Feasibility of Coacervate-Like Nanostructure for Instant Drug Nanoformulation.

Despite the enormous advancements in nanomedicine research, a limited number of nanoformulations are available on the market, and few have been translated to clinics. An easily scalable, sustainable, and cost-effective manufacturing strategy and long-term stability for storage are crucial for successful translation. Here, we report a system and method to instantly formulate NF achieved with a nanoscale polyelectrolyte coacervate-like system, consisting of anionic pseudopeptide poly(l-lysine isophthalamide) derivatives, polyethylenimine, and doxorubicin (Dox) via simple "mix-and-go" addition of precursor solutions in seconds. The coacervate-like nanosystem shows enhanced intracellular delivery of Dox to patient-derived multidrug-resistant (MDR) cells in 3D tumor spheroids. The results demonstrate the feasibility of an instant drug formulation using a coacervate-like nanosystem. We envisage that this technique can be widely utilized in the nanomedicine field to bypass the special requirement of large-scale production and elongated shelf life of nanomaterials.

Separation of Radioactive Ruthenium from Alkaline Solution: A Solvent Extraction and Detailed Mechanistic Approach.

A solvent extraction-based technique has been utilized to study the separation of ruthenium from simulated alkaline solution using Aliquat 336 as the extractant and isodecyl alcohol (IDA) as the phase modifier in n-dodecane. The effects of various experimental parameters such as solution pH, mixing time, concentration of Aliquat 336 and IDA, role of citric acid as the aqueous phase modifier/complexing agent, and stripping agents have been evaluated. It was observed that with the increase in the solution pH, the extraction efficiency increases gradually. However, when citric acid was added into the aqueous solution, an overall increase (from ∼20 to 91%) in ruthenium extraction is observed. 20 min of the mixing time was found to be sufficient to reach the extraction equilibrium. Solution composition was optimized as 50% Aliquat 336 and 10% IDA in n-dodecane (v/v) for maximum extraction. The stripping of ruthenium from the loaded organic phase has been studied using HCl and HNO3. The result indicates that in the presence of 8 M HNO3, ∼73% of ruthenium can be back extracted to the aqueous phase in a single contact. The stripping efficiency of HNO3 was found to be higher than that of HCl. Active studies with 106Ru as the radiotracer were also performed and monitored using a HPGe detector. The same method was implemented for extraction studies with real waste solution in the presence of other radionuclides such as 137Cs, 90Sr, and 125Sb. The presence of the chemical species in aqueous as well as organic phase has been identified using UV-vis spectrophotometry, Fourier transform infrared spectroscopy, and Raman spectroscopy. Density functional theory-based quantum mechanical calculations have been performed in order to unravel the extraction mechanism with the present solvent system.

Non-random distribution of vacuoles in Schizosaccharomyces pombe.

A central question in eukaryotic cell biology asks, during cell division, how is the growth and distribution of organelles regulated to ensure each daughter cell receives an appropriate amount. For vacuoles in budding yeast, there are well described organelle-to-cell size scaling trends as well as inheritance mechanisms involving highly coordinated movements. It is unclear whether such mechanisms are necessary in the symmetrically dividing fission yeast, Schizosaccharomyces pombe, in which random partitioning may be utilized to distribute vacuoles to daughter cells. To address the increasing need for high-throughput analysis, we are augmenting existing semi-automated image processing by developing fully automated machine learning methods for locating vacuoles and segmenting fission yeast cells from brightfield and fluorescence micrographs. All strains studied show qualitative correlations in vacuole-to-cell size scaling trends, i.e. vacuole volume, surface area, and number all increase with cell size. Furthermore, increasing vacuole number was found to be a consistent mechanism for the increase in total vacuole size in the cell. Vacuoles are not distributed evenly throughout the cell with respect to available cytoplasm. Rather, vacuoles show distinct peaks in distribution close to the nucleus, and this preferential localization was confirmed in mutants in which nucleus position is perturbed. Disruption of microtubules leads to quantitative changes in both vacuole size scaling trends and distribution patterns, indicating the microtubule cytoskeleton is a key mechanism for maintaining vacuole structure.

Annotation-free learning of plankton for classification and anomaly detection.

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.

Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

Linear Support Tensor Machine With LSK Channels: Pedestrian Detection in Thermal Infrared Images.

Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here, we propose a mid-level attribute in the form of the multidimensional template, or tensor, using local steering kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e.g., histograms in histogram of oriented gradients). In order to learn the LSK tensor, we introduce a new image similarity kernel following the popular maximum margin framework of support vector machines facilitating a relatively short and simple training phase for building a rigid pedestrian detector. Tensor representation has several advantages, and indeed, LSK templates allow exact acceleration of the sluggish but de facto sliding window-based detection methodology with multichannel discrete Fourier transform, facilitating very fast and efficient pedestrian localization. The experimental studies on publicly available thermal infrared images justify our proposals and model assumptions. In addition, the proposed work also involves the release of our in-house annotations of pedestrians in more than 17 000 frames of OSU color thermal database for the purpose of sharing with the research community.

One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity.

One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace. Unlike principal components that preserve global structure of feature space, we actually seek a linear approximation to the Laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors. Our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection, obviating the computationally expensive sliding window search. We leverage the power of Fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses (for pose estimation) within a reasonably short time. Our approach to one shot detection is training-free, and experiments on the standard data sets confirm the efficacy of our model. Besides, low computation cost of the proposed (codebook-free) object detector facilitates rather straightforward query detection in large data sets including movie videos.

Development of an extractive spectrophotometric method for estimation of uranium in ore leach solutions using 2-ethylhexyl phosphonic acid-mono-2-ethylhexyl ester (PC88A) and tri-n-octyl phosphine oxide (TOPO) mixture as extractant and 2-(5-bromo-2-pyridylozo)-5-diethyl aminophenol (Br-PADAP) as chromophore.

An extractive spectrophotometric analytical method has been developed for the determination of uranium in ore leach solution. This technique is based on the selective extraction of uranium from multielement system using a synergistic mixture of 2-ethylhexyl phosphonic acid-mono-2-ethylhexyl ester (PC88A) and tri-n-octyl phosphine oxide (TOPO) in cyclohexane and color development from the organic phase aliquot using 2-(5-Bromo-2-pyridylazo)-5-diethyl aminophenol (Br-PADAP) as chromogenic reagent. The absorption maximum (λ(max)) for UO(2)(2+)-Br-PADAP complex in organic phase samples, in 64% (v/v) ethanol containing buffer solution (pH 7.8) and 1,2-cyclohexylenedinitrilotetraacetic acid (CyDTA) complexing agent, has been found to be at 576 nm (molar extinction coefficient, ɛ: 36,750 ± 240 L mol(-1)cm(-1)). Effects of various parameters like stability of complex, ethanol volume, ore matrix, interfering ions etc. on the determination of uranium have also been evaluated. Absorbance measurements as a function of time showed that colored complex is stable up to > 24h. Presence of increased amount of ethanol in colored solution suppresses the absorption of a standard UO(2)(2+)-Br-PADAP solution. Analyses of synthetic standard as well as ore leach a solution show that for 10 determination relative standard deviation (RSD) is < 2%. The accuracy of the developed method has been checked by determining uranium using standard addition method and was found to be accurate with a 98-105% recovery rate. The developed method has been applied for the analysis of a number of uranium samples generated from uranium ore leach solutions and results were compared with standard methods like inductively coupled plasma emission spectrometry (ICPAES). The determined values of uranium concentrations by these methods are within ± 2%. This method can be used to determine 2.5-250 µg mL(-1) uranium in ore leach solutions with high accuracy and precision.

Development of a 99Mo/99mTc generator using alumina microspheres for industrial radiotracer applications.

A chromatographic (99)Mo/(99m)Tc generator for industrial applications has been developed using alumina microspheres synthesized through sol-gel process to obtain (99m)Tc in both aqueous and non-aqueous media. The sorbent was mesoporous, mechanically strong and possessed high surface area. (99m)Tc could be eluted from generator system using either acetone or 0.9% NaCl solution with appreciably high yields and high radiochemical as well as radionuclidic purity. The facile, versatile generator provides an efficient way to access (99m)Tc at industrial sites for radiotracer applications.

Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM.

We propose a generative model for constructing an efficient set of distinctive textures for recognizing architectural distortion in digital mammograms. In the first layer of the proposed two-layer architecture, the mammogram is analyzed by a multiscale oriented filter bank to form texture descriptor of vectorized filter responses. Our model presumes that every mammogram can be characterized by a "bag of primitive texture patterns" and the set of textural primitives (or textons) is represented by a mixture of Gaussians which builds up the second layer of the proposed model. The observed textural descriptor in the first layer is assumed to be a stochastic realization of one (hard mapping) or more (soft mapping) textural primitive(s) from the second layer. The results obtained on two publicly available datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM), demonstrate the efficacy of the proposed approach.