Acquisition Method Research Articles

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

SAB-YOLOv5: An Improved YOLOv5 Model for Permanent Magnetic Ferrite Magnet Rotor Detection

Surface defects on the permanent magnetic ferrite magnet rotor are the primary cause for the decline in performance and safety hazards in permanent magnet motors. Machine-vision methods offer the possibility to identify defects automatically. In response to the challenges in the permanent magnetic ferrite magnet rotor, this study proposes an improved You Only Look Once (YOLO) algorithm named SAB-YOLOv5. Utilizing a line-scan camera, images capturing the complete surface of a general object are obtained, and a dataset containing surface defects is constructed. Simultaneously, an improved YOLOv5-based surface defect algorithm is introduced. Firstly, the algorithm enhances the capability to extract features at different scales by incorporating the Atrous Spatial Pyramid Pooling (ASPP) structure. Then, the fusion of features is improved by combining the tensor concatenation operation of the feature-melting network with the Bidirectional Feature Pyramid Network (BiFPN) structure. Finally, the introduction of the spatial pyramid dilated (SPD) convolutional structure into the backbone network and output end enhances the detection performance for minute defects on the target surface. In the study, the SAB-YOlOv5 algorithm shows an obvious increase from 84.2% to 98.3% in the mean average precision (mAP) compared to that of the original YOLOv5 algorithm. The results demonstrate that the data acquisition method and detection algorithm designed in this paper effectively enhance the efficiency of defect detection permanent magnetic ferrite magnet rotors.

Abstract 4948: Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray

Abstract Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4948.

How to learn and teach a foreign language through computational thinking: Suggestions based on a systematic review

There has been an increasing interest in the integration of technology into language education. Meanwhile, computational thinking (CT) skills are fostered which influence communicative language teaching and learning. Despite CT's established importance in science education, its significance in language teaching remains scarce. This study contributes to knowledge synthesis and methodological development by reviewing 16 journal articles, analyzing CT applications in language learning across educational contexts, intervention tools, CT skills, language acquisition, and assessment methods. The results indicate that (a) more CT-involved activities are needed for primary, secondary, and tertiary education, (b) educational robots and Scratch stand out as the most trending intervention tools, (c) CT skills such as decomposition, abstraction, generalization and so on have a positive effect on students’ foreign language acquisition, especially in grammar and writing, (d) assessment of CT development can be surveyed through a validated and reliable instrument such as computer programming self-efficacy scale or an online platform: Dr. Scratch; Conversely, there is a diversity of evaluation tools in foreign language learning. This study is the first to review experimental research on integrating computational thinking into foreign language learning and offers significant implications for future research.

Abstract 1161: Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment

Abstract Glioblastoma is the most frequent and malignant brain tumor, with a median survival of approximately 14 months. The tumor microenvironment is composed of different cell types and is known to support the glioblastoma cells and contribute to poor patient prognosis. Since accumulating studies report discordance between mRNA and protein abundances both on population and single-cell levels, and since proteins are the effector molecules of the cells and the actual target molecules of most drugs, our aim is to dissect the tumor microenvironment by spatial LC-MS based proteomics. Glioblastoma formalin-fixed paraffin-embedded (FFPE) tissue was sectioned and placed on membrane glass slides suitable for laser capture microdissection (LCM), followed by precise isolation of single cells from the heterogeneous tumor microenvironment. We used the Zeiss Palm MicroBeam laser microdissection instrument, combined with an Orbitrap Eclipse Tribrid Mass Spectrometer running our in-house sensitivity tailored Data Independent Acquisition method (WISH-DIA) to analyze and quantify the proteomes. We obtained up to 3000 proteins out of only 100 cells and were able to demonstrate the feasibility of collecting and analyzing various cell types at the single-cell level. The proteins identified belonged to many different classes/families of proteins and were able to distinguish the different cell types. In conclusion, this on-going study demonstrates our ability to isolate single cells from the highly heterogeneous tumor microenvironment of glioblastoma with ultra-high sensitivity, thereby allowing the characterization of the proteome of the glioblastoma microenvironment followed by identification of potential therapeutic and/or diagnostic targets. Citation Format: Kartikey Saxena, Rune Daucke, Vilde Pedersen, Pia Helene Klausen, Erwin Schoof, Bjarne Winther Kristensen. Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1161.

Abstract 7090: A scaled proteomic discovery study for prostate cancer diagnostic signatures using Proteograph workflow with trapped ion mobility mass spectrometry

Abstract Background and Aims: The low cancer specificity of serum Prostate-Specific Antigen (PSA), the principal biomarker for prostate cancer detection, leads to a high frequency of unwarranted prostate biopsies. To alleviate this deficit, we executed a proteomic discovery study seeking PSA reflex signatures using the Proteograph™ Product Suite, a multi-nanoparticle-based deep plasma/serum proteomics workflow (Seer, Inc.), with timsTOF Pro mass spectrometry (Bruker) to interrogate over 900 serum specimens from patients that underwent prostate biopsy due to elevated PSA and/or abnormal digital rectal exam. Methods: Our study followed rigorous design principles including uniform collection, randomization and blinding of specimens, all of which were processed with the Proteograph Product Suite. Liquid chromatography-mass spectrometry (LC-MS) analyses leveraged the Bruker timsTOF Pro MS platform utilizing 30-minute reversed-phase chromatography and a label-free dia-PASEF (data independent acquisition - parallel accumulation serial fragmentation) data acquisition method. Peptides deriving from a chosen subset of specimens designed to maximize peptide diversity were pooled, fractionated and analyzed using DDA (data-dependent acquisition)-PASEF to build a spectral reference library. Results: The DIA-NN algorithm was employed through the cloud-based Proteograph Analysis Suite (PAS) to search LC-MS data. Leveraging our study-specific spectral library proteomic depth achieved was approximately 3600 protein groups identified (median) per patient serum specimen and more than 4400 in at least 25% of samples across the study. Customized machine learning workflows and the SeerML pipeline were used to identify and assess diagnostic power of new proteomic signatures. Conclusions: Our data demonstrate the remarkable proteomic depth achievable in a scaled patient serum specimen discovery study using a combination of Proteograph and timsTOF platforms. This significant effort revealed serum proteomic signatures with increased diagnostic performance over that provided by PSA blood measurement and traditional patient-associated metadata alone, providing a foundation for future clinical tests aimed to reduce the current high frequency of unnecessary prostate biopsy. Citation Format: Mark Flory, Matthew Chang, Jessie May Cartier, Jane Lange, Travis Moore, James McGann, Ryan Kopp, Michael Liss, Robin Leach, Brenna Albracht, James Dai, Michael Krawitzky, Eltaher Elgierari, Jessica Chu, Paul Pease, Max Mahoney, Shadi Ferdosi, Ryan Benz, Khatereh Motamedchaboki, Asim Siddiqui, Mahdi Zamanighomi, Amir Alavi, Harendra Guturu, Daniel Hornburg, Serafim Batzoglou. A scaled proteomic discovery study for prostate cancer diagnostic signatures using Proteograph workflow with trapped ion mobility mass spectrometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7090.

Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management.

This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables-demographic, lifestyle and family history, personal health device, and laboratory data-organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.

Improved playstore review sentiment classification accuracy with stacking ensemble

In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that are critical for service improvement. Previous research has explored the application of stacking ensemble methods, such as in the context of predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail the data acquisition process, leaving a gap in understanding the applicability of these methods to different domains. This research aims to bridge this gap by applying the stacking ensemble approach to improve the accuracy of sentiment classification in Playstore reviews, with a clear exposition of the data collection method. Utilizing Logistic Regression as the meta classifier, this methodology is executed in several stages. Initially, data was collected from user reviews of online loan applications on Google Playstore, ensuring transparency in the data acquisition process. The data is then classified using three basic models: Random Forest, Naive Bayes, and SVM. The outputs of these models serve as inputs to the Logistic Regression meta model. A comparison of each base model output with the meta model was subsequently carried out. The test results on the Playstore review dataset demonstrated an increase in accuracy, precision, recall, and F1 score compared to using a single model, achieving an accuracy of 87.05%, which surpasses Random Forest (85.6%), Naive Bayes (85.55%), and SVM (86.5%). This indicates the effectiveness of the stacking ensemble method in providing deeper and more accurate insights into user sentiment, overcoming the limitations of single models and previous research by explicitly addressing data acquisition methods.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

BIBLIOTECA DIRECȚIEI GENERALE A ARHIVELOR STATULUI. ÎNCEPUTURI ȘI EVOLUȚIE (1862-1914)

In 1862, the Bucharest and Iași Archives have unified under a General Directorate. A document from 1862 mentions the beginnings of the establishment of the Archives library, by allocating funds for the purchase of books. In the same year, the position of librarian appears in the staff organization chart. All the directors were concerned with enriching the book fund necessary for the research. Thus, the methods of book acquisition: purchase, donations, internal and international exchanges, as well as the record-keeping tools were outlined. In this sense, the efforts of Grigore Manu, Bogdan Petriceicu Hașdeu, Dimitrie Onciul, who developed the Archives library and they insisted on appointing a specialized staff to deal with it.

Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry

Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D model of the environment. This study investigates the existing gap in fully immersive teleoperation systems within the mining industry, aiming to identify the most optimal methods for their development and ensure operator’s safety. To achieve this purpose, a literature search is employed to identify and extract information from the most relevant sources. The most advanced teleoperation systems are examined by focusing on their visualization types. Then, various 3D reconstruction techniques applicable to mining VR teleoperation are investigated, and their data acquisition methods, sensor technologies, and algorithms are analyzed. Ultimately, the study discusses challenges associated with 3D reconstruction techniques for mining teleoperation. The findings demonstrated that the real-time 3D reconstruction of underground mining environments primarily involves depth-based techniques. In contrast, point cloud generation techniques can mostly be employed for 3D reconstruction in open-pit mining operations.

Quick Extraction of Joint Surface Attitudes and Slope Preliminary Stability Analysis: A New Method Using Unmanned Aerial Vehicle 3D Photogrammetry and GIS Development

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices.

Optimal Design of Sparse Matrix Phased Array Using Simulated Annealing for Volumetric Ultrasonic Imaging with Total Focusing Method.

The total focusing method (TFM) is often considered to be the 'gold standard' for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated with a huge volume of data to be acquired and processed. This severely limits the frame rate of volumetric imaging with 2D probes and necessitates the use of high-end equipment. Thus, the aim of this research was to develop a novel design method for determining the optimal sparse 2D probe configuration for specific conditions of ultrasonic imaging. The developed approach is based on simulated annealing and involves implementing the solution of the sparse matrix phased array layout optimization problem. In order to implement simulated annealing for the aforementioned task, its parameters were set, the acceptance function was introduced, and the approaches were proposed to compute beam directivity diagrams of sparse matrix phased arrays in TFM imaging. Experimental studies have shown that the proposed approach provides high-quality volumetric imaging with a decrease in data volume of up to 84% compared to that obtained using the FMC data acquisition method.

Comparison of surface-wave techniques to estimate S- and P-wave velocity models from active seismic data

Abstract. The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (Vs and Vp) models. We used Poisson's ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m2. The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.

A framework of transportation mode detection for people with mobility disability

Transportation mode detection (TMD) is an important computational technique that aids human life at the social and individual levels. However, previous studies on TMD were focused on people without mobility disabilities, and research involving people with mobility disability is limited. Therefore, this study aimed to provide a TMD framework for people with mobility disability. We propose a method for data acquisition, and acquired data pertaining to 120 participants including manual and electric wheelchairs for 15,350 min. We analyzed the acquired data to determine the characteristics of each transportation mode, and applied machine learning and deep learning models to TMD. Our results showed that a recurrent neural network, known as long short-term memory, could classify five transportation modes (still, manual wheelchair, electric wheelchair, subway, and car) for people with and without disabilities, with an accuracy of 96.17%. Our results will be beneficial for enhancing the quality of life and enabling the social inclusion of people with mobility disabilities.

X-ray scattering tensor tomography based finite element modelling of heterogeneous materials

Among micro-scale imaging technologies of materials, X-ray micro-computed tomography has evolved as most popular choice, even though it is restricted to limited field-of-views and long acquisition times. With recent progress in small-angle X-ray scattering these downsides of conventional absorption-based computed tomography have been overcome, allowing complete analysis of the micro-architecture for samples in the dimension of centimetres in a matter of minutes. These advances have been triggered through improved X-ray optical elements and acquisition methods. However, it has not yet been shown how to effectively transfer this small-angle X-ray scattering data into a numerical model capable of accurately predicting the actual material properties. Here, a method is presented to numerically predict mechanical properties of a carbon fibre-reinforced polymer based on imaging data with a voxel-size of 100 μm corresponding to approximately fifteen times the fibre diameter. This extremely low resolution requires a completely new way of constructing the material’s constitutive law based on the fibre orientation, the X-ray scattering anisotropy, and the X-ray scattering intensity. The proposed method combining the advances in X-ray imaging and the presented material model opens for an accurate tensile modulus prediction for volumes of interest between three to six orders of magnitude larger than those conventional carbon fibre orientation image-based models can cover.

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS2) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS1-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis.

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BackgroundBy analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.ResultsHere, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.ConclusionsOur approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes.DtQfodvXmxPWcwvjCbxEj1Video

Translation as a Mediation Activity for Vocabulary Retention: An Empirical Study

This study investigates whether translation as a mediation activity in the Common European Framework of Reference (CEFR) fosters better vocabulary retention than English-only vocabulary exercises for students of English as a foreign language. Translation as a language instruction and acquisition method has been the topic of much debate between proponents of English-only methodologies and advocates of bi/multilingual philosophies. This study intends to spotlight the pedagogical value of translation in the EFL classroom. To that end, quantitative data were collected from 82 students at the Classes Préparatoires aux Grandes Écoles (Preparatory Classes for Engineering and Management Schools) (henceforth, CPGE) in Marrakesh, Morocco. The Statistical Package for Social Science (henceforth, SPSS) was used to analyze the data collected from two posttests. The results prove that translation as a mediation activity leads to better vocabulary retention than English-only vocabulary exercises. This study suggests that translation as mediation is a valid language learning activity to implement in English classrooms.