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Many polymer-stabilized silver nanoparticles (AgNPs) with enhanced antibacterial properties have been synthesized, but very little is known about the fate of these materials and their interactions with microbes in physiological solutions. In this study, we evaluated the role of cellulose nanofibrils (CNFs) in stabilizing AgNPs (CNF-AgNPs) in a bacterial growth medium, determined the antibacterial properties of CNF-AgNPs and assessed the CNF and CNF-AgNP interactions with bacteria. The attachment of AgNPs to CNFs significantly improved the stability and dispersibility of AgNPs in the bacterial growth medium compared to colloidal AgNPs. The CNF-AgNPs exerted a concentration-dependent growth-inhibitory effect on E. coli. An extracellular polysaccharide (EPS)-like structure was formed around the E. coli bacterium when incubated with a sub-lethal CNF-AgNP concentration, which led to the clustering of neighboring bacteria. No EPS-like structure was observed around the bacterium incubated with high concentration of CNF-AgNPs. Overall, this study demonstrated that CNFs significantly improved the stability and dispersibility of AgNPs in physiological medium, validated the antimicrobial potential of CNF-AgNPs, and provided an insight into the physio-chemical interactions between bacteria and CNF-AgNPs in a physiological growth medium.

Selection of personalized chemotherapy regimen for individual patients has significant potential to improve chemotherapy efficacy and to reduce the deleterious effects of ineffective chemotherapy drugs. In this study, a rapid and high-throughput in vitro drug response assay was developed using a combination of microwell array and molecular imaging. The microwell array provided high-throughput analysis of drug response, which was quantified based on the reduction in intracellular uptake (2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy-D-glucose) (2-NBDG). Using this synergistic approach, the drug response measurement was completed within 4 h, and only a couple thousand cells were needed for quantification. The broader application of this microwell molecular imaging approach was demonstrated by evaluating the drug response of two cancer cell lines, cervical (HeLa) and bladder (5637) cancer cells, to two distinct classes of chemotherapy drugs (cisplatin and paclitaxel). This approach did not require an extended cell culturing period, and the quantification of cellular drug response was 4-16 times faster compared with other cell-microarray drug response studies. Moreover, this molecular imaging approach had comparable sensitivity to traditional cell viability assays, i.e., the MTT assay and propidium iodide labeling of cellular nuclei;and similar throughput results as flow cytometry using only 1,000-2,000 cells. Given the simplicity and robustness of this microwell molecular imaging approach, it is anticipated that the assay can be adapted to quantify drug responses in a wide range of cancer cells and drugs and translated to clinical settings for a rapid in vitro drug response using clinically isolated samples.

A highly ordered array of T7 bacteriophages was created by the electrophoretic capture of phages onto a nanostructured array with wells that accommodated the phages. Electrophoresis of bacteriophages was achieved by applying a positive potential on an indium tin oxide electrode at the bottom of the nanowells. Nanoscale arrays of phages with different surface densities were obtained by changing the electric field applied to the bottom of the nanowells. The applied voltage was shown to be the critical factor in generating a well-ordered phage array. The number of wells occupied by a phage, and hence the concentration of phages in a sample solution, could be quantified by using a DNA intercalating dye that rapidly stains the T7 phage. The fluorescence signal was enhanced by the intrinsic photonic effect made available by the geometry of the platform. It was shown that the quantification of phages on the array was 6 orders of magnitude better than could be obtained with a fluorescent plate reader. The device opens up the possibility that phages can be detected directly without enrichment or culturing, and by detecting phages that specifically infect bacteria of interest, rapid pathogen detection becomes possible.

Abstract Background Overexpression and abnormal accumulation of aggregated α-synuclein (αS) have been linked to Parkinson's disease (PD) and other synucleinopathies. αS can misfold and adopt a variety of morphologies but recent studies implicate oligomeric forms as the most cytotoxic species. Both genetic mutations and chronic exposure to neurotoxins increase αS aggregation and intracellular reactive oxygen species (ROS), leading to mitochondrial dysfunction and oxidative damage in PD cell models. Results Here we show that curcumin can alleviate αS-induced toxicity, reduce ROS levels and protect cells against apoptosis. We also show that both intracellular overexpression of αS and extracellular addition of oligomeric αS increase ROS which induces apoptosis, suggesting that aggregated αS may induce similar toxic effects whether it is generated intra- or extracellulary. Conclusions Since curcumin is a natural food pigment that can cross the blood brain barrier and has widespread medicinal uses, it has potential therapeutic value for treating PD and other neurodegenerative disorders.

Background: Overexpression and abnormal accumulation of aggregated a-synuclein (aS) have been linked to Parkinson's disease (PD) and other synucleinopathies. aS can misfold and adopt a variety of morphologies but recent studies implicate oligomeric forms as the most cytotoxic species. Both genetic mutations and chronic exposure to neurotoxins increase aS aggregation and intracellular reactive oxygen species (ROS), leading to mitochondrial dysfunction and oxidative damage in PD cell models. Results: Here we show that curcumin can alleviate aS-induced toxicity, reduce ROS levels and protect cells against apoptosis. We also show that both intracellular overexpression of aS and extracellular addition of oligomeric aS increase ROS which induces apoptosis, suggesting that aggregated aS may induce similar toxic effects whether it is generated intra- or extracellulary. Conclusions: Since curcumin is a natural food pigment that can cross the blood brain barrier and has widespread medicinal uses, it has potential therapeutic value for treating PD and other neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Background: Overexpression and abnormal accumulation of aggregated alpha-synuclein (alphaS) have been linked to Parkinson's disease (PD) and other synucleinopathies. alphaS can misfold and adopt a variety of morphologies but recent studies implicate oligomeric forms as the most cytotoxic species. Both genetic mutations and chronic exposure to neurotoxins increase alphaS aggregation and intracellular reactive oxygen species (ROS), leading to mitochondrial dysfunction and oxidative damage in PD cell models.Results: Here we show that curcumin can alleviate alphaS-induced toxicity, reduce ROS levels and protect cells against apoptosis. We also show that both intracellular overexpression of alphaS and extracellular addition of oligomeric alphaS increase ROS which induces apoptosis, suggesting that aggregated alphaS may induce similar toxic effects whether it is generated intra- or extracellulary.Conclusions: Since curcumin is a natural food pigment that can cross the blood brain barrier and has widespread medicinal uses, it has potential therapeutic value for treating PD and other neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

The article explores the impact of pretrial detention on individuals with various disabilities, such as physical disabilities, chronic illnesses, developmental disabilities, and psychiatric disabilities. It discusses how the Rehabilitation Act and the Americans with Disabilities Act require alternatives to detention as a reasonable modification of government practice. It argues that pretrial incarceration often violates the Rehabilitation Act and the Americans with Disabilities Act.

With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energyefficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors. Finally, to decrease the sampling frequency of energy-intensive sensors, we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors. To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks. [ABSTRACT FROM AUTHOR]

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of > 80%, 11-15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression. [ABSTRACT FROM AUTHOR]

A fast algorithm for computing the non-parametric maximum likelihood estimate of a mixing distribution is presented. At each iteration, the algorithm adds new important points to the support set as guided by the gradient function, updates all mixing proportions via a quadratically convergent method and discards redundant support points straightaway. With its convergence being theoretically established, numerical studies show that it is very fast and stable, compared with several other algorithms that are available in the literature. [ABSTRACT FROM AUTHOR]