A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems

The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper

The protective role of transferrin in Müller glial cells after iron-induced toxicity

PURPOSE: Transferrin (Tf) expression is enhanced by aging and inflammation in humans. We investigated the role of transferrin in glial protection. METHODS: We generated transgenic mice (Tg) carrying the complete human transferrin gene on a C57Bl/6J genetic background. We studied human (hTf) and mouse (mTf) transferrin localization in Tg and wild-type (WT) C57Bl/6J mice using immunochemistry with specific antibodies. Müller glial (MG) cells were cultured from explants and characterized using cellular retinaldehyde binding protein (CRALBP) and vimentin antibodies. They were further subcultured for study. We incubated cells with FeCl(3)-nitrilotriacetate to test for the iron-induced stress response; viability was determined by direct counting and measurement of lactate dehydrogenase (LDH) activity. Tf expression was determined by reverse transcriptase-quantitative PCR with human- or mouse-specific probes. hTf and mTf in the medium were assayed by ELISA or radioimmunoassay (RIA), respectively. RESULTS: mTf was mainly localized in retinal pigment epithelium and ganglion cell layers in retina sections of both mouse lines. hTf was abundant in MG cells. The distribution of mTf and hTf mRNA was consistent with these findings. mTf and hTf were secreted into the medium of MG cell primary cultures. Cells from Tg mice secreted hTf at a particularly high level. However, both WT and Tg cell cultures lose their ability to secrete Tf after a few passages. Tg MG cells secreting hTf were more resistant to iron-induced stress toxicity than those no longer secreted hTf. Similarly, exogenous human apo-Tf, but not human holo-Tf, conferred resistance to iron-induced stress on MG cells from WT mice. CONCLUSIONS: hTf localization in MG cells from Tg mice was reminiscent of that reported for aged human retina and age-related macular degeneration, both conditions associated with iron deposition. The role of hTf in protection against toxicity in Tg MG cells probably involves an adaptive mechanism developed in neural retina to control iron-induced stress

Overexpressed or intraperitoneally injected human transferrin prevents photoreceptor degeneration in rd10 mice

PURPOSE: Retinal degeneration has been associated with iron accumulation in age-related macular degeneration (AMD), and in several rodent models that had one or several iron regulating protein impairments. We investigated the iron concentration and the protective role of human transferrin (hTf) in rd10 mice, a model of retinal degeneration. METHODS: The proton-induced X-ray emission (PIXE) method was used to quantify iron in rd10 mice 2, 3, and 4 weeks after birth. We generated mice with the β-phosphodiesterase mutation and hTf expression by crossbreeding rd10 mice with TghTf mice (rd10/hTf mice). The photoreceptor loss and apoptosis were evaluated by terminal deoxynucleotidyl transferase dUTP nick end labeling in 3-week-old rd10/hTf mice and compared with 3-week-old rd10 mice. The neuroprotective effect of hTf was analyzed in 5-day-old rd10 mice treated by intraperitoneal administration with hTf for up to 25 days. The retinal hTf concentrations and the thickness of the outer nuclear layer were quantified in all treated mice at 25 days postnatally. RESULTS: PIXE analysis demonstrated an age-dependent iron accumulation in the photoreceptors of rd10 mice. The rd10/hTf mice had the rd10 mutation, expressed high levels of hTf, and showed a significant decrease in photoreceptor death. In addition, rd10 mice intraperitoneally treated with hTf resulted in the retinal presence of hTf and a dose-dependent reduction in photoreceptor degeneration. CONCLUSIONS: Our results suggest that iron accumulation in the retinas of rd10 mutant mice is associated with photoreceptor degeneration. For the first time, the enhanced survival of cones and rods in the retina of this model has been demonstrated through overexpression or systemic administration of hTf. This study highlights the therapeutic potential of Tf to inhibit iron-induced photoreceptor cell death observed in degenerative diseases such as retinitis pigmentosa and age-related macular degeneration

Regulation of AMH type 2 receptor expression by Bone Morphogenetic Protein 15 in ovine granulosa cells (abstract)

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Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at https://doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).</p

Anti-Müllerian Hormone and Its Clinical Use in Pediatrics with Special Emphasis on Disorders of Sex Development

Using measurements of circulating anti-Müllerian hormone (AMH) in diagnosing and managing reproductive disorders in pediatric patients requires thorough knowledge on normative values according to age and gender. We provide age- and sex-specific reference ranges for the Immunotech assay and conversion factors for the DSL and Generation II assays. With this tool in hand, the pediatrician can use serum concentrations of AMH when determining the presence of testicular tissue in patients with bilaterally absent testes or more severe Disorders of Sex Development (DSD). Furthermore, AMH can be used as a marker of premature ovarian insufficiency (POI) in both Turner Syndrome patients and in girls with cancer after treatment with alkylating gonadotoxic agents. Lastly, its usefulness has been proposed in the diagnosis of polycystic ovarian syndrome (PCOS) and ovarian granulosa cell tumors and in the evaluation of patients with hypogonadotropic hypogonadism

Dr. Ahmed Ouali, 1948–2020

International audienceAhmed Ouali was born on October 4, 1948 in Tigzirt, Tizi-Ouzou, Algeria. In 1952, he moved with his parents to Montluçon, France. In 1974, he was trained and graduated with a bachelor's degree in Biochemistry at the University of Lyon. He then, in 1976, earned a joint Ph.D. in Animal Science at the University of Blaise Pascal (Clermont-Ferrand) where he studied at the National Institute of Agricultural Research (INRA, Theix). The title of his doctorate thesis was “The role of muscle proteases on meat tenderization”. Subsequently, he was employed in a private laboratory for medical analysis from 1976 to 1978 and thereafter at the Meat Research Laboratory group at INRA, Theix as a permanent researcher. In 1990, he was appointed as a research director and led the “Biochemistry and Functions of Muscle Proteins” unit for 8 years. The Meat Research Station focused their research on many topics including colour and protein oxidation; enzymology and tenderness; and muscle protein functionalities. During his entire scientific career at INRA, but before his retirement on October 2013, Ahmed was living in Clermont-Ferrand, the city of the famous volcanic chain of the Puy-de-Dôme, with his wife Anne-Marie with whom he had two lovely children: Armelle (41 years) and Gäel (38 years). In 2019, they moved to their new house in Montpellier in the South of France

Co-limitation towards lower latitudes shapes global forest diversity gradients

Funding Information: The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159 ; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/ ; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108-00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/ S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)—‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change ’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007–2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109–00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10-2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.Peer reviewedPostprin

La sculpture romaine en Occident

Cet ouvrage réunit les résultats de deux manifestations complémentaires  : d’une part, la table ronde intitulée «  Rendre à César  », organisée le mercredi 20 juin 2012, à Paris, au Musée du Louvre et, d’autre part, les «  Rencontres autour de la sculpture romaine conservée en France  » qui ont eu lieu du 18 au 20 octobre 2012 au Musée départemental Arles antique. La richesse des interventions lors de ces deux manifestations permet de restituer un ouvrage composé de trente-huit articles, répartis en trois parties et une conclusion. La première partie, en écho et en développement de la table ronde du Louvre, porte sur le portrait du «  César du Rhône  », aussi bien que sur «  Le portrait romain en Gaule  ». La deuxième partie publie cinq études autour des «  nouvelles techniques d’investigations scientifiques  » et présente l’analyse des matériaux des sculptures en pierre et en bronze, découvertes dans le Rhône à Arles, ainsi qu’une étude ethnoarchéologique sur les techniques de production du portrait. Enfin une troisième partie présente les «  découvertes récentes et les nouvelles recherches  », déclinées en seize études qui sont consacrées à des études de cas (Autun, Vaison-la-Romaine, Nîmes, Metz-Divodurum, Apt), ainsi qu’à des relectures novatrices de sculptures méconnues (Plouarzel, Langres, Avignonet-Lauragais, Vernègues, vallée de l’Ubaye, Besançon, Lyon). Robert Turcan signe la conclusion. Ainsi, «  La sculpture romaine en Occident. Nouveaux regards   » reflète la variété et l’intérêt des questionnements actuels dans ce domaine