A comprehensive strategy based on ultra-high performance liquid chromatography with diode array detector fingerprinting and multi-component ultra-performance liquid chromatography with tandem mass spectrometry technology for quality control of Jiawei Huoxiang Zhengqi Pill.

Jiawei Huoxiang Zhengqi Pill (JHZP) is a commonly used Chinese patent medicine for the clinical treatment of headache, dizziness, chest tightness as well as abdominal distension, and pain caused by wind-cold flu. In this study, a comprehensive strategy combining ultra-high performance liquid chromatography with diode array detector (UHPLC-DAD) fingerprinting and multi-component quantitative analysis was established and validated for quality evaluation of JHZP. A total of 49 characteristic common peaks were selected in a chromatographic fingerprinting study to assess the similarity of 15 batches of JHZP. Furthermore, 109 compounds were identified or preliminarily identified from JHZP by coupling with an advanced hybrid linear ion trap-Orbitrap mass spectrometer. For quantification, the optimized ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method was employed for the simultaneous determination of 13 target compounds within 12 min. The sensitivity, precision, reproducibility, and accuracy of the method were satisfactory. This validated UPLC-MS/MS method was successfully applied to analyzing 15 batches of JHZP. The proposed comprehensive strategy combining UHPLC-DAD fingerprinting and multi-component UPLC-MS/MS analysis proved to be highly efficient, accurate, and reliable for the quality evaluation of JHZP, which can be considered as a reference for the overall quality evaluation of other Chinese herbal formulations.

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis.

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

Impacts of farmland decontamination on 137Cs transfers in rivers after Fukushima nuclear accident: Evidence from a retrospective sediment core study.

Following the Fukushima Daiichi Nuclear Power Plant disaster in March 2011, the Japanese government initiated an unprecedented decontamination programme to remediate 137Cs-contaminated soils and allow population return. This programme involved the removal of topsoil under farmland and residential land, and its replacement with "fresh soil" composed of granitic saprolite. However, decontamination was limited to these two land uses, without remediating forests, which cover 70 % of the surface area in the affected region. In this unprecedented context, the specific impact of this unique decontamination programme on 137Cs transfers in river systems remains to be quantified at the catchment scale. In this study, based on the analysis of a sediment core collected in June 2021 in the Mano Dam reservoir draining a decontaminated catchment, the effects of soil decontamination on particle-bound 137Cs dynamics and sediment source contributions in response to a succession of extreme precipitation events were retrospectively assessed. The sequence of sediment layer deposition and its chronology were reconstructed through the analysis of several diagnostic properties (organic matter, elemental geochemistry, visible colourimetry, granulometry) and contextual information. During abandonment (2011-2016), cropland contribution decreased (31 %). Concurrently, 137Cs activity and deposition flux decreased (19 and 29%year-1, respectively). Following decontamination (2017), sediment transfer increased (270 %) in response to increased contributions from decontaminated cropland and "fresh soil" (625 % and 180 % respectively). Meanwhile, forest contributions remained stable. In contrast, 137Cs activity dropped (65 %), although 137Cs deposition flux remained constant. Forests acted as a stable source of 137Cs. Accordingly, 137Cs deposition flux after decontamination (2016-2021) was similar to that observed during the 5-years period of land abandonment (2011-2016), as a result of the regrowth of spontaneous vegetation over farmland, protecting soil against erosion. Future research should further investigate the impact of longer land abandonment that prevailed in some regions decontaminated lately on the 137Cs fluxes in the rivers.

A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers.

Triadica sebifera is an important landscaping tree species because of its colorful autumn leaves. In recent years, some cultivars have been bred and licensed, but it can be difficult to identify them from their morphological traits due to their similar phenotypes. To explore the genetic relationships and construct a fingerprint of the cultivars, the licensed T. sebifera cultivars were analyzed using SSR markers. A total of 179 alleles were identified among the 21 cultivars at 16 SSR loci, and these alleles exhibited a high level of genetic diversity (He = 0.86). The genetic variations mainly occurred among cultivars based on an analysis of molecular variance (AMOVA). According to phylogenetic analysis, principal coordinate analysis (PCoA), and Bayesian clustering analysis, the genetic relationships were independent of geographic distances, which may be mainly due to transplantations between regions. Some cultivars with different leaf colors showed obvious genetic differentiation and may be preliminary candidates for cross-breeding. Finally, the fingerprint for the licensed cultivars was constructed with two SSR markers. The results of this study can provide technical support for the application and legal protection of licensed Triadica sebifera cultivars.

Lipids and Fatty Acid Composition Reveal Differences between Durum Wheat Landraces and Modern Cultivars.

Durum wheat (Triticum turgidum L. ssp. durum) landraces, traditional local varieties representing an intermediate stage in domestication, are gaining attention due to their high genetic variability and performance in challenging environments. While major kernel metabolites have been examined, limited research has been conducted on minor bioactive components like lipids, despite their nutritional benefits. To address this, we analyzed twenty-two tetraploid accessions, comprising modern elite cultivars and landraces, to (i) verify if the selection process for yield-related traits carried out during the Green Revolution has influenced lipid amount and composition; (ii) uncover the extent of lipid compositional variability, giving evidence that lipid fingerprinting effectively identifies evolutionary signatures; and (iii) identify genotypes interesting for breeding programs to improve yield and nutrition. Interestingly, total fat did not correlate with kernel weight, indicating lipid composition as a promising trait for selection. Tri- and di-acylglycerol were the major lipid components along with free fatty acids, and their relative content varied significantly among genotypes. In particular, landraces belonging to T. turanicum and carthlicum ecotypes differed significantly in total lipid and fatty acid profiles. Our findings provide evidence that landraces can be a genetically relevant source of lipid variability, with potential to be exploited for improving wheat nutritional quality.

Odor Fingerprinting of Chitosan and Source Identification of Commercial Chitosan: HS-GC-IMS, Multivariate Statistical Analysis, and Tracing Path Study.

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R2X = 0.866, R2Y = 0.996, Q2 = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications.

Antibiotyping, RAPD- and ERIC-PCR fingerprinting of Klebsiella pneumoniae clinical isolates at a tertiary reference hospital in Denpasar, Bali, Indonesia.

Background and Objectives: Klebsiella pneumoniae is a healthcare-associated infections agent and could be an extended spectrum ß-lactamase (ESBL) producer. Understanding the transmission of this bacterium in a hospital setting needs accurate typing methods. An antibiogram is used to detect the resistance pattern of the isolates. Random Amplified Polymorphic DNA (RAPD) and Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR are rapid, technically simple, and easy-to-interpret DNA typing methods. This study aimed to evaluate the use of antibiotyping, RAPD-, and ERIC-PCR to investigate the heterogeneity of K. pneumoniae isolated from clinical specimens. Materials and Methods: The antibiograms of 46 K. pneumoniae clinical isolates were determined by Vitek® 2 Compact. All isolates underwent RAPD-PCR using AP4 primer and ERIC-PCR using ERIC-2 primer. The dendrogram was generated using the GelJ software and analyzed to determine its similarity. The analysis of antibiogram and the molecular typing diversity index was calculated using the formula of the Simpson's diversity index. Results: About 71.7% of the isolates were ESBL-producers, and more than 80% of isolates were susceptible to amikacin, ertapenem, and meropenem. The antibiotyping produced 32 diverse types with DI = 0.964. In addition, the RAPD-PCR produced 47 different types with DI = 1, while ERIC-PCR was 46 (DI=0.999). Conclusion: Antibiotyping, RAPD- and ERIC-PCR showed powerful discrimination power among the isolates, supported the diversity of K. pneumoniae isolates in current study. These combination could be promising tools for clonal relationship determination, including in tracking the transmission of the outbreak's agent in hospital setting.

Source differentiation of BTEX compounds in groundwater contaminated due to refinery activities.

This study aims to identify sources of groundwater contamination in a refinery area using integrated compound-specific stable isotope analysis (CSIA), oil fingerprinting techniques, hydrogeological data, and distillation analysis. The investigations focused on determination of the origin of benzene, toluene, ethylbenzene, and xylenes (BTEX), and aliphatic hydrocarbons as well. Groundwater and floating oil samples were collected from extraction wells for analysis. Results indicate presence of active leaks in both the northern and southern zones. In the northern zone, toluene was found to primarily originate from oil products like aviation turbine kerosene (ATK or aviation fuel), kerosene, regular gasoline, and diesel fuel. Additionally, stable isotope ratios of carbon and hydrogen for ethylbenzene, o-xylene (ortho xylene) and p-xylene (para xylene) in zone A suggested the pollution originated from gasoline within the northern zone. The origin of super gasoline (with higher octane) identified in southern zone using δ13C and δ2H values of toluene in the floating oil and groundwater samples. Further, biodegradation of toluene likely occurred in southern zone according to δ13C and δ2H. The findings underscore the critical importance of integrating CSIA and fingerprinting techniques to effectively address the challenges of source identification and relying solely on each method independently is insufficient. Accordingly, comparing the GC-MS results of floating oil samples with ATK and jet fuel (JP4) standards can be effectively utilized for source differentiation. However, this method showed no practical application to distinguish different types of diesel or gasoline. The accuracy and reliability of source identification of BTEX compounds may significantly improve when hydrogeological data incorporates with stable isotopes analysis. Additionally, the results of this study will elevate the procedures for fuel-related contaminants source identification of the polluted groundwater that is crucial to develop effective remediation strategies.

Enhancing Molecular Characterization of Dissolved Organic Matter by Integrative Direct Infusion and Liquid Chromatography Nontargeted Workflows.

Dissolved organic matter (DOM) in aquatic systems is a highly heterogeneous mixture of water-soluble organic compounds, acting as a major carbon reservoir driving biogeochemical cycles. Understanding DOM molecular composition is thus of vital interest for the health assessment of aquatic ecosystems, yet its characterization poses challenges due to its complex and dynamic chemical profile. Here, we performed a comprehensive chemical analysis of DOM from highly urbanized river and seawater sources and compared it to drinking water. Extensive analyses by nontargeted direct infusion (DI) and liquid chromatography (LC) high-resolution mass spectrometry (HRMS) through Orbitrap were integrated with novel computational workflows to allow molecular- and structural-level characterization of DOM. Across all water samples, over 7000 molecular formulas were calculated using both methods (∼4200 in DI and ∼3600 in LC). While the DI approach was limited to molecular formula calculation, the downstream data processing of MS2 spectral information combining library matching and in silico predictions enabled a comprehensive structural-level characterization of 16% of the molecular space detected by LC-HRMS across all water samples. Both analytical methods proved complementary, covering a broad chemical space that includes more highly polar compounds with DI and more less polar ones with LC. The innovative integration of diverse analytical techniques and computational workflow introduces a robust and largely available framework in the field, providing a widely applicable approach that significantly contributes to understanding the complex molecular composition of DOM.

A comparative metabolomics analysis of Açaí (Euterpe oleracea Mart.) fruit, food powder, and botanical dietary supplement extracts.

INTRODUCTION: Euterpe oleracea Mart. (açaí) is a botanical of interest to many who seek functional foods that provide antioxidant and anti-inflammatory properties. Cancer patients are increasingly taking botanical dietary supplements containing açaí to complement their conventional therapeutics, which may lead to serious adverse events. Before testing our açaí extracts in vitro for botanical-drug interactions, the goal is to chemically characterize our extracts for compounds whose biological activity in açaí is unknown. OBJECTIVE: The objective of this work was to develop a chemical fingerprinting method for untargeted characterization of açaí samples from a variety of sources, including food products and botanical dietary supplement capsules, made with multiple extraction solvents. METHODS: An optimized LC-MS method was generated for in-depth untargeted fingerprinting of chemical constituents in açaí extracts. Statistical analysis models were used to describe relationships between the açaí extracts based on molecular features found in both positive and negative mode ESI. RESULTS: In an attempt to elucidate the differences in metabolites among açaí extracts from different cultivars, we identified or tentatively identified 173 metabolites from the 16 extracts made from 6 different sources. Of these compounds, there are 138 reported in açaí for the first time. Statistical models showed similar yet distinct differences between the extracts tested based on the polarity of compounds present and the origin of the source material. CONCLUSION: A high-resolution mass spectrometry method was generated that allowed us to greatly characterize 16 complex extracts made from different sources of açaí with different extraction solvent polarities.

Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing.

BACKGROUND: The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources. RESULTS: We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties. CONCLUSIONS: The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Conventional management has a greater negative impact on Phaseolus vulgaris L. rhizobia diversity and abundance than water scarcity.

Introduction: Drought is one of the biggest problems for crop production and also affects the survival and persistence of soil rhizobia, which limits the establishment of efficient symbiosis and endangers the productivity of legumes, the main source of plant protein worldwide. Aim: Since the biodiversity can be altered by several factors including abiotic stresses or cultural practices, the objective of this research was to evaluate the effect of water availability, plant genotype and agricultural management on the presence, nodulation capacity and genotypic diversity of rhizobia. Method: A field experiment was conducted with twelve common bean genotypes under irrigation and rain-fed conditions, both in conventional and organic management. Estimation of the number of viable rhizobia present in soils was performed before the crop establishment, whereas the crop yield, nodule number and the strain diversity of bacteria present in nodules were determined at postharvest. Results: Rainfed conditions reduced the number of nodules and of isolated bacteria and their genetic diversity, although to a lesser extent than the agrochemical inputs related to conventional management. In addition, the effect of water scarcity on the conventional management soil was greater than observed under organic conditions. Conclusions: The preservation of diversity will be a key factor to maintain crop production in the future, as problems caused by drought will be exacerbated by climate change and organic management can help to maintain the biodiversity of soil microbiota, a fundamental aspect for soil health and quality.

ITC-Net-blend-60: a comprehensive dataset for robust network traffic classification in diverse environments.

OBJECTIVES: Recognition of mobile applications within encrypted network traffic holds considerable effects across multiple domains, encompassing network administration, security, and digital marketing. The creation of network traffic classifiers capable of adjusting to dynamic and unforeseeable real-world settings presents a tremendous challenge. Presently available datasets exclusively encompass traffic data obtained from a singular network environment, thereby restricting their utility in evaluating the robustness and compatibility of a given model. DATA DESCRIPTION: This dataset was gathered from 60 popular Android applications in five different network scenarios, with the intention of overcoming the limitations of previous datasets. The scenarios were the same in the applications set but differed in terms of Internet service provider (ISP), geographic location, device, application version, and individual users. The traffic was generated through real human interactions on physical devices for 3-15 min. The method used to capture the traffic did not require root privileges on mobile phones and filtered out any background traffic. In total, the collected dataset comprises over 48 million packets, 450K bidirectional flows, and 36 GB of data.

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning.

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

Quantification of the provenance contribution and sedimentary mixing effect of sediments in the Yellow River Basin, China.

Quantifying the source contributions of sediments in large fluvial systems with active wind erosion problems has crucial implications for understanding morphological evolution and ecological progression in the Earth system. Much effort have been focused on characterizing sediments of the Yellow River, but quantitation of the sediment source proportions at the basin-wide scale is lacking. To this end, the research aims to quantitatively elucidate the potential source contributions of sediments in the Yellow River based on geochemical characteristics and sediment fingerprinting technique, in order to identify sedimentary mixing effect and propose sustainable development strategies. In total, samples of four source groups (n = 107) and target floodplain sediments (n = 61) were collected and tested for elemental composition, grain size, magnetic susceptibility, and quartz grain microtextures. The results indicated that the optimal tracer combination was determined as P, Zn, and Ca. The average contributions of the "Tibetan Plateau", "Sandy deserts-Loess Plateau", "Loess Plateau", and "Loess Plateau-Qinling Mountains" source groups to the target sediments were 23.0 %, 21.5 %, 31.6 %, and 23.9 %, respectively. The accuracy of source apportionments was supported by the goodness of fit (GOF) and virtual mixtures tests. Meanwhile, large amounts of debris from surrounding mountains was transported to the Loess Plateau through fluvial processes and ultimately mixed with aeolian deposits, leading to sedimentary mixing effect. To maintain water balance and minimize erosion risk, the drought-resistant perennial planting and moderate grazing were recommended. The findings are instrumental in promoting soil and water conservation and disclosing fluvial and aeolian interaction on a global scale.

Chronobiology of Viscum album L.: a time series of daily metabolomic fingerprints spanning 27 years.

Introduction: European mistletoe (Viscum album L.) has been gaining increasing interest in the field of oncology as a clinically relevant adjunctive treatment in many forms of cancer. In the field of phytopharmacology, harvesting time is pivotal. In the last century, a form of metabolomic fingerprinting based on pattern formation was proposed as a way to determine optimal harvesting times to ensure high quality of mistletoe as raw material for pharmaceutical use. In order to further evaluate the information obtained with this metabolomic fingerprinting method, we analysed a large time series of previously undigitised daily mistletoe chromatograms dating back to the 1950s. Methods: These chromatograms were scanned and evaluated using computerized image analysis, resulting in 12 descriptors for each individual chromatogram. We performed a statistical analysis of the data obtained, investigating statistical distributions, cross-correlations and time self-correlations. Results: The analysed dataset spanning about 27 years, contains 19,037 evaluable chromatograms in daily resolution. Based on the distribution and cross-correlation analyses, the 12 descriptors could be clustered into six independent groups describing different aspects of the chromatograms. One descriptor was found to mirror the annual rhythm being well correlated with temperature and a phase shift of 10 days. The time self-correlation analysis showed that most other descriptors had a characteristic self-correlation of ∼50 days, which points to further infradian rhythms (i.e., more than 24 h). Discussion: To our knowledge, this dataset is the largest of its type. The combination of this form of metabolomic fingerprinting with the proposed computer analysis seems to be a promising tool to characterise biological variations of mistletoe. Additional research is underway to further analyse the different rhythms present in this dataset.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

The neurophysiological brain-fingerprint of Parkinson's disease.

BACKGROUND: Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS: The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).