A missense SNP in the tumor suppressor SETD2 reduces H3K36me3 and mitotic spindle integrity in Drosophila.

Mutations in SETD2 are among the most prevalent drivers of renal cell carcinoma (RCC). We identified a novel single nucleotide polymorphism (SNP) in SETD2, E902Q, within a subset of RCC patients, which manifests as both an inherited or tumor-associated somatic mutation. To determine if the SNP is biologically functional, we used CRISPR-based genome editing to generate the orthologous mutation within the Drosophila melanogaster Set2 gene. In Drosophila, the homologous amino acid substitution, E741Q, reduces H3K36me3 levels comparable to Set2 knockdown, and this loss is rescued by reintroduction of a wild-type Set2 transgene. We similarly uncovered significant defects in spindle morphogenesis, consistent with the established role of SETD2 in methylating α-Tubulin during mitosis to regulate microtubule dynamics and maintain genome stability. These data indicate the Set2 E741Q SNP affects both histone methylation and spindle integrity. Moreover, this work further suggests the SETD2 E902Q SNP may hold clinical relevance.

Automated Image Analysis for Detection of Coccidia in Poultry.

Coccidiosis, caused by the protozoan Eimeria sp., is one of the most common and costly diseases impacting the poultry industry. To establish effective control measures, it is essential to identify these protozoa. Typical methods for identifying and determining the severity of the protozoal infection include intestinal lesion scoring or enumeration of the protozoal oocysts in fecal samples. Standard analysis methods require highly skilled technicians or veterinarians to manually identify and manually enumerate these protozoal parasites. This process is labor intensive, time-consuming, and susceptible to human error. None of the current methods available, including molecular flow cytometry or even digital image analysis, can determine if an oocyst is sporulated or not. Oocysts are not infectious until they sporulate. The goal of this study was to design an automated model using Artificial Intelligence (AI) to expedite the process of enumeration, improve the efficiency and accuracy of the species identification, and determine the ability of the oocysts to infect. To this end, we trained and evaluated computer vision models based on the Mask RCNN neural network architecture. A model was trained to detect and differentiate three species and to determine sporulation for each (totaling six detection groups). This model achieved a mean relative percentage difference (RPD) of 5.64%, representing a slight overcount compared to manual counts, averaging across all groups. The mean RPD for each group individually fell within a range from -33.37% to 52.72%. These results demonstrate that these models were speedy and had high agreement with manual counts, with minimal processing of field-quality samples. These models also could differentiate the sporulation status of the oocysts, providing critical diagnostic information for potential field applications.

Understanding and tackling the reproducibility crisis - Why we need to study scientists' trust in data.

In the life sciences, there is an ongoing discussion about a perceived 'reproducibility crisis'. However, it remains unclear to which extent the perceived lack of reproducibility is the consequence of issues that can be tackled and to which extent it may be the consequence of unrealistic expectations of the technical level of reproducibility. Large-scale, multi-institutional experimental replication studies are very cost- and time-intensive. This Perspective suggests an alternative, complementary approach: meta-research using sociological and philosophical methodologies to examine researcher trust in data. An improved understanding of the criteria used by researchers to judge data reliability will provide crucial, initial evidence on the actual scale of the reproducibility crisis and on measures to tackle it.

Efficacy of social cognition and interaction training in outpatients with schizophrenia spectrum disorders: randomized controlled trial.

Given the relationship between social cognition and functional outcome in schizophrenia, a number of social cognitive interventions have been developed, including Social Cognition Interaction Training (SCIT), a group-based, comprehensive, manualized intervention. In the current trial, we examined SCIT efficacy as well as potential moderators of treatment effects. Fifty-one outpatients were randomized to SCIT or a wait-list-control (WLC), with assessments of social cognition, neurocognition, self-report, symptoms, and functioning conducted at baseline and end of the active phase. Relative to WLC, we did not find significant improvements for SCIT on neurocognition, social cognition, self-report, or symptoms, though there was a trend-level, medium effect favoring the SCIT condition on interpersonal and instrumental role function. Post-hoc analyses indicated that baseline neurocognition did not impact degree of social cognitive or functional change. Shorter duration of illness was significantly associated with better post-training neurocognition and self-esteem and, at trend-level with better symptoms and social functioning. We discuss the importance of outcome measure selection and the need for continued evaluation of potential treatment moderators in order to better match people to existing treatments. Clinical trial registration: Clinicaltrials.gov, Identifier NCT00587561.

A randomized controlled trial of social cognition and interaction training for persons with first episode psychosis in Hong Kong.

Social cognitive impairment is a core limiting factor of functional recovery among persons with first episode psychosis (FEP). Social Cognition and Interaction Training (SCIT) is a group-based, manualized training with demonstrated evidence in improving social cognitive performance among people with schizophrenia. However, there are few studies on the effect of SCIT for people with FEP and for people in non-Western societies. This study evaluated the feasibility, acceptability and initial effectiveness of the locally-adapted SCIT in improving social cognitive functioning in Chinese people with FEP. The SCIT was delivered two sessions per week over a 10-weeks period, each session lasted for 60-90 min. A total of 72 subjects with FEP were recruited from an outpatient clinic and randomized to conventional rehabilitation ("Rehab") and experimental ("SCIT and Rehab") groups. Primary outcome measures included four social cognitive domains including emotion perception, theory-of-mind, attributional bias and jumping-to-conclusion, and secondary measures included neurocognition, social competence and quality of life. Participants were assessed at baseline, post-treatment, and 3-months post-treatment. Repeated measures ANCOVAs, with baseline scores as covariates, were used to compare the group differences in various outcomes across time. The results showed that the SCIT was well-accepted, with a satisfactory completion rate and subjective ratings of relevance in the experimental group. Moreover, treatment completers (n = 28) showed evidence of an advantage, over conventional group (n = 31), in reduced attributional bias and jumping-to-conclusions at treatment completion, lending initial support for the SCIT in Chinese people with FEP. Future research should address the limitations of this study, using more refined outcome measurements and higher treatment intensity of the SCIT.

Understanding the drivers of expert opinion when classifying species as extinct.

The criteria as laid out by the International Union for the Conservation of Nature (IUCN) Red List are the gold standard by which the extinction risk of a species is assessed and where appropriate biological extinctions are declared. However, unlike all other categories, the category of extinct lacks a quantitative framework for assigning this category. Given its subjective nature, we surveyed expert assessors working on a diversity of taxa to explore the attributes they used to declare a species extinct. Using a choice experiment approach, we surveyed 674 experts from the IUCN Species Survival Commission specialist groups and taskforces. Data availability, time from the last sighting, detectability, habitat availability, and population decline were all important attributes favored by assessors when inferring extinction. Respondents with red-listing experience assigned more importance to the attributes data availability, time from the last sighting, and detectability when considering a species extinction, whereas those respondents working with well-known taxa gave more importance to the time from the last sighting. Respondents with no red-listing experience and those working with more well-known taxa (i.e., mammals and birds) were overall less likely to consider species extinct. Our findings on the importance assessors place on attributes used to declare a species extinct provide a basis for informing the development of specific criteria for more accurately assessing species extinctions.

Los criterios establecidos por la Unión Internacional para la Conservación de la Naturaleza (UICN) son la regla de oro con la cual se evalúa el riesgo de extinción de una especie y en donde se declaran las extinciones biológicas. Sin embargo, como con todas las demás categorías, la categoría "extinto" carece de un marco de trabajo cuantitativo para asignar esta categoría. Dada su naturaleza subjetiva, pedimos a los asesores expertos que trabajan con la diversidad de taxones que exploraran los atributos que usan para declarar extinta a una especie. Mediante un experimento de elección, sondeamos a 674 expertos de los grupos especialistas y de trabajo de la Comisión de Supervivencia de Especies de la UICN. La disponibilidad de datos, el tiempo desde la última detección, la detectabilidad, la disponibilidad del hábitat y la declinación poblacional fueron atributos importantes que los asesores favorecieron al inferir las extinciones. Los respondientes con experiencia con la lista roja les asignaron mayor importancia a los atributos de disponibilidad de datos, tiempo desde la última detección y detectabilidad cuando consideraron la extinción de una especie, mientras que los respondientes que trabajan con taxones conocidos le dieron más importancia al tiempo desde la última detección. En general fue menos probable que los respondientes sin experiencia con la lista roja y aquellos que trabajan con los taxones más conocidos (es decir, mamíferos y aves) consideraran extinta a una especie. Nuestros descubrimientos sobre la importancia que los asesores colocan sobre los atributos utilizados para declarar extinta a una especie proporcionan una base para orientar el desarrollo de criterios específicos para evaluar de manera más acertada las extinciones de las especies.

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs.

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

Hormetic association between perceived stress and human epigenetic aging based on resilience capacity.

Chronic stress is associated with deleterious health outcomes and mortality risk. A potential mechanism by which stress affects healthspan and lifespan is acceleration of cellular aging. Biologic age prediction models, termed epigenetic clocks, have been developed to estimate biologic age differences among people with the same chronologic age. This study evaluates the simultaneous impact of perceived chronic stress and resilience on Grim Age acceleration. The perceived stress score (PSS) and Connor-Davidson Resilience Scale (CD-RISC) were used to measure chronic stress and resilience, respectively. DNA was extracted from whole blood and analyzed using the MethylationEPIC BeadChip. GrimAge estimates were calculated using the methylation age calculator. Forty-seven business executives were categorized by levels of high or low stress and resilience scores. Compared to participants with low stress and high resilience, those with low stress and low resilience demonstrated the strongest association with Grim Age acceleration (p = 0.044), after controlling for age and estimated cellular proportions. Interestingly, among participants with low resilience, those with high perceived stress had a weaker association with Grim Age acceleration than participants with low perceived stress. However, among participants with high resilience, low perceived stress had a weaker association with Grim Age acceleration than high perceived stress. Our findings suggest that the impact of perceived stress on epigenetic age acceleration may differ based on resilience capacity, with a potential paradoxical beneficial effect among those with low resilience.

Societal extinction of species.

The ongoing global biodiversity crisis not only involves biological extinctions, but also the loss of experience and the gradual fading of cultural knowledge and collective memory of species. We refer to this phenomenon as 'societal extinction of species' and apply it to both extinct and extant taxa. We describe the underlying concepts as well as the mechanisms and factors that affect this process, discuss its main implications, and identify mitigation measures. Societal extinction is cognitively intractable, but it is tied to biological extinction and thus has important consequences for conservation policy and management. It affects societal perceptions of the severity of anthropogenic impacts and of true extinction rates, erodes societal support for conservation efforts, and causes the loss of cultural heritage.

Hair cortisol, perceived stress, and resilience as predictors of coronary arterial disease.

The widespread prevalence of cardiovascular disease underscores the continuing need for identifying modifiable risk factors and novel targets for therapeutic intervention. Hair cortisol concentration (HCC) is a promising biomarker for evaluating the contribution of chronic stress to the pathogenesis and prognosis of coronary arterial disease (CAD). In this cross-sectional study of 24 participants, we assessed the risk of CAD associated with HCC and with perceived chronic stress (Perceived Stress Score), controlling for the established risk factors of age, diabetes, hypertension, dyslipidemia, and obesity. In fully adjusted Poisson regression models, we additionally evaluated CAD risk with the simultaneous inclusion of psychological and physiologic resilience measures (CD-RISC, DHEA-S). Our results show that HCC, but not PSS, is significantly associated with CAD (incident rate ratio 0.99, confidence interval 0.98-1.00, p = 0.01), but the magnitude of the association is weak and inverse, and less than with dyslipidemia and age. The association remained significant after inclusion of the sum of resilience measures via a combined resiliency score. Resilience was not independently significantly associated with CAD. Our findings indicate the contribution of HCC to CAD risk is small in an average-risk population and remains after adjustment for multisystem resilience.

Suicidal Ideation Across Three Timepoints in Patients Discharged from Psychiatric Hospitalization.

This article describes the rate of suicidal ideation (SI) across three timepoints among treatment-seeking patients recently discharged from psychiatric hospitalization-a group that is at ultrahigh-risk for suicide. Retrospective chart review was used to quantify the rate of SI in 252 consecutive patients discharged to a post-hospital mental health clinic. Data include patients' lifetime history of SI, SI at the time of hospital intake, and SI at post-hospital outpatient clinic intake, as well as demographics and diagnosis. Overall, 67% of the sample reported a lifetime history of SI, 49% reported SI during hospital intake, and 6% reported SI at post-hospital clinic intake. Age was the only variable associated with history of SI (p = .04), with younger patients more likely (OR = 1.85) to report a history of SI. These results may help inform the development of interventions for the population of ultrahigh-risk patients being discharged from hospital after psychiatric care.

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage.

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

Correction: Biogeographical patterns and speciation of the genus Pinguicula (Lentibulariaceae) inferred by phylogenetic analyses.

[This corrects the article DOI: 10.1371/journal.pone.0252581.].

Dating first cases of COVID-19.

Questions persist as to the origin of the COVID-19 pandemic. Evidence is building that its origin as a zoonotic spillover occurred prior to the officially accepted timing of early December, 2019. Here we provide novel methods to date the origin of COVID-19 cases. We show that six countries had exceptionally early cases, unlikely to represent part of their main case series. The model suggests a likely timing of the first case of COVID-19 in China as November 17 (95% CI October 4). Origination dates are discussed for the first five countries outside China and each continent. Results infer that SARS-CoV-2 emerged in China in early October to mid-November, and by January, had spread globally. This suggests an earlier and more rapid timeline of spread. Our study provides new approaches for estimating dates of the arrival of infectious diseases based on small samples that can be applied to many epidemiological situations.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Biogeographical patterns and speciation of the genus Pinguicula (Lentibulariaceae) inferred by phylogenetic analyses.

Earlier phylogenetic studies in the genus Pinguicua (Lentibulariaceae) suggested that the species within a geographical region was rather monophyletic, although the sampling was limited or was restricted to specific regions. Those results conflicted with the floral morphology-based classification, which has been widely accepted to date. In the current study, one nuclear ribosomal DNA (internal transcribed spacer; ITS) and two regions of chloroplast DNA (matK and rpl32-trnL), from up to ca. 80% of the taxa in the genus Pinguicula, covering all three subgenera, were sequenced to demonstrate the inconsistency and explore a possible evolutionary history of the genus. Some incongruence was observed between nuclear and chloroplast topologies and the results from each of the three DNA analyses conflicted with the morphology-based subgeneric divisions. Both the ITS tree and network, however, corresponded with the biogeographical patterns of the genus supported by life-forms (winter rosette or hibernaculum formation) and basic chromosome numbers (haploidy). The dormant strategy evolved in a specific geographical region is a phylogenetic constraint and a synapomorphic characteristic within a lineage. Therefore, the results denied the idea that the Mexican group, morphologically divided into the three subgenera, independently acquired winter rosette formations. Topological incongruence among the trees or reticulations, indicated by parallel edges in phylogenetic networks, implied that some taxa originated by introgressive hybridisation. Although there are exceptions, species within the same geographical region arose from a common ancestor. Therefore, the classification by the floral characteristics is rather unreliable. The results obtained from this study suggest that evolution within the genus Pinguicula has involved; 1) ancient expansions to geographical regions with gene flow and subsequent vicariance with genetic drift, 2) acquirement of a common dormant strategy within a specific lineage to adapt a local climate (i.e., synapomorphic characteristic), 3) recent speciation in a short time span linked to introgressive hybridisation or multiplying the ploidy level (i.e., divergence), and 4) parallel evolution in floral traits among lineages found in different geographical regions (i.e., convergence). As such, the floral morphology masks and obscures the phylogenetic relationships among species in the genus.

Association Between Business Travel, Health-Related Behaviors, and Adiposity.

OBJECTIVE: To evaluate the associations between frequency of business travel and health behaviors and adiposity. METHODS: Retrospective cross-sectional analysis of de-identified electronic medical records from 795 corporate physical exams. RESULTS: Business travel frequency demonstrates a curvilinear relationship with body mass index and body composition in men and women, with domestic and international travel. Linear and quadratic term beta coefficients indicate stronger associations between the sum of domestic and international travel and BMI, body fat percentage, and visceral adipose tissue in women than men, after accounting for age, exercise, and sleep. Based on our male sample population, international travel frequency has a greater influence on adiposity than summed (mostly domestic) travel. CONCLUSIONS: Frequent business travel adversely affects body composition, with differences by gender and type of travel.

Ethics and governance for internet-based conservation science research.

Internet-based research is increasingly important for conservation science and has wide-ranging applications and contexts, including culturomics, illegal wildlife trade, and citizen science. However, online research methods pose a range of ethical and legal challenges. Online data may be protected by copyright, database rights, or contract law. Privacy rights may also restrict the use and access of data, as well as ethical requirements from institutions. Online data have real-world meaning, and the ethical treatment of individuals and communities must not be marginalized when conducting internet-based research. As ethics frameworks originally developed for biomedical applications are inadequate for these methods, we propose that research activities involving the analysis of preexisting online data be treated analogous to offline social science methods, in particular, nondeceptive covert observation. By treating internet users and their data with respect and due consideration, conservationists can uphold the public trust needed to effectively address real-world issues.

Ética y Gestión para la Investigación Científica de la Conservación Basada en Internet Resumen La investigación basada en internet es cada vez más importante para las ciencias de la conservación, además de tener contextos y aplicaciones de gran alcance como el análisis de textos, el mercado ilegal de fauna y la ciencia ciudadana. Sin embargo, los métodos de investigación en línea representan una gama de retos éticos y legales pues los datos virtuales pueden estar protegidos por derechos de autor, derechos de base de datos o leyes contractuales. Además, los derechos de privacidad pueden restringir el uso y el acceso a los datos, así como también los requerimientos éticos impuestos por las instituciones. Los datos virtuales tienen valor en el mundo real y el tratamiento ético de los individuos y de las comunidades no se debe marginalizar cuando se realiza una investigación por internet. Ya que los marcos éticos desarrollados originalmente para aplicarse en temas biomédicos son inadecuados para estos métodos, proponemos que las actividades de investigación que involucran el análisis de los datos virtuales preexistentes sean tratadas como análogas a los métodos no virtuales de las ciencias sociales, especialmente la observación encubierta no engañosa. Si se trata a los usuarios del internet y a sus datos con respeto y la consideración debida, los conservacionistas pueden mantener la confianza pública necesaria para tratar efectivamente los asuntos del mundo real.

Development of a Dissemination Platform for Spatiotemporal and Phylogenetic Analysis of Avian Infectious Bronchitis Virus.

Infecting large portions of the global poultry populations, the avian infectious bronchitis virus (IBV) remains a major economic burden in North America. With more than 30 serotypes globally distributed, Arkansas, Connecticut, Delaware, Georgia, and Massachusetts are among the most predominant serotypes in the United States. Even though vaccination is widely used, the high mutation rate exhibited by IBV is continuously triggering the emergence of new viral strains and hindering control and prevention measures. For that reason, targeted strategies based on constantly updated information on the IBV circulation are necessary. Here, we sampled IBV-infected farms from one US state and collected and analyzed 65 genetic sequences coming from three different lineages along with the immunization information of each sampled farm. Phylodynamic analyses showed that IBV dispersal velocity was 12.3 km/year. The majority of IBV infections appeared to have derived from the introduction of the Arkansas DPI serotype, and the Arkansas DPI and Georgia 13 were the predominant serotypes. When analyzed against IBV sequences collected across the United States and deposited in the GenBank database, the most likely viral origin of our sequences was from the states of Alabama, Georgia, and Delaware. Information about vaccination showed that the MILDVAC-MASS+ARK vaccine was applied on 26% of the farms. Using a publicly accessible open-source tool for real-time interactive tracking of pathogen spread and evolution, we analyzed the spatiotemporal spread of IBV and developed an online reporting dashboard. Overall, our work demonstrates how the combination of genetic and spatial information could be used to track the spread and evolution of poultry diseases, providing timely information to the industry. Our results could allow producers and veterinarians to monitor in near-real time the current IBV strain circulating, making it more informative, for example, in vaccination-related decisions.

Statistical inference of earlier origins for the first flaked stone technologies.

Identifying when hominins first produced Lomekwian, Oldowan, and Acheulean technologies is vital to multiple avenues of human origins research. Yet, like most archaeological endeavors, our understanding is currently only as accurate as the artifacts recovered and the sites identified. Here we use optimal linear estimation (OLE) modelling to identify the portion of the archaeological record not yet discovered, and statistically infer the date of origin of the earliest flaked stone technologies. These models provide the most accurate framework yet for understanding when hominins first produced these tool types. Our results estimate the Oldowan to have originated 2.617 to 2.644 Ma, 36,000 to 63,000 years earlier than current evidence. The Acheulean's origin is pushed back further through OLE, by at least 55,000 years to 1.815 to 1.823 Ma. We were unable to infer the Lomekwian's date of origin using OLE, but an upper bound of 5.1 million years can be inferred using alternative nonparametric techniques. These dates provide a new chronological foundation from which to understand the emergence of the first flaked stone technologies, alongside their behavioral and evolutionary implications. Moreover, they suggest there to be substantial portions of the artifact record yet to be discovered.