Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Identifying RNA Sensors in Antiviral Innate Immunity.

The innate immune system plays a pivotal role in pathogen recognition and the initiation of innate immune responses through its Pathogen Recognition Receptors (PRRs), which detect Pathogen-Associated Molecular Patterns (PAMPs). Nucleic acids, including RNA and DNA, are recognized as particularly significant PAMPs, especially in the context of viral pathogens. During RNA virus infections, specific sequences in the viral RNA mark it as non-self, enabling host recognition through interactions with RNA sensors, thereby triggering innate immunity. Given that some of the most lethal viruses are RNA viruses, they pose a severe threat to human and animal health. Therefore, understanding the immunobiology of RNA PRRs is crucial for controlling pathogen infections, particularly RNA virus infections. In this chapter, we will introduce a "pull-down" method for identifying RIG-I-like receptors, related RNA helicases, Toll-like receptors, and other RNA sensors.

Nivel de cumplimiento de metas internacionales de seguridad en un Sanatorio privado 2023 / Level of compliance with international safety goals in a private Sanatorium 2023

La presente investigación pretende evaluar el nivel de cumplimiento de las metas internacionales, que representan el foco principal para la mejora de calidad y seguridad de atención de los pacientes. La seguridad del paciente involucra a todos los estudios, prácticas y acciones promovidas por las instituciones sanitarias para disminuir y eliminar los riesgos de daños innecesarios relacionados con el cuidado de la salud. Metodología: Estudio descriptivo, observacional y transversal. De fuentes primaria y secundaria, Resultados: se abordaron las metas N° 1 la cual consiste en Identificar a los pacientes correctamente y la meta N° 6 la cual se refiere a Reducir el riesgo de lesiones en pacientes como resultado de caídas. En primer lugar, se destaca el cumplimiento en la identificación correcta del paciente y en segunda instancia la mejora del cumplimiento de medidas de prevención de caídas. Conclusión: Esta proximidad de los valores obtenidos genera un aspecto positivo para mejorar la seguridad de los pacientes y que, si bien el cumplimiento de las metas no es el deseado, es cercano al porcentaje planteado. Lo que en definitiva hace a este estudio un antecedente importante en la mejora continua con vista al futuro cercano[AU]

This research aims to evaluate the level of compliance with international goals, which represent the main focus for improving the quality and safety of patient care. Patient safety involves all studies, practices and actions promoted by health institutions to reduce and eliminate the risks of unnecessary harm related to health care. Methodology: Descriptive, observational and transversal study. From primary and secondary sources, Results: goals No. 1 were addressed, which consists of Identifying patients correctly and goal No. 6, which refers to Reducing the risk of injuries in patients as a result of falls. Firstly, compliance with correct patient identification stands out and secondly, improvement in compliance with fall prevention measures. Conclusion: This proximity of the values obtained generates a positive aspect to improve patient safety and that, although the fulfillment of the goals is not as desired, it is close to the proposed percentage. Which ultimately makes this study an important precedent in continuous improvement for the near future[AU]

Esta pesquisa tem como objetivo avaliar o nível de cumprimento das metas internacionais, que representam o foco principal para a melhoria da qualidade e segurança do atendimento ao paciente. A segurança do paciente envolve todos os estudos, e ações promovidas pelas instituições de saúde para reduzir e eliminar os riscos de danos desnecessários relacionados à assistência à saúde. Metodologia: Estudo descritivo, observacional e transversal. De fontes primárias e secundárias, dependendo do indicador e do objetivo para o qual a medição está sendo realizada. Resultados: Nesta pesquisa serão abordadas as metas nº1, que consiste em Identificar corretamente os pacientes e a meta nº 6, que es Reduzir o risco de lesões nos pacientes em decorrência de quedas. Foi realizado nos dois locais. Em primeiro lugar, destaca-se o cumprimento da correta identificação do paciente e, em segundo lugar, a melhoria no cumprimento das medidas de prevenção de quedas. Conclusão: Essa proximidade dos valores obtidos gera um aspecto positivo para melhorar a segurança do paciente e que, embora o cumprimento das metas não seja o desejado, está próximo do percentual proposto. O que acaba por tornar este estudo um precedente importante na melhoria contínua com vista ao futuro próximo.

CAIM: coverage-based analysis for identification of microbiome.

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Identification and prevalence of ixodid ticks of cattle in case of Aleltu district, Oromia regional state, northern Ethiopia.

BACKGROUND: In Ethiopia, ticks are the major threat to cattle productivity and production, leading to considerable economic losses. The current study was designed to estimate the prevalence of ixodid tick infestation, identify species, assess major risk factors associated with tick infestation and assess public awareness. METHODS: A cross-sectional and questionnaire-based study was conducted from January 2022 to June 2022 in the Aleltu district. The study animals were selected using a simple random sampling method. RESULTS: Of the 400 cattle examined, 303 (75.8%) were found to be infested by one or more tick species. Six species of ticks were identified that belonged to three genera: Amblyomma, Hyalomma and Rhipicephalus, and the subgenus Rhipicephalus (Boophilus). The most common tick species identified in terms of their prevalence and dominance were Rh. (Bo) decoloratus, Rh. evertsi, Am. variegatum, Hy. rufipes, Rh. bergeoni and Rh. praetextatus. In the present study, Rh. (Bo) decoloratus was the most prevalent (56.8%) in the study area. Among the risk factors considered, the prevalence of tick species had a statistically significant (p < 0.05) association with the age, production systems and body condition of animals. Out of 110 people interviewed, 107 (97.3%) believed there was a tick infestation in their village, and almost all farmers 103(93.6%) in the study area were unaware that ticks serve as vectors. CONCLUSIONS: The present study provides preliminary information on the prevalence of tick infestation and the composition of ticks in the Aleltu district. Ticks are a major problem for the cattle in the study area. Therefore, the problem observed in the study area alarms the district and calls for a comprehensive control strategy.

Radio frequency identification technology; A method of analysis of falsified pharmaceutical products: Literature review.

Quality has been a concern of the World Health Organization since its inception and is defined as fitness for use. Since our ancestors began trading several millennia ago, Falsified Pharmaceutical Products has been a recurring problem and still threatens economic stability and public health. Its definition various from country to country and according to World Health Organization, 2017, it is 'a product that is deliberately and fraudulently mislabelled with respect to identity and/or source'. The implementation of anti-falsified nanomaterial technologies is the prominent preventive measure to track and/or detect Falsified Pharmaceutical Products. Software and hardware companies had made encouraging progress towards implementing Radio Frequency Identification devices for ensuring the authenticity of pharmaceutical products. The purpose of the review was to critically appraise Radio Frequency Identification devices technology for the purpose of track and trace Falsified Pharmaceutical Products circulating in the market. Different search engines such as Google Scholar, Science Direct and PubMed were applied and mesh terms and keywords were searched. Different guides and related books were investigated in addition to the articles. Radio Frequency Identification devices technology is a compact electronic device that contains a small chip and reader with antenna that enables wireless transmission of identity of pharmaceutical products. The authenticated Radio Frequency Identification devices model is used for pharmaceutical products' authentication from origin of pharmaceutical industry to the pharmacy at any point along the chain of the distribution. Popular pharmaceutical products, such as OxyContin and Sildenafil Citrate, which are particular targets of falsification have mandated the use of Radio Frequency Identification devices technology.

Letters, Words, Sentences, and Reading.

In this personal, and therefore highly selective, review article I summarize work performed in collaboration with numerous colleagues on how skilled adult readers perform identification tasks and speeded binary decision tasks involving single letters and visually presented words and sentences. The overarching aim is to highlight similarities in the processing performed at three key levels involved in written language comprehension (in languages that use an alphabetic script): letters, words, and sentences. The comparisons are made using behavioral data obtained with: i) speeded (response-limited) binary decision tasks; and ii) the effects of simultaneous surrounding context on letter and word identification using both data-limited (non-speeded) and response-limited procedures. I then propose a general framework that combines the three levels of processing, and that connects core processes at each level with the processing involved in tasks designed to reflect those core processes, and I end by suggesting possible avenues for future research with an aim to extend this general framework.

Dissociating the Effects of Visual Similarity for Brand Names and Common Words.

Abstractionist models of visual word recognition can easily accommodate the absence of visual similarity effects in misspelled common words (e.g., viotin vs. viocin) during lexical decision tasks. However, these models fail to account for the sizable effects of visual similarity observed in misspelled brand names (e.g., anazon produces longer responses and more errors than atazon). Importantly, this dissociation has only been reported in separate experiments. Thus, a crucial experiment is necessary to simultaneously examine the role of visual similarity with misspelled common words and brand names. In the current experiment, participants performed a lexical decision task using both brand names and common words. Nonword foils were created by replacing visually similar letters (e.g., anazon [baseword: amazon], anarilllo [amarillo, yellow]) or visually dissimilar letters (e.g., atazon, atarillo). Results showed sizeable visual letter similarity effects for misspelled brand names in response times and percent error. Critically, these effects were absent for misspelled common words. The pervasiveness of visual similarity effects for misspelled brand names, even in the presence of common words, challenges purely abstractionist accounts of visual word recognition. Instead, these findings support instance-based and weakly abstractionist theories, suggesting that episodic traces in the mental lexicon may retain perceptual information, particularly when words are repeatedly presented in a similar format.

Comprehensive smart smartphone image dataset for plant leaf disease detection and freshness assessment from Bangladesh vegetable fields.

Bangladesh's agricultural landscape is significantly influenced by vegetable cultivation, which substantially enhances nutrition, the economy, and food security in the nation. Millions of people rely on vegetable production for their daily sustenance, generating considerable income for numerous farmers. However, leaf diseases frequently compromise the yield and quality of vegetable crops. Plant diseases are a common impediment to global agricultural productivity, adversely affecting crop quality and yield, leading to substantial economic losses for farmers. Early detection of plant leaf diseases is crucial for improving cultivation and vegetable production. Common diseases such as Bacterial Spot, Mosaic Virus, and Downy Mildew often reduce vegetable plant cultivation and severely impact vegetable production and the food economy. Consequently, many farmers in Bangladesh struggle to identify the specific diseases, incurring significant losses. This dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones. The dataset includes images of vegetable leaves such as Bitter Gourd (2223 images), Bottle Gourd (1803 images), Eggplants (2944 images), Cauliflowers (1598 images), Cucumbers (1626 images), and Tomatoes (2449 images). Each vegetable class encompasses several common diseases that affect cultivation. By identifying early leaf diseases, this dataset will be invaluable for farmers and agricultural researchers alike.

Phlebotomine sand fly (Diptera: Psychodidae) fauna, blood meal source, and detection of Leishmania (Kinetoplastida: Trypanosomatidae) DNA in the Gurupi Biological Reserve, Eastern Amazon, Brazil.

This study was conducted in the Gurupi Biological Reserve (REBIO-Gurupi), the largest area of Amazon rainforest in Maranhão State, Brazil. The objectives were to survey the sand fly (Diptera: Psychodidae) fauna of REBIO-Gurupi, identify blood meal sources, and investigate the presence of Leishmania (Ross, 1903) (Kinetoplastida: Trypanosomatidae) DNA. Individuals were collected using Centers for Disease Control (CDC) light traps and black and white Shannon traps in May and Jun 2022 and Jan 2023. DNA was extracted from female sand flies and subjected to amplification and sequencing of cytochrome b molecular marker (CYTB) for identification of blood meal sources and the first internal transcribed spacer (ITS-1) of ribosomal DNA for Leishmania detection. A total of 514 sand flies individuals were sampled, of which 93 were identified at the genus or series level (9 taxa) and 421 were identified at the species level (24 taxa). Psychodopygus davisi (Root, 1934) (41.1%), Nyssomyia antunesi (Coutinho, 1939) (10.3%), and Psychodopygus (Mangabeira, 1941) Chagasi Series Barretto, 1962 (9.7%) were the most frequently collected. Human (Homo sapiens, Primates, Hominidae) and tapir (Tapirus terrestris, Perissodactyla, Tapiridae) DNA was detected in 10 female sand flies. Leishmania (Leishmania) infantum Cunha and Chagas, 1937 DNA was detected in 2 specimens of Ps. davisi. Given the presence of vectors of Leishmania in REBIO-Gurupi, it is imperative to conduct more comprehensive studies on the interactions among sand flies, Leishmania, and pathogen reservoirs in the area.

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into tumor molecular subtyping. However, the high dimensionality and small sample size of the data may result in ambiguous and overlapping cancer subtypes during clustering. In this study, we propose a novel contrastive-learning-based approach to address this issue. The proposed end-to-end deep learning method can extract crucial information from the multi-omics features by self-supervised learning for patient clustering. RESULTS: By applying our method to nine public cancer datasets, we have demonstrated superior performance compared to existing methods in separating patients with different survival outcomes (p < 0.05). To further evaluate the impact of various omics data on cancer survival, we developed an XGBoost classification model and found that mRNA had the highest importance score, followed by DNA methylation and miRNA. In the presented case study, our method successfully clustered subtypes and identified 14 cancer-related genes, of which 12 (85.7%) were validated through literature review. CONCLUSIONS: Our findings demonstrate that our method is capable of identifying cancer subtypes that are both statistically and biologically significant. The code about COLCS is given at: https://github.com/Mercuriiio/COLCS .

Multilevel Printed Wearable Radio-Frequency Intelligent Identification Platform for Object Recognition.

As a noncontact target recognition technique, radio-frequency identification (RFID) technology demonstrates attractive potential in constructing human-machine interaction (HMI) systems. However, the current development of RFID technologies in HMI systems is hampered by critical challenges in manufacturing high-performance RFID readers with superior flexibility and wearing comfort. Hence, we propose a multilevel printing strategy to overcome the difficulties in manufacturing high-performance large-scale microwave systems. Compared to traditional processes, the RFID system fabricated by the hybrid additive manufacturing technique exhibits equivalent electromagnetic performance and has obvious advantages in terms of manufacturing cost and environmental friendliness. A printed reconfigurable antenna with intelligent radiation mode is seamlessly integrated with the reader circuit via a "one-step" printing technology. Additionally, through chemical doping and artificial intelligence (AI) prediction, we have developed a modified polydimethylsiloxane (PDMS) encapsulation to miniaturize the system volume and enhance reliability. Electromagnetic and mechanical measurements demonstrated that our flexible RFID platform offers superior reliability and stability during long-term daily use. The RFID platform possesses exceptional capabilities in target positioning and accurate identification, demonstrating unique potential in noncontact sensing and recognition, which are highly demanded by flexible and wearable HMI systems.

Mechanisms of evolution and pollution source identification in groundwater quality of the Fen River Basin driven by precipitation.

Groundwater pollution has attracted widespread attention as a threat to human health and aquatic ecosystems. However, the mechanisms of pollutant enrichment and migration are unclear, and the spatiotemporal distributions of human health risks are poorly understood, indicating insufficient groundwater management and monitoring. This study assessed groundwater quality, human health risks, and pollutant sources in the Fen River Basin(FRB). Groundwater quality in the FRB is good, with approximately 87 % of groundwater samples rated as "excellent" or "good" in both the dry and rainy seasons. Significant precipitation elevates groundwater levels, making it more susceptible to human activities during the rainy season, slightly deteriorating water quality. Some sampling points in the southern of Taiyuan Basin are severely contaminated by mine drainage, with water quality index values up to 533.80, over twice the limit. Human health risks are mainly from As, F, NO3-, and Cr. Drinking water is the primary pathway of risk. From 2019 to 2020, the average non-carcinogenic risk of As, F, and NO3- increased by approximately 28 %, 170 % and 8.5 %, respectively. The average carcinogenic risk of As and Cr increased by 28 % and 786 %, the overall trend of human health risks is increasing. Source tracing indicates As and F mainly originate from geological factors, while NO3- and Cr are significantly influenced by human activities. Various natural factors, such as hydrogeochemical conditions and aquifer environments, and processes like evaporation, cation exchange, and nitrification/denitrification, affect pollutant concentrations. A multi-tracer approach, integrating hydrochemical and isotopic tracers, was employed to identify the groundwater pollution in the FRB, and the response of groundwater environment to pollutant enrichment. This study provides a scientific basis for the effective control of groundwater pollution at the watershed scale, which is very important in the Loess Plateau.

Importance of powder diffraction raw data archival in a curated database for materials science applications.

In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X-ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDDR) in archiving powder X-ray diffraction raw data in the Powder Diffraction FileTM (PDFR) database is outlined. The upcoming 2025 release of the PDF-5+ database will have more than 20 800 raw powder diffraction patterns that are available for reference.

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case.

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Ecotoxicological assessment of sanitary sewer overflows and rainfall dynamics offers insights into conditions for potential adverse ecological outcomes.

Sewer overflows are an environmental concern due to their potential to introduce contaminants that can adversely affect downstream aquatic ecosystems. As these overflows can occur during rainfall events, the influence of rainwater ingress from inflow and infiltration on raw untreated wastewater (influent) within the sewer is a critical factor influencing the dilution and toxicity of the contaminants. The Vineyard sewer carrier in the greater city of Sydney, Australia, was selected for an ecotoxicological investigation of a sanitary (separate to stormwater) sewerage system and a wet-weather overflow (WWO). Three influent samples were collected representing dry-weather (DW), intermediate wet-weather (IWW) and wet-weather (WW). In addition, a receiving water sample was also collected downstream in Vineyard Creek (WW-DS) coinciding with a WWO. We employed direct toxicity assessment (DTA) and toxicity identification evaluation (TIE) approaches to gain comprehensive insights into the nature and magnitude of the impact on influent from rainwater ingress into the sewer. Three standard ecotoxicological model species, a microalga, Chlorella vulgaris, the water flea, Ceriodaphnia dubia and the midge larva, Chironomus tepperi were used for both acute and chronic tests. The study revealed variable toxicity responses, with the sample of influent collected in wet-weather displaying lower toxicity compared to the dry-weather sample of influent. Ammonia, and metals, were identified in dry weather as contributors to the observed toxicity, however, this risk was alleviated through rainwater ingress in wet-weather with further dilution within the receiving water. Based on toxicity data, dilutions of influent to minimise effects on C. vulgaris and C. dubia ranged from 1 in 12 in DW to 1 in 2.8 in WW, and further diminished in the receiving water to 1 in 1.8. The successful application of ecotoxicological approaches enabled the assessment of cumulative effects of contaminants in influent, offering valuable insights into sanitary sewer system under rainwater ingress.

Odor identification dysfunction in late-life depression with suicidal ideation.

BACKGROUND: Suicide is more prevalent among older adults compared to younger individuals. Late-life depression (LLD) poses the highest risk for suicide. However, early recognition of suicidal ideation is challenging. Dysfunction in odor identification (OI), a characteristic of LLD, may hold potential for early identification of suicidal ideation. This study aims to compare OI between LLD patients with suicidal ideation (LLD-S) and LLD patients without suicidal ideation (LLD-NS), and examine its relationship with cognitive function. METHODS: A total of 262 LLD-NS patients, 63 LLD-S patients, and 316 healthy normal older adults (HOAs) underwent OI testing, standardized clinical interviews, and comprehensive neuropsychological assessments. RESULTS: (1) LLD-S patients exhibited lower OI scores and poorer cognitive performance (including global cognition, information processing speed, memory, language, executive function, and visuospatial ability) compared to LLD-NS patients and HOAs. (2) There were interactive effects between suicidal ideation and OI dysfunction, leading to lower scores in information processing speed and visuospatial ability. (3) OI dysfunction mediated the differences in cognition between the LLD-NS and LLD-S groups. LIMITATIONS: The present study was a cross-sectional design. CONCLUSIONS: LLD-S patients had worse odor identification than LLD-NS patients and HOAs, suggesting that OI testing could be a valuable approach for identifying suicidal ideation in LLD and screening for suicide risk. The presence of both OI impairment and suicidal ideation was associated with poorer cognitive performance in LLD.

Two simplified tooth sample preparation methods for conventional laboratory and RapidHIT™ ID workflows.

Disaster victim identification (DVI) refers to the forensic identification of unknown individuals following a mass disaster event. Human dental structures can contain viable DNA sources when other soft tissues are compromised. However, labor-intensive sample preparation performed by extensively trained personnel is needed to expose the nuclear material for traditional forensic DNA workflows. With this in mind, we evaluated two simplified sample preparation protocols for processing tooth samples using either a conventional forensic DNA workflow or the Applied Biosystems® RapidHIT™ ID instrument. Briefly, sample sets for both protocols included 10 deciduous teeth that were cleaned prior to either fragmentation with a claw hammer (for RapidHIT™ ID processing) or fine-powder pulverization with a consumer-grade coffee grinder (for traditional workflows). The average percentage of expected STR alleles that were detected above analytical threshold for these tooth samples were comparable between methods: RapidHIT™ ID = 99.0% and GlobalFiler™ = 99.8%. Average intralocus heterozygote peak height ratios (PHRs) were comparable: RapidHIT™ ID = 0.80 and GlobalFiler™ = 0.86. Importantly, 9 of 10 samples analyzed via the RapidHIT™ ID required analyst review for flagged artifact peaks and quality issues. Across all profiles, 91% of alleles passed quality metrics for the RapidHIT™ workflow versus 100% for conventional GlobalFiler™ analysis. Collectively, these results suggest that quick, low-tech tooth sample fragmentation followed by analysis with the RapidHIT™ ID instrument can produce complete STR profiles from aged tooth samples. Future studies should include larger samples sets, more challenging tooth samples, and further simplification of sample preparation to enable field-forward, on-scene DVI.

Identification and expression profiling of the bone morphogenetic protein gene family based on pearl culture in mantle and visceral mass of Hyriopsis cumingii.

Bone morphogenetic proteins (BMPs) play an important biological role in pearl biomineralization in pearl mussels. In this study, based on the genome data of the triangular sail mussel (Hyriopsis cumingii), the genome-wide identification and bioinformatic analysis of BMP gene family were performed, and the expression pattern of the BMP genes was investigated by the insertion experiments. The results showed that a total of 12 BMP gene family members (BMP2a/2b, BMP3, BMP5a/5b, BMP7a/7b/7c, BMP9, BMP10a/10b, and BMP11) were identified, which were unevenly distributed on chromosome 3/14/18, encoding 169-583 amino acids, with molecular weights ranging from 19.32 to 65.99 kDa. BMP2a, BMP7b, and BMP10a were distributed, respectively, in the cytoplasm, endoplasmic reticulum and mitochondria, others were distributed in the nucleus. qRT-PCR results showed the significant tissue specificity in BMPs gene expression. The HcBMPs were differentially expressed in the mantle and visceral mass, and the expression level was higher in the visceral mass. The expressing trends of HcBMPs were not consistent between the mantle and visceral mass insertion, suggesting that HcBMPs may perform different functions. We also found that insertion surgery in the mantle and visceral mass significantly alters the expression profiling of the BMP gene family. Insertion of the mantle induced the biomineralization function of BMP2a, BMP7a, and BMP7b, while BMP3 and BMP10b played opposite roles in visceral mass insertion. Visceral mass insertion could suppress BMP9 expression at 5 d and BMP5b expression at 90 d after insertion This work lays the foundation and data support for the preliminary elucidation of regulatory role and mechanism of HcBMPs in the pearl-cultivating process of mantle and visceral mass.

How Decent Work Influences Internal Whistleblowing Intentions in Nurses: The Parallel Mediating Roles of Felt Obligation and Organisational Identification-A Cross-Sectional Survey.

INTRODUCTION: Fostering nurses' intentions to blow the whistle internally is a salient issue in healthcare. However, there are debates on the antecedents of these intentions, and the explanation of how these antecedents influence such intentions is missing in the nursing literature. AIM: Based on the social exchange theory and the social identity theory, this study aimed to unveil how decent work influences nurses' internal whistleblowing intentions, specifically through the parallel mediating roles of felt obligation and organisational identification. METHODS: A cross-sectional survey was conducted with a convenience sample of 268 clinical nurses from three tertiary public hospitals in Mansoura, Egypt. Data were collected between June and November 2023 using the Decent Work Scale, the Felt Obligation Scale, the Organisational Identification Scale and the Internal Whistleblowing Intentions Scale. Study hypotheses were tested using structural equation modelling. RESULTS: Decent work significantly influenced nurses' internal whistleblowing intentions. Felt obligation and organisational identification partially mediated the positive relationship between decent work and internal whistleblowing intentions. CONCLUSION: Decent work is a significant predictor in nurturing felt obligation and organisational identification, leading to an increased willingness among nurses to engage in internal whistleblowing. IMPACT: This study offers valuable insights into how hospital administrators can leverage internal whistleblowing intentions among nurses. It showed that securing decent work conditions, fostering felt obligation and organisational identification contribute to an increased willingness to blow the whistle. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.