Convective infux/glymphatic system: tracers injected into the CSF enter and leave the brain along separate periarterial basement membrane pathways

Tracers injected into CSF pass into the brain alongside arteries and out again. This has been recently termed the "glymphatic system" that proposes tracers enter the brain along periarterial "spaces" and leave the brain along the walls of veins. The object of the present study is to test the hypothesis that: (1) tracers from the CSF enter the cerebral cortex along pial-glial basement membranes as there are no perivascular "spaces" around cortical arteries, (2) tracers leave the brain along smooth muscle cell basement membranes that form the Intramural Peri-Arterial Drainage (IPAD) pathways for the elimination of interstitial fluid and solutes from the brain. 2 μL of 100 μM soluble, fluorescent fixable amyloid β (Aβ) were injected into the CSF of the cisterna magna of 6-10 and 24-30 month-old male mice and their brains were examined 5 and 30 min later. At 5 min, immunocytochemistry and confocal microscopy revealed Aβ on the outer aspects of cortical arteries colocalized with α-2 laminin in the pial-glial basement membranes. At 30 min, Aβ was colocalised with collagen IV in smooth muscle cell basement membranes in the walls of cortical arteries corresponding to the IPAD pathways. No evidence for drainage along the walls of veins was found. Measurements of the depth of penetration of tracer were taken from 11 regions of the brain. Maximum depths of penetration of tracer into the brain were achieved in the pons and caudoputamen. Conclusions drawn from the present study are that tracers injected into the CSF enter and leave the brain along separate periarterial basement membrane pathways. The exit route is along IPAD pathways in which Aβ accumulates in cerebral amyloid angiopathy (CAA) in Alzheimer's disease. Results from this study suggest that CSF may be a suitable route for delivery of therapies for neurological diseases, including CAA

Contribution by the Indian and Pakistani Authors to Library Philosophy and Practice: A Bibliometric Analysis 2008-2017.

A bibliometric analysis of the research papers explores different aspects of the contribution from an individual author to the country as a whole. The growth of publications, authorship patterns, paper lengths, referencing trends, prolific contribution of authors, etc. about a particular journal are includes in bibliometric studies. Keeping in mind the Indian contribution to a peer review e-journal namely Library Philosophy and Practice (e-Journal) this study began. Meanwhile, it is observed that already work has done on several related aspect. Further, it is also found that the same study has already conducted by Anwar, (2018), exploring the Pakistani contribution to the same journals from 2008-2017. Finally, the present study has designed to explore and compare the contribution of Indian and Pakistani authors to Library Philosophy and Practice (LPP) for a period of ten years from 2008-17. The India and Pakistan are two significant countries in South-East Asia those shares historical, political, and economic background together. This study is based on the bibliometric analysis on LPP covered a period from 2008-2017 in which 432 articles (86 articles from Pakistani & 346 articles from Indian authors) were published during the marked period. Study examines the various bibliometric parameters such as authorship pattern, geographical distribution, major authors, and length of articles. Study finds that 41.8% (181) articles were contributed by two authors. Further, the study found that 11-15 pages of articles published in majority by the authors in both countries

High dynamic range color image enhancement using fuzzy logic and bacterial foraging

High dynamic range images contain both the underexposed and the overexposed regions. The enhancement of the underexposed and the overexposed regions is the main concern of this paper. Two new transformation functions are proposed to modify the fuzzy membership values of under and the overexposed regions of an image respectively.For the overexposed regions, a rectangular hyperbolic function is used while for the underexposed regions, an S-function is applied. The shape and range of these functions can be controlled by the parameters involved, which are optimized using the bacterial foraging optimization algorithm so as to obtain the enhanced image. The hue, saturation, and intensity (HSV) color space is employed for the purpose of enhancement, where the hue component is preserved to keep the original color composition intact. This approach is applicable to a degraded image of mixed type. On comparison, the proposed transforms yield better results than the existing transformation functions17 for both the underexposed and the overexposed regions

3D Textured Model Encryption via 3D Lu Chaotic Mapping

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI

The role of casein in supporting the operation of surface bound kinesin

Microtubules and associated motor proteins such as kinesin are envisioned for applications such as bioseparation and molecular sorting to powering hybrid synthetic mechanical devices. One of the challenges in realizing such systems is retaining motor functionality on device surfaces. Kinesin motors adsorbed onto glass surfaces lose their functionality or ability to interact with microtubules if not adsorbed with other supporting proteins. Casein, a milk protein, is commonly used in microtubule motility assays to preserve kinesin functionality. However, the mechanism responsible for this preservation of motor function is unknown. To study casein and kinesin interaction, a series of microtubule motility assays were performed where whole milk casein, or its αs1 and αs2, β or κ subunits, were introduced or omitted at various steps of the motility assay. In addition, a series of epifluorescence and total internal reflection microscopy (TIRF) experiments were conducted where fluorescently labeled casein was introduced at various steps of the motility assay to assess casein-casein and casein-glass binding dynamics. From these experiments it is concluded that casein forms a bi-layer which supports the operation of kinesin. The first tightly bound layer of casein mainly performs the function of anchoring the kinesin while the second more loosely bound layer of casein positions the head domain of the kinesin to more optimally interact with microtubules. Studies on individual casein subunits indicate that β casein was most effective in supporting kinesin functionality while κ casein was found to be least effective

High-efficiency WSi superconducting nanowire single-photon detectors for quantum state engineering in the near infrared

We report on high-efficiency superconducting nanowire single-photon detectors based on amorphous WSi and optimized at 1064 nm. At an operating temperature of 1.8 K, we demonstrated a 93% system detection efficiency at this wavelength with a dark noise of a few counts per second. Combined with cavity-enhanced spontaneous parametric down-conversion, this fiber-coupled detector enabled us to generate narrowband single photons with a heralding efficiency greater than 90% and a high spectral brightness of $0.6\times10^4$ photons/(s$\cdot$mW$\cdot$MHz). Beyond single-photon generation at large rate, such high-efficiency detectors open the path to efficient multiple-photon heralding and complex quantum state engineering

Machine learned calibrations to high-throughput molecular excited state calculations

Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions), so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique [eXtended Tight Binding based simplified Tamm-Dancoff approximation (xTB-sTDA)] against a higher accuracy one (time-dependent density functional theory). Testing the calibration model shows an approximately sixfold decrease in the error in-domain and an approximately threefold decrease in the out-of-domain. The resulting mean absolute error of ∼0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates that machine learning can be used to develop a cost-effective and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines