Isoform-specific effects of a novel BmK 11(2) peptide toxin on Na channels.

BmK 11(2) is a 7216Da polypeptide toxin purified from the venom of the scorpion Buthus martensii Karsch. Nanomolar concentrations of the toxin prolong amphibian nerve action potentials without attenuation of the amplitude. The pharmacological action of the toxin and its sequence similarity to other alpha-scorpion toxins suggest that BmK 11(2) selectively alters voltage-gated Na channels. In order to test whether BmK 11(2) preferentially modulates the gating or kinetics of certain channel isoforms, we applied BmK 11(2) to muscle, heart and neuronal Na channels. 100nM BmK 11(2) increased the peak current amplitude of skeletal muscle (micro1) and neuronal (N1E-115) Na currents by 40 and 20%, respectively, and reduced the cardiac Na (hH1) current by 15%. The toxin slowed current decay of all isoforms, most prominently in N1E-115 (tau(BmK)/tau(Control)=12), micro1 (11), and less so for hH1 (1.3). BmK 11(2) shifted the voltage dependence of activation of micro1 and N1E-115 currents. BmK 11(2) had no effect on steady-state inactivation, use-dependent availability, and the kinetics of entry into slowly recovering inactivated states.     

beta-Catenin and ras oncogenes detect most human colorectal cancer.

PURPOSE AND STUDY DESIGN: Recent studies have shown that beta-catenin translocated into the cell nucleus functions like an oncogene. Accumulating evidence suggests that activation of the beta-catenin oncogenic signaling cascade along with its twin, the K-ras cascade, may exert syngeneic or synergistic effects on tumor development and progression. In the study reported here, we analyzed oncogenic beta-catenin activation on the basis of its nuclear accumulation (NA) and compared the results with those of mutational activation of K-ras in 74 patients with colorectal cancer to determine whether the two oncogene-mediated signaling cascades interact. RESULTS: We found two distinct patterns of beta-catenin activation, i.e., diffuse NA in 20 cases (27%) and selective NA at the tumor invasion front (NAinv) in 19 cases (26%). The presence of the NAinv pattern was significantly correlated with advanced Dukes' stage tumor (P = 0.0005) and the presence of distant metastases (P = 0.0064). K-ras proto-oncogene was mutated in the tumors of 31 cases (42%). Activated beta-catenin or K-ras was detected in most (78%) colorectal cancers analyzed, although a weak inverse correlation was found between the activities of the two oncogenes in the tumors. Importantly, most (7 of 8) patients with tumor showing both K-ras activation and the NAinv pattern of beta-catenin activation were in Dukes' stage C at surgery, and half of them developed distant metastases to the liver and lungs. CONCLUSION: The results suggest that although oncogenic activation of beta-catenin and K-ras is independent in the process of clinical cancer development, combined analysis of the two major oncogenes can detect most colorectal cancers and identify a subset of patients with poorer outcomes. Consequently, activation of either or both of these oncogenes may serve as a genetic marker for molecular diagnosis.     

Engineering a DNA origami mediated multicolour quantum dot platform for sub-diffraction spectral separation imaging

The validation of super-resolution optical imaging techniques requires well-defined reference samples that can be used repeatedly and reliably as model standards. Here, we engineer a DNA origami scaffold-mediated multicolour quantum dot hybrid nanostructure and test it using a recently proposed Quantum Dot-based spectral separation technique. We show that multivalent DNA structures offer a robust and precise nanoscale quantum dot placement scaffold, while the spectral resolution method provides relatively simple and fast image acquisition capabilities using any standard confocal or fluorescence microscope capable of spectral signal separation and a single excitation laser wavelength.

The validation of super-resolution optical imaging techniques requires well-defined reference samples that can be used repeatedly and reliably as model standards.     

Immunobiological Characteristics of the Attenuated African Swine Fever Virus Strain Katanga-350

The African swine fever virus (ASFV) is the cause of a recent pandemic that is threatening the global pig industry. The virus infects domestic and wild pigs and manifests with a variety of clinical symptoms, depending on the strain. No commercial vaccine is currently available to protect animals from this virus, but some attenuated and recombinant live vaccine candidates might be effective against the disease. This article describes the immunobiological characteristics of one such candidate—the laboratory-attenuated ASFV strain, Katanga-350—which belongs to genotype I. In this study, we assessed clinical signs and post-mortem changes, the levels of viremia and the presence of viral DNA caused by injection of ASF virus strains Katanga-350, Lisbon-57, and Stavropol 08/01. Intramuscular injection of this strain protected 80% of pigs from a virulent strain of the same genotype and seroimmunotype (Lisbon-57). At least 50% of the surviving pigs received protection from subsequent intramuscular infection with a heterologous (genotype II, seroimmunotype VIII) virulent strain (Stavropol 08/01). Virus-specific antibodies were detectable in serum and saliva samples between 8–78 days after the first inoculation of the Katanga-350 strain (the observational period). The results suggested that this strain could serve as a basis for the development of a recombinant vaccine against ASF viruses belonging to seroimmunotype I.     

Optimizing the human learnability of abstract network representations

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core–periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

From a young age, humans demonstrate the capacity to learn the relationships between concepts (1–3). During the learning process, humans are exposed to discrete chunks of information that combine and interconnect to form cognitive maps that can be represented as complex networks (4–9). These chunks of information often appear in a natural sequential order, such as words in language, notes in music, and abstract concepts in stories and classroom lectures (10–14). Further, these sequences are encoded in the brain as networks, with links between items reflecting observed transitions (see refs. 15–18 for empirical studies and 19 for a recent review). Broadly, the fact that many different types of information exhibit temporal order (and therefore network structure) motivates investigations into the processes that underlie the human learning of transition networks (8, 19, 20).To understand the network-learning process, recent studies have investigated how humans internally construct abstract representations of associations (21–23). Using a variety of approaches, from computational models to artificial neural networks, such studies have consistently found that the mind builds network representations by integrating information over time. Such integration enables humans to compress exact sequences of experienced events into broader, but less precise, representations of context (24). These mental representations allow learners to make better generalizations about new information, at the cost of accuracy (22). Here, we focus on one particular modeling approach that accounts for the temporal integration and inaccuracies inherent in human learning. In particular, we build upon a maximum-entropy model, which posits that the mind learns a network representation of the world in a manner guided by a tradeoff between accuracy and complexity (21, 25). Specifically, in order to conserve mental resources, humans will tend to reduce the complexity of their representations at the cost of accuracy by allowing for errors during the learning process.While inaccuracies in human learning can aid flexibility across contexts, they present fundamental obstacles for the human comprehension of transition networks. Thus, a clear question emerges: What strategies should be employed to most effectively communicate the structure of a network to an inaccurate human learner? Prior studies of animal communication and behavior have demonstrated the utility of exaggerating the presentation of certain signals to receivers in offsetting erroneous information processing (26, 27). Similarly, one could imagine that, by emphasizing some features of a network over others, one may be able to correct for errors in human learning. Such an approach of targeted modulation of emphasis may be helpful not only in learning a whole network, but also in optimally learning particularly challenging parts of a network. In fact, humans show consistent difficulties in learning certain motifs in networks, such as the connections between modules (21, 28–30). Taken together, these observations suggest that disproportionately weighting specific network features that are difficult to learn may facilitate human network learning.     

Design of Green Silver Nanoparticles Based on Primula Officinalis Extract for Textile Preservation

The present study aims to bring an addition to biomass resources valorization for environmental-friendly synthesis of nanoparticles. Thus, the green synthesis of silver nanoparticles (AgNPs) was performed, using a novel and effective reducing agent, Primula officinalis extract. The synthesis was optimized by monitoring the characteristic absorption bands, using UV–Vis spectroscopy, and by evaluating the size and physical stability. The phenolic consumption was established using Folin-Ciocâlteu method (1.40 ± 0.42 mg, representing ~5% from the total amount of poly--phenols) and the antioxidant activity was evaluated using chemiluminescence and TEAC methods. The optimum ratio extract to Ag ions was 1:3, for which the AgNPs presented a zeta potential value of −29.3 ± 1.2 mV and particles size of 5–30 nm. For characterization, EDS and XRD techniques were used, along with microscopy techniques (TEM). The AgNPs dispersions were applied on natural textile samples (cotton and wool), as a novel antimicrobial treatment for textile preservation. The treated fabrics were further characterized in terms of chromatic parameters and antimicrobial effect against Escherichia coli, Staphylococcus aureus, Bacillus subtilis, and Penicillium hirsutum strains. The high percentages of bacterial reduction, >99%, revealed that the AgNPs produced are a good candidate for textiles preservation against microbial degradation.     

VOx Phase Mixture of Reduced Single Crystalline V2O5: VO2 Resistive Switching

The strongly correlated electron material, vanadium dioxide (VO2), has seen considerable attention and research application in metal-oxide electronics due to its metal-to-insulator transition close to room temperature. Vacuum annealing a V2O5(010) single crystal results in Wadsley phases (VnO2n+1, n > 1) and VO2. The resistance changes by a factor of 20 at 342 K, corresponding to the metal-to-insulator phase transition of VO2. Macroscopic voltage-current measurements with a probe separation on the millimetre scale result in Joule heating-induced resistive switching at extremely low voltages of under a volt. This can reduce the hysteresis and facilitate low temperature operation of VO2 devices, of potential benefit for switching speed and device stability. This is correlated to the low resistance of the system at temperatures below the transition. High-resolution transmission electron microscopy measurements reveal a complex structural relationship between V2O5, VO2 and V6O13 crystallites. Percolation paths incorporating both VO2 and metallic V6O13 are revealed, which can reduce the resistance below the transition and result in exceptionally low voltage resistive switching.     

Modeling of Hydrogen Diffusion in Inhomogeneous Steel Welded Joints

Hydrogen is a main factor in cold cracking or hydrogen-induced cracking. The most crack susceptible region of a steel welded joint is the heat affected zone (HAZ). The formulation and functional-analytical solution of the one-dimensional problem of hydrogen diffusion in an inhomogeneous butt-welded joint considering weld and joint dimensions and initial hydrogen distribution as well as hydrogen diffusion coefficients and solubilities are presented. It is shown that the peak hydrogen concentration in the HAZ of inhomogeneous joints varies in direct proportion to the initial hydrogen concentration in the weld metal. It is inversely proportional to the ratio of hydrogen solubilities in the weld metal and the HAZ metal and is nonlinear in the diffusion coefficient ratio of these metals. The peak hydrogen concentration in the HAZ can exceed 170% of the average initial concentration in the joint if martensitic steel is welded using low-carbon low-alloy welding consumables. The utilization of austenitic consumables leads to a dramatic reduction in the hydrogen concentration in the HAZ in comparison with the non-austenitic consumables. No direct relationship was found between the hydrogen concentration in the HAZ and the hydrogen evolution from the joint surface.     

The Discrepancy between Coal Ash from Muffle,Circulating Fluidized Bed (CFB), and Pulverized Coal (PC) Furnaces,with a Focus on the Recovery of Iron and Rare Earth Elements

Coal ash (CA) is not only one of the most solid wastes from combustion, easily resulting in a series of concerns, but it is also an artificial deposit with considerable metals, such as iron and rare earth. The variation in the coal ash characteristics due to the origins, combustion process, and even storage environment has been hindering the metal utilization from coal ash. In this study, three ash sample from lab muffle, circulating fluidized bed (CFB), and pulverized coal (PC) furnace was derived for the discrepancy study from the combustion furnace, including properties, iron, and rare earth recovery. The origins of the coal feed samples have more of an effect on their properties than combustion furnaces. Magnetic separation is suitable for coal ash from PC because of the magnetite product, and the iron content is 58% in the Mag-1 fraction, with a yield of 3%. The particles in CA from CFB appear irregular and fragmental, while those from PC appear spherical with a smooth surface. The results of sequential chemical extraction and observation both indicated that the aluminosilicate phase plays an essential role in rare earth occurrences. Rare earth in CA from muffling and CFB is facilely leached, with a recovery of approximately 50%, which is higher than that from PC ash. This paper aims to offer a reference to easily understand the difference in metal recovery from coal ash.     

Femtosecond laser dentistry for precise and efficient cavity preparation in teeth

High fluence focused femtosecond laser pulses were used to perform fast, high precision and minimally damaging cavity cutting of teeth at room temperature without using any irrigation or cooling system. The optimal ablation rates were established for both enamel and dentin, and the surfaces were assessed with optical and scanning electron microscopy, Raman spectroscopy and optical profilometry. No chemical change in the composition of enamel and dentin was observed. We explored temperature variations inside the dental pulp during the laser procedure and showed the maximum increase was 5.5°C, within the acceptable limit of temperature increase during conventional dental treatments.