Il corpo spezzato. Costruire e decostruire la figura umana nella tradizione funeraria egiziana

This book is focused on the anthropologically and historically complex subject of the body in ancient Egypt, with particular emphasis on the so-called ‘funerary literature’ and, more specifically, on the corpus of the Pyramid Texts and its ‘mutilated’ anthropomorphic determinatives. In this perspective, it was necessary to establish a framework for the perception and formal elaboration of the social, political, living, and dead body in iconographic and textual sources, in order to provide an emic basis to start from. Particular attention was paid to the ‘broken body’, understood not only as the physical body but also as its iconographical or material representation, sometimes mutilated, decapitated, treated and manipulated in different ways and contexts. Thus, a deductive process has been carried out, starting from the general and arriving at the particular, to propose some suggestions for a long-debated but still unsolved phenomenon. We thus arrive at the practice of mutilation, or partialisation of the body, which is still scarce in archaeological contexts, but more abundant in iconography and hieroglyphics, as a deliberate, reasoned and motivated work of construction and deconstruction of the human body and its representation. The work will have served its purpose if it succeeds in stimulating new reflections and more in-depth studies of the subject, or at least in throwing a glimmer of light on the shadows that Egyptian thought still ‘casts on the walls of the cave’

Best practices and software for themanagement and sharing of camera trap data for small and large scales studies

Camera traps typically generate large amounts of bycatch data of non-target species that are secondary to the study’s objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi-collaborator projects that use standardized methods to answer broad-scale research questions are rare and limited in geographical scope. Many small, fixed-term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range-wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research

Di sangue e di vino: analisi della correlazione tra le figure oltremondane Shezmu e Medjed

Nel capitolo 17 del Libro dei Morti, uno dei più rilevanti e complessi dell’intera raccolta, compaiono due entità “demoniache” dalle caratteristiche peculiari, legate da nessi reciproci tali da meritare un approfondimento specifico. Ad un’osservazione superficiale i due esseri potrebbero apparire molto distanti, giacché il primo, Shezmu – connesso al sangue del macello, al vino e agli olii sacri – vanta una lunga storia di attestazioni testuali e iconografiche, dall’Antico Regno all’Epoca Tarda; l’altro, Medjed –“L’Oppressore” – presenta un aspetto enigmatico e una diffusione estremamente circoscritta nel tempo e nella documentazione (XXI-XXII dinastia). Tramite una più attenta analisi delle fonti disponibili, testuali e iconografiche, sarà invece possibile individuare inaspettati punti di contatto tra le due figure ed elaborare nuove proposte interpretative sulle loro caratterizzazioni, rappresentazioni e funzionalità nel panorama funerario egiziano

Under the lion’s shadow. Iconographic evidence of Apedemak in the Meroitic Royal District at Napata

The lion is one of the most widespread and evergreen symbols of the Egyptian kingship; the lion-king motif recurs in traditional iconography and in royal inscriptions as an attribute of power, domination, strength. At the same time, the lion gods are characterized by ambivalent value and invested with destructive as well as protective potentiality. In Nubia the lion divinity begins to take on importance in the passage between Napatan and Meroitic phases: a leonine god joins Amun like the protector of royalty, especially in central and northern Sudan; he could be the result of syncretic phenomena with the lion-headed god Mahes, but his name is purely Meroitic: Apedemak. This work is intended to give an overview about the iconographic evidence of the lion-god Apedemak, protector of kingship and guardian of the Meroitic Royal District at Jebel Barkal, currently being excavated by the Italian Archaeological Mission in Sudan

Spectral modulation for full linear polarimetry

Linear (spectro) polarimetry is usually performed using separate photon flux measurements after spatial or temporal polarization modulation. Such classical polarimeters are limited in sensitivity and accuracy by systematic effects and noise. We describe a spectral modulation principle that is based on encoding the full linear polarization properties of light in its spectrum. Such spectral modulation is obtained with an optical train of an achromatic quarter-wave retarder, an athermal multiple-order retarder, and a polarizer. The emergent spectral modulation is sinusoidal with its amplitude scaling with the degree of linear polarization and its phase scaling with the angle of linear polarization. The large advantage of this passive setup is that all polarization information is, in principle, contained in a single spectral measurement, thereby eliminating all differential effects that potentially create spurious polarization signals. Since the polarization properties are obtained through curve fitting, the susceptibility to noise is relatively low. We provide general design options for a spectral modulator and describe the design of a prototype modulator. Currently, the setup in combination with a dedicated retrieval algorithm can be used to measure linear polarization signals with a relative accuracy of 5%.Comment: accepted for publication in Applied Optic

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Perceived and observed biases within scientific communities: a case study in movement ecology

Who conducts biological research, where they do it and how results are disseminated vary among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical step towards addressing them. We documented perceived and observed biases in movement ecology, a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. We surveyed attendees before an international conference to assess a baseline within-discipline perceived bias (uninformed perceived bias). We analysed geographic patterns in Movement Ecology articles, finding discrepancies between the country of the authors’ affiliation and study site location, related to national economics. We analysed race-gender identities of USA biology researchers (the closest to our sub-discipline with data available), finding that they differed from national demographics. Finally, we discussed the quantitatively observed bias at the conference, to assess within-discipline perceived bias informed with observational data (informed perceived bias). Although the survey indicated most conference participants as bias-aware, conversations only covered a subset of biases. We discuss potential causes of bias (parachute-science, fieldwork accessibility), solutions and the need to evaluate mitigatory action effectiveness. Undertaking data-driven analysis of bias within sub-disciplines can help identify specific barriers and move towards the inclusion of a greater diversity of participants in the scientific process

Introducing a unique animal ID and digital life history museum for wildlife metadata

Funding: C.R. acknowledges funding from the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20). G.B., R.K., S.C.D. and D.E.-S. acknowledge funding from NASA. A.S. and F.I. acknowledge support from the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions Individual Fellowships (grant no. 101027534 and no. 101107666, respectively). S.C.D. acknowledges funding from NASA Ecological Forecasting Program Grant 80NSSC21K1182. A.M.M.S. was supported by an ARC DP DP210103091. This project is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF10539 to M.W., as well as the Academy for the Protection of Zoo Animals and Wildlife e.V., Germany.1. Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships. 2. Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens. 3. To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases. 4. MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation.Peer reviewe

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August-24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication