The set of 55 ancestry informative SNPs (AISNPs) originally developed by the Kidd Lab has been studied on a large number of populations and continues to be applied to new population samples. The existing reference database of population samples allows the relationships of new population samples to be inferred on a global level. Analyses show that these autosomal markers constitute one of the better panels of AISNPs. Continuing to build this reference database enhances its value. Because more than half of the 25 ethnic groups recently studied with these AISNPs are from Southwest Asia and the Mediterranean region, we present here various analyses focused on populations from these regions along with selected reference populations from nearby regions where genotype data are available. Many of these ethnic groups have not been previously studied for forensic markers. Data on populations from other world regions have also been added to the database but are not included in these focused analyses. The new population samples added to ALFRED and FROG-kb increase the total to 164 population samples that have been studied for all 55 AISNPs.

BACKGROUND The aim of this study is to investigate the association of gene variations of the monoamine oxidase A (MAOA) and the serotonin transporter solute carrier family 6 member 4 (SLC6A4) gene with posttraumatic stress disorder (PTSD) severity and coping strategies in patients with war related PTSD. SUBJECTS AND METHODS The study included 747 individuals who had experienced war trauma in the South Eastern Europe conflicts between 1991 and 1999. Genotyping of the MAOA VNTR and SLC6A4 tandem repeat polymorphism in combination with rs25531 was done in 719 participants: 232 females and 487 males. Among them, 369 have had current or lifetime PTSD and 350 have had no PTSD symptoms. For psychometric approach we used the Clinician Administrated PTSD Scale (CAPS), the Brief Symptom Inventory (BSI), the adapted Hoffman-Lazarus Coping scale and a basic socio-demographic data questionnaire. RESULTS There were no significant intergroup (PTSD versus non PTSD) differences in the genotype distribution of MAOA and SLC6A4 gene polymorphisms. The primary finding of our study was that the MAOA short allele (MAOA-S) was nominally significantly associated with the severity of PTSD symptoms in the total subgroup of participants with lifetime PTSD; males for symptoms of hyperarrousal and females with symptoms of re-experience and hyperarousal. In our research the male subsample with current PTSD and MAOA-S genotype had nominally significantly higher scores for some positive coping strategies compared to those carrying the long allele genotype (MAOA-L). There was no significant association between the severity of PTSD symptoms, BSI phenotype, coping scores and the SLC6A4 genotype. CONCLUSION The present results support the notion that MAOA VNTR gene variation modulates development and recovery of posttraumatic stress disorder in a war traumatised population, but did not support a connection between SLC6A4 gene variations and war related PTSD.

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions in graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish an analogy with classical signal processing, spectral- and vertex-domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by a study of filtering and inversion of the localized graph Fourier transform. For rigour, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the need to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the particular paradigm of signal smoothness on graphs. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, and important but missing link for more advanced applications of graphs signal processing. The theory is supported by illustrative and practically relevant examples.

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions in graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish an analogy with classical signal processing, spectral- and vertex-domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by a study of filtering and inversion of the localized graph Fourier transform. For rigour, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the need to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the particular paradigm of signal smoothness on graphs. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, and important but missing link for more advanced applications of graphs signal processing. The theory is supported by illustrative and practically relevant examples.

This study presents the results from a bench-scale UV irradiation experiment of benzophenone (BP) in different water matrices. Despite the reported resistance of BP to UV-irradiation, it was successfully degraded in solution when intensively irradiated by UV light in a batch reactor for 4 h. In this way, it was possible to remove 56 and 45 % of 1.0 and 10 ?g?L-1 of BP in Milli-Q water, respectively. The addition of a mixture of anions suppressed the degradation of BP at the lower concentration level by 20 %. The addition of a dissolved organic carbon (DOC) surrogate mixture (DOC:BP mole ratio of 3000:1) increased the degradation of BP in Milli-Q water (removal 80 %). At 1.0 ?g?L-1 of BP and 1.0 mg?L-1 of DOC, removal was 70 %, while at 10 ?g?L-1 of BP and 10 mg?L-1 of DOC, there was no observable difference in removal compared with the removal in Milli-Q water. The results show that both DOC and anions influence the photodegradation of BP. Furthermore, it was shown that the removal efficiency depends on the concentration of BP. These findings show the importance of a detailed characterisation of the water matrix in studies relating to the removal of micropollutants during UV irradiation.

Proper inclusion of van der Waals interactions (vdW) in ab initio calculations based on the density functional theory (DFT) is crucial to describe soft, organic, layered solids such as κ -(BEDT-TTF) 2 X . Since no consensus has been reached on the reliability of available vdW DFT functionals, most of the first principles calculations have been based on experimental crystal structure data without any structural optimization. Here, we explore optimal DFT-based schemes that account for the effects of vdW interactions on the structural and electronic band properties of three paradigmatic charge transfer salts, κ -(BEDT-TTF) 2 Cu 2 (CN) 3 , κ -(BEDT-TTF) 2 Ag 2 (CN) 3 , and κ -(BEDT-TTF) 2 Cu[N(CN) 2 ]Cl, for which a unified optimization of the structure is possible. Detailed examination of the prototype test system κ -(BEDT-TTF) 2 Cu[N(CN) 2 ]Cl shows that the optB88-vdW functional performs slightly better than the PBE-vdW and that the choice of pseudopotentials is critical to obtaining realistic results.

Uvod Poznato je da starenje za posljedicu ima opadanje tjelesnih sposobnosti čovjeka, što je uz globalni problem hipokinezije, jedan od vodećih savremenih problema društva (Čaušević, Ormanović, Doder, & Čović, 2017; Ćirić, Čaušević, & Bejdić, 2015). Individualni aerobni fi tness se smanjuje za 8-10% tokom svake decenije života, dok veoma aktivni ljudi mogu smanjiti ovaj nivo na 2-3%. Opadanje snage je osjetno u šezdesetim, a posebno u sedamdesetim godinama života (15% odnosno 30%), (Sharkey & Gaskill, 2008). Takođe u brojnim studijama, utvrđeno je da su promjene koje nastaju kao posljedica biološkog starenja, povezane sa smanjenim stepenom fi zičke aktivnosti osoba starije dobi, a koje za posljedicu imaju smanjenje mišićne mase i tjelesne funkcije. Poznato je da organizovano tjelesno vježbanje ima mnogo pozitivnih efekata na organizam čovjeka u cjelini, te da ima jasan i prioritetan fi zički i zdravstveni uticaj na osobe i njihove radne aktivnosti. Međutim, bez obzira na to šta osoba radi – bilo da se bavi prostim fi zičkim poslom (nošenjem, podizanjem, poljoprivrednim radom) ili vrši fi zičke vježbe, njen mišić ni, kardio-vaskularni, respiratorni i centralni nervni sistem aktivno funkcionišu. Shodno tome će fi zička aktivnost, bilo kao tjelesna vježba ili fi zički rad, uvijek uticati na naše tijelo i tako dovesti do poveć anog nivoa funkcionisanja (Bjelica & Krivikapić, 2019). S druge strane, neaktivnost uzrokuje 9% prerane smrtnosti, što brojem iznosi više nego 5,3 od 57 miliona smrtnih slučajeva širom svijeta tokom 2008. godine (Lee i sar. 2012). Istoimeni autori navode da ukoliko neaktivnost nije eliminisana ali jeste umanjena za 10% ili 25%, onda bi se smrtnost tokom svake godine mogla izbjeći za više od 533 000 odnosno za više od 1,3 miliona slučajeva. Ovakav navod potvrđuje i činjenica da Abstract

In this paper, we propose a new concept of a knowledge management framework to enable a self-optimizing and self-learning for wireless system operation in real time. The framework encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning, and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions improving the quality of future decisions. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness problem of joint channel and location optimization in a wireless system. Extensive simulations are run to validate its fast convergence, high throughput, and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to propose autonomous self-optimization with knowledge management.