The genetic history of the Southern Arc: a bridge between West Asia and Europe

By sequencing 727 ancient individuals from the Southern Arc (Anatolia and its neighbors in Southeastern Europe and West Asia) over 10,000 years, we contextualize its Chalcolithic period and Bronze Age (about 5000 to 1000 BCE), when extensive gene flow entangled it with the Eurasian steppe. Two streams of migration transmitted Caucasus and Anatolian/Levantine ancestry northward, and the Yamnaya pastoralists, formed on the steppe, then spread southward into the Balkans and across the Caucasus into Armenia, where they left numerous patrilineal descendants. Anatolia was transformed by intra–West Asian gene flow, with negligible impact of the later Yamnaya migrations. This contrasts with all other regions where Indo-European languages were spoken, suggesting that the homeland of the Indo-Anatolian language family was in West Asia, with only secondary dispersals of non-Anatolian Indo-Europeans from the steppe

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Dynamic Sensory Probabilistic Maps for Mobile Robot Localization

In order to localize itself, a mobile robot tries to match its sensory information at any instant against a prior environment model, the map. A probabilistic map can be regarded as a model that stores at each robot configuration q the probability density function of the sensor readings at q. By combining the knowledge of its current position, the new-coming sensory information, and the probabilistic map the robot is capable of improving its prior position estimate. In this paper we propose a novel sensor model and a method for maintaining a probabilistic map in cases of dynamic environments. When the environment structure changes, the map must adapt to this change by modifying the sensor densities at the respective configurations. We propose a combined algorithm for map update and robot localization. 1 Introduction Recently, there has been an increasing interest in the mobile robots community in probabilistic models for robot localization and navigation in metric maps [7, 1, 4, 11, 8]..

The Probabilistic Growing Cell Structures Algorithm

The growing cell structures (GCS) algorithm is an adaptive k-means clustering algorithm in which new clusters are added dynamically to produce a Dirichlet tessellation of the input space. In this paper we extend the non-parametric model of the GCS into a probabilistic one, assuming that samples are distributed in each cluster according to a multi-variate normal probability density function. We show that by recursively estimating the means and the variances of the clusters, and by introducing a new criterion for the insertion and deletion of a cluster, our approach can be more powerful to the original GCS algorithm. We demonstrate our results within the mobile robots paradigm. 1 Introduction The growing cell structures (GCS) algorithm [1] is an adaptive k-means algorithm [3] that performs data clustering. Based on the earlier work of Kohonen [4], GCS is a self-organizing neural network model [2] that incrementally builds a Dirichlet or Voronoi tessellation of the input space, while it ..

The emergence of social media for natural disasters management: A big data perspective

Social media is rapidly emerging as a potential resource of information capable to support natural disasters management. Despite the growing research interest focused on using social media during natural disasters, many challenges may arise on how to handle the 'big data' problem: huge amounts of geo-social data are available, in different formats and varying quality that must be processed quickly. This article presents a state-of-the-art approach towards the enhancement of decision support tools for natural disaster management with information from the Twitter social network. The novelty of the approach lies in the integration of Geographic Information Systems (GIS) modeling outputs with real-time information from Twitter. A first prototype has been implemented that integrates geo-referenced Twitter messages into a Web GIS for wildfire risk management and real-time earthquake monitoring. Following a highly scalable architecture that relies on big data components, the proposed methodology can be applied in different geographical areas, different types of social media and a variety of natural disasters. The article aims at highlighting the role of social big data, towards a more sophisticated transfer of knowledge among civil protection agencies, emergency response crews and affected population

Altered Adipokine Expression in Tumor Microenvironment Promotes Development of Triple Negative Breast Cancer

Obesity is a remarkably important factor for breast carcinogenesis and aggressiveness. The implication of increased BMI in triple negative breast cancer (TNBC) development is also well established. A malignancy-promoting role of the adipose tissue has been supposed, where the adipocytes that constitute the majority of stromal cells release pro-inflammatory cytokines and growth factors. Alterations in adipokines and their receptors play significant roles in breast cancer initiation, progression, metastasis, and drug response. Classic adipokines, such as leptin, adiponectin, and resistin, have been extensively studied in breast cancer and connected with breast cancer risk and progression. Notably, new molecules are constantly being discovered and the list is continuously growing. Additionally, substantial progress has been made concerning their differential expression in association with clinical and pathological parameters of tumors and the prognostic and predictive value of their dysregulation in breast cancer carcinogenesis. However, evidence regarding the mechanisms by which adipose tissue is involved in the development of TNBC is lacking. In the present article we comment on current data on the suggested involvement of these mediators in breast cancer development and progression, with particular emphasis on TNBC, to draw attention to the design of novel targeted therapies and biomarkers