Profiling Techniques Research Articles

DL-TODA: A Deep Learning Tool for Omics Data Analysis.

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon.

Minimum profile hellinger distance estimation of semiparametric multiple linear regression models

As the most fundamental tool to analyse associations, the semiparametric multiple linear regression model is considered in this work where the error distribution is assumed symmetric but otherwise completely unknown. To handle the common concern about possible outlying observations, especially in the era of big data, we propose a robust estimator of regression coefficients based on Hellinger distance and profiling technique. We prove in theory that the resulting estimator, minimum profile Hellinger distance estimator (MPHDE), is consistent. Its finite-sample performance is examined via both extensive simulation studies and a real data application. Our numerical results demonstrate that the proposed MPHDE has good efficiency and simultaneously is very robust against outlying observations.

Determination of conduction type in high-resistivity silicon by combining spreading resistance profiling (SRP) with hydrofluoric acid treatment

The effect of hydrofluoric acid (HF) treatment on the surface electrical properties was observed in high-resistivity bulk silicon by the spreading resistance profiling (SRP) technique. It is found that the near-surface resistivity decreased in n-type silicon and increased in p-type silicon after HF treatment according to the SRP measurement. The variation of surface chemical elements and energy bands of HF-treated n-type and p-type (100) silicon has been characterized by x-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy measurements. The results indicate that the surface Fermi level is shifted toward the conduction band minimum after HF treatment. The surface energy band bending caused by the surface electronegative groups (–F and –OH) was investigated by the method of First-principles calculation. Based on these findings, a method combining the SRP measurement with HF treatment to determine the conduction type of high-resistivity silicon was proposed, which is critical for the development of high-resistivity CZ silicon crystal growth.

Abstract ED7-4: Beyond the lab: Clinical implications

Abstract During this talk, I will review the ever expanding repertoire of single cell and spatially resolved profiling techniques which enable the interrogation breast cancer pathlogy, immuno-biology and treatment response at unprecedented resolution. I will outline considerations for throughput, plex and resolution across different methods before providing several case studies in their application. As one example, I will outline the use of multi-omic single cell profiling and spatial proteomic profiling to characterize changes throughout the course of neoadjuvant Her2-targeted therapy and leading to the identification of candidate predictive biomarkers. I will go on to discuss translational and potential clinical applications of these techniques. Citation Format: Christina Curtis. Beyond the lab: Clinical implications [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED7-4.

Application of Metabolomics in Obesity-Related Childhood Asthma Subtyping: A Narrative Scoping Review.

Obesity-related asthma is a heterogeneous childhood asthma phenotype with rising prevalence. Observational studies identify early-life obesity or weight gain as risk factors for childhood asthma development. The reverse association is also described, children with asthma have a higher risk of being obese. Obese children with asthma have poor symptom control and an increased number of asthma attacks compared to non-obese children with asthma. Clinical trials have also identified that a proportion of obese children with asthma do not respond as well to usual treatment (e.g., inhaled corticosteroids). The heterogeneity of obesity-related asthma phenotypes may be attributable to different underlying pathogenetic mechanisms. Although few childhood obesity-related asthma endotypes have been described, our knowledge in this field is incomplete. An evolving analytical profiling technique, metabolomics, has the potential to link individuals' genetic backgrounds and environmental exposures (e.g., diet) to disease endotypes. This will ultimately help define clinically relevant obesity-related childhood asthma subtypes that respond better to targeted treatment. However, there are challenges related to this approach. The current narrative scoping review summarizes the evidence for metabolomics contributing to asthma subtyping in obese children, highlights the challenges associated with the implementation of this approach, and identifies gaps in research.

Profiling and Optimizing Java Streams

The Stream API was added in Java 8 to allow the declarative expression of data-processing logic, typically map-reduce-like data transformations on collections and datasets. The Stream API introduces two key abstractions. The stream, which is a sequence of elements available in a data source, and the stream pipeline, which contains operations (e.g., map, filter, reduce) that are applied to the elements in the stream upon execution. Streams are getting popular among Java developers as they leverage the conciseness of functional programming and ease the parallelization of data processing. Despite the benefits of streams, in comparison to data processing relying on imperative code, streams can introduce significant overheads which are mainly caused by extra object allocations and reclamations, and the use of virtual method calls. As a result, developers need means to study the runtime behavior of streams in the goal of both mitigating such abstraction overheads and optimizing stream processing. Unfortunately, there is a lack of dedicated tools able to dynamically analyze streams to help developers specifically locate issues degrading application performance. In this paper, we address the profiling and optimization of streams. We present a novel profiling technique for measuring the computations performed by a stream in terms of elapsed reference cycles, which we use to locate problematic streams with a major impact on application performance. While accuracy is crucial to this end, the inserted instrumentation code causes the execution of extra cycles, which are partially included in the profiles. To mitigate this issue, we estimate and compensate for the extra cycles caused by the inserted instrumentation code. We implement our approach in StreamProf that, to the best of our knowledge, is the first dedicated stream profiler for the Java Virtual Machine (JVM). With StreamProf, we find that cycle profiling is effective to detect problematic streams whose optimization can enable significant performance gains. We also find that the accurate profiling of tasks supporting parallel stream processing allows the diagnosis of load imbalance according to the distribution of stream-related cycles at a thread level. We conduct an evaluation on sequential and parallel stream-based workloads that are publicly available in three different sources. The evaluation shows that our profiling technique is efficient and yields accurate profiles. Moreover, we show the actionability of our profiles by guiding stream-related optimizations on two workloads from Renaissance. Our optimizations require the modification of only a few lines of code while achieving speedups up to a factor of 5x. Java streams have been extensively studied by recent work, focusing on both how developers are using streams and how to optimize them. Current approaches in the optimization of streams mainly rely on static analysis techniques that overlook runtime information, suffer from important limitations to detect all streams executed by a Java application, or are not suitable for the analysis of parallel streams. Understanding the dynamic behavior of both sequential and parallel stream processing and its impact on application performance is crucial to help users make better decisions while using streams.

Cognition and curiosity:Strategies for firms to recruit curious employees

ABSTRACT Curiosity has long been touted as important for the ability to learn and has been linked with innovation and entrepreneurship. It is also important that employers know how to identify curious potential employees in cost-effective ways during recruitment processes. This paper explores the association between curiosity and cognitive ability. Recruitment processes rely heavily on educational attainment, especially when recruiting young people with low labour market experience. This paper explores the association between curiosity and maths, science, and reading ability in youth. Using six waves of data from the 2003, 2006, and 2009 cohorts of the Longitudinal Surveys of Australian Youth, we examine the extent to which the curiosity of young adults is associated with their school-age cognition level. We find that curious individuals are more likely to have had higher levels of science and reading ability in school, yet curiosity is negatively associated with school-age mathematics ability. These findings provide clear strategies for employers wanting to recruit curious employees without access to expensive profiling techniques.

Knowledge graph embedding for experimental uncertainty estimation

PurposeExperiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.Design/methodology/approachThis work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.FindingsThe analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.Originality/valueThe employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

ScAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences.

Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.

Research Advances Through Activity‐Based Lipid Hydrolase Profiling

AbstractActivity‐based proteomic profiling (ABPP) enables the functional study of enzymes by employing small molecule probes that bind covalently to the active site of an enzyme. Activity‐based probes can penetrate cells and tissues and thereby allow enzymes to be targeted/labelled in their native state. Probes can be designed to target individual enzymes or whole enzyme groups, which makes ABPP a versatile protein profiling technique. In this review, we give an overview of research advances through ABPP in the context of lipid hydrolase research. We report of lipid hydrolases that were discovered and characterized through ABPP, and aim to give an overview of commonly used probes as well as inhibitors that were discovered and characterized by competitive ABPP. Lastly, this review aims to raise caveats and current limitations of this protein profiling technique.

MIMT1 and LINC01550 are uncharted lncRNAs down-regulated in colorectal cancer.

Incomplete knowledge of the molecular basis of colorectal cancer, with subsequent limitations in early diagnosis and effective treatment, has contributed to this form of malignancy becoming the second most common cause of cancer-related death worldwide. With the advances in high-throughput profiling techniques and the availability of public data sets such as The Cancer Genome Atlas Program (TCGA), a broad range of coding transcripts have been profiled and their underlying modes of action have been mapped. However, there is still a huge gap in our understanding of noncoding RNA dysregulation. To this end, we used a bioinformatics approach to shortlist and evaluate yet-to be-profiled long noncoding RNAs (lncRNAs) in colorectal cancer. We analysedthe TCGA RNA-seq data and followed this by validating the expression patterns using a qPCR technique. Analysing in-house clinical samples, the real-time PCR method revealed that the shortlisted lncRNAs, that is MER1 Repeat Containing Imprinted Transcript 1 (MIMT1) and Non-Protein Coding RNA 1550 (LINC01550), were down-regulated in colorectal cancer tumours compared with the paired adjacent normal tissues. Mechanistically, the in silico results suggest that LINC01550 could form a complex competitive endogenous RNA (ceRNA) network leading to the subsequent regulation of colorectal cancer-related genes, such as CUGBP Elav-Like Family Member (CELF2), Polypyrimidine Tract Binding Protein 1 (PTBP1) and ELAV Like RNA Binding Protein 1 (ELAV1). The findings of this work indicate that MIMT1 and LINC01550 could be novel tumour suppressor genes that can be studied further to assess their roles in regulating the cancer signalling pathway(s).

Epigenetic Regulations in Mammalian Cells: Roles and Profiling Techniques.

The genome is almost identical in all the cells of the body. However, the functions and morphologies of each cell are different, and the factors that determine them are the genes and proteins expressed in the cells. Over the past decades, studies on epigenetic information, such as DNA methylation, histone modifications, chromatin accessibility, and chromatin conformation have shown that these properties play a fundamental role in gene regulation. Furthermore, various diseases such as cancer have been found to be associated with epigenetic mechanisms. In this study, we summarized the biological properties of epigenetics and single-cell epigenomic profiling techniques, and discussed future challenges in the field of epigenetics.

Applications of X-Chromosome Short Tandem Repeats for Human Identification: A Review

The forensic DNA profiling technique has tremendously contributed to forensic human identification, an important aspect in forensic investigations. In instances whereby comparison samples are unavailable, utilization of short tandem repeats of X chromosome (X-STRs) may prove useful to resolve complex kinship investigations involving missing persons and mass disasters. Despite such evidential values, the use of X-STRs during investigations remains scarce in many Southeast Asian countries including Malaysia, requiring concerted efforts for establishing forensic statistical support for its diverse populations (especially the admixture populations), standardizing core loci and procedure, improving the knowledge among practitioners as well as developing suitable standard operating procedure for incorporating X-STRs analysis in the overall DNA profiling framework. Hence, this review paper aims to highlight the developments, applications and population data of X-STRs, as well as its challenges and future insights for forensic casework.

Evaluation of the Performance of a Discharge Gauging Station on Tigris River in Baiji, Iraq

Gauging the discharge of theTigris River in Baiji is very important, as it reflects the final accumulated flows from Mosul Dam, upper and lower Zab tributaries, which send downstream to the control system in Samarra. The current study aimed to evaluate the performance of the Baiji gauging station for measuring water levels and discharge, and thus suggest alternative locations. Large disagreement was found between the manually measured discharge, and that measured by the Acoustic Doppler Current Profiler, and in comparison with the discharge coming from the stations of Shirqat and Al-Dibs upstream Baiji, or those discharges sent to Tikrit downstream the station. A topographical survey of the flow section was done to determine the area using manual and Acoustic Doppler Current Profiler techniques for the original and alternative locations. Then flow velocity was measured, then the discharge was calculated according to the flow section area, and the average flow velocity for different levels, the stage-discharge equation was deduced and compared with the other equations used previously. The study concluded that the original station is inefficient, and it is better to move it to the second alternative site, by comparing the three stations, we found that the most accurate measurements were at the second alternative station.

A comprehensive study of some features from characteristics of enhanced ground‐penetrating radar wave images through convenient data processing within carbonate rock, west of Assiut, Egypt

AbstractThe Eocene limestone plateau, west of Assiut, is characterized by different sedimentary structural features such as fractures, karst, cavities and marble. All conventional methods failed to detect these features due to their inhomogeneities. In this study, a ground‐penetrating radar survey was applied to an area including an old quarry of marble lying at the eastern part of the limestone plateau. The field survey was carried out using a 200‐MHz antenna following the profiling and areal mapping techniques. The acquired ground‐penetrating radar wave images were processed and interpreted using the available software program to enhance the different reflection characteristics from the subsurface anomalous materials. A series of the three‐dimensional model cross section with variable depths were constructed to identify and characterize different georadar facies changes vertically and laterally. The obtained results were adjusted and compared with the excavated features of the quarry; therefore, ground‐penetrating radar anomalies and reflections were calibrated. In the present study, the ground‐penetrating radar survey succeeded in differentiating between dense limestone rocks, onyx marble and microstructural features resulting from recent tectonism. Moreover, the investigation of ground‐penetrating radar reflection waves is useful in differentiating between low‐ and high‐quality limestone and onyx marble. This study identifies the various features of the limestone plateau lying below the ground surface, west of Assiut, that are to be taken into account by planners when establishing new urban centres on the plateau in the future.

Immunotherapy with immune checkpoint inhibitors and predictive biomarkers in malignant mesothelioma: Work still in progress.

Malignant mesothelioma (MM) is a rare and aggressive neoplasm, usually associated with a poor prognosis (5 years survival rate <10%). For unresectable disease, platinum and pemetrexed chemotherapy has been the only standard of care in first line for more than two decades, while no standard treatments have been approved in subsequent lines. Recently, immunotherapy has revolutionized the therapeutic landscape of MM. In fact, the combination of ipilimumab plus nivolumab has been approved in first line setting. Moreover, immune checkpoint inhibitors (ICIs) showed promising results also in second-third line setting after platinum-based chemotherapy. Unfortunately, approximately 20% of patients are primary refractory to ICIs and there is an urgent need for reliable biomarkers to improve patient's selection. Several biological and molecular features have been studied for this goal. In particular, histological subtype (recognized as prognostic factor for MM and predictive factor for chemotherapy response), programmed death ligand 1 (PD-L1) expression, and tumor mutational burden (widely hypothesized as predictive biomarkers for ICIs in several solid tumors) have been evaluated, but with unconclusive results. On the other hand, the deep analysis of tumor infiltrating microenvironment and the improvement in genomic profiling techniques has led to a better knowledge of several mechanisms underlying the MM biology and a greater or poorer immune activation. Consequentially, several potential biomarkers predictive of response to immunotherapy in patients with MM have been identified, also if all these elements need to be further investigated and prospectively validated. In this paper, the main evidences about clinical efficacy of ICIs in MM and the literature data about the most promising predictive biomarkers to immunotherapy are reviewed.

In-situ quality monitoring of laser hot wire cladding process based on multi-sensing diagnosis and machine learning model

This study aimed to build an in-situ monitoring system based on a multi-sensing diagnosis strategy combined with a machine learning model for laser hot wire cladding applications. A high speed camera and spectrometer were mounted to obtain in situ synchronization information. Eight process signal features were extracted based on the molten pool and plasma. The silica brittle inclusions result in a higher, narrower, and rougher clad layer with higher and more uneven hardness than the normal clad layer and eventually caused undercut defects. Three quality features based on a seam tracker based surface profiling technique were extracted to label the process signal features, and an empirical database was produced based on the quantified experimental results. The minimum redundancy maximum relevance algorithm was then used to rank the importance of the process signal features, through which, a new feature vector was created. Finally, the eXtreme gradient boosting (XGBoost) model was used to develop an in-situ monitoring system for the laser hot wire cladding process. This MRMR-XGBoost based system reduces the redundancy of process information and achieved the cladding quality classification with an accuracy of 0.9727 and identify time of 7.979 ms.

Concurrent Validity and Reliability of Different Technologies for Sprint-Derived Horizontal Force-Velocity-Power Profiling.

Cormier, P, Tsai, M-C, Meylan, C, Agar-Newman, D, Epp-Stobbe, A, Kalthoff, Z, and Klimstra, M. Concurrent validity and reliability of different technologies for sprint-derived horizontal force-velocity-power profiling. J Strength Cond Res 37(6): 1298-1305, 2023-This study evaluated the validity and reliability of common systems to assess sprint-derived horizontal force-velocity-power ( FVPH ) profile metrics. Two double constellation athlete monitoring systems (STATSports Apex, Catapult Vector S7) and one timing gate system were compared with a radar gun for the computation of FVPH metrics. Intersystem validity was assessed using intraclass correlation coefficients (ICC), Pearson's correlation coefficients ( R2 ), and Bland-Altman plots with absolute and percent agreement. Intrasystem reliability was assessed with agreement bias and ICC. STATSports demonstrated moderate agreement for F0 , Pmax , τ, and Drf (8.62, 6.46, -9.81, and 9.96%, respectively) and good agreement for V0 and MSS (-2.18 and -1.62%). Catapult displayed good agreement across all metrics ( F0 , V0 , Pmax , MSS, τ, and Drf : -0.96, -0.89, -1.85, -0.84, 0.38, and -0.27%, respectively). Timing gates demonstrated good agreement with V0 and MSS (-2.62 and -1.71%) and poor agreement with F0 , Pmax , τ, and Drf (19.17, 16.64, -20.49, and 20.18%, respectively). Intrasystem reliability demonstrated good agreement (<2% bias) with very large to near-perfect ICC (0.84-0.99) for Catapult and STATSports systems. Overall, GPS/GNSS 10 Hz technology is reliable across devices and can provide moderate-to-good accuracy of FVPH metrics in single maximal effort sprints. However, Catapult provided better agreement for more FVPH metrics than STATSports, which may be related to differences in proprietary algorithms. Also, modeling timing gate data using current FVPH profiling techniques results in poor bias that requires greater investigation. GPS/GNSS data can be used for FVPH profiling, which could inform performance and rehabilitation processes.

Solar Occultation Experiments (SOE) in the Venusian Atmosphere: effect of orbital parameters on the spatiotemporal distribution of measurements

AbstractSolar occultation is a satellite-based technique for high-resolution vertical profiling of planetary atmospheres. Owing to the distinctive observational geometry, the deduction of the spatiotemporal coverage of solar occultation measurements as a function of the spacecraft orbit is non-trivial. In this work, we have implemented python-based 3D simulations of the occultation-viewing geometry for a hypothetical Solar Occultation Experiment (SOE) to study the atmosphere of Venus. The simulations incorporate planetary motions and orbital propagation using the astropy and poliastro packages, and compute the instantaneous line-of-sight (LoS) tangent point using 3D vector algebra. SPICAV/SOIR data from Venus Express was used to validate the simulations, showing excellent agreement. Using the simulations, we conducted a first-of-its-kind theoretical study on the effect of varying different spacecraft orbital elements on the spatiotemporal distribution of solar occultation measurements in the Venusian atmosphere, confirming a highly sensitive dependence. The semimajor axis (a) and inclination (i) of the spacecraft orbit are found to influence the latitudinal extent of observations and the nature/duration of occultation seasons, while the eccentricity (e) and argument of periapsis (ω) determine the distinct regions of sparse observations. The spatiotemporal spread of individual SOE profiles is found to depend on the orbital parameters as well as the solar beta angle. Our results show that spacecraft orbits can be designed with appropriate parameters to optimize the coverage of SOE measurements in view of achieving specific science goals, providing valuable inputs for future missions to Venus that aim to implement the solar occultation technique.

Integrative Genomic and Proteomic Profiling for Personalized Oncological Treatments: Enhancing Therapeutic Efficacy and Reducing Adverse Effects in Breast Cancer Patients

Breast cancer is still one of the most common and difficult tumors to cure that impact women globally. Conventional treatment methods frequently result in inconsistent patient outcomes and serious side effects. By using integrative genomic and proteomic profiling to produce individualized oncological medicines, this study tackles the serious issue of the one-size-fits-all treatment paradigm. The study aimed to ascertain distinct genetic mutations and protein expression profiles in tumors pertaining to breast cancer, establish a connection between these molecular discoveries and clinical results, and customize treatments to optimize therapeutic effectiveness while reducing adverse effects. The most important contribution of this study is the comprehensive identification of both common and unique genetic mutations such as TP53, PIK3CA, BRCA1 and BRCA2 and the elucidation of their corresponding protein expression profiles. This molecular knowledge has helped develop individualized treatment protocols that have been shown to significantly improve treatment outcomes. Specifically, patients who received personalized treatment based on their molecular profiles experienced an average 60% reduction in tumor size compared to 35% with standard treatment. In addition, individualized treatments resulted in a lower mean adverse event score of 3 compared to conventional treatments of 7, highlighting a significant reduction in treatment-related adverse events. The results of this study highlight the potential of personalized medicine to optimize breast cancer treatment by matching treatment strategies to the unique molecular characteristics of each patient's tumor. These findings pave the way for future research aimed at improving individualized treatments, improving the availability and affordability of molecular profiling techniques, and expanding the application of these techniques to wider and more diverse patient populations. With continued innovation and clinical validation, integrated genomic and proteomic profiling promises to set new standards for personalized oncology care, ultimately improving patient outcomes and quality of life.