "not IEM-related." Pathologists reviewed the paragraphs for curation, and the algorithm's performance was evaluated. RESULTS: Out of 46,419 patients with IEM-related tests, the algorithm identified 100 as "IEM-related." After pathologists' validation, 96 cases were confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yielded a sensitivity of 92.3% compared to our previously published IEM cohort. CONCLUSIONS: Our artificial intelligence approach provides a novel method to identify IEM patients, facilitating the creation of a centralized, computer-assisted rare disease patient registry at the local and national levels. This data can potentially be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases.

High-yield hybrid breeding of Camellia oleifolia based on ISSR molecular markers.

BACKGROUND: C. Oleifera is among the world's largest four woody plants known for their edible oil production, yet the contribution rate of improved varieties is less than 20%. The species traditional breeding is lengthy cycle (20-30 years), occupation of land resources, high labor cost, and low accuracy and efficiency, which can be enhanced by molecular marker-assisted selection. However, the lack of high-quality molecular markers hinders the species genetic analysis and molecular breeding. RESULTS: Through quantitative traits characterization, genetic diversity assessment, and association studies, we generated a selection population with wide genetic diversity, and identified five excellent high-yield parental combinations associated with four reliable high-yield ISSR markers. Early selection criteria were determined based on kernel fresh weight and cultivated 1-year seedling height, aided by the identification of these 4 ISSR markers. Specific assignment of selected individuals as paternal and maternal parents was made to capitalize on their unique attributes. CONCLUSIONS: Our results indicated that molecular markers-assisted breeding can effectively shorten, enhance selection accuracy and efficiency and facilitate the development of a new breeding system for C. oleifera.