Sample Processing Research Articles

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples.

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCD industry

ABSTRACT In the thin film transistor-liquid crystal display (TFT-LCD) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCD industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products.

Development of a portable coal rock charge monitoring instrument and its application for rockburst control

Effective monitoring techniques and equipment are essential for the prevention and control of coal and rock dynamic disasters such as rockburst. Based on the fact that there is charge generation during deformation and rupture of coal rock body and the charge signals contain a large amount of information about the mechanical process of deformation and rupture of coal rock, the rockburst charge sensing monitoring technology has been formed. In order to improve the charge sensing technology for monitoring and early warning of rockburst disasters, this paper develops a new generation of portable coal rock charge monitoring instrument on the basis of the original instrument and carries out laboratory and underground field application. The primary advancement involves enhancing the external structure of the sensor and increasing the charge sensing area, which can more comprehensively capture the charge signals from the loaded rupture of the coal rock body. The overall structure of the data acquisition instrument has been improved, the monitoring channels have been increased, and the function of displaying the monitoring data curve has been added, so that the coal and rock body force status can be grasped in time. The results of the experimental study show that the abnormal charge signals can be monitored during the rupture process of rock samples under loading, and the monitored charge signals are in good agreement with the sudden change of stress in the rock samples and the formation of crack extension. There is a precursor charge signal before the stress mutation, and the larger the loading rate is, the earlier the precursor charge signal appears. The charge monitoring instrument can monitor the charge signal of the coal seam roadway under strong mining pressure. In the zone of elevated overburden pressure, the amount of induced charge is large, and anomalously high value charge signals can be monitored when a coal shot occurs. The change trend of the charge at different measuring points of strike and inclination has a good consistency with the distribution of overrunning support pressure and lateral support pressure, which can reflect the stress distribution and the degree of stress concentration of the coal body through the size and location of the charge, foster early warning and analysis of rockburst, and provide target guidance for the prevention and control of rockburst.

Enhanced differentiation between 3‐hydroxyglutaric and 2‐hydroxyglutaric acids facilitates diagnostic testing for glutaric aciduria type 1

AbstractGlutaric aciduria type 1 (GA1) is an inherited neurometabolic disorder, in which deficiency of glutaryl‐CoA dehydrogenase leads to accumulation of glutaric acid (GA) and 3‐hydroxyglutaric acid (3‐HG). Some low excretors may exhibit only slight elevation of urinary 3‐HG, with normal urinary GA, yet are at significant risk of severe clinical disease. Accurate quantitation of urinary 3‐HG is crucial in diagnostic workup for GA1, but in this context, current gas chromatography–mass spectrometry (GC–MS) methods have inherent analytical challenges. Co‐elution and spectral similarities of the 3‐HG and 2‐HG structural isomers can cause difficulties in quantitation of slightly elevated 3‐HG. Our laboratory recently acquired a gas chromatography system coupled to a triple quadrupole mass spectrometer (GC–MS/MS), and we took advantage of its increased sensitivity and specificity to improve our existing GC–MS method. A stable isotope dilution process is used, with sample treatment consisting of a double liquid–liquid extraction followed by a trimethylsilyl derivatization. The transitions m/z 349 → 333 for 3‐HG and m/z 349 → 321 for 2‐HG were selected to differentiate these two isobaric molecules based on their characteristic fragments, thus minimizing interferences despite co‐elution. Method validation demonstrated satisfactory precision and accuracy. Using GC–MS/MS instead of GC–MS allowed us to decrease the required specimen volume, number of sample processing steps, chromatographic run time, and instrument maintenance. This enhanced assay facilitates clinical laboratory testing for GA1, both in confirmatory protocols following positive newborn screening and in diagnostic investigation of patients with suggestive signs or symptoms.

Damage inference of statically deterministic structural trusses is carried out by Bayesian updating

In the face of harsh environments, aging construction materials, and increased traffic, the performance of Bridges in service continues to deteriorate. To avoid the serious consequences caused by structural damage, it is of great significance to infer the damage of the truss structure and evaluate the reliability of the bridge. In this paper, a random sampling process is used to characterize the resistance degradation, and the historical load test information and Bayesian method are used to update the resistance of serving Bridges. The modeling of structural mechanics solver determines the maximum axial force on the damaged structural member by loading different nodes. Secondly, node displacement is calculated. Then, the displacement results are taken as new information as the parameters of prior distribution for Bayesian update, and finally, the maximum probability of structural damage is obtained. The results show that the probability of EA values between 1.34 and 1.38 is the greatest. The uncertainty and variability in the process of resistance degradation of reinforced concrete bridge members can be described more reasonably by the Bayesian updating process. By integrating the real resistance degradation information detected during bridge service into practical application, and using the research method in this paper to analyze and calculate, Bridges' future service status and remaining service life can be predicted more accurately.

A hybrid sampling-based RRT* path planning algorithm for autonomous mobile robot navigation

The path-planning algorithms for autonomous mobile robot navigation are crucial, often relying on sampling-based methods. RRT* is a robust, sampling-based path planning algorithm. The sampling process in RRT* plays a pivotal role, where uniform sampling can lead to slow convergence, while non-uniform sampling offers faster convergence but may struggle in complex environments due to its limited exploration. Thus, achieving a balance between exploitation and exploration is essential when selecting the sampling method for the RRT* path-planning algorithm. To address this issue, this research paper introduces Hybrid-RRT*, a path planning method that utilizes hybrid sampling. This unique approach generates samples using both non-uniform and uniform samplers. Hybrid-RRT* is evaluated against three baseline path planning algorithms—RRT*-N, Informed RRT*, and RRT*—across three different 384x384 2D simulation environments. Compared to these baseline methods, Hybrid-RRT* achieves superior results across all five performance metrics: convergence rate, success rate, number of nodes visited, path length, and planning time. According to the numerical results, the proposed algorithm achieves a faster average convergence rate that is 76.14% higher than RRT*, 24% higher than Informed RRT*, and 3.33% higher than RRT*-N. Moreover, it reduces node exploration by an average of 48.53% compared to RRT* and 40.83% compared to Informed RRT*. The simulation results demonstrate that the proposed Hybrid-RRT* algorithm effectively addresses the issue of slow convergence with uniform sampling and the challenge of limited exploration with non-uniform sampling methods.

Comparative Analysis of Digestion Methods for Bile Proteomics: The Key to Unlocking Biliary Biomarker Potential.

Bile's potential to reflect the health of the biliary system has led to increased attention, with proteomic analysis offering deeper understanding of biliary diseases and potential biomarkers. With the emergence of normothermic machine perfusion (NMP), bile can be easily collected and analyzed. However, the composition of bile can make the application of proteomics challenging. This study systematically evaluated various trypsin digestion methods to optimize proteomics of bile from human NMP livers. Bile was collected from 12 human donor livers that were accepted for transplantation after the NMP viability assessment. We performed tryptic digestion using six different methods: in-gel, in-solution, S-Trap, SMART, EasyPep, and filter-aided sample purification, with or without additional precipitation before digestion. Proteins were analyzed using untargeted proteomics. Methods were assessed for total protein IDs, variation, and protein characteristics to determine the most optimal method. Methods involving precipitation surpassed crude methods in protein identifications (4500 vs 3815) except for in-gel digestion. Filtered data (40%) resulted in 3192 versus 2469 for precipitated and crude methods, respectively. We found minimal differences in mass, cellular components, or hydrophobicity of proteins between methods. Intermethod variability was notably diverse, with in-gel, in-solution, and EasyPep outperforming others. Age-related biological comparisons revealed upregulation of metabolic-related processes in younger donors and immune response and cell cycle-related processes in older donors. Variability between methods emphasizes the importance of cross-validation across multiple analytical approaches to ensure robust analysis. We recommend the in-gel crude method for its simplicity and efficiency, avoiding additional precipitation steps. Sample processing speed, cost, cleanliness, and reproducibility should be considered when a digestion method is selected for bile proteomics.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

The challenges of children's safety in pre-hospital emergencies: a qualitative study

Objective: Exploring the challenges experienced by the pre-hospital emergency personnel to prevent the secondary injuries and provide more effective services to children can be helpful. This study aims to explain the safety challenges of children in the pre-hospital emergencies. Method: This qualitative study, which was conducted with the content analysis approach, was a semi-structured, and in-depth data collection process with the field notes for pre-hospital emergency personnel in Dezful city in 2022. Sampling was done in a targeted and available manner. The sampling process continued until the data saturation was reached. The trustworthiness of data was improved by reviewing and revising the data, experience and expertise of the interviewer, review of research participants and researchers. The research data, including interview texts and field notes, were divided into the semantic units, and then summarized by compression, and then abstracted in the order of subclasses, main classes, and finally themes. Results: Qualitative data analysis of interviews and notes led to the extraction of eight themes, which expressed the nature of children's safety challenges in the pre-hospital emergency: the lack of pre-hospital equipment for children, physical and mental characteristics of children, the lack of safety of vehicles for children, the lack of clinical skills of personnel for children, anxiety and non-cooperation of parents, operational and legal problems, the lack of attention to professional responsibility and high work pressure in children's missions . Conclusion: This study’s results showed that identifying children's safety challenges for pre-hospital emergency personnel can help better understand the care needs of this age group and help managers plan in order to reduce children's safety challenges in the pre-hospital missions.

Wear behaviour analysis of thermo-mechanically processed AA7075 and AA7075/SiC/Graphite composite

The current work focuses on investigating the tribological performance of AA7075 alloy and AA7075 hybrid composite with SiC and graphite reinforcements of 2 and 0.5 wt percentages, respectively. The alloy and hybrid composite are fabricated through stir casting and thermo-mechanically processed through hot rolling routes. Dry sliding wear characterization has been carried out using a pin-on-disc wear testing setup for alloy and composite under various processing conditions using experiments designed according to Taguchi's L9 orthogonal array. The technique for order preference by similarity to ideal solution (TOPSIS) method has been adopted to achieve a single multi-response index (MRI) considering the minimization of multiple objective functions for specific wear rate (SWR) and coefficient of friction (COF). The analysis of variance (ANOVA) results of the MRI indicated that sample processing condition and load were the most significant parameters. The wear mechanism at different experimental conditions was studied using Scanning electron microscope (SEM) micrographs, energy dispersive spectroscopy analysis and 3D surface profile of wear track. The AA7075 composites are found to be comparatively good in all experimental conditions compared to the base alloy due to load-bearing SiC particles and solid lubricant graphite as reinforcing elements. The mechanically mixed layer (MML) formation in hybrid composites helped achieve good tribological properties. The AA7075 alloy and hybrid composites cross rolled at 350 °C yielded better results than other samples. The wear mechanism was abrasive and adhesive at low temperature and low load conditions in both alloy and hybrid composites. Whereas, at higher temperatures and high loads oxidative wear mechanism with delamination was prevalent. The transition of SWR from mild to severe value occurs at 250 °C in all experimental cases. The specific wear rate was improved by 85.64 % between worst and best conditions, i.e., annealed alloy and cross rolled composite.

A laboratory investigation for assessing the effectiveness of wastewater treatment using flocculation process in building stone processing industry on stone surface quality and abrasive tool wear

In this study, the effect of presence and concentration of two synthetic polymeric flocculants with different charging mechanisms (anionic and cationic) on surface quality and abrasive tool wear in polishing process of building stones was investigated. The polishing tests were performed in laboratory scale on two stone samples (granite and marble). An index, entitled improvement index (I) was defined for evaluating the effectiveness of flocculants on surface quality and abrasive tool wear. The results revealed that flocculation process had a noticeable effect on enhancement of surface quality and reduction of abrasive tool wear for both stone samples. The cationic flocculant was more effective for granite sample and anionic flocculant was more effective for marble sample. The granite particles mainly exhibit negative surface charge while the marble particles have positive surface charge in the pH range of wastewater used for polishing tests. Therefore, charge neutralization mechanism plays a major role in flocculation process of granite and marble samples. It was found that the use of flocculation process for treatment of wastewater in stone processing plants leads to increase of abrasive tool lifespan and more high-quality products.

Zooplankton communities of small lakes of the Kuibyshev district Novosibirsk region in the spring and summer

In the course of hydrobiological studies conducted in the spring and summer of 2023 on small lakes of the Kuibyshev district of the Novosibirsk region, zooplankton communities were studied, which is relevant in the field of studying the fish food supply. The purpose of this study was to determine the species diversity and quantitative characteristics of zooplankton communities of lakes. Sampling and in-house processing of samples was carried out according to generally accepted methods. The characteristics of zooplanktonic invertebrates in terms of qualitative and quantitative indicators are considered. The assessment of the trophic and feeding capacity of the lakes is given. 16 species of organisms of 12 genera from 9 families were found in the zooplankton, 5 of which are rotifers, 9 are branched crustaceans and 2 are oar-footed crustaceans. It was found that the greatest species diversity was observed in Lake Tsybovo — 13, the smallest in Lake Maloe Puchkovo — 9 taxa. The average zooplankton abundance and biomass in the studied lakes ranged from 16.0 to 67.645 thousand specimens/m3 and from 652.2 to 7118.5 mg/m3, respectively. The maximum average values of zooplankton abundance and biomass were found in Lake Fedino, the minimum values in Lake Cemetery. In terms of frequency of occurrence and specific abundance, Copepods are mainly dominant. The lowest frequency of occurrence and specific abundance is mainly in Rotifera. According to the average indicators of zooplankton biomass, lakes have different trophic status and feeding capacity of the reservoir: from oligotrophic type (low-fat) to eutrophic type (high-fat). Fedino Lake was classified as a high-grade eutrophic reservoir with an increased productivity class. Cemetery Lake with the lowest abundance and biomass indicators was assessed as an oligotrophic reservoir of low productivity class, from the point of view of feeding opportunities for planktophage fish — low-feeding. Tsybovo and Maloe Puchkovo lakes were assessed as mesotrophic reservoirs.

Saliva-STAT: Sample-to-answer saliva test for COVID-19

Highly accessible and highly accurate diagnostics are necessary to combat rapidly-spreading infectious diseases, such as the recent COVID-19 pandemic. While lateral flow antigen tests have become pervasive, they are insufficiently sensitive to detect early or asymptomatic disease. Nucleic acid amplification tests provide the needed sensitivity, but accessibility of these tests continues to be a challenge due to the need for precise sample processing steps. Here we report a sample-to-answer test for saliva samples (saliva-STAT) that utilizes a battery-powered handheld instrument and a low-cost easily-manufacturable sample cassette to perform a nucleic acid amplification test for viral pathogens. To enable a completely automated assay, we leverage thermally responsive alkane partitions (TRAPs) and paramagnetic beads for virus purification and concentration, as well as reagent addition and mixing. Notably, the saliva STAT easily accommodates directly-dispensed saliva samples (in contrast to microfluidic devices), which is necessary for self-testing. Using the saliva-STAT platform, we demonstrate detection of down to 0.2 copies/μL of SARS-CoV-2 virus in saliva samples. We envision that the saliva-STAT could be used in walk-in clinics, mobile clinics, public testing locations, and in the home. With minor adjustments to the assay, the saliva-STAT platform can easily be adapted for other respiratory viruses, such as Influenza.

Risk factors for non-communicable diseases in Afghanistan: insights of the nationwide population-based survey in 2018

BackgroundNoncommunicable diseases (NCDs) account for a substantial number of deaths in Afghanistan. Understanding the prevalence and correlates of major NCD risk factors could provide a benchmark for future public health policies and programs to prevent and control NCDs. Therefore, this study aimed to examine the prevalence and correlates of NCD risk factors among adults aged 18–69 years in Afghanistan.MethodsWe used data from the Afghanistan STEPS Survey 2018. The study population were 3650 (1896 males and 1754 females) adults aged 18–69 years sampled from all 34 provinces through a multistage cluster sampling process. Information on behavioural and biological risk factors was collected. We used STATA (version 18.0) for data analysis.ResultsOf the total participants, 42.8% were overweight or obese, 8.6% were current smokers, 26.9% had insufficient physical activities, 82.6% had low consumption of fruits and vegetables, and only 0.5% had ever consumed alcohol. Approximately 15% of participants had a high salt intake, while 25% and 8% had elevated blood pressure and blood glucose levels, respectively. Similarly, around 18% had elevated total cholesterol. The study revealed a lower prevalence of current smoking among females [AOR = 0.17, 95%CI (0.09–0.30)] compared with males, but a higher prevalence in those who had higher education levels [1.95 (1.13–3.36)] compared with those with no formal education. Insufficient physical activity was higher in participants aged 45–69 years [1.96 (1.39–2.76)], females [4.21 (1.98–8.84)], and urban residents [2.38 (1.46–3.88)] but lower in those with higher education levels [0.60 (0.37–0.95)]. Participants in the 25th to 75th wealth percentiles had higher odds of low fruit and vegetable consumption [2.11 (1.39–3.21)], while those in the > 75th wealth percentile had lower odds of high salt intake [0.63 (0.41–0.98)]. Being overweight/obese was more prevalent in participants aged 45–69 years [1.47 (1.03–2.11)], females [1.42 (0.99–2.01)], currently married [3.56 (2.42–5.21)] or ever married [5.28 (2.76–10.11)], and urban residents [1.39 (1.04–1.86)]. Similarly, high waist circumference was more prevalent in participants aged 45–69 years [1.86 (1.21–2.86)], females [5.91 (4.36–8.00)], those being currently married [4.82 (3.12–7.46)], and those being in 25th to 75th wealth percentile [1.76 (1.27–2.43)]. A high prevalence of elevated blood pressure was observed in participants aged 45–69 years [3.60 (2.44–5.31)] and currently married [2.31 (1.24–4.31)] or ever married [6.13 (2.71–13.8)] participants. Elevated blood glucose was more prevalent in older adults ([1.92 (1.09–3.39)] for 45–69 and [3.45 (2.44–5.31)] for 30–44 years), urban residents [2.01 (1.33–3.03)], and ever-married participants [4.89 (1.48–16.2)]. A higher prevalence of elevated cholesterol was observed in females [2.68 (1.49–4.82)] and those currently married [2.57 (1.17–5.63)] or ever married [4.24 (1.31–13.73)].ConclusionThis study used up-to-date available data from a nationally representative sample and identified the prevalence of NCDs and associated risk factors in Afghanistan. Our findings have the potential to inform and influence health policies by identifying people at high risk of developing NCDs and can assist policymakers, health managers, and clinicians to design and implement targeted health interventions.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Do Parental and Children’s Dental Anxiety Affect Childhood Dental Caries?

Background: Dental anxiety is an unpleasant reaction to fear or stress related to the dental environment and can be observed in both parents and children as dental patients. Despite several studies challenging the significance of parental anxiety in influencing a child's response to dental treatment, conflicting results have emerged regarding the impact of both parental and children's anxiety on the development of dental caries. Objectives: This study aimed to explore the association between parental and children's dental anxiety and the occurrence of dental caries in children. Methods: A cross-sectional study involving 7 - 12-year-old students and their parents in schools of Rasht, Iran, utilized the Modified Dental Anxiety Scale (MDAS) for parents and the Modified Child Dental Anxiety Scale (MCDAS) questionnaire for children. Participants were chosen through a multistage sampling process. A trained dentist assessed children's dental caries using the DMFT/dmft (decayed, missing, and filled teeth) Index. The data were analyzed using the chi-square test and linear regression. Results: The study included 275 parents and an equal number of children, with a mean child age of 9.32 ± 1.85 years. The findings indicated a significant relationship between maternal dental anxiety and DMFT + dmft scores in boys (P = 0.049). The study revealed that the number of teeth with caries experience (P = 0.023), child's sex (P = 0.007), history of dental clinic visits (P = 0.045), and maternal dental anxiety (P = 0.043) significantly influenced the child's dental anxiety. Conclusions: Children with anxious parents showed lower DMFT/dmft values, suggesting increased attention to their dental health. The study highlights the impact of parental dental anxiety on children's oral well-being and emphasizes the need for comprehensive strategies to address dental anxiety in both parents and children.

Application of the Agilent 2100 Bioanalyzer instrument as quality control for next-generation sequencing.

Next-generation sequencing (NGS) technologies have expanded the spectrum of forensic DNA analysis by facilitating efficient and precise genotyping of a large number of genetic markers. Yet, challenges persist regarding complex sample processing and assurance of equal molar concentrations across pooled samples. Since optimal cluster density is crucial for sequencing performance, the determination of both quantity and quality is indispensable for library preparation. In this study, we investigated the application of the Agilent 2100 Bioanalyzer for library quality control, as studies for forensic approaches, particularly for highly degraded postmortem samples, are rare. Our analysis encompassed assessing total DNA concentrations, fluorescence unit (FU) values, and adapter dimer concentrations in purified DNA libraries derived from buccal swabs and tissue samples of decomposed corpses. The sensitivity study tested a serial dilution derived from buccal swabs and revealed a decrease in FU values and an increase in adapter dimers with declining DNA input concentrations. Deviations in total DNA concentrations and average peak heights between the Agilent 2100 Bioanalyzer runs indicated a lack of repeatability in data and presented challenges in accurate quantification, which was also observed in previous studies. Yet, the analysis of degraded samples from decomposed human remains has shown the ability to detect adapter dimer concentrations, which can be crucial for the quality of subsequent NGS library preparation and sequencing success. Therefore, the Agilent 2100 Bioanalyzer proves to be a valuable tool for NGS quality control.

General heterogeneous real-time baseband backend system scheme applied to QTT based on RFSoC+GPU

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system’s performance and the design’s rationality. This paper explores and confirms the RFSoC’s applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes.

Optimising in-cell NMR acquisition for nucleic acids.

Understanding the structure and function of nucleic acids in their native environment is crucial to structural biology and one focus of in-cell NMR spectroscopy. Many challenges hamper in-cell NMR in human cell lines, e.g. sample decay through cell death and RNA degradation. The resulting low signal intensities and broad line widths limit the use of more complex NMR experiments, reducing the possible structural and dynamic information that can be extracted. Here, we optimize the detection of imino proton signals, indicators of base-pairing and therefore secondary structure, of a double-stranded DNA oligonucleotide in HeLa cells, using selective excitation. We demonstrate the reproducible quantification of in-cell selective longitudinal relaxation times (selT1), which are reduced compared to the in vitro environment, as a result of interactions with the complex cellular environment. By measuring the intracellular selT1, we optimize the existing proton pulse sequences, and shorten measurement time whilst enhancing the signal gained per unit of time. This exemplifies an advantage of selective excitation over conventional methods like jump-return water suppressionfor in-cell NMR. Furthermore, important experimental controls are discussed, including intracellular quantification, supernatant control measurements, as well as the processing of lowly concentrated in-cell NMR samples. We expect that robust and fast in-cell NMR experiments of nucleic acids will facilitate the study of structure and dynamics and reveal their functional correlation.

RegMamba: An Improved Mamba for Medical Image Registration

Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets.