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Poor oral health is associated with cardiovascular disease and dementia. Potential pathways include sepsis from oral bacteria, systemic inflammation, and nutritional deficiencies. However, in post-industrialized populations, links between oral health and chronic disease may be confounded because the lower socioeconomic exposome (poor diet, pollution, and low physical activity) often entails insufficient dental care. We assessed tooth loss, caries, and damaged teeth, in relation to cardiovascular and brain aging among the Tsimane, a subsistence population living a relatively traditional forager-horticulturalist lifestyle with poor dental health, but minimal cardiovascular disease and dementia. Dental health was assessed by a physician in 739 participants aged 40–92 years with cardiac and brain health measured by chest computed tomography (CT; n = 728) and brain CT (n = 605). A subset of 356 individuals aged 60+ were also assessed for dementia and mild cognitive impairment (n = 33 impaired). Tooth loss was highly prevalent, with 2.2 teeth lost per decade and a 2-fold greater loss in women. The number of teeth with exposed pulp was associated with higher inflammation, as measured by cytokine levels and white blood cell counts, and lower body mass index. Coronary artery calcium and thoracic aortic calcium were not associated with tooth loss or damaged teeth. However, aortic valve calcification and brain tissue loss were higher in those who had more teeth with exposed pulp. Overall, these results suggest that dental health is associated with indicators of chronic diseases in the absence of typical confounds, even in a population with low cardiovascular and dementia risk factors.

 

Despite its potential to overcome the design and processing barriers of traditional subtractive and formative manufacturing techniques, the use of laser powder bed fusion (LPBF) metal additive manufacturing is currently limited due to its tendency to create flaws. A multitude of LPBF-related flaws, such as part-level deformation, cracking, and porosity are linked to the spatiotemporal temperature distribution in the part during the process. The temperature distribution, also called the thermal history, is a function of several factors encompassing material properties, part geometry and orientation, processing parameters, placement of supports, among others. These broad range of factors are difficult and expensive to optimize through empirical testing alone. Consequently, fast and accurate models to predict the thermal history are valuable for mitigating flaw formation in LPBF-processed parts. In our prior works, we developed a graph theory-based approach for predicting the temperature distribution in LPBF parts. This mesh-free approach was compared with both non-proprietary and commercial finite element packages, and the thermal history predictions were experimentally validated with in- situ infrared thermal imaging data. It was found that the graph theory-derived thermal history predictions converged within 30–50% of the time of non-proprietary finite element analysis for a similar level of prediction error. However, these prior efforts were based on small prismatic and cylinder-shaped LPBF parts. In this paper, our objective was to scale the graph theory approach to predict the thermal history of large volume, complex geometry LPBF parts. To realize this objective, we developed and applied three computational strategies to predict the thermal history of a stainless steel (SAE 316L) impeller having outside diameter 155 mm and vertical height 35 mm (700 layers). The impeller was processed on a Renishaw AM250 LPBF system and required 16 h to complete. During the process, in-situ layer-by-layer steady state surface temperature measurements for the impeller were obtained using a calibrated longwave infrared thermal camera. As an example of the outcome, on implementing one of the three strategies reported in this work, which did not reduce or simplify the part geometry, the thermal history of the impeller was predicted with approximate mean absolute error of 6% (standard deviation 0.8%) and root mean square error 23 K (standard deviation 3.7 K). Moreover, the thermal history was simulated within 40 min using desktop computing, which is considerably less than the 16 h required to build the impeller part. Furthermore, the graph theory thermal history predictions were compared with a proprietary LPBF thermal modeling software and non-proprietary finite element simulation. For a similar level of root mean square error (28 K), the graph theory approach converged in 17 min, vs. 4.5 h for non-proprietary finite element analysis. 

The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history in additively manufactured parts that was recently published in the ASME transactions. In the present paper the graph theory approach is validated with in-situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize this objective through the following three tasks. First, two types of test parts (stainless steel) are made in two corresponding build cycles on a Renishaw AM250 LPBF machine. The intent of both builds is to influence the thermal history of the part by changing the cooling time between melting of successive layers, called interlayer cooling time. Second, layer-wise thermal images of the top surface of the part are acquired using an in-situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds were used to validate the graph theory-predicted surface temperature trends. Furthermore, the surface temperature trends predicted using graph theory are compared with results from finite element analysis. As an example, for one the builds, the graph theory approach accurately predicted the surface temperature trends to within 6% mean absolute percentage error, and approximately 14 Kelvin root mean squared error of the experimental data. Moreover, using the graph theory approach the temperature trends were predicted in less than 26 minutes which is well within the actual build time of 171 minutes.

 

Abstract The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history in additively manufactured parts that was recently published in these transactions. In the present paper the graph theory approach is validated with in-situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize this objective through the following three tasks. First, two types of test parts (stainless steel) are made in two corresponding build cycles on a Renishaw AM250 LPBF machine. The intent of both builds is to influence the thermal history of the part by changing the cooling time between melting of successive layers, called interlayer cooling time. Second, layer-wise thermal images of the top surface of the part are acquired using an in-situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds were used to validate the graph theory-predicted surface temperature trends. Furthermore, the surface temperature trends predicted using graph theory are compared with results from finite element analysis. As an example, for one the builds, the graph theory approach accurately predicted the surface temperature trends to within 6% mean absolute percentage error, and approximately 14 Kelvin root mean squared error of the experimental data. Moreover, using the graph theory approach the temperature trends were predicted in less than 26 minutes which is well within the actual build time of 171 minutes. 

To address claims of human exceptionalism, we determine where humans fit within the greater mammalian distribution of reproductive inequality. We show that humans exhibit lower reproductive skew (i.e., inequality in the number of surviving offspring) among males and smaller sex differences in reproductive skew than most other mammals, while nevertheless falling within the mammalian range. Additionally, female reproductive skew is higher in polygynous human populations than in polygynous nonhumans mammals on average. This patterning of skew can be attributed in part to the prevalence of monogamy in humans compared to the predominance of polygyny in nonhuman mammals, to the limited degree of polygyny in the human societies that practice it, and to the importance of unequally held rival resources to women’s fitness. The muted reproductive inequality observed in humans appears to be linked to several unusual characteristics of our species—including high levels of cooperation among males, high dependence on unequally held rival resources, complementarities between maternal and paternal investment, as well as social and legal institutions that enforce monogamous norms.

 

Women experience higher morbidity than men, despite living longer. This is often attributed to biological differences between the sexes; however, the majority of societies in which these disparities are observed exhibit gender norms that favor men. We tested the hypothesis that female-biased gender norms ameliorate gender disparities in health by comparing gender differences in inflammation and hypertension among the matrilineal and patrilineal Mosuo of China. Widely reported gender disparities in health were reversed among matrilineal Mosuo compared with patrilineal Mosuo, due to substantial improvements in women’s health, with no concomitant detrimental effects on men. These findings offer evidence that gender norms limiting women’s autonomy and biasing inheritance toward men adversely affect the health of women, increasing women’s risk for chronic diseases with tremendous global health impact.

 

El grado de igualitarismo o jerarquización social en el seno de las sociedades prehispánicas del norte de las tierras altas del suroeste de Estados Unidos y los cambios de dicho aspecto a través del tiempo continúan siendo objeto de debate. Este trabajo examina la plausibilidad del surgimiento de sistemas de gobierno a nivel de villas múltiples en la región del Suroeste a través de simulaciones sobre la coevolución de la jerarquía y del conflicto utilizando una extensión de la modelización basada en agentes del proyecto Village Ecodynamics. Además, recopilamos datos empíricos sobre la distribución de los tamaños poblacionales en los lugares de habitación y los espacios rituales (kivas), y sobre los grupos sociales que las utilizaron, para tres de las mayores regiones del Suroeste norteamericano, analizando estos datos a través del tiempo. Todas evidencias refutan el modelo de villas autónomas durante el periodo Pueblo II (890–1145 d.C.). Al contrario, las evidencias sugieren el surgimiento de sistemas de gobierno a nivel de villas múltiples durante el periodo Pueblo II y probablemente durante el Pueblo III (1145–1285 d.C.) en algunas áreas. Parece plausible que durante el periodo Pueblo II, uno o más sistemas de gobierno conectaron la zona norte del suroeste de Estados Unidos mediante un sistema de tributos que fluyó hacia un epicentro situado en Chaco Canyon. Probablemente durante el periodo Pueblo III y hasta la despoblación de la región del final del siglo XIII, las organizaciones locales ganaron en influencia.