Profiling Techniques Research Articles

Influence of voltage profile and fitting technique on the accuracy of lithium-ion battery degradation identification through the Voltage Profile Model

Accurate estimation of degradation in lithium-ion batteries is essential in predicting remaining useful life and understanding how to better operate batteries for extended lifetimes. A common way of estimating degradation modes in lithium-ion batteries is to analyse the cells voltage profile through techniques such as incremental capacity analysis, differential voltage analysis or direct fitting of half-cell potential profiles. To extract degradation modes such as loss of lithium inventory or loss of active material requires accurate knowledge of the individual electrode half-cell potential profiles, which is often not available for commercial cells without performing a cell teardown. This work investigates how the choice of half-cell potential profile influences the accuracy of a parametric voltage profile model to estimate electrode capacity and simulated degradation modes through a combination of half-cell and three electrode testing on a LiNi0.5Mn0.3Co0.2O2/Graphite cell. Results demonstrate that whilst half-cell potential data from the same electrode material batch gives the most accurate voltage profile fit, other data sources for the same electrode chemistry can also accurately estimate individual electrode capacity in a fresh cell. However, when degradation modes are induced into the voltage profile, only the model using half-cell profiles obtained from similar sources to the full-cell configurations are able to accurately distinguish between loss of lithium inventory and loss of active material of the positive electrode. It is also shown that the diagnostic accuracy of the parametric voltage profile model can be improved for all data sources through combined fitting of the voltage, incremental capacity and differential voltage analysis compared to voltage profile fitting alone.

Hematological Oncology journal women in lymphoma special issue: Latest updates in nodal peripheral T-cell lymphoma.

In the last decade, there has been increased understanding of the pathologic features and biology of peripheral T cell lymphomas (PTCLs) through development of multi omics and molecular profiling techniques. In addition, international collaborations through multi center trials as well as prospective registry studies have improved our knowledge of host and tumor genomic factors and treatment factors affecting disease outcomes. In our review today, we aim to highlight the current epidemiology, latest advances in classification, disease biology and the evolving treatment landscape for nodal PTCLs.

Mechanical and Sensory Properties of Pulse Gels in the Development of New Plant Based Food

The shift towards healthier and sustainable food consumption requires a greater variety of plant-based products. This study aimed to determine how the mechanical and sensory properties of three different pulse gels (chickpea, lentil, or red lentil flour) vary with the ingredients (flour, oil, lemon, and salt content). All pulse flours were able to form self-standing gels. Mechanical properties varied with the pulse type and with the formulation differently depending on the pulse. For all pulses, the hardness and stiffness increased with the flour content and decreased with salt. They decreased with lemon in chickpea gels and increased with oil content in the red lentil gel. The Flash Profile technique provided distinctive sensory characteristics of each pulse gel. The red lentil gels were homogeneous, creamy, and compact. The chickpea gels were also compact, but harder. Lentil flour resulted in rough and sandy gels. For the three pulses, including oil and lemon reduced pulse flavors and increased sour taste. The results of this study give insights into the suitability of pulse gels to be the basis of new solid plant-based products whose sensory properties can be modulated by varying the concentration of natural ingredients.

Development of a novel beam profiling prototype with laser self-mixing via the knife-edge approach

Laser is widely used in industry, biomedical and other kinds of fields. Beam size is the most important parameter among the laser variables. Typical state-of-the-art profiling techniques employ either a scanning-based or camera-based system, using photodiodes or image sensors as the signal receiver. Despite their profiling capabilities, these systems do not tend to be budget-friendly and easy to operate. In this paper, a novel cost-effective beam profiling prototype based on self-mixing interference was developed to measure the Full Width Half Maximum (FWHM) of a range of laser diodes by the knife-edge approach. The difference between our prototype and other systems is that the photodiode is placed behind the laser source, and beam size is calculated by analyzing the feedback signal. A commercial camera beam profiler was used to benchmark our prototype. Results show that though there is a variation of 45.29 % and 65.44 % between the measured beam size and the integrated beam size in the x and y directions due to diffuse and specular reflection, our USD 200 prototype has an accuracy ranging from 89.59 % to 98.74 % on the prediction of laser beam sizes. Our prototype could provide accurate predicted beam size both for Gaussian-alike beam and top-hat-alike beam. This is the very first study to explore the application of self-mixing interference in laser beam profiling. It is believed that our proposed approach has contributed to the on-going development of laser beam profiling methodology.

Fundamentals of thin film depth profiling by glow discharge optical emission spectroscopy

Glow discharge optical emission spectroscopy (GDOES) is a useful technique for qualitative plasma characterization. It also enables depth profiling of solid materials upon exposure of samples to energetic positively charged ions from gaseous plasma, providing specifics of both surface- and gas-phase collision phenomena that are considered. The early stages of developing GDOES useful for the determination of surface composition and depth profiling of solid materials are reviewed and analyzed, stressing the contribution of early authors. The advantages as well as drawbacks of the GDOES technique are presented and discussed. The recent applications of this technique for depth profiling of various materials are presented, and the directions for constructing a laboratory-scale device are provided.

Optimization of aqueous extraction of henna leaves (Lawsonia inermis L.) and evaluation of biological activity by HPLC-based profiling and molecular docking techniques

Due to the antimicrobial capabilities of henna leaf extract (Lawsonia inermis L.), this study intends to identify environmentally friendly ideal conditions for extracting bioactive compounds from henna leaf powder. For this purpose, the maceration method with a 100% aqueous solvent was selected for further analysis. Optimization of the extraction method was carried out by central composite design (CCD) protocol as a subset of Response Surface Methodology (RSM). The temperature of 40–90 °C, extraction time of 2–24 h, and the solvent-plant weight ratio ranging from 10 to 100 mL/mg were all considered independent variables. Also, the amount of lawsone, as an active component, and the total phenolic content were tracked as responses. Finally, a yield of 30.59% was achieved under the best extraction condition at 40 °C, 2 h, and a solvent-plant weight ratio of 1:100. The biological activity of an aqueous extract was evaluated using an HPLC-based activity profiling and TLC bioautography method. Peaks collected as fractions between 3 and 30 min were tested for antibacterial activity against Staphylococcus aureus ATCC 25923, and micro fractions from 5, 6, 7.5, 9, 10.5, 12, 13.5, 15, 19, and 30 min exhibited significant inhibitory effects. All spotted fractions on the TLC plate inhibited the growth of Escherichia coli ATCC 25922, except for micro fractions from 3, 18, and 21 min. Moreover, molecular docking analysis of quinones in the extract identified 3a, 4a-Dihydroxy-a-tetralone (C-2) as the most potent compound in interacting with bacteria sterol 14-demethylase protein, with docking scores of − 6.715 and − 7.196 kcal/mol, respectively.

The resolutions of drive-level capacitance profiling technique.

Recently, the drive level capacitance profiling (DLCP) technique has been proven to be effective in resolving the spatial and energetic distributions of defects through feasible measurements. However, the discussion on its ranges and resolutions is still missing, which is urgent in evaluating the validity of the calculated defect profiles. In this work, we propose a method to analyze the ranges and resolutions of DLCP. Assuming that the test instrument has more influence on the experimental results than the experimental environment, this method can be used to evaluate the resolution of DLCP for different test instruments. Through revisiting the equations involved in DLCP, we learned that the sources of the limits and resolutions are (1) the instrument system error and inherent resolution and (2) the device impedance. Consequently, from the study of device impedance and the measuring instrument system error, the resolutions of DLCP could be calculated according to the error propagation theory. We provide the spatial distribution of the minimum selection range of AC signal δV used by DLCP and the spatial resolution of DLCP technology. This method can be used to evaluate the resolution of DLCP for different test instruments.

Exploring the crop epigenome: a comparison of DNA methylation profiling techniques.

Epigenetic modifications play a vital role in the preservation of genome integrity and in the regulation of gene expression. DNA methylation, one of the key mechanisms of epigenetic control, impacts growth, development, stress response and adaptability of all organisms, including plants. The detection of DNA methylation marks is crucial for understanding the mechanisms underlying these processes and for developing strategies to improve productivity and stress resistance of crop plants. There are different methods for detecting plant DNA methylation, such as bisulfite sequencing, methylation-sensitive amplified polymorphism, genome-wide DNA methylation analysis, methylated DNA immunoprecipitation sequencing, reduced representation bisulfite sequencing, MS and immuno-based techniques. These profiling approaches vary in many aspects, including DNA input, resolution, genomic region coverage, and bioinformatics analysis. Selecting an appropriate methylation screening approach requires an understanding of all these techniques. This review provides an overview of DNA methylation profiling methods in crop plants, along with comparisons of the efficacy of these techniques between model and crop plants. The strengths and limitations of each methodological approach are outlined, and the importance of considering both technical and biological factors are highlighted. Additionally, methods for modulating DNA methylation in model and crop species are presented. Overall, this review will assist scientists in making informed decisions when selecting an appropriate DNA methylation profiling method.

DNA Methylation: Bridging life’s experience and genetics within forensic scenario

DNA serving as the “blueprints of life” is a complex found in all biological cells carrying the genetic instructions for development and functioning of the organism. Inheritance of unique combination of genetic polymorphism results in DNA profile. In past few decades, the field of forensic genetics has witnessed several advancements from the radio-labelled DNA probes to the Short Tandem Repeats (STR) and Single Nucleotide Polymorphism (SNP) that have fostered the emergence of biological materials as most reliable evidences at a crime scene for the purpose of human identification but then, are inadequate in distinguishing monozygotic twins. Also, these techniques are of unavailing in providing informations such as tissue-specificity of the DNA source, phenotypic identification, and age estimation. Epigenetics, particularly DNA methylation is evolving as a promising technique to overcome such drawbacks of conventional profiling techniques.

The S2 Subunit of Infectious Bronchitis Virus Affects Abl2-Mediated Syncytium Formation.

The S2 subunit serves a crucial role in infectious bronchitis virus (IBV) infection, particularly in facilitating membrane fusion. Using reverse genetic techniques, mutant strains of the S2 locus exhibited substantially different syncytium-forming abilities in chick embryonic kidney cells. To determine the precise formation mechanism of syncytium, we demonstrated the co-ordinated role of Abl2 and its mediated cytoskeletal regulatory pathway within the S2 subunit. Using a combination of fluorescence quantification, RNA silencing, and protein profiling techniques, the functional role of S2 subunits in IBV-infected cells was exhaustively determined. Our findings imply that Abl2 is not the primary cytoskeletal regulator, the viral S2 component is involved in indirect regulation, and the three different viral strains activate various cytoskeletal regulatory pathways through Abl2. CRK, CRKL, ABI1, NCKAP1, and ENAH also play a role in cytoskeleton regulation. Our research provides a point of reference for the development of an intracellular regulatory network for the S2 subunit and a foundation for the rational design of antiviral drug targets against Abl2.

A novel co-culture model of human prostate epithelial and stromal cells for androgenic and antiandrogenic screening

The risk assessment of endocrine-disrupting chemicals (EDCs) greatly relies on in vitro screening. A 3-dimensional (3D) in vitro prostate model that can reflect physiologically-relevant prostate epithelial and stromal crosstalk can significantly advance the current androgen assessment. This study built a prostate epithelial and stromal co-culture microtissue model with BHPrE and BHPrS cells in scaffold-free hydrogels. The optimal 3D co-culture condition was defined, and responses of the microtissue to androgen (dihydrotestosterone, DHT) and anti-androgen (flutamide) exposure were characterized using molecular and image profiling techniques. The co-culture prostate microtissue maintained a stable structure for up to seven days and presented molecular and morphological features of the early developmental stage of the human prostate. The cytokeratin 5/6 (CK5/6) and cytokeratin 18 (CK18) immunohistochemical staining indicated epithelial heterogeneity and differentiation in these microtissues. The prostate-related gene expression profiling did not efficiently differentiate androgen and anti-androgen exposure. However, a cluster of distinctive 3D image features was identified and could be applied in the androgenic and anti-androgenic effect prediction. Overall, the current study established a co-culture prostate model that provided an alternative strategy for (anti-)androgenic EDC safety assessment and highlighted the potential and advantage of utilizing image features to predict endpoints in chemical screening.

Transcriptional and translational landscape fine-tune genome annotation and explores translation control in cotton

IntroductionThe unavailability of intergenic region annotation in whole genome sequencing and pan-genomics hinders efforts to enhance crop improvement. ObjectivesDespite advances in research, the impact of post-transcriptional regulation on fiber development and translatome profiling at different stages of fiber growth in cotton (G. hirsutum) remains unexplored. MethodsWe utilized a combination of reference-guided de novo transcriptome assembly and ribosome profiling techniques to uncover the hidden mechanisms of translational control in eight distinct tissues of upland cotton. ResultsOur study identified P-site distribution at three-nucleotide periodicity and dominant ribosome footprint at 27 nucleotides. Specifically, we have detected 1,589 small open reading frames (sORFs), including 1,376 upstream ORFs (uORFs) and 213 downstream ORFs (dORFs), as well as 552 long non-coding RNAs (lncRNAs) with potential coding functions, which fine-tune the annotation of the cotton genome. Further, we have identified novel genes and lncRNAs with strong translation efficiency (TE), while sORFs were found to affect mRNA transcription levels during fiber elongation. The reliability of these findings was confirmed by the high consistency in correlation and synergetic fold change between RNA-sequencing (RNA-seq) and Ribosome-sequencing (Ribo-seq) analyses. Additionally, integrated omics analysis of the normal fiber ZM24 and short fiber pag1 cotton mutant revealed several differentially expressed genes (DEGs), and fiber-specific expressed (high/low) genes associated with sORFs (uORFs and dORFs). These findings were further supported by the overexpression and knockdown of GhKCS6, a gene associated with sORFs in cotton, and demonstrated the potential regulation of the mechanism governing fiber elongation on both the transcriptional and post-transcriptional levels. ConclusionReference-guided transcriptome assembly and the identification of novel transcripts fine-tune the annotation of the cotton genome and predicted the landscape of fiber development. Our approach provided a high-throughput method, based on multi-omics, for discovering unannotated ORFs, hidden translational control, and complex regulatory mechanisms in crop plants.

A New Term Representation Method for Gender and Age Prediction

Author Profiling is a kind of text classification method that is used for detecting the personality profiles such as age, gender, educational background, place of origin, personality traits, native language, etc., of authors by processing their written texts. Several applications like forensic analysis, security and marking are used the techniques of author profiling for finding the basic details of authors. The main problem in the domain of author profiling is preparation of suitable dataset for predicting the characteristics of authors. PAN is one organization conducting competitions on various types of shared tasks. In 2013, PAN organizers presented the task of author profiling in their series of competitions and continued this task in further years. They arranged different kinds of datasets in different varieties of languages. From 2013 onwards several researchers proposed solutions for author profiling to predict different personality features of authors by utilizing the datasets provided in PAN competitions. Researchers used different kinds of features like character based, lexical or word based, structural features, syntactic, content based, style based features for distinguishing the author’s writing styles in their texts. Most of the researchers observed that the content based features like words or phrases those are used in the text are most useful for detecting the personality features of authors. In this work, the experiment conducted with the content based features like most important words or terms for predicting age group and gender from the PAN competition datasets. Two datasets such as PAN 2014 and 2016 author profiling datasets are used in this experiment. The documents of dataset are converted in to a vector representation which is a suitable format for giving training to machine learning algorithms. The term representation in a document vector plays a crucial role to improve the performance of gender and age group prediction.The Term Weight Measures (TWMs) are such techniques used for this purpose to represent the significance of a term value in document vector representation. In this work, we developed a new TWM for representing the term value in document vector representation. The proposed TWM’s efficiency is compared with the efficiency of other existing TWMs. Two Machine Learning (ML) algorithms like SVM (Support Vector Machine) and RF (Random Forest) are considered in this experiment for estimating the accuracy of proposed approach. We recognized that the proposed TWM accomplished best accuracies for gender and age prediction in two PAN Datasets.

Run, Ribosome, Run: From Compromised Translation to Human Health.

Significance: Translation is an essential cellular process, and diverse signaling pathways have evolved to deal with problems arising during translation. Erroneous stalls and unresolved ribosome collisions are implicated in many pathologies, including neurodegeneration and metabolic dysregulation. Recent Advances: Many proteins involved in detection and clearance of stalled and collided ribosomes have been identified and studied in detail. Ribosome profiling techniques have revealed extensive and nonprogrammed ribosome stalling and leaky translation into the 3' untranslated regions of mRNAs. Impairment of protein synthesis has been linked to aging in yeast and mice. Critical Issues: Ribosomes act as sensors of cellular states, but the molecular mechanisms, as well as physiological relevance, remain understudied. Most of our current knowledge stems from work in yeast and simple multicellular organisms such as Caenorhabditis elegans, while we are only beginning to comprehend the role of ribosome surveillance in higher organisms. As an example, the ribotoxic stress response, a pathway responding to global translational stress, has been studied mostly in response to small translation inhibitors and ribotoxins, and has only recently been explored in physiological settings. This review focuses on ribosome-surveillance pathways and their importance for cell and tissue homeostasis upon naturally occurring insults such as oxidative stress, nutrient deprivation, and viral infections. Future Directions: A better insight into the physiological roles of ribosome-surveillance pathways and their crosstalk could lead to an improved understanding of human pathologies and aging. Antioxid. Redox Signal. 39, 336-350.

Winthropping as an Investigative Tool in Clandestine Grave Discovery and Psychological Profiling

Missing persons cases are some of the hardest investigations to solve. The lack of forensic evidence impedes an investigation and critical time can be lost attempting to find the person or body. Recently, researchers have attempted to develop new approaches to help police in these types of investigations. The current paper continues research into Winthropping as a means of assisting police investigations when searching for clandestine, hidden graves and missing persons. A novel approach, using geocachers, to help inform Winthropping processes is outlined, and support gained through a survey of geocachers. The findings are then applied to a series of real-world homicides, including several serial killers. Analyses of gravesite locations in serial killer cases matched many of the features and properties outlined by geocachers. The results also indicate the potential of using Keatley and colleagues’ Winthropping as a psychological profiling as well as geographical profiling technique. Support was provided through the case of a murdered child who was later dumped. Recommendations for further research and application are provided.

DNA profiling in forensic investigation – A review

DNA Profiling is a revolutionary method for individual and relationship analysis, crime investigation, hereditary disorders, etc. It is a universal method used to establish accurate results during the process of forensic investigation. DNA profiling techniques, which are based on repetitive sequences within DNA, have proven to be of paramount importance, albeit the complete utilization of knowledge still remaining unexplored. Even a hair strand, blood drop or even skin flakes can be used to identify DNA sequences. It has a wide range of applications both in forensics and law. Because of the advancement in the field of forensics in the past four decades, DNA evidence now stands as one of the most reliable forms of proof in a court of law. In the following article, the authors explore the main concepts of DNA Profiling, and the techniques which are widely used in forensic laboratories such as RFLP, VNTR, STR, AFLP, mtDNA analysis, Y-chromosome analysis and gender typing.

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis

ObjectiveGene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. MethodsWe firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. ResultsPathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. ConclusionsOur method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

Development and validation of a proteomic correlation profiling technique to detect and identify enzymes involved in metabolism of drugs of concern.

It is important to be able to detect previously unknown and unsuspected enzymes involved in drug metabolism, in order to predict the variation of pharmacological or toxicological effect caused by pharmacokinetic variance. We investigated the use of proteomic correlation profiling (PCP) as a technique to identify the enzymes involved in metabolism of drugs of concern. By evaluating the metabolic activities of each enzyme (including isoforms of cytochrome P450, uridine 5' diphospho‑glucuronosyltransferase, and hydrolases, plus aldehyde oxidase and carbonyl reductase) on their typical substrates using a panel of human liver samples, we were able to show the validity of PCP for this purpose. Pearson or Spearman's rank correlation coefficient and P values were calculated for the association between the protein abundance profile of each protein and the metabolic rate profile of each typical substrate. For the 18 enzymatic activities examined, 13 of the enzymes reported to be responsible for the reactions had correlation coefficients higher than 0.7, and were ranked first to third. For the remaining five activities, the responsible enzymes had correlation coefficients lower than 0.7 and lower rankings. The reasons for this were diverse, including confounding resulting from low protein abundance ratios, artificially high correlations of other enzymes due to limited sample numbers, the presence of inactive enzyme forms, and genetic polymorphisms. Overall, PCP was able to identify the majority of responsible drug-metabolizing enzymes across several enzyme classes (oxidoreductase, transferase, hydrolase); use of this methodology could allow more timely and accurate identification of unknown drug-metabolizing enzymes. Significance Statement Proteomic correlation profiling using samples from individual human donors was proven to be a useful methodology for the identification of enzymes responsible for drug-metabolism. This methodology could accelerate the identification of unknown drug-metabolizing enzymes in the future.

NTRK fusions: A novel diagnostic and therapeutic methodology for cancer

This commentary discusses the case from the publication “Metastatic Triple Negative Breast Cancer with NTRK Gene Fusion on Tissue but not on ctDNA Molecular Profile.” This paper stresses the importance of molecular profiling to find characteristic mutations such as NTRK gene fusions which increase therapeutic options including tyrosine receptor kinase inhibitors such as Larotrectinib and entrectinib. The current profiling techniques are also discussed with both advantages and limitations discussed. Genomic testing in relation to current pharmaceutical therapies will shape the future of cancer diagnosis and treatment.

Scattered Emission Profile Technique for Accurate and Fast Assessment of Optical Gain in Thin Film Lasing Materials

Accurate measurement of optical gain is essential to screen materials as viable active media for thin-film laser applications. The net modal gain is typically measured using the variable stripe length (VSL) method, which has been extensively studied for the last few decades. In this work, we propose an alternative method, which we name scattered emission profile (SEP) method, to measure the net modal gain. It relies on the collection of amplified spontaneous emission (ASE) scattered from the surface of the film illuminated by a pump stripe. By using an appropriate setup, the new method results in a significantly faster measurement of net modal gain, while simultaneously providing a more accurate gain value. The setup and algorithm to extract the net modal gain are detailed in this paper and are demonstrated on lead halide perovskite films. The influence of the stripe length on the measured gain value is shown. Gain measurements performed over two different perovskite films, fabricated either via spin-coating or thermal evaporation, confirm the broad applicability of the SEP method. Finally, we show a quantitative comparison of the SEP method with VSL measurements and highlight the advantages and shortcomings of each method.