Single Sample Research Articles

Variation in Muskox (Ovibos moschatus) Guard Hair Growth Rates: Implications for Measuring Chronological Biomarkers.

Segmental analyses of hair may be useful for measuring biomarkers over several seasons to years from a single sample. To attribute hair segments to specific time periods, a known chronological marker, or a hair growth rate, is needed. We examined guard hair growth rates of captive muskoxen (Ovibos moschatus) in Fairbanks, Alaska, USA. We sought to determine if a general growth rate could be applied across muskox populations, thus facilitating the use of segmental analyses for various biomarkers. We used archived samples from 16 muskoxen that had guard hairs sampled at six, 14, and 30 wk after shaving. We measured the lengths of 10 guard hairs per sample, calculated weekly and annual growth rates, and then fitted linear mixed-effects models to assess the effect of different covariates on hair growth rate. The period in which hair had been grown had a significant effect (P<0.05) on growth rate. Extrapolated annual hair growth rates were 277±40 mm/yr (weeks 0-6), 248±47 mm/yr (weeks 7-14), and 165±36 mm/yr (weeks 15-30), with an overall average rate of 210±14 mm/yr. These rates were significantly faster than those of free-ranging Greenland muskoxen-78 mm/yr as measured by stable isotope analyses-and varied intra-annually. This suggests that a universal growth rate cannot be generalized across muskox populations and time.

Parasitological investigation of Artemia parthenogenetica: A Cestode case from Ayvalık Saltwork

One of the primary food supplies for the aquaculture sector and wild animal populations, such as flamingos (Phoenicopterus roseus), is Artemia (Artemia parthenogenetica). Along with the rise in cases and reports from different salterns in recent years, the prevalence of Artemia cestode parasitism has also grown. Flamingolepis liguloides eggs are consumed by Artemia and transform into cysticercoids which mature into adult worms in the final host, flamingos. We described the first investigation of cestode parasitism in the natural populations of Artemia parthenogenetica from the Ayvalık saltwork (Balıkesir, Türkiye) in this study. A total of 965 A. parthenogenetica were examined and 320 of them had F. liguloides infection. The overall prevalence was estimated to be 33.2%. Most of the parasites were found in the abdomen and intestinal tract of A. parthenogenetica. Parasitism intensity ranged from 1 to 4 per individual, with a maximum of 4 cysticercoids detected in a single Artemia sample. To understand the influence of parasitism on Artemia populations and the transmission of species, it is important to establish Artemia parasitism in the saltwork. This initial report from the Ayvalık saltwork provides information on the infestation of F. liguloides in Artemia; therefore, the interactions between the saltworks’ species should be investigated in depth.

Accuracy and sensitivity of soil erosion estimation using 137Cs technology: A statistical perspective

Random spatial variation of the 137Cs inventory is the principal contributor to uncertainty in soil erosion estimation using 137Cs technology. A statistically sound sampling design is imperative for obtaining reliable soil erosion estimations. The objectives of this study are to: 1) characterize the effect of sample size on the estimates of mean inventories; 2) evaluate the sensitivity of the estimated soil redistribution to sample size using a new 137Cs model; 3) compare erosion detection limits among five conversion models; and 4) assess three spatial interpolation methods. Two small watersheds were sampled in an irregular grid design, and 30 samples were taken on a reference site. A moving window scheme was used to compute the 7-point and 11-point means of 137Cs inventories. The Welch’s modified t-test was used to test the mean estimates and to compute the detection limits for the replicated samples. Five models were selected to convert 137Cs inventories to soil redistribution rates. Since most single samples without replication yielded insignificant test results, the 137Cs technique cannot be used to estimate soil erosion at a single point due to random spatial variation. With 30 reference samples, 7 replicate samples on the sampling site provided reliable estimations of 137Cs inventories and soil redistribution. The spatial patterns of the estimated 137Cs inventories and soil redistribution became more regular and systematic as sample size increased, which agreed increasingly well with topography and surface hydrology. Replicate samples can be taken from each landform element or slope position where the erosion rate is expected to be uniform. Alternatively, a systematic grid sampling scheme can be used, and the nearest neighbors can be treated as replicates to calculate moving averages. The Type I and II errors with replicated samples showed that all sampling positions should be included in calculating watershed or area mean erosion rates even though the Type I errors indicated insignificant redistribution rates for some samples. Due to the lack of spatial autocorrelation, spline and inverse distance weighting methods performed better than kriging for interpolation. Detection limits and magnitudes of soil redistribution varied substantially with conversion models, depending on their sensitivities to changes in 137Cs inventories.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

PurposeThe aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci. MethodsTo do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA. ResultsOur research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose. ConclusionWe searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Additive Manufacturing of Nanocellulose Aerogels with Structure-Oriented Thermal, Mechanical, and Biological Properties.

Additive manufacturing (AM) is widely recognized as a versatile tool for achieving complex geometries and customized functionalities in designed materials. However, the challenge lies in selecting an appropriate AM method that simultaneously realizes desired microstructures and macroscopic geometrical designs in a single sample. This study presents a direct ink writing method for 3D printing intricate, high-fidelity macroscopic cellulose aerogel forms. The resulting aerogels exhibit tunable anisotropic mechanical and thermal characteristics by incorporating fibers of different length scales into the hydrogel inks. The alignment of nanofibers significantly enhances mechanical strength and thermal resistance, leading to higher thermal conductivities in the longitudinal direction (65mWm-1K-1) compared to the transverse direction (24mWm-1K-1). Moreover, the rehydration of printed cellulose aerogels for biomedical applications preserves their high surface area (≈300m2g-1) while significantly improving mechanical properties in the transverse direction. These printed cellulose aerogels demonstrate excellent cellular viability (>90% for NIH/3T3 fibroblasts) and exhibit robust antibacterial activity through in situ-grown silver nanoparticles.

Transfer Learning for Bearing Fault Diagnosis based on Graph Neural Network with Dilated KNN and Adversarial Discriminative Domain Adaptation

Graph neural networks (GNNs) have emerged as a forefront in deep learning, notably influencing research in mechanical fault diagnosis. Transfer learning, particularly through domain adaptation (DA) techniques, has found application in machinery fault diagnosis by training models under one working condition and deploying them under another. While efforts have been made to integrate GNNs with DA techniques to alleviate data distribution discrepancies by investigating the inter-sample relationships, challenges persist: reliance on K-nearest neighbor (KNN) for graph generation emphasizes close relationships, neglecting distant ones; batch processing limits real-time fault diagnosis; and transfer between different-sized bearings is nearly unexplored. To address these limitations, a novel framework for GNN-based domain adaptation in machinery fault diagnosis is proposed. Initially, a convolutional neural network extracts node embeddings from the continuous wavelet transform graph of raw vibration signals. Subsequently, a graph generation layer based on dilated KNN captures both close and distant sample relationships, addressing the long-range dependency issue. Two GNN blocks are then applied for inter-sample relationships investigation and further feature extraction with the outputs directed to a linear classifier during source domain pretraining. Following pretraining, adversarial discriminative domain adaptation is leveraged to mitigate domain distribution discrepancies. Additionally, a novel graph construction method that combines existing training samples with a new single sample is proposed, enabling fault prediction with single instances for real-time online fault diagnosis. Evaluation on datasets with varying working conditions and bearings of different sizes demonstrates the superior performance of our method to other comparison methods.

Morphology and Molecular Phylogeny of the Genus Stigeoclonium (Chaetophorales, Chlorophyta) from China, Including Descriptions of the Pseudostigeoclonium gen. nov.

Stigeoclonium is a genus of green algae that is widely distributed in freshwater habitats around the world. The genus comprises species with variously developed prostrates and erect systems of uniseriate branched filaments and grows attached to a wide range of different surfaces. It holds significant promise for applications in water quality indicators, sewage treatment, and the development of high-value-added products. Nevertheless, our comprehension of Stigeoclonium remains unclear and perplexing, particularly regarding its fundamental systematic taxonomy. Recent molecular analyses have revealed that the morphologically well-defined genus Stigeoclonium is polyphyletic and requires taxonomic revision. Phylogenetic analysis based on a single molecular marker and limited samples is insufficient to address the polyphyletic nature of Stigeoclonium. In the present study, 34 out of 45 strains of Stigeoclonium were newly acquired from China. Alongside the morphological data, a concatenated dataset of three markers (18S rDNA + ITS2 + tufA) was utilized to determine their molecular phylogeny. The phylogenetic analysis successfully resolved the broadly defined Stigeoclonium into three robustly supported clades (Stigeoclonim tenue clade, S. farctum clade, and S. helveticum clade). The morphological characteristics assessment results showed that the cell type of the main axis-producing branch, considered a crucial morphological characteristic of the Stigeoclonium taxonomy, did not accurately reflect the real phylogeny of the genus. A new taxonomical classification of the genus Stigeoclonium was proposed based on zoospores' germination types, which aligned well with the phylogenetic topologies. Species where zoospores showed erect germination (S. helveticum clade) formed a distinct monophyletic clade, clearly separated from the other two clades, with zoospores showing prostrate germination or pseudo-erect germination. Consequently, a new genus, Pseudostigeoclonium gen. nov., is suggested to include all species in the broadly defined Stigeoclonium with zoospores with erect germination. The taxonomic diversity is supported by distinctive morphological differences and phylogenetic divergence within the broadly defined Stigeoclonium identified in this study. Further evaluation of the genus Stigeoclonium is necessary, especially via examining additional specimens and re-evaluating morphological characters under precisely defined laboratory conditions.

Development and evaluation of three automated media pooling and molecular diagnostic systems for the detection of SARS-CoV-2.

Pooled testing combined with molecular diagnostics for the detection of SARS-CoV-2 is a promising method that can increase testing capacities and save costs. However, pooled testing is also associated with the risks of decreased test sensitivity and specificity. To perform reliable pooled testing, we developed and validated three automated media pooling and molecular diagnostic systems. These pooling systems (geneLEAD-PS, Panther-PS, and Biomek-PS) comprised existing automated molecular detection platforms, corresponding automated media pooling devices, and laboratory information management systems. Analytical sensitivity analysis and mock sample evaluation were performed, and the obtained data were used to determine the sizes of the pool for the validation study. In the validation study, a total of 2,448, 3,228, and 6,420 upper respiratory samples were used for geneLEAD-PS, Panther-PS, and Biomek-PS, respectively, and the diagnostic performances were compared with the reference RT‒PCR assay. A pool size of 6 for geneLEAD-PS and a pool size of 4 for Panther-PS and Biomek-PS were selected for the validation studies. All three systems showed high positive percent agreement values of ≥90.5% and negative percent agreement values of ≥99.8% for any specimen type. Pooled testing resulted in a 65%-71% reduction in cost per sample. The testing capacities of geneLEAD-PS, Panther-PS, and Biomek-PS were 144 samples in 3 hours, 384 samples in 5.5 hours, and 376 samples in 4 hours, respectively. The developed pooling systems showed robust diagnostic performances and will increase the testing capacities of molecular diagnostic tests while saving costs and may contribute to infection control of COVID-19.IMPORTANCEDuring the COVID-19 pandemic, there have been surges in demand for accurate molecular diagnostic testing and laboratory supply shortages. Pooled testing combined with highly sensitive molecular testing, which entails mixing multiple samples as a single sample, is a promising approach to increase testing capacities while reducing the use of consumables. However, pooled testing is associated with risks that compromise diagnostic performance, such as false negatives due to dilution of positive samples or false positives due to cross-contamination. To perform reliable pooled testing, three different pooling systems (an automated pooling device, an automated molecular detection platform, and a laboratory information management system) were developed to accurately interpret pooled testing results. These three systems were validated using multiple clinical samples and showed high concordance with individual testing. The developed pooling systems will contribute to increasing reliable molecular testing capacities while using fewer consumables and saving costs.

VALIDATION OF A NOVEL ENZYME-LINKED IMMUNOSORBENT ASSAY FOR DETECTING PREGNANCY-SPECIFIC PROTEIN B IN MASAI GIRAFFE (GIRAFFA CAMELOPARDALIS TIPPELSKIRCHI).

The Masai giraffe (Giraffa camelopardalis tippelskirchi) is endangered in the wild, and successful reproduction in managed care is important to help maintain assurance populations of this highly charismatic subspecies. Detection of pregnancy in giraffes using hormonal monitoring requires multiple samples and cannot distinguish pregnancy from pseudopregnancy. A novel enzyme-linked immunosorbent assay that can detect pregnancy-specific protein B (PSPB) for pregnancy diagnosis with a single serum sample was developed from a reticulated giraffe (Giraffa camelopardalis reticulata) placenta. Seventy-eight serum samples were analyzed from three female Masai giraffes before and during five gestation periods that resulted in live calf births. Using an optical density cutoff of 0.2, the assay showed a sensitivity of 93% and specificity of 100% for all samples tested. At 59 d of gestation, sensitivity increased to 100%. The earliest pregnancy detection was at 40 d of gestation. This study documents the successful development of a blood-based PSPB assay for pregnancy diagnosis in Masai giraffe, which can help advance conservation efforts in this endangered species.

Powering of IOTs through single jersey wearable tribo-electric nano generator

A simple, lightweight, and easy to develop Single Jersey Wearable Tribo-Electric Nano Generators (SJ-WTENG) were constructed using Cotton and Acrylic fabrics (Triboelectric series materials). Fabrics were also coated with Maghemite (γ-Fe2O3) nanoparticles (13 nm) to increase the electrical conductance of the samples. Compression and Vertical contact-separation modes were adopted for studying the performance of the developed samples. Along with a Single WTENG sample, the outputs of two samples connected in series were also measured. To study the effect of developed Maghemite (γ-Fe2O3) nanoparticle coating, non-coated fabrics WTENGs were also constructed and tested. The maximum voltage reached with the Maghemite (γ-Fe2O3) nanoparticle-coated SJ-WTENG samples was a time-varying signal of 7.68 volts peak to peak volts with an approximate frequency of 50.5 Hertz. A shotky diode-based full bridge rectifier was used to get the DC voltage. The rectified DC signal was observed to be 5 volts which was enough to light up an LED with a threshold voltage of 1.7 volts DC as well as charge 3.7 volts, 3.6Ah Li-ion battery pack. Results confirmed that the application of Maghemite (γ-Fe2O3) nanoparticles was useful in augmenting the output of the proposed SJ-WTENG design. The proposed system can be used to power the battery powered IOT (Internet of Things) devices, widely used in medical and body sensor network applications.

Seasonal variations and water chemical control mechanism of water quality in a suburban river near a coal city: a case study in the Xinbian River of Suzhou City, Anhui Province, China

Abstract To study the seasonal change rules of water quality indicators and water chemical control mechanisms in the nearby city river Xinbian River, in Suzhou City, Anhui Province, China, nine points were selected and periodically sampled for 12 months. Totally 108 water samples were collected, and basic physical–chemical indicators and routine ions (e.g., Ca2+, Mg2+, Na+, K+, SO42−, Cl−, F−, NO3−, CO32−, and HCO3−) were measured. Piper diagram, Gibbs diagram, and ion ratio method were used. The result shows that the seasonal and upstream-to-downstream variations in pH were less varied, whereas the fluctuation in dissolved oxygen was large. Four ions, namely, SO42-, F−, Cl−, and Na+ generally first decreased overall and then changed from spring to winter. The maximum contents of SO42- (406.03 mg/L), Cl− (250.22 mg/L), and Na+ (269.99 mg/L) in single water samples appeared at the S9 sampling point (Suzhou control gate) in March 2020. The dominant hydrochemical types in summer were Na–Ca–HCO3 and Na–Mg–SO4, while Na–Mg–SO4 and Na–Mg–HCO3 were the main hydrochemical types in the other seasons. The control factors of water chemical composition vary according to the season. However, rock weathering (e.g., silicate dissolution) is the dominant control factor of water chemistry in the studied river section.

The effectiveness of albendazole against hookworm infections and the impact of bi-annual treatment on anaemia and body mass index of school children in the Kpandai district of northern Ghana.

The impact of the Global Programme to Eliminate Lymphatic Filariasis (GPELF) (initiated in 2000 in Ghana and ran for 12 years) in mitigating soil-transmitted helminth (STH) infections in LF-endemic areas is unknown. During a 1-year hiatus which ensued between 2011 and 2012, a longitudinal study was conducted to determine GPELF effect on hookworm infections in selected communities involved in the programme since its inception, while measuring the effectiveness of biannual ALB treatments on schoolchildren living in such communities. A total of 399 school children aged 3 to 18 years were randomly selected from four communities in the Kpandai district of northern Ghana. Each presented a single stool sample at baseline, 21 days post-treatment, at the 3rd and 6th months, 21 days post-second intervention (i.e. following sample collection and treatment with ALB in the 6th month), and in the ninth month of the study period. Haemoglobin (hb) levels were also measured at all time points using finger prick blood samples and a URIT digital test kit. Each participant submitting a sample, was treated with a single-dose ALB (400mg) at baseline and in the sixth month. Stool samples were processed by preparing duplicate Kato-Katz slides per sample, and examined by microscopy. The Body Mass Index-for-age z-scores (BAZ) of participants were assessed following the determination of BMIs at each time point by measuring their height and weight with a stadiometer and weighing scale. Overall hookworm prevalences were 25.68% (95% CI = 20.51-31.75) at baseline, 11.18% (95% CI = 7.87-15.41) 21 days post-treatment, 11.78% (95% CI = 8.38-16.11) and 6.95% (95% CI = 4.41-10.43) in the 3rd and 6th months, 0.91% (95% CI = 0.19-2.65) 21 days post-second intervention, and 8.46% (95% CI = 5.62-12.23) in the ninth month. Observed overall faecal egg count reduction rates (ERRs) were 94.21% (95% CI = 81.50%- 100.00%) 21 days after baseline treatment, 97.70% (95% CI = 85.08-100.00) and 96.95% (95% CI = 84.18%- 100.00%) in the 3rd and 6th months, 99.98% (95% CI = 86.42%- 100.00%) 21 days post-second intervention, and 17.18% (95% CI = 14.07%- 20.67%) in the 9th month. Respective cure rates (CRs) were 62.35% (95% CI = 46.71-81.56%), 85.88% (95% CI = 67.32-100.00%), 87.06% (95% CI = 68.36%- 100.00%), 98.82% (95% CI = 78.83%- 100.00%), and 36.36% (95% CI = 9.91%- 93.11%). Additionally, increases in the percent frequency of 'normal hb' (p < 0.01) were observed across the study time points, whilst 'normal BAZ' cases remained high (from 94.87% to 98.87%) throughout the study period. These findings primarily indicate satisfactory effectiveness of ALB which may be maintainable in mass drug administration programmes by the modification of treatment strategies from annual to bi-annual regimes. This could minimize the likelihood of emerging poorly-responding hookworm phenotypes in Ghana. Additionally, a positive impact of bi-annual treatment on participant anaemia status is herein indicated with particular regard to the school children in our cohort.

Laser temperature programmed desorption: A flexible technique to study ion-surface interaction.

Understanding the physical-chemical processes ruling the interaction of particles (atoms, molecules, and ions) with surfaces is fundamental in several research fields, such as heterogeneous catalysis, astrochemistry, and nuclear fusion. In particular, the interaction of hydrogen isotopes with plasma facing materials represents a high-priority research task in the fusion community. Such studies are essential to ensure the successful operation of experimental fusion reactors, such as the tokamak ITER. In this work, we present a surface science apparatus developed to study ion-surface interaction in fusion relevant systems. It combines laser-based techniques with contaminant-free ion/molecular beams, mass spectrometry, and surface science tools such as low-energy electron diffraction and Auger electron spectroscopy. It allows to cover a wide range of sample temperatures, from 130 to 2300K, by changing the heating rate of samples from 0.1 to 135 K/s and maintaining the linearity of the heating ramps, a powerful feature to gain insight on adsorption, absorption, and desorption mechanisms. Experimental calibration and performance are presented in detail. Moreover, to provide a factual overview of the experimental capabilities, we focus on two different applications: the protocol used to clean a W(110) single crystal sample and the development of laser temperature programmed desorption to study helium retention in tungsten.

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.

Rationale: Distinguishing connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. Objectives: To identify proteins that separate and classify patients with CTD-ILD and those with IPF. Methods: Four registries with 1,247 patients with IPF and 352 patients with CTD-ILD were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using recursive feature elimination to construct a proteomic classifier. Multiple machine learning models, including support vector machine, LASSO (least absolute shrinkage and selection operator) regression, random forest, and imbalanced Random Forest, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. Measurements and Main Results: A classifier with 37 proteins (proteomic classifier 37 [PC37]) was enriched in the biological process of bronchiole development and smooth muscle proliferation and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver operating characteristic curve analyses of these scores demonstrated consistent areas under the curve of 0.85-0.90 in the test cohort and 0.94-0.96 in the single-sample dataset. Binary classification demonstrated 78.6-80.4% sensitivity and 76-84.4% specificity in the test cohort and 93.5-96.1% sensitivity and 69.5-77.6% specificity in the single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194 of 248) accuracy in the test cohort and 82.9% (208 of 251) in the single-sample classification dataset. Conclusions: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Many of the identified proteins are involved in immune pathways. We further developed a novel approach for single-sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision making.

Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients.

Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies. The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia. This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques. A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects. Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer.

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

Corporate Governance Structure and Corporate Risk-Taking: Evidence of Malaysian Agriculture Sector

Purpose: This study explores the role of corporate governance structure and corporate risk-taking of on Malaysian agriculture sector. Despite its importance in mitigating climate change, the agricultural sector also faces global competition, market liberalisation, rapid technological advances and the starter of stricter quality and safety procedures, all of w hich require f irms t o take g reater r isks.&#x0D; Design/methodology/approach: This study explores this relationship by applying generalised least square (GLS), random effect methodologies (REM). The sample of this study uses panel data from 2015 to 2021.&#x0D; Findings: The findings report a negative relationship between corporate governance structure and corporate risk-taking using a sample of firms from an emerging market.&#x0D; Research limitations/implications: The effects of these results for management practice and recommendations for further research were examined. The limitation of this study provides a sample of study using a limited number of agricultural firms in Malaysia.&#x0D; Originality/value: While this empirical study used a single industry-focused sample, most previous studies have focused on multiple industries. A key feature of this study is the analysis of how corporate governance structures affect the risk appetite of Malaysian agribusinesses.

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatments. Focusing on gene expression, the transcriptomic market exploits the benefits of next-generation sequencing (NGS), leveraging RNA sequencing (RNA-seq) as standard for measuring genome-wide gene expression in biological samples. The cumbersome sample preparation, including RNA extraction, conversion to cDNA and amplification, prevents high-throughput translation of RNA-seq technologies. Bulk RNA barcoding and sequencing (BRB-seq) addresses this limitation by enabling sample preparation in multi-well plate format. Sample multiplexing combined with early pooling into a single tube reduces reagents consumption and manual steps. Enabling simultaneous pooling of all samples from the multi-well plate into one tube, our technology relies on smart labware: a pooling lid comprising fluidic features and small pins to transport the liquid, adapted to standard 96-well plates. Operated with standard fluidic tubes and pump, the system enables over 90% recovery of liquid in a single step in less than a minute. Large scale manufacturing of the lid is demonstrated with the transition from a milled polycarbonate/steel prototype into an injection molded polystyrene lid. The pooling lid demonstrated its value in supporting high-throughput barcode-based sequencing by pooling 96 different DNA barcodes directly from a standard 96-well plate, followed by processing within the single sample pool. This new pooling technology shows great potential to address medium throughput needs in the BRB-seq workflow, thereby addressing the challenge of large-scale and cost-efficient sample preparation for RNA-seq.Graphical abstract

Multiplexed Capillary-Flow Driven Immunoassay for Respiratory Illnesses.

Multiplexed analysis in medical diagnostics is widely accepted as a more thorough and complete method compared to single-analyte detection. While analytical methods like polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) exist for multiplexed detection of biomarkers, they remain time-consuming and expensive. Lateral flow assays (LFAs) are an attractive option for point-of-care testing, and examples of multiplexed LFAs exist. However, these devices are limited by spatial resolution of test lines, large sample volume requirements, cross-reactivity, and poor sensitivity. Recent work has developed capillary-flow microfluidic ELISA platforms as a more sensitive alternative to LFAs; however, multiplexed detection on these types of devices has yet to be demonstrated. In the aftermath of the initial SARS-CoV-2 pandemic, the need for rapid, sensitive point-of-care devices has become ever clearer. Moving forward, devices that can distinguish between diseases with similar presenting symptoms would be the ideal home diagnostic. Here, the first example of a multiplexed capillary-flow immunoassay device for the simultaneous detection of multiple biomarkers is reported. From a single sample addition step, the reagents and washing steps required for two simultaneous ELISAs are delivered to spatially separated test strips. Visual results can be obtained in <15 min, and images captured with a smartphone can be analyzed for quantitative data. This device was used to distinguish between and quantify H1N1 hemagglutinin (HA) and SARS-CoV-2 nucleocapsid protein (N-protein). Using this device, analytical detection limits of 840 and 133 pg/mL were obtained for hemagglutinin and nucleocapsid protein, respectively. The presence of one target in the device did not increase the signal on the other test line, indicating no cross-reactivity between the assays. Additionally, simultaneous detection of both N-protein and HA was performed as well as simultaneous detection of N-protein and human C-reactive protein (CRP). Elevated levels of CRP in a patient infected with SARS-CoV-2 have been shown to correlate with more severe outcomes and a greater risk of death as well. To further expand on the simultaneous detection of two biomarkers, CRP and N-protein were detected simultaneously, and the presence of SARS-CoV-2 N-protein did not interfere with the detection of CRP when both targets were present in the sample.

A spatially resolved analysis of dislocation loop and nanohardness evolution in proton irradiated Zircaloys

Proton irradiation is increasingly used as a surrogate for neutron irradiation, providing similar damage structures at significantly lower costs and less time compared to neutron irradiation. However, in contrast to neutrons, protons produce a depth dependent damage profile that incorporates a plateau region and a Bragg peak in the tens of microns region depending on the material and proton energy. Here we demonstrate that this depth dependent damage can be utilised to obtain new understanding about irradiation induced damage at different dose levels from a single sample by combining spatially resolved micro-beam synchrotron X-ray diffraction-based dislocation analysis and nanoindentation. For this purpose, and to study the damage evolution starting from very early stages, we have investigated microstructure evolution and hardening behaviour of Zircaloy-2 and Zircaloy-4 proton irradiated samples between 0.01 dpa and 17 dpa at 350 °C. The results highlight good agreements of SRIM-based damage depth predictions with irradiation-induced dislocation appearance and nanohardness. However, at even relatively low damage levels within the plateau region, dislocation line density and nanohardness profiles appear saturated across the entire irradiated region, even when the dpa levels differed significantly. When relating dislocation loop line densities to nanohardness, it was found that from a certain point hardness increased only very slightly with increasing line densities. This apparent discrepancy can be explained by a decreasing loop size for the highest line densities identified by from the diffraction line profile analysis and the application of a Dispersed Barrier Hardening model that relates obstacle strength to dislocation loop size.