From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization.

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Hydrological connectivity drives longitudinal movement of endangered endemic Chilean darter Percilia irwini (Eigenmann, 1927).

Movement is a fundamental aspect of fish ecology, and it therefore represents an important trait to monitor for the management and conservation of fish populations. This is especially true for small benthic fish, as they often inhabit part of the catchment where their movement may be restricted by alterations to river connectivity due to human activity. Still, the movement of these small benthic fish remains poorly understood, partly because of their small size and their cryptic nature. This applies to Percilia irwini, an endangered small darter native to the south-central region of Chile. Its habitat has been affected by the presence of large hydroelectric dams and is currently threatened by the construction of several others. In this study, the authors investigated movement patterns of P. irwini from populations inhabiting different parts of the Biobío catchment, with different levels of connectivity due to natural and/or human-induced features. The authors combined chronological clustering with random forest classification to reconstruct lifelong movements from multi-elemental otolith microchemistry transects. The majority of the movements detected occurred in an undisturbed part of the catchment. These were directional upstream movements occurring between capture sites from the lower and the middle reaches of the river, representing a distance of nearly 30 km, a distance much larger than previously thought. Nonetheless, in the part of the catchment where connectivity was affected by human activity, no such movements were identified. This study shows that connectivity alteration could impede naturally occurring movement and further threaten the resilience of populations of P. irwini. Furthermore, the results presented are used to discuss advantages and disadvantages of microchemistry analysis for studying movement of small benthic fish.

Concentration and Distribution of Naphthenic Acids in the Produced Water from Offshore Norwegian North Sea Oilfields.

Naphthenic acids (NAs) constitute one of the toxic components of the produced water (PW) from offshore oil platforms discharged into the marine environment. We employed liquid chromatography (LC) coupled to high-resolution mass spectrometry with electrospray ionization (ESI) in negative mode for the comprehensive chemical characterization and quantification of NAs in PW samples from six different Norwegian offshore oil platforms. In total, we detected 55 unique NA isomer groups, out of the 181 screened homologous groups, across all tested samples. The frequency of detected NAs in the samples varied between 14 and 44 isomer groups. Principal component analysis (PCA) indicated a clear distinction of the PW from the tested platforms based on the distribution of NAs in these samples. The averaged total concentration of NAs varied between 6 and 56 mg L-1, among the tested platforms, whereas the concentrations of the individual NA isomer groups ranged between 0.2 and 44 mg L-1. Based on both the distribution and the concentration of NAs in the samples, the C8H14O2 isomer group appeared to be a reasonable indicator of the presence and the total concentration of NAs in the samples with a Pearson correlation coefficient of 0.89.

Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data.

Nontargeted feature detection in data from high resolution mass spectrometry is a challenging task, due to the complex and noisy nature of data sets. Numerous feature detection and preprocessing strategies have been developed in an attempt to tackle this challenge, but recent evidence has indicated limitations in the currently used methods. Recent studies have indicated the limitations of the currently used methods for feature detection of LC-HRMS data. To overcome these limitations, we propose a self-adjusting feature detection (SAFD) algorithm for the processing of profile data from LC-HRMS. SAFD fits a three-dimensional Gaussian into the profile data of a feature, without data preprocessing (i.e., centroiding and/or binning). We tested SAFD on 55 LC-HRMS chromatograms from which 44 were composite wastewater influent samples. Additionally, 51 of 55 samples were spiked with 19 labeled internal standards. We further validated SAFD by comparing its results with those produced via XCMS implemented through MZmine. In terms of ISs and the unknown features, SAFD produced lower rates of false detection (i.e., ≤ 5% and ≤10%, respectively) when compared to XCMS (≤11% and ≤28%, respectively). We also observed higher reproducibility in the feature area generated by SAFD algorithm versus XCMS.

Mass spectrometric strategies for the investigation of biomarkers of illicit drug use in wastewater.

The analysis of illicit drugs in urban wastewater is the basis of wastewater-based epidemiology (WBE), and has received much scientific attention because the concentrations measured can be used as a new non-intrusive tool to provide evidence-based and real-time estimates of community-wide drug consumption. Moreover, WBE allows monitoring patterns and spatial and temporal trends of drug use. Although information and expertise from other disciplines is required to refine and effectively apply WBE, analytical chemistry is the fundamental driver in this field. The use of advanced analytical techniques, commonly based on combined chromatography-mass spectrometry, is mandatory because the very low analyte concentration and the complexity of samples (raw wastewater) make quantification and identification/confirmation of illicit drug biomarkers (IDBs) troublesome. We review the most-recent literature available (mostly from the last 5 years) on the determination of IDBs in wastewater with particular emphasis on the different analytical strategies applied. The predominance of liquid chromatography coupled to tandem mass spectrometry to quantify target IDBs and the essence to produce reliable and comparable results is illustrated. Accordingly, the importance to perform inter-laboratory exercises and the need to analyze appropriate quality controls in each sample sequence is highlighted. Other crucial steps in WBE, such as sample collection and sample pre-treatment, are briefly and carefully discussed. The article further focuses on the potential of high-resolution mass spectrometry. Different approaches for target and non-target analysis are discussed, and the interest to perform experiments under laboratory-controlled conditions, as a complementary tool to investigate related compounds (e.g., minor metabolites and/or transformation products in wastewater) is treated. The article ends up with the trends and future perspectives in this field from the authors' point of view. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:258-280, 2018.

Assessing Alternative Population Size Proxies in a Wastewater Catchment Area Using Mobile Device Data.

Modeling and prediction of a city's (Oslo, Norway) daily dynamic population using mobile device-based population activity data and three low cost markers is presented for the first time. Such data is useful for wastewater-based epidemiology (WBE), which is an approach used to estimate the population level use of licit and illicit drugs, new psychoactive substances, human exposure to a wide range of pollutants, such as pesticides or phthalates, as well as the release of endogenous substances such as oxidative stress and allergen biomarkers. Comparing WBE results between cities often requires normalization to population size, and inaccuracy in the measured population can introduce high levels of uncertainty. In this study mobile phone data from 8-weeks in 2016 was used to train three linear models based on drinking water production, electricity consumption and online measurements of ammonium in wastewater. The ammonium model showed the best correlation with R2 = 0.88 while drinking water production and electricity consumption showed more discrepancies. The three models were then re-evaluated against 5-week of mobile phone data from 2017 showing mean absolute errors <10%. The ammonium-based estimated mean annual population for Oslo in 2017 was 645 000 inhabitants, 4% higher than the "de jure" population reported by the wastewater treatment plant. Due to changing conditions and seasonality, drinking water production underestimated the population by 27% and electricity consumption overestimated the population by 59%. Therefore, the results of this work showed that the ammonium mass loads can be used as an anthropogenic proxy to monitor and correct the fluctuations in population for a specific catchment area. Furthermore, this approach uses a simple, yet reliable indicator for population size that can be used also in other areas of research.

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results.

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS2 spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.

Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry.

A key challenge in the environmental and exposure sciences is to establish experimental evidence of the role of chemical exposure in human and environmental systems. High resolution and accurate tandem mass spectrometry (HRMS) is increasingly being used for the analysis of environmental samples. One lauded benefit of HRMS is the possibility to retrospectively process data for (previously omitted) compounds that has led to the archiving of HRMS data. Archived HRMS data affords the possibility of exploiting historical data to rapidly and effectively establish the temporal and spatial occurrence of newly identified contaminants through retrospective suspect screening. We propose to establish a global emerging contaminant early warning network to rapidly assess the spatial and temporal distribution of contaminants of emerging concern in environmental samples through performing retrospective analysis on HRMS data. The effectiveness of such a network is demonstrated through a pilot study, where eight reference laboratories with available archived HRMS data retrospectively screened data acquired from aqueous environmental samples collected in 14 countries on 3 different continents. The widespread spatial occurrence of several surfactants (e.g., polyethylene glycols ( PEGs ) and C12AEO-PEGs ), transformation products of selected drugs (e.g., gabapentin-lactam, metoprolol-acid, carbamazepine-10-hydroxy, omeprazole-4-hydroxy-sulfide, and 2-benzothiazole-sulfonic-acid), and industrial chemicals (3-nitrobenzenesulfonate and bisphenol-S) was revealed. Obtaining identifications of increased reliability through retrospective suspect screening is challenging, and recommendations for dealing with issues such as broad chromatographic peaks, data acquisition, and sensitivity are provided.

Wastewater Analysis for Community-Wide Drugs Use Assessment.

Wastewater-based epidemiology (WBE) complements existing epidemiology-based estimation techniques and provides objective, evidence-based estimates of illicit drug use. After consumption, biomarkers - drugs and their metabolites - excreted to toilets and flushed into urban sewer networks can be measured in raw wastewater samples. The quantified loads can serve as an estimate for the collective consumption of all people contributing to the wastewater sample. This transdisciplinary approach, further explained in this chapter, has developed, matured and is now established for monitoring substances such as cocaine and amphetamine-type stimulants. Research currently underway is refining WBE to new applications including new psychoactive substances (NPS).

Statistical Variable Selection: An Alternative Prioritization Strategy during the Nontarget Analysis of LC-HR-MS Data.

Liquid chromatography coupled to high resolution mass spectrometry (LC-HR-MS) has been one of the main analytical tools for the analysis of small polar organic pollutants in the environment. LC-HR-MS typically produces a large amount of data for a single chromatogram. The analyst is therefore required to perform prioritization prior to nontarget structural elucidation. In the present study, we have combined the F-ratio statistical variable selection and the apex detection algorithms in order to perform prioritization in data sets produced via LC-HR-MS. The approach was validated through the use of semisynthetic data, which was a combination of real environmental data and the artificially added signal of 31 alkanes in that sample. We evaluated the performance of this method as a function of four false detection probabilities, namely: 0.01, 0.02, 0.05, and 0.1%. We generated 100 different semisynthetic data sets for each F-ratio and evaluated that data set using this method. This design of experiment created a population of 30 000 true positives and 32 000 true negatives for each F-ratio, which was considered sufficiently large enough in order to fully validate this method for analysis of LC-HR-MS data. The effect of both the F-ratio and signal-to-noise ratio (S/N) on the performance of the suggested approach were evaluated through normalized statistical tests. We also compared this method to the pixel-by-pixel as well as peak list approaches. More than 92% of features present in the final feature list via the F-ratio method were also present in the conventional peak list generated by MZmine. However, this method was the only approach successful in the classification of samples, and thus prioritization, when compared to the other evaluated approaches. The application potential and limitations of the suggested method are discussed.

Use of Mobile Device Data To Better Estimate Dynamic Population Size for Wastewater-Based Epidemiology.

Wastewater-based epidemiology is an established approach for quantifying community drug use and has recently been applied to estimate population exposure to contaminants such as pesticides and phthalate plasticizers. A major source of uncertainty in the population weighted biomarker loads generated is related to estimating the number of people present in a sewer catchment at the time of sample collection. Here, the population quantified from mobile device-based population activity patterns was used to provide dynamic population normalized loads of illicit drugs and pharmaceuticals during a known period of high net fluctuation in the catchment population. Mobile device-based population activity patterns have for the first time quantified the high degree of intraday, week, and month variability within a specific sewer catchment. Dynamic population normalization showed that per capita pharmaceutical use remained unchanged during the period when static normalization would have indicated an average reduction of up to 31%. Per capita illicit drug use increased significantly during the monitoring period, an observation that was only possible to measure using dynamic population normalization. The study quantitatively confirms previous assessments that population estimates can account for uncertainties of up to 55% in static normalized data. Mobile device-based population activity patterns allow for dynamic normalization that yields much improved temporal and spatial trend analysis.

The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS).

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(-1) and 0.0437 ± 0.02 L d(-1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.

Comparison of pharmaceutical, illicit drug, alcohol, nicotine and caffeine levels in wastewater with sale, seizure and consumption data for 8 European cities.

BACKGROUND: Monitoring the scale of pharmaceuticals, illicit and licit drugs consumption is important to assess the needs of law enforcement and public health, and provides more information about the different trends within different countries. Community drug use patterns are usually described by national surveys, sales and seizure data. Wastewater-based epidemiology (WBE) has been shown to be a reliable approach complementing such surveys. METHOD: This study aims to compare and correlate the consumption estimates of pharmaceuticals, illicit drugs, alcohol, nicotine and caffeine from wastewater analysis and other sources of information. Wastewater samples were collected in 2015 from 8 different European cities over a one week period, representing a population of approximately 5 million people. Published pharmaceutical sale, illicit drug seizure and alcohol, tobacco and caffeine use data were used for the comparison. RESULTS: High agreement was found between wastewater and other data sources for pharmaceuticals and cocaine, whereas amphetamines, alcohol and caffeine showed a moderate correlation. methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA) and nicotine did not correlate with other sources of data. Most of the poor correlations were explained as part of the uncertainties related with the use estimates and were improved with other complementary sources of data. CONCLUSIONS: This work confirms the promising future of WBE as a complementary approach to obtain a more accurate picture of substance use situation within different communities. Our findings suggest further improvements to reduce the uncertainties associated with both sources of information in order to make the data more comparable.

Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis.

In this article, a dataset from a collaborative non-target screening trial organised by the NORMAN Association is used to review the state-of-the-art and discuss future perspectives of non-target screening using high-resolution mass spectrometry in water analysis. A total of 18 institutes from 12 European countries analysed an extract of the same water sample collected from the River Danube with either one or both of liquid and gas chromatography coupled with mass spectrometry detection. This article focuses mainly on the use of high resolution screening techniques with target, suspect, and non-target workflows to identify substances in environmental samples. Specific examples are given to emphasise major challenges including isobaric and co-eluting substances, dependence on target and suspect lists, formula assignment, the use of retention information, and the confidence of identification. Approaches and methods applicable to unit resolution data are also discussed. Although most substances were identified using high resolution data with target and suspect-screening approaches, some participants proposed tentative non-target identifications. This comprehensive dataset revealed that non-target analytical techniques are already substantially harmonised between the participants, but the data processing remains time-consuming. Although the objective of a "fully-automated identification workflow" remains elusive in the short term, important steps in this direction have been taken, exemplified by the growing popularity of suspect screening approaches. Major recommendations to improve non-target screening include better integration and connection of desired features into software packages, the exchange of target and suspect lists, and the contribution of more spectra from standard substances into (openly accessible) databases. Graphical Abstract Matrix of identification approach versus identification confidence.

The use of lithium as a marker for the retention of liquids in the oral cavity after rinsing.

OBJECTIVE: The aim of this study was to validate the use of lithium as a marker to indicate the retention of simple liquids in the oral cavity and use this to determine how much liquid is retained in the oral cavity following 30 s of rinsing. MATERIALS AND METHODS: This is a validation study in which saliva was spiked with known concentrations of lithium. Twenty healthy participants then rinsed their mouths with either water or a 1 % w/v carboxymethylcellulose (CMC) solution for 30 s before expectorating into a collection cup. Total volume and concentration of lithium in the expectorant were then measured, and the percentage of liquid retained was calculated. RESULTS: The mean amount of liquid retained was 10.4 ± 4.7 % following rinsing with water and 15.3 ± 4.1 % following rinsing with 1 % w/v CMC solution. This difference was significant (p < 0.01). CONCLUSIONS: Lithium was useful as a marker for the retention of liquids in the oral cavity, and a value for the amount of water and 1 % w/v CMC solution remaining in the oral cavity following a 30-s rinse was established. CLINICAL RELEVANCE: The present study quantifies the retention of simple fluids in the oral cavity, validating a technique that may be applied to more complex fluids such as mouth rinses. Further, the application of this method to specific population groups such as those with severe xerostomia may assist in developing effective saliva substitutes.

Quantity and Size of Titanium Particles Released from Different Mechanical Decontamination Procedures on Titanium Discs: An In Vitro Study.

Complications such as peri-implantitis could ultimately affect the survival of a dental implant. The prevention and treatment of peri-implant diseases require managing bacterial biofilm and controlling environmental risks, including the presence of pro-inflammatory titanium (Ti) particles in the peri-implant niche. Objectives included the evaluation of the size and quantity of Ti particles released from moderately roughened Ti surfaces during common mechanical surface decontamination methods. One hundred and forty moderately roughened Ti discs were divided into seven groups (n = 20 per group); six groups received mechanical decontamination procedures (ultrasonic scaling (US) with a metal tip and poly-ether-ketone (PEEK) under low and medium power settings, air-polishing with erythritol powder, and Ti brush), and the control group underwent air-water spray using a dental triplex. The rinsing solution was collected for Ti mass analysis using inductively coupled plasma mass spectrometry (ICPMS), as well as for Ti particle size and count analysis under scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS). US metal tip instrumentation generated 34.00 ± 12.54 µg and 34.44 ± 6.08 µg of Ti under low and medium power settings, respectively. This amount of Ti generation was significantly higher than other instrumentation methods. The mean Ti particle size of the US groups ranged from 0.89 ± 0.27 µm to 1.25 ± 0.24 µm. No statistically significant difference was found in the particle size among US groups and Ti brush group (1.05 ± 0.11 µm), except for US with the PEEK tip, where a significantly smaller mean particle diameter was found at the low power setting (0.89 ± 0.27 µm). Mechanical instrumentation can produce Ti particulates and modify the implant surfaces. US using a metal tip generated the highest amount of Ti with smaller Ti size particles compared to all other commonly used mechanical surface instrumentations. The EDS analysis confirmed Ti in PEEK US tips. It can be suggested that deterioration from the PEEK US tip and Ti brush, as observed under SEM, is an additional source of Ti release during Ti surface decontamination.

Extracting the truth through chemical analyses: Early life histories of Victorian-era dental patients in Aotearoa New Zealand.

OBJECTIVES: There are few bioarcheological analyses of life experiences in colonial period Aotearoa New Zealand, despite this being a time of major adaptation and social change. In our study, early life histories are constructed from multi-isotope and enamel peptide analysis of permanent first molars associated with Victorian era dental practices operating between AD 1881 and 1905 in Invercargill. Chemical analyses of the teeth provide insight into the childhood feeding practices, diet, and mobility of the people who had their teeth extracted. MATERIALS AND METHODS: Four permanent left mandibular first molars were analyzed from a cache of teeth discovered at the Leviathan Gift Depot site during excavations in 2019. The methods used were: (1) enamel peptide analysis to assess chromosomal sex; (2) bulk (δ13 Ccarbonate ) and incremental (δ13 Ccollagen and δ15 N) isotope analysis of dentin to assess childhood diet; and (3) strontium (87 Sr/86 Sr) and oxygen (δ18 O) isotope analysis of enamel to assess childhood residency. Two modern permanent first molars from known individuals were analyzed as controls. RESULTS: The archaeological teeth were from three chromosomal males and one female. The protein and whole diets were predominately based on C3 -plants and domestic animal products (meat and milk). A breastfeeding signal was only identified in one historic male. All individuals likely had childhood residences in Aotearoa. DISCUSSION: Unlike most bioarcheological studies that rely on the remains of the dead, the teeth analysed in this study were extracted from living people. We suggest that the dental patients were likely second or third generation colonists to Aotearoa, with fairly similar childhood diets. They were potentially lower-class individuals either living in, or passing through, the growing colonial center of Invercargill.

Earliest known funerary rites in Wallacea after the last glacial maximum.

The insular region of Wallacea has become a focal point for studying Pleistocene human ecological and cultural adaptations in island environments, however, little is understood about early burial traditions during the Pleistocene. Here we investigate maritime interactions and burial practices at Ratu Mali 2, an elevated coastal cave site on the small island of Kisar in the Lesser Sunda Islands of eastern Indonesia dated to 15,500-3700 cal. BP. This multidisciplinary study demonstrates extreme marine dietary adaptations, engagement with an extensive exchange network across open seas, and early mortuary practices. A flexed male and a female, interred in a single grave with abundant shellfish and obsidian at Ratu Mali 2 by 14.7 ka are the oldest known human burials in Wallacea with established funerary rites. These findings highlight the impressive flexibility of our species in marginal environments and provide insight into the earliest known ritualised treatment of the dead in Wallacea.