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Wide-field temporal focused (WF-TeFo) two-photon microscopy allows for the simultaneous imaging of a large planar area, with a potential order of magnitude enhancement in the speed of volumetric imaging. To date, low repetition rate laser sources with over half a millijoule per pulse have been required in order to provide the high peak power densities for effective two-photon excitation over the large area. However, this configuration suffers from reduced signal intensity due to the low repetition rate, saturation effects due to increased excitation fluences, as well as faster photobleaching of the fluorescence probe. In contrast, with the recent advent of high repetition rate, high pulse energy laser systems could potentially provide the advantages of high repetition rate systems that are seen in traditional two-photon microscopes, while minimizing the negatives of high fluences in WF-TeFo setups to date. Here, we use a 100 microjoule/high repetition rate (50−100 kHz) laser system to investigate the performance of a WF-TeFo two-photon microscope. While using micro-beads as a sample, we demonstrate a proportionate increase in signal intensity with repetition rate, at no added cost in photobleaching. By decreasing pulse intensity, via a corresponding increase in repetition rate to maintain fluorescence signal intensity, we find that the photobleaching rate is reduced by ~98.4%. We then image live C. elegans at a high repetition rate for 25 min. as a proof-of-principle. Lastly, we identify the steady state temperature increase as the limiting process in further increasing the repetition rate, and we estimate that repetition rate in the range between 0.5 and 5 MHz is ideal for live imaging with a simple theoretical model. With new generation low-cost fiber laser systems offering high pulse energy/high repetition rates in what is essentially a turn-key solution, we anticipate increased adoption of this microscopy technique by the neuroscience community.

The present study deals with the synthesis and characterization of nanocrystalline vanadium pentoxide (V2O5) nanoparticles and their antibacterial and antifungal activity on Staphylococcus aureus and Aspergillus niger, respectively, by agar diffusion method. The metal oxide has been synthesized by employing the sol-gel method, polyaniline (PANI) has been synthesized by chemical oxidation, and PANI/V2O5 composites have been synthesized by in situ polymerization method with different ratios (10, 20, 30, 40, and 50 weight%) of V2O5 in PANI. The newly prepared composites were characterized by FTIR and powder X-ray diffraction (P-XRD) techniques and are found to be formed of PANI/V2O5 nanocomposites, and also the compounds showed moderate antibacterial and antifungal activity.

The five important parameters to characterize tannery effluents viz.total chromium, sulfide, TDS, BOD and COD were determined for samples obtained from different streams as well as for composite liquor. The remedial measures to contain their pollution load on the treatment plant are discussed. The correlation between TDS and BOD, TDS and CODfor these samples are also reported. [ABSTRACT FROM AUTHOR]

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized - (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.