The earliest evidence for Upper Paleolithic occupation in the Armenian Highlands at Aghitu-3 Cave

With its well-preserved archaeological and environmental records, Aghitu-3 Cave permits us to examine the settlement patterns of the Upper Paleolithic (UP) people who inhabited the Armenian Highlands. We also test whether settlement of the region between ∼39–24,000 cal BP relates to environmental variability. The earliest evidence occurs in archaeological horizon (AH) VII from ∼39–36,000 cal BP during a mild, moist climatic phase. AH VI shows periodic occupation as warm, humid conditions prevailed from ∼36–32,000 cal BP. As the climate becomes cooler and drier at ∼32– 29,000 cal BP (AH V-IV), evidence for occupation is minimal. However, as cooling continues, the deposits of AH III demonstrate that people used the site more intensively from ∼29–24,000 cal BP, leaving behind numerous stone artifacts, faunal remains, and complex combustion features. Despite the climatic fluctuations seen across this 15,000-year sequence, lithic technology remains attuned to one pattern: unidirectional reduction of small cores geared towards the production of bladelets for tool manufacture. Subsistence patterns also remain stable, focused on medium-sized prey such as ovids and caprids, as well as equids. AH III demonstrates an expansion of social networks to the northwest and southwest, as the transport distance of obsidian used to make stone artifacts increases. We also observe the addition of bone tools, including an eyed needle, and shell beads brought from the east, suggesting that these people manufactured complex clothing and wore ornaments. Remains of micromammals, birds, charcoal, pollen, and tephra relate the story of environmental variability. We hypothesize that UP behavior was linked to shifts in demographic pressures and climatic changes. Thus, by combining archaeological and environmental data, we gain a clearer picture about the first UP inhabitants of the Armenian Highlands

Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos

Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 ± 0.07% and 99.71 ± 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis

Իրանի ճանապարհային քարտեզը որպես նոր այլընտրանք տարածաշրջանում / The Road Map of Iran as a New Alternative in the Region

From the 80s of the 20th century until today, Iran is under sanctions imposed by the United States, which does not allow the country to develop international economic ties with American and European countries and enter the world market. In an attempt to counterbalance and avoid domestic economic collapse in the face of imposed sanctions, Iran made a decision and since the last decades of the 20th century began to engage in a higher level of cooperation with regional countries, which was reflected in the policy of connecting its railway network with neighboring and other regional countries, most famously is called "railway policy". Iran also wants to have a second, alternative option in addition to having a railway connection with any country, because that road may cease to be operational for various reasons, as a result of which the countries will lose this vital path between them. In order to prevent this, Iran wants to have alternative versions of all its interstate railways with its projects, which mainly operate or are being designed through the territory of other states.</jats:p

Multicentric validation of EndoDigest: a computer vision platform for video documentation of the critical view of safety in laparoscopic cholecystectomy

Background: A computer vision (CV) platform named EndoDigest was recently developed to facilitate the use of surgical videos. Specifically, EndoDigest automatically&nbsp;provides short video clips&nbsp;to effectively document the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). The aim of the present study is to validate EndoDigest on a multicentric dataset of LC videos. Methods: LC videos from 4 centers were manually annotated with the time of the cystic duct division and an assessment of CVS criteria. Incomplete recordings, bailout procedures and procedures with an intraoperative cholangiogram were excluded. EndoDigest leveraged predictions of deep learning models for workflow analysis in a rule-based inference system designed to estimate the time of the cystic duct division. Performance was assessed by computing the error in estimating the manually annotated time of the cystic duct division. To provide concise video documentation of CVS, EndoDigest extracted video clips showing the 2&nbsp;min preceding and the 30&nbsp;s following the predicted cystic duct division. The relevance of the documentation was evaluated by assessing CVS in automatically extracted 2.5-min-long video clips. Results: 144 of the 174 LC videos from 4 centers were analyzed. EndoDigest located the time of the cystic duct division with a mean error of 124.0 ± 270.6&nbsp;s despite the use of fluorescent cholangiography in 27 procedures and great variations in surgical workflows across centers. The surgical evaluation found that 108 (75.0%) of the automatically extracted short video clips documented CVS effectively. Conclusions: EndoDigest was robust enough to reliably locate the time of the cystic duct division and efficiently video document CVS despite the highly variable workflows. Training specifically on data from each center could improve results; however, this multicentric validation shows the potential for clinical translation of this surgical data science tool to efficiently document surgical safety