Suboptimal growth temperatures enhance the biological activity of cultured cyanobacterium Gloeocapsa sp.

The cytotoxic, antibacterial, and antifungal activities of cyanobacterium Gloeocapsa sp. strain Gacheva 2007/R-06/1 were investigated and the possibility for an enhancement of these activities by changing the culture conditions evaluated. Fatty acids of this cyanobacterium were found to be active against Streptococcus pyogenes. Exopolysaccharides inhibited the growth of both Gram-positive and Gram-negative bacteria and the fungus Candida albicans. Both exopolysaccharides and fatty acid mixtures also significantly decreased the viability of human cervical carcinoma cells, HeLa. Greater biological activities were exhibited by Gloeocapsa sp., cultured at suboptimal temperatures (15–26°C) than at optimal and supraoptimal ones. In comparison with higher light intensity, the low-light cultivation stimulated the cytotoxicity of the fatty acids. In general, low temperatures decreased the growth of Gloeocapsa sp., but promoted its biological activity. Prolonged cultivation also had a beneficial impact on the bioactivity. Compared to 4 days, the 17-day cultivation resulted in fourfold higher antibacterial and antifungal activities of exopolysaccharides and more than twice increases in their cytotoxicity. The study revealed that this cyanobacterial isolate is a new source of natural products with potential for pharmacological and medical applications.     

The Freter model: A simple model of biofilm formation

A simple, conceptual model of biofilm formation, due to R. Freter et al. (1983), is studied analytically and numerically in both CSTR and PFR. Two steady state regimes are identified, namely, the complete washout of the microbes from the reactor and the successful colonization of both the wall and bulk fluid. One of these is stable for any particular set of parameter values and sharp and explicit conditions are given for the stability of each. The effects of adding an anti-microbial agent to the CSTR are examined.Supported by NSF Grant DMS 0107439 and UTA Grant REP 14748717Supported by NSF Grant DMS 0107160     

Hierarchical genetic algorithm versus static optimization-investigation of elbow flexion and extension movements

The applicability of static optimization (and, respectively, frequently used objective functions) for prediction of individual muscle forces for dynamic conditions has often been discussed. Some of the problems are whether time-independent objective functions are suitable, and how to incorporate muscle physiology in models. The present paper deals with a twofold task: (1) implementation of hierarchical genetic algorithm (HGA) based on the properties of the motor units (MUs) twitches, and using multi-objective, time-dependent optimization functions; and (2) comparison of the results of the HGA application with those obtained through static optimization with a criterion "minimum of a weighted sum of the muscle forces raised to the power of n". HGA and its software implementation are presented. The moments of neural stimulation of all MUs are design variables coding the problem in the terms of HGA. The main idea is in using genetic operations to find these moments, so that the sum of MUs twitches satisfies the imposed goals (required joint moments, minimal sum of muscle forces, etc.). Elbow flexion and extension movements with different velocities are considered as proper illustration. It is supposed that they are performed by two extensor muscles and three flexor muscles. The results show that HGA is a suitable means for precise investigation of motor control. Many experimentally observed phenomena (such as antagonistic co-contraction, three-phasic behavior of the muscles during fast movements) can find their explanation by the properties of the MUs twitches. Static optimization is also able to predict three-phasic behavior and could be used as practicable and computationally inexpensive method for total estimation of the muscle forces.     

Enhanced resistance to Salmonella enterica serovar Typhimurium infection in mice after coumarin treatment

Coumarin and its derivatives are naturally occurring substances with multiple biological activities. Here we demonstrate that prophylactic peroral administration of coumarin or 7-hydroxycoumarin (7-OHC) enhances resistance to subsequent lethal Salmonella enterica Serovar Typhimurium infection in mice. 7-OHC decreased bacterial load in liver and spleen, and enhanced phagocytosis and bacterial killing by macrophages when applied in vitro and in vivo. 7-OHC treatment induced significant NO release in peritoneal macrophage cultures. The observed protective effect correlated with the induction of Th1-associated cytokines, such as IL-12, IFN-gamma, and TNF-alpha. These data demonstrate a clear immunomodulatory potential of coumarins which might have important therapeutic implications to enhance resistance to infection.     

Experimental and modelling investigation of learning a fast elbow flexion in the horizontal plane

Changes in the kinematic and electromyographic characteristics that occur while learning to move as fast as possible have been studied experimentally. Experimental investigation of what happens to the individual motor units (MUs) is more difficult. Access to each MU is impossible, and the recruitment and force developing properties of all individual MUs cannot be known. Thus, what is currently known about MU firing is based on experiments that have recorded relatively few MUs compared to what exists in the entire muscle. A recently developed muscle model (Raikova and Aladjov, 2002, J. Biomechanics, 35, 1123-1135) composed of MUs with different properties can be used for such investigation. The process of learning fast elbow flexion in the horizontal plane was simulated and the results were compared with experimentally measured data. Comparing the simulation results of the very first trial of a particular subject with those of the last trail (at the end of the learning process), it can be concluded that the speed of limb motion and muscle forces increase initially as a result of the more synchronous MUs activation and the increase of firing rate of active MUs. Further improvement necessitated an appreciable reduction in the motor task requirements (i.e. less muscle force and less MUs' activity) set in the computational algorithm by optimization criteria. This forced the next process-inclusion of additional MUs.     

Molecular dynamics study of the metallochaperone Hah1 in its apo and Cu(I)-loaded states: role of the conserved residue M10

Molecular dynamics simulations were performed on both apo and copper forms of the human copper chaperone, Hah1. Wild-type Hah1 and a methionine (M10) to serine mutant were investigated. We have evidenced the central role of residue M10 in stabilizing the hydrophobic core of Hah1 as well as the internal structure of the metal-binding site. When copper(I) is bound, the mobility of Hah1 is reduced whereas mutation of M10 implies a drastic increase of the mobility of apoHah1, stressing the importance of this highly conserved hydrophobic residue for copper sequestration by the apoprotein.     

PIV-measured versus CFD-predicted flow dynamics in anatomically realistic cerebral aneurysm models

Computational fluid dynamics (CFD) modeling of nominally patient-specific cerebral aneurysms is increasingly being used as a research tool to further understand the development, prognosis, and treatment of brain aneurysms. We have previously developed virtual angiography to indirectly validate CFD-predicted gross flow dynamics against the routinely acquired digital subtraction angiograms. Toward a more direct validation, here we compare detailed, CFD-predicted velocity fields against those measured using particle imaging velocimetry (PIV). Two anatomically realistic flow-through phantoms, one a giant internal carotid artery (ICA) aneurysm and the other a basilar artery (BA) tip aneurysm, were constructed of a clear silicone elastomer. The phantoms were placed within a computer-controlled flow loop, programed with representative flow rate waveforms. PIV images were collected on several anterior-posterior (AP) and lateral (LAT) planes. CFD simulations were then carried out using a well-validated, in-house solver, based on micro-CT reconstructions of the geometries of the flow-through phantoms and inlet/outlet boundary conditions derived from flow rates measured during the PIV experiments. PIV and CFD results from the central AP plane of the ICA aneurysm showed a large stable vortex throughout the cardiac cycle. Complex vortex dynamics, captured by PIV and CFD, persisted throughout the cardiac cycle on the central LAT plane. Velocity vector fields showed good overall agreement. For the BA, aneurysm agreement was more compelling, with both PIV and CFD similarly resolving the dynamics of counter-rotating vortices on both AP and LAT planes. Despite the imposition of periodic flow boundary conditions for the CFD simulations, cycle-to-cycle fluctuations were evident in the BA aneurysm simulations, which agreed well, in terms of both amplitudes and spatial distributions, with cycle-to-cycle fluctuations measured by PIV in the same geometry. The overall good agreement between PIV and CFD suggests that CFD can reliably predict the details of the intra-aneurysmal flow dynamics observed in anatomically realistic in vitro models. Nevertheless, given the various modeling assumptions, this does not prove that they are mimicking the actual in vivo hemodynamics, and so validations against in vivo data are encouraged whenever possible.     

How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

Post‐traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE‐exposed people who develop PTSD is small. To be cost‐effective, risk prediction rules are needed to target high‐risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio‐demographics, and prior histories of cumulative TE exposure and DSM‐IV disorders. DSM‐IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest‐risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0‐1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic‐geographic sub‐samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.     

Ancient split of major genetic lineages of European Black Pine: evidence from chloroplast <Emphasis>DNA</Emphasis>

The European Black Pine (Pinus nigra Arn.) has a long and complex history. Genetic distance and frequency analyses identified three differentiated genetic groups, which corresponded to three wide geographical areas: Westerns Mediterranean, Balkan Peninsula and Asia Minor. These groups shared common ancestors (14.75 and 10.72 Ma). The most recent splits occurred after the Messinian Salinity Crisis (4.37 Ma) and the Early–Middle Pleistocene Transitions (0.93 Ma). The posterior ancestral population size (Na) is 260,000–265,000 individuals. Each pool is further fragmented, with evidence of a phylogeographic structure (N _st  > G _st ) typically observed in some natural populations from the Western Mediterranean region and the Balkan Peninsula. The laboratory analysis was performed by fragment analysis—i.e. electrophoretic sizing of polymerase chain reaction fragments, combined with the sequencing analysis of 33 % of all individuals as a control. Intense sampling of chloroplast DNA polymorphisms (3154 individuals and 13 markers: SNPs and SSRs) over the full area of the species’ natural distribution indicated moderate among-population variability (G _st(nc) ≤ 0.177) in various parts of its range. These results indicate that the natural populations have long migration histories that differ from one another and that they have been strongly phylogeographically affected by complex patterns of isolation, speciation and fragmentation. Long and varying climatic fluctuations in the region of the principal genetic group have been the probable cause of different forest community associations with different successional patterns resulting in interglacial refugia vs. macro long-term refugia.