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1. Using a multimethod life history approach to navigate the osteological paradox: A case study from Prehispanic Nasca, Peru

   https://doi.org/10.1002/ajpa.24279  

   McCool, Weston C. ; Anderson, Amy S. ; Kennett, Douglas J. ( March 2021 , American Journal of Physical Anthropology)

   We leverage recent bioarchaeological approaches and life history theory to address the implications of the osteological paradox in a study population. The goal of this article is to evaluate morbidity and mortality patterns as well as variability in the risk of disease and death during the Late Intermediate period (LIP; 950–1450 C.E.) in the Nasca highlands of Peru. We demonstrate how the concurrent use of multiple analytical techniques and life history theory can engage the osteological paradox and provide salient insights into the study of stress, frailty, and resilience in past populations.

   Crania from LIP burial contexts in the Nasca highlands were examined for cribra orbitalia (n = 325) and porotic hyperostosis (n = 270). All age groups and both sexes are represented in the sample. Survivor/nonsurvivor analysis assessed demographic differences in lesion frequency and severity. Hazard models were generated to assess differences in survivorship. The relationship between dietary diversity and heterogeneity in morbidity was assessed using stable δ¹⁵N and δ¹³C isotope values for bone collagen and carbonate. One hundred and twenty‐four crania were directly AMS radiocarbon dated, allowing for a diachronic analysis of morbidity and mortality.

   The frequency and expression of both orbital and vault lesions increases significantly during the LIP. Survivor/nonsurvivor analysis indicates cranial lesions co‐vary with frailty rather than robusticity or longevity. Hazard models show (1) decreasing survivorship with the transition into the LIP, (2) significantly lower adult life expectancy for females compared to males, and (3) individuals with cranial lesions have lower survivorship across the life course. Stable isotope results show very little dietary diversity. Mortality risk and frequency of pathological skeletal lesions were highest during Phase III (1300–1450 C.E.) of the LIP.

   Results provide compelling evidence of increasing physiological stress and mortality in the Nasca highlands during the LIP, but also reveal substantial heterogeneity in frailty and the risk of death. Certain members of society experienced a heavier disease burden and higher mortality compared to their contemporaries. Elevated levels of disease and lethal trauma among females account for some of the sex differences in survivorship but cannot explain the large degree of female‐biased mortality. We hypothesize that parental investment in males or increased female fertility rates may explain these differences.

    

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2. Survivorship and the second epidemiological transition in industrial‐era London

   https://doi.org/10.1002/ajpa.24797  

   Yaussy, Samantha L. ; DeWitte, Sharon N. ; Hughes‐Morey, Gail ( June 2023 , American Journal of Biological Anthropology)

   The second epidemiological transition describes a shift in predominant causes of death from infectious to degenerative (non‐communicable) diseases associated with the demographic transition from high to low levels of mortality and fertility. In England, the epidemiological transition followed the Industrial Revolution, but there is little reliable historical data on cause of death beforehand. Because of the association between the demographic and epidemiological transitions, skeletal data can potentially be used to examine demographic trends as a proxy for the latter. This study uses skeletal data to examine differences in survivorship in London, England in the decades preceding and following initial industrialization and the second epidemiological transition.

   We use data (fromn = 924 adults) from London cemeteries (New Churchyard, New Bunhill Fields, St. Bride's Lower Churchyard, and St. Bride's Church Fleet Street) in use prior to and during industrialization (c.1569–1853 CE). We assess associations between estimated adult age at death and time period (pre‐industrial vs. industrial) using Kaplan–Meier survival analysis.

   We find evidence of significantly lower adult survivorship prior to industrialization (c.1569–1669 and 1670–1739 CE) compared to the industrial period (c.1740–1853 CE) (p < 0.001).

   Our results are consistent with historical evidence that, in London, survivorship was improving in the later 18th century, prior to the recognized beginning of the second epidemiological transition. These findings support the use of skeletal demographic data to examine the context of the second epidemiological transition in past populations.

    

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3. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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4. Comparing demography inferred from age vs. stage in a perennial plant

   https://doi.org/10.1002/ecy.3322  

   Alahuhta, Kirsi M. ; Crone, Elizabeth E. ; Lesica, Peter ( May 2021 , Ecology)

   Life history theories analyze and predict variation in vital rates, such as survival and reproduction, based on age. The age‐from‐stage method to derive age‐specific vital rates from stage data was developed because age‐specific data are rarely obtained for plants. Age‐specific vital rates derived by this method might underestimate effects of age on vital rates, because the models assume that vital rates do not vary within stage classes. Consequently, population models and life history summaries relying on these vital rates could be biased against detecting senescence. Here, we perform a comparative study of methods to estimate age‐specific vital rates using monitoring data with known age and stage. We derived age‐, stage‐, and age‐and‐stage‐specific vital rates with demographic data from a long‐lived perennial,Silene spaldingii. Then, we derived three age‐specific population matrix models (age, age‐from‐stage, and age‐and‐stage). For each model, we derived life history summaries commonly used in ecology: population growth rate, net reproductive value, relative reproductive values, stable age distribution, generation time, and sensitivity and elasticity of population growth rate. Many vital rates depended on both age and stage inS. spaldingii. However, this species does not senesce; in fact, the number of flowers increased with age. As expected, the age‐from‐stage method was not able to accurately recreate the age dependence in some life history summaries, such as relative reproductive value. The age‐from‐stage model suggested faster reproductive dynamics inS. spaldingiithan the models based on known age, i.e., plants started to reproduce earlier, and fertility remained constant thereafter, which may lead to biased predictions about evolutionary consequences of age‐dependent life history traits. However, population growth rate, generation time, and net reproductive rate did not differ significantly among the models. Our study demonstrated that some metrics are robust to imprecision in model structure, while others are more sensitive. In spite of these biases, this case study provides another example of the diversity of aging patterns in plants. Age can be essential information when studying senescence in plants, but demographic metrics that were not about age per se were similar across model structures.

    

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5. Increased clonal growth in heavily harvested ecosystems failed to rescue ayahuasca lianas from decline in the Peruvian Amazon rainforest

   https://doi.org/10.1111/1365-2664.14488  

   Coe, Michael A. ; Gaoue, Orou G. ( August 2023 , Journal of Applied Ecology)

   Increasing harvest and overexploitation of wild plants for non‐timber forest products can significantly affect population dynamics of harvested populations. While the most common approach to assess the effect of harvest and perturbation of vital rates is focused on the long‐term population growth rate, most management strategies are planned and implemented over the short‐term.

   We developed an integral projection model to investigate the effects of harvest on the demography and the short‐ and long‐term population dynamics ofBanisteriopsis caapiin the Peruvian Amazon rainforest.

   Harvest had no significant effect on the size‐dependent growth of lianas, but survival rates increased with size. Harvest had a significant negative effect on size‐dependent survival where larger lianas experienced greater mortality rates under high harvest pressure than smaller lianas. In the populations under high harvest pressure, survival of smaller lianas was greater than that of populations with low harvest pressure. Harvest had no significant effect on clonal or sexual reproduction, but fertility was size‐dependent.

   The long‐term population growth rates ofB. caapipopulations under high harvest pressure were projected to decline at a rate of 1.3% whereas populations with low harvest pressure are expected to increase at 3.2%. However, before reaching equilibrium, over the short‐term, allB. caapipopulations were in decline by 26% (high harvested population) and (low harvested population) 20.4% per year.

   Elasticity patterns were dominated by survival of larger lianas irrespective of harvest treatments. Life table response experiment analyses indicated that high harvest caused the 6% reduction in population growth rates by significantly reducing the survival of large lianas and increasing the survival‐growth of smaller lianas including vegetative reproductive individuals.

   Synthesis and applications. This study emphasizes how important it is for management strategies forB. caapilianas experiencing anthropogenic harvest to prioritize the survival of larger size lianas and vegetative reproducing individuals, particularly in increased harvested systems often prone to multiple stressors. From an applied conservation perspective, our findings illustrate the importance of both prospective and retrospective perturbation analyses in population growth rates in understanding the population dynamics of lianas in general in response to human‐induced disturbance.

    

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