The building stones of Roman Sagalassos (SW Turkey): Facies analysis and provenance

Summary Different types of builing stones have been macro-scopically and petrographically characterized at the ancient city of Sagalassos (SW Turkey). The natural building stones include limestone, conglomerate, breccia, marble, travertine, granite and sand-to siltstone of different qualities. The provenance of most of the building stones may be related to local lithological units, both in the immediate area of the city and on its territory. Also, some stone types were clearly imported from considerable distance. Throughout the history of the city, local beige and pink good quality limestone remained the most important building stone. Both the high quality white limestone from the territory of the city and the marbles imported from a distance of 250 km, represent only a small fraction of the total amount of building stones used. While the use of the white limestone can be considered to form a clear but limited trend from the Trajanic period (98–117 AD) onwards, the import of the marbles must be considered as a rare event. The selection of building stone went hand in hand with the appreciation for structural strength and suitability for carving complex architectural decoration, together with the desire to obtain a polychrome architecture.     

东亚和东南亚旧石器初期重型工具的类型学：评Movius的分类体系

实践证明,Ｍｏｖｉｕｓ在本世纪４０年代建立的东亚,东南亚旧石器分类体系有严重缺陷。经过厘定,目前在欧洲和非洲通用的吹斫器,手斧,薄刃斧,手镐,球状器和刮削器等分类概念,也适用于东亚,东南亚旧石器初期的重型工具工业。     

Applications of Population Genetics to Animal Breeding,from Wright,Fisher and Lush to Genomic Prediction

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher’s infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with “genomic selection” is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.THE success of breeders in effecting immense changes in domesticated animals and plants greatly influenced Darwin’s insight into the power of selection and implications to evolution by natural selection. Following the Mendelian rediscovery, attempts were soon made to accommodate within the particulate Mendelian framework the continuous nature of many traits and the observation by Galton (1889) of a linear regression of an individual’s height on that of a relative, with the slope dependent on degree of relationship. A polygenic Mendelian model was first proposed by Yule (1902) (see Provine 1971; Hill 1984). After input from Pearson, Yule again, and Weinberg (who developed the theory a long way but whose work was ignored), its first full exposition in modern terms was by Ronald A. Fisher (1918) (biography by Box 1978). His analysis of variance partitioned the genotypic variance into additive, dominance and epistatic components. Sewall Wright (biography by Provine 1986) had by then developed the path coefficient method and subsequently (Wright 1921) showed how to compute inbreeding and relationship coefficients and their consequent effects on genetic variation of additive traits. His approach to relationship in terms of the correlation of uniting gametes may be less intuitive at the individual locus level than Malécot’s (1948) subsequent treatment in terms of identity by descent, but it transfers directly to the correlation of relatives for quantitative traits with additive effects.From these basic findings, the science of animal breeding was largely developed and expounded by Jay L. Lush (1896–1982) (see also commentaries by Chapman 1987 and Ollivier 2008). He was from a farming family and became interested in genetics as an undergraduate at Kansas State. Although his master’s degree was in genetics, his subsequent Ph.D. at the University of Wisconsin was in animal reproductive physiology. Following 8 years working in animal breeding at the University of Texas he went to Iowa State College (now University) in Ames in 1930. Wright was Lush’s hero: ‘I wish to acknowledge especially my indebtedness to Sewall Wright for many published and unpublished ideas upon which I have drawn, and for his friendly counsel” (Lush 1945, in the preface to his book Animal Breeding Plans). Lush commuted in 1931 to the University of Chicago to audit Sewall Wright’s course in statistical genetics and consult him. Speaking at the Poultry Breeders Roundtable in 1969: he said, “Those were by far the most fruitful 10 weeks I ever had.” (Chapman 1987, quoting A. E. Freeman). Lush was also exposed to and assimilated the work and ideas of R. A. Fisher, who lectured at Iowa State through the summers of 1931 and 1936 at the behest of G. W. Snedecor.Here I review Lush’s contributions and then discuss how animal breeding theory and methods have subsequently evolved. They have been based mainly on statistical methodology, supported to some extent by experiment and population genetic theory. Recently, the development of genomic methods and their integration into classical breeding theory has opened up ways to greatly enhance rates of genetic improvement. Lush focused on livestock improvement and spin-off into other areas was coincidental; but he had contact with corn breeders in Ames and beyond and made contributions to evolutionary biology and human genetics mainly through his developments in theory (e.g., Falconer 1965; Robertson 1966; Lande 1976, 1979; see also Hill and Kirkpatrick 2010). I make no attempt to be comprehensive, not least in choice of citations.     

Refining the standardized growth change method for pointer year detection: Accounting for statistical bias and estimating the deflection period

Detecting pointer years in tree-ring data is a central aspect of dendroecology. Pointer years are usually represented by extraordinary secondary tree growth, which is often interpreted as a response to abnormal environmental conditions such as late-frosts or droughts. Objectively identifying pointer years in larger tree-ring networks and relating those to specific climatic conditions will allow for refining our understanding of how trees perform under extreme climate and consequently, under anticipated climate change. Recently, Buras et al. (2020) demonstrated that frequently used pointer-year detection methods were either too sensitive or insensitive for such large scale analyses. In their study, Buras et al. (2020) proposed a novel approach for detecting pointer years – the standardized growth change (SGC) method which outperformed other pointer-year detection methods in pseudopopulation trials. Yet, the authors concluded that SGC could be improved further to account for the inability to detect pointer years following successive growth decline. Under this framework, we here present a refined version of the SGC-method – the bias-adjusted standardized growth change method (BSGC). The methodological adjustment to the SGC approach comprises conflated probabilities derived from standardized growth changes with probabilities derived from a time-step specific global standardization of growth changes. In addition, BSGC allows for estimating the length of the deflection period, i.e. the period before extraordinary growth values have reached normal levels. Application of BSGC to simulated and measured tree-ring data indicated an improved performance in comparison to SGC which allows for the identification of pointer years following years of successive growth decline. Also, deflection period lengths were estimated well and revealed plausible results for an existing tree-ring data set. Based on these validations, BSGC can be considered a further refinement of pointer-year detection, allowing for a more accurate identification and consequently better understanding of the radial growth response of trees to extreme events.     

Stone artifacts and hominins in island Southeast Asia: new insights from Flores, eastern Indonesia

This study reexamines the current understanding of Pleistocene stone-artifact assemblages in island Southeast Asia. A differentiation has long been made between assemblages of large-sized "core tools" and assemblages of small-sized "flake tools." "Core tool" assemblages are often argued to be the handiwork of early hominin species such as Homo erectus, while small-sized "flake tool" assemblages have been attributed to Homo sapiens. We argue that this traditional Southeast Asian perspective on stone tools assumes that the artifacts recovered from a site reflect a complete technological sequence. Our analyses of Pleistocene-age artifact assemblages from Flores, Indonesia, demonstrate that large pebble-based cores and small flake-based cores are aspects of one reduction sequence. We propose that the Flores pattern applies across island Southeast Asia: large-sized "core tool" assemblages are in fact a missing element of the small-sized flake-based reduction sequences found in many Pleistocene caves and rock-shelters. We conclude by discussing the implications of this for associating stone-artifact assemblages with hominin species in island Southeast Asia.     

Announcing a Still Bay industry at Sibudu Cave, South Africa

Sibudu Cave in KwaZulu-Natal, South Africa, has a long Middle Stone Age (MSA) sequence with good organic preservation. The uppermost MSA sequence includes industries attributed to the final and late MSA and the Howiesons Poort. Below the Howiesons Poort are two layers containing some thin, bifacial lanceolate points, mostly in the form of distal and proximal fragments. These double-pointed foliates are the fossile directeur of the Still Bay Industry, and importantly, this Sibudu industry provides confirmation that the Still Bay predates the Howiesons Poort Industry. Technologically, the points from Sibudu are comparable to those from other South African sites with Still Bay occurrences (e.g., Blombos Cave and Hollow Rock Shelter). Although dating of the Sibudu Still Bay is preliminary, its age falls within the range of that at Blombos. For the past two decades, archaeologists have rejected the idea of a Still Bay Industry occurring outside of the Western Cape, but the Still Bay at Sibudu shows that this industry was, indeed, geographically widespread.     

Identifying bipolar knapping in the Mesolithic site of Font del Ros (northeast Iberia)

Despite recent advances in the identification of bipolar knapping, its role in many sites is not well known. We propose to assess the significance of this technique in the context of changes that occur in the Mesolithic. A lithic assemblage was recovered from unit SG at Font del Ros (Catalunya, Spain) in which pitted stones, cores and products arising from bipolar reduction (flakes, fragments and splintered pieces) were identified. This study indicates that the bipolar technique is fundamental in the settlement. These results are key to defining the organization of Holocene hunter-gatherer subsistence in northeast Iberia.     

Science in Risk Assessment and Policy (SciRAP): An Online Resource for Evaluating and Reporting In Vivo (Eco)Toxicity Studies

(Eco)toxicity studies conducted according to internationally standardized test guidelines are often considered reliable by default and preferred as key evidence in regulatory risk assessment. At the same time regulatory agencies emphasize the use of all relevant (eco)toxicity data in the risk assessment process, including non-standard studies. However, there is a need to facilitate the use of such studies in regulatory risk assessment. Therefore, we propose a framework that facilitates a systematic and transparent evaluation of the reliability and relevance of (eco)toxicity in vivo studies for health and environmental risk assessment. The framework includes specific criteria to guide study evaluation, as well as a color-coding tool developed to aid the application of these criteria. In addition we provide guidance intended for researchers on how to report non-standard studies to ensure that they meet regulatory requirements. The intention of the evaluating and reporting criteria is to increase the usability of all relevant data that may fill information gaps in chemical risk assessments. The framework is publically available online, free of charge, at the Science in Risk Assessment and Policy (SciRAP) website: www.scirap.org. The aim of this article is to present the framework and resources available at the SciRAP website.     

A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system

Even if European river management and restoration are largely supported by the use of reliable tools, these tools are most often “generalist” and provide only initial leads of alteration sources. Acknowledging that young-of-the-year (YOY) fish assemblages are highly dependent on riverine habitat conditions, the development of a YOY-based tool might be very useful or even essential in the design and implementation of conservation or restoration plan of large rivers, in measuring more straight-forward the losses and gains of hydro-ecological functionalities. In the past 20 years, new modeling techniques have emerged from a growing sophistication of statistical model applied to ecology. “Machine learning methods” (ML) are now recognized as holding great promise for the advancement of understanding and prediction of ecological phenomena. The aim of this work was to select the appropriate statistical technique to model YOY assemblages according to different meso-scale habitat variables that are meaningful to planners. To do this, two “Machine Learning” methods, Classification and Regression Trees (CART) and Boosted Regression Trees (BRT), were compared to Generalized Linear Models (GLM). We modeled the occurrence of 9 species from the Seine River basin (France) in order to compare models abilities to accurately predict the presence and absence of each species. BRT appeared to be the best technique for modeling 0+ fish occurrences in our dataset.