Advanced Multivariate Analysis Research Articles

Critical aspects of Raman spectroscopy as a tool for postmortem interval estimation

The estimation of the postmortem interval (PMI) from skeletal remains represents a challenging task in forensic science. PMI is often influenced by extrinsic factors (humidity, dryness, scavengers, etc.) and intrinsic factors (age, sex, pathology, way of life, medical treatments, etc.). Raman spectroscopy combined with multivariate data analysis represents a promising tool for forensic anthropologists. Despite all the advantages of the technique, Raman spectra of skeletal remains are influenced by these extrinsic and intrinsic factors, which impairs precision and reproducibility. Both parameters have to reach a high level of confidence when such spectroscopy is used as a way to predict PMI. As a consequence, advanced multivariate data analysis is necessary to quantify the effect of all factors to improve the estimation of the PMI.The objective of this work is to evaluate the effect of intrinsic and extrinsic factors on the Raman spectra of skeletal remains. We designed a protocol close to a real-world scenario. We used ANOVA-simultaneous component analysis (ASCA) to unmix and quantify the effect of 1 intrinsic (source body) and 1 extrinsic (burial time) factors on the Raman spectra. In our model, the burial time was found to generate the highest variability after the source body. ASCA showed that the variability due to the burial time has 2 mixed contributions. Seasonal variations are the first contribution. The second contribution is attributed to diagenesis. A decrease in the mineral bands and an increase in the organic bands are observed. The source body was also found to contribute to the variability in Raman spectra. ASCA showed that the source body induces variability related to the composition of bones. This quantification cannot be assessed by basic chemometrics methods such as PCA. The results of this study highlighted the need to use an advanced chemometric data analysis tool (like ASCA) combined with Raman spectroscopy to estimate the postmortem interval.

350 Investigating the Architecture of Speech Processing Pathways in the Brain

OBJECTIVES/GOALS: Speech production requires mapping between sound-based and motor-based neural representations of a word – accomplished by learning internal models. However, the neural bases of these internal models remain unclear. The aim of this study is to provide experimental evidence for these internal models in the brain during speech production. METHODS/STUDY POPULATION: 16 healthy human adults were recruited for this electrooculography speech study. 20 English pseudowords were designed to vary on confusability along specific features of articulation (place vs manner). All words were controlled for length and voicing. Three task conditions were performed: speech perception, covert and overt speech production. EEG was recorded using a 64-channel Biosemi ActiveTwo system. EMG was recorded on the orbicularis orbis inferior and neck strap muscles. Overt productions were recorded with a high-quality microphone to determine overt production onset. EMG during was used to determine covert production onset. Neuroimaging: Representational Similarity Analysis (RSA), was used to probe the sound- and motor-based neural representations over sensors and time for each task. RESULTS/ANTICIPATED RESULTS: Production (motor) and perception (sound) neural representations were calculated using a cross-validated squared Euclidean distance metric. The RSA results in the speech perception task show a strong selectivity around 150ms, which is compatible with recent human electrocorticography findings in human superior temporal gyrus. Parietal sensors showed a large difference for motor-based neural representations, indicating a strong encoding for production related processes, as hypothesized by previous studies on the ventral and dorsal stream model of language. Temporal sensors, however, showed a large change for both motor- and sound-based neural representations. This is a surprising result since temporal regions are believed to be primarily engaged in perception (sound-based) processes. DISCUSSION/SIGNIFICANCE: This study used neuroimaging (EEG) and advanced multivariate pattern analysis (RSA) to test models of production (motor-) and perception (sound-) based neural representations in three different speech task conditions. These results show strong feasibility of this approach to map how the perception and production processes interact in the brain.

Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson\u2019s disease

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.

Archaeometric perspective on the emergence of brass north of the Alps around the turn of the Era

Ancient brass (aurichalcum) was a valued commodity in the Antiquity, notably because of its gold-like appearance. After mastering brass fabrication using the cementation procedure in the first century BC in the Mediterranean, this material became widely used by the Romans for coins, jewellery and other artefacts. Because of its visual qualities, it is believed that since this period, brass played an important role in diplomatic and economic contacts with indigenous communities, notably Celtic and Germanic tribes north of Danube and west of Rhine. To test this hypothesis, we performed for the first time the advanced statistical multivariate analysis based on chemical composition and lead isotope systematics, coupled with informed typo-chronological categorisation, of a suite of late Iron Age and Early Roman period (first century BC – first century AD) brass and other copper-alloy artefacts from the territory of Bohemia. In order to to discuss their provenance, the results were compared to known contemporary sources of material. The new results for brass artefacts from this early phase of the massive occurrence of Roman aurichalcum in the Barbarian territories point to the ore deposits in the western Mediterranean or the Massif Central area in Gaul, consistent with historical events. These new findings underscore the great economic and political importance of the new and rich mineral resources in the Transalpine Gaul acquired due to Caesar's military campaigns.

Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.

Unravelling the Encapsulation of DNA and Other Biomolecules in HAp Microcalcifications of Human Breast Cancer Tissues by Raman Imaging.

Simple SummaryAlthough microcalcifications can be considered one of the first indicators of suspicious cancer lesions, depending on their morphology and distribution, the formation of hydroxyapatite calcifications and their relationship with malignancy remains unknown. In this work, we investigate in depth the biochemical composition of breast cancer microcalcifications, combining Raman spectroscopy imaging and advanced multivariate analysis. We demonstrate that DNA is naturally adsorbed and encapsulated inside hydroxyapatite found in breast cancer tissue. Furthermore, we also show the encapsulation of other relevant biomolecules such as lipids, proteins, cytochrome C and polysaccharides. The demonstration of the natural DNA biomineralization in cancer tissues represents an unprecedented advance in the field, as it can pave the way to understanding the role of hydroxyapatite in malignant tissues.Microcalcifications are detected through mammography screening and, depending on their morphology and distribution (BI-RADS classification), they can be considered one of the first indicators of suspicious cancer lesions. However, the formation of hydroxyapatite (HAp) calcifications and their relationship with malignancy remains unknown. In this work, we report the most detailed three-dimensional biochemical analysis of breast cancer microcalcifications to date, combining 3D Raman spectroscopy imaging and advanced multivariate analysis in order to investigate in depth the molecular composition of HAp calcifications found in 26 breast cancer tissue biopsies. We demonstrate that DNA has been naturally adsorbed and encapsulated inside HAp microcalcifications. Furthermore, we also show the encapsulation of other relevant biomolecules in HAp calcifications, such as lipids, proteins, cytochrome C and polysaccharides. The demonstration of natural DNA biomineralization, particularly in the tumor microenvironment, represents an unprecedented advance in the field, as it can pave the way to understanding the role of HAp in malignant tissues.

Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin

An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography – time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity.

Advanced multivariate analysis methods for use by the experiments at the Large Hadron Collider*

In the course of the past four years, AMVA4NewPhysics, a Horizon2020-funded Marie Skłodowska-Curie (MSCA) Innovative Training Network, focused on the study of Multivariate Analysis Methods and Statistical Learning tools for the High Energy Physics research. Through the individual and collaborative work of its members, AMVA4NewPhysics succeeded in developing and optimising several such tools for use by the ATLAS and CMS experiments at the Large Hadron Collider, at CERN, promising to improve their measurement and search sensitivity. In this paper, some of these new tools are presented, along with their related results.

Electronic waste generation, economic values, distribution map, and possible recycling system in Indonesia

Waste electrical and electronic equipment (WEEE) or simply e-waste have been gaining attention around the world due to their rapid generation and potential economic values. While a number of global and regional estimations of e-waste have been conducted, the current and future magnitude of e-waste in Indonesia, the world’s fourth largest population, has not been thoroughly assessed. This study aims to provide an estimation and projection of e-waste generation in Indonesia, as well as its potential recoverable metals’ value from 1996 to 2040 to address this gap. An advanced multivariate Input-Output Analysis (IOA) of sales-stock-lifespan model and a dynamic, time-variant lifespan of products through Weibull distribution function were used to provide more accurate and extended estimation of e-waste generation in Indonesia. The results show that Indonesia’s e-waste generation is projected to increase from approximately 2.0 (in 2021) to 3.2 million tonnes (in 2040), which corresponds to 7.3 (in 2021) to 10 kg/capita (in 2040). These represent economic values from US$ 2.2 billion to US$ 14 billion of Copper, Gold, Silver, Platinum and Palladium in the e-waste. This study also maps the distribution of e-waste generation in Indonesia and found that in 2021, the Java Island contributes up to 56% of the total e-waste generation in the country. Lastly, the study proposes a recycling system framework that include possible processing routes and scenarios that integrate mobile/sub-station recycling facilities to existing large metallurgical facilities in the context of Indonesia archipelago.

The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura.

The purpose of the present study was to explore whether and to what extent the neuroimaging markers could predict the relief of the symptoms of patients with migraine without aura (MWoA) following a 4-week acupuncture treatment period. In study 1, the advanced multivariate pattern analysis was applied to perform a classification analysis between 40 patients with MWoA and 40 healthy subjects (HS) based on the z-transformed amplitude of low-frequency fluctuation (zALFF) maps. In study 2, the meaningful classifying features were selected as predicting features and the support vector regression models were constructed to predict the clinical efficacy of acupuncture in reducing the frequency of migraine attacks and headache intensity in 40 patients with MWoA. In study 3, a region of interest–based comparison between the pre- and post-treatment zALFF maps was conducted in 33 patients with MwoA to assess the changes in predicting features after acupuncture intervention. The zALFF value of the foci in the bilateral middle occipital gyrus, right fusiform gyrus, left insula, and left superior cerebellum could discriminate patients with MWoA from HS with higher than 70% accuracy. The zALFF value of the clusters in the right and left middle occipital gyrus could effectively predict the relief of headache intensity (R2 = 0.38 ± 0.059, mean squared error = 2.626 ± 0.325) and frequency of migraine attacks (R2 = 0.284 ± 0.072, mean squared error = 20.535 ± 2.701) after the 4-week acupuncture treatment period. Moreover, the zALFF values of these two clusters were both significantly reduced after treatment. The present study demonstrated the feasibility and validity of applying machine learning technologies and individual cerebral spontaneous activity patterns to predict acupuncture treatment outcomes in patients with MWoA. The data provided a quantitative benchmark for selecting acupuncture for MWoA.

Network estimation for censored time-to-event data for multiple events based on multivariate survival analysis.

In general survival analysis, multiple studies have considered a single failure time corresponding to the time to the event of interest or to the occurrence of multiple events under the assumption that each event is independent. However, in real-world events, one event may impact others. Essentially, the potential structure of the occurrence of multiple events can be observed in several survival datasets. The interrelations between the times to the occurrences of events are immensely challenging to analyze because of the presence of censoring. Censoring commonly arises in longitudinal studies in which some events are often not observed for some of the subjects within the duration of research. Although this problem presents the obstacle of distortion caused by censoring, the advanced multivariate survival analysis methods that handle multiple events with censoring make it possible to measure a bivariate probability density function for a pair of events. Considering this improvement, this paper proposes a method called censored network estimation to discover partially correlated relationships and construct the corresponding network composed of edges representing non-zero partial correlations on multiple censored events. To demonstrate its superior performance compared to conventional methods, the selecting power for the partially correlated events was evaluated in two types of networks with iterative simulation experiments. Additionally, the correlation structure was investigated on the electronic health records dataset of the times to the first diagnosis for newborn babies in South Korea. The results show significantly improved performance as compared to edge measurement with competitive methods and reliability in terms of the interrelations of real-life diseases.

Unraveling the Dynamic Nexus Between Trade Liberalization, Energy Consumption, CO2 Emissions, and Health Expenditure in Southeast Asian Countries.

ObjectiveMost of the Southeast Asian countries witnessed remarkable economic growth in the last few decades. Trade is a primary factor in achieving this exponential economic progress in these countries. Besides, the positive implications of trade, it has associated cost of escalated energy consumption, CO2 emissions, and resulting health complications which leads to higher public health expenditures. This study examines the nexus between trade liberalization, energy consumption, CO2 emissions, and health expenditures in Southeast Asian countries for the period of 1991 to 2018.MethodsThe empirical methods used in this study entail diagnostic testing, correlation analysis, and structure equation modeling (SEM) technique. SEM is an advanced multivariate analysis technique that can test complex multivariate causal associations among a set of variables. Therefore, it is the most suitable econometric approach to explore the dynamic association between trade openness, energy consumption, CO2 emissions, and health expenditures.ResultsThe empirical results reveal a nexus between trade openness, energy consumption, CO2 emissions, and health expenditure in Southeast Asian countries. Nevertheless, pollutant emissions have a direct impact on health expenditures, whereas trade and energy consumption shows an indirect influence on the escalation in public health spending in sample Southeast Asian countries. Moreover, the mediating path of each indirect effect is energy consumption.ConclusionThese results imply that Southeast Asian countries heavily rely on fossil energy to fuel economic growth. Hence, to promote sustainable and eco-friendly economic development, the respective governments need to reform their energy sectors by tapping into renewable energy resources and deploy green technologies to reduce pollutant emissions for the development of a healthy society. In addition, governments should levy taxes on highly polluting industries so as to curtail carbon emissions and resulting health expenditures.

Bicomponent supramolecular self‐assemblies studied with tip‐enhanced Raman spectroscopy

AbstractThe formation of well‐ordered supramolecular self‐assembly based on cytosine and 4,4′‐bipyridine on Ag(111) has been investigated with subnanometer‐resolved tip‐enhanced Raman spectroscopy (TERS) using a low‐temperature ultrahigh‐vacuum scanning tunneling microscope (STM). The co‐deposition of two molecules on the Ag(111) surface held at room temperature results in well‐ordered hydrogen‐bonded monolayer islands, which transform into well‐aligned double‐stranded chains after thermal annealing. Site‐dependent TERS spectra help to identify the packing structures of molecular assemblies. The changes of molecule‐specific vibrational fingerprints across the assemblies can be clearly monitored by combining high‐resolution TERS spectra with advanced multivariate analysis. Furthermore, the peak positions and relative intensities for characteristic vibrational modes of the two molecules are found to vary due to the changes of the local hydrogen bonding and interaction environment in different molecular samples. Our findings demonstrate that TERS is sensitive to the local molecular interactions, provides new potentials to monitor the functionality of molecular architectures on demand, and opens new opportunities to probe surface chemistry at the nanoscale.

Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG.

Theta-band (∼6 Hz) rhythmic activity within and over the medial PFC ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in 9 human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level because of signal mixing.SIGNIFICANCE STATEMENT "Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous because of "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal demixing to understand the narrowband neural dynamics underlying cognitive processes.

Raman Spectroscopy vs Quantitative Polymerase Chain Reaction In Early Stage Huanglongbing Diagnostics

Raman spectroscopy (RS) is an emerging analytical technique that can be used to develop and deploy precision agriculture. RS allows for confirmatory diagnostic of biotic and abiotic stresses on plants. Specifically, RS can be used for Huanglongbing (HLB) diagnostics on both orange and grapefruit trees, as well as detection and identification of various fungal and viral diseases. The questions that remain to be answered is how early can RS detect and identify the disease and whether RS is more sensitive than qPCR, the “golden standard” in pathogen diagnostics? Using RS and HLB as case study, we monitored healthy (qPCR-negative) in-field grown citrus trees and compared their spectra to the spectra collected from healthy orange and grapefruit trees grown in a greenhouse with restricted insect access and confirmed as HLB free by qPCR. Our result indicated that RS was capable of early prediction of HLB and that nearly all in-field qPCR-negative plants were infected by the disease. Using advanced multivariate statistical analysis, we also showed that qPCR-negative plants exhibited HLB-specific spectral characteristics that can be distinguished from unrelated nutrition deficit characteristics. These results demonstrate that RS is capable of much more sensitive diagnostics of HLB compared to qPCR.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet.

The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss. The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight. Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research. Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma.

ObjectiveThe objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma.Patients and MethodsWe proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer.ResultsSERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%.ConclusionOur results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.

OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.

Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

High speed determination of laser wavelength using Poincaré descriptors of speckle

Laser speckle can provide a powerful tool that may be used for metrology, for example measurements of the incident laser wavelength with a resolution beyond that which may be achieved in a commercial device. However, to realise highest resolution requires advanced multi-variate analysis techniques, which limit the acquisition rate of such a wavemeter. Here we show an arithmetically simple method to measure wavelength changes with dynamic speckle, based on a Poincarè descriptor of the speckle pattern. We demonstrate the measurement of wavelength changes at femtometer-level with a measurement time reduced by two orders of magnitude compared to the previous state-of-the-art, which offers promise for applications such as speckle-based laser wavelength stabilisation.