32 research outputs found
One-step simultaneous liquid phase exfoliation-induced chirality in graphene and their chirality-mediated microRNA delivery
Graphene (G) has established itself as an exciting prospect for a broad range of applications owing to its remarkable properties. Recent innovations in chiral nanosystems have led to sensors, drug delivery, catalysis, etc. owing to the stereospecific interactions between various nanosystems and enantiomers. As the molecular structure of G itself is achiral introducing chirality in G by simple attachment of a functional group (a chiral ligand) on the G nanosheet may result in more diverse applications. Herein, we demonstrate direct liquid phase exfoliation and chiral induction in G nanosheets abbreviated as L-graphene and D-graphene in the presence of chiral L-tyrosine and D-tyrosine and by applying high-temperature sonication. The obtained exfoliated nanosheets demonstrated stable chirality confirmed by circular dichroism. Fourier transform infrared (FTIR) spectra, Raman spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and differential scanning calorimetry (DSC) showed functional, structural, morphological, surface, and thermal characteristics of L-graphene and D-graphene. The hemo-compatibility of these chiral graphenes was evaluated for the very first time utilizing human red blood cells. Lastly, for the very first time, an attempt was made to explore enantiomeric binding between chiral L-graphene and D-graphene with microRNA (miR-205) and their possibility towards chirality-mediated gene delivery in prostate cancerous cells
End-to-End Spoken Language Understanding using RNN-Transducer ASR
We propose an end-to-end trained spoken language understanding (SLU) system
that extracts transcripts, intents and slots from an input speech utterance. It
consists of a streaming recurrent neural network transducer (RNNT) based
automatic speech recognition (ASR) model connected to a neural natural language
understanding (NLU) model through a neural interface. This interface allows for
end-to-end training using multi-task RNNT and NLU losses. Additionally, we
introduce semantic sequence loss training for the joint RNNT-NLU system that
allows direct optimization of non-differentiable SLU metrics. This end-to-end
SLU model paradigm can leverage state-of-the-art advancements and pretrained
models in both ASR and NLU research communities, outperforming recently
proposed direct speech-to-semantics models, and conventional pipelined ASR and
NLU systems. We show that this method improves both ASR and NLU metrics on both
public SLU datasets and large proprietary datasets
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages
Search for High-energy Neutrinos from Binary Neutron Star Merger GW170817 with ANTARES, IceCube, and the Pierre Auger Observatory
Technology Mapping for Low Power in Logic Synthesis
Traditionally, three metrics have been used to evaluate the quality of logic circuits -- size, speed and testability. Consequently, synthesis techniques have strived to optimize for one or more of these metrics, resulting in a large body of research in optimal logic synthesis. As a consequence of this research, we have today very powerful techniques for synthesis targeting area and testability; and to a lesser extent, circuit speed. The last couple of years have seen the addition of another dimension in the evaluation of circuit quality -- its power requirements. Low power circuits are emerging as an important application domain, and synthesis for low power is demanding attention. The research presented in this paper addresses one aspect of low power synthesis. It focuses on the problem of mapping a technology independent circuit to a technology specific one, using gates from a given library, with power as the optimization metric. We believe that the difficulty in obtaining accurate mo..
Guarded Evaluation: Pushing Power Management to Logic Synthesis/Design
The need to reduce the power consumption of the next generation of digital systems is clearly recognized. At the system level, power management is a very powerful technique and delivers large and unambiguous savings. This paper describes the development and application of algorithms that use ideas similar to power management, but that are applicable to logic level synthesis/design. The proposed approach is termed guarded evaluation. The main idea here is to determine, on a per clock cycle basis, which parts of a circuit are computing results that will be used, and which are not. The sections that are not needed are then “shut off”, thus saving the power used in all the useless transitions in that part of the circuit. Initial experiments indicate substantial power savings and the strong potential of this approach. While thi
The combined effect of thermal and chemotherapy on HeLa cells using magnetically actuated smart textured fibrous system
Thermal therapy combined with chemotherapy is one of the advanced and efficient methods to eradicate cancer. In this work, we fabricated magnetically actuated smart textured (MAST) fibrous systems and studied their candidacy for cancer treatment. The polycaprolactone-Fe3O4 based MAST fibers were fabricated using electrospinning technique. These MAST fibrous systems contained carbogenic quantum dots as a tracking agent and doxorubicin hydrochloride anticancer drug. Additionally, as fabricated MAST fibrous systems were able to deliver anticancer drug and heat energy simultaneously to kill HeLa cells in a 10 min period in vitro. After treatment, the metabolic activity and morphology of HeLa cells were analyzed. In addition, the mechanism of cell death was studied using flow cytometry. Interestingly, the navigation of these systems in the fluid can be controlled with the application of gradient magnetic field. (c) 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 40-51, 2018
Graphical Basis Partitions
A partition of an integer n is graphical if it is the degree sequence of a simple, undirected graph. It is an open question whether the fraction of partitions of n which are graphical approaches 0 as n approaches infinity. A partition is basic if the number of dots in its Ferrers graph is minimum among all partitions with the same rank vector as π. In this paper, we investigate graphical partitions via basis partitions. We show how to efficiently count and generate graphical basis partitions and how to use them to count graphical partitions. We give empirical evidence which leads us to conjecture that, as n approaches infinity, the fraction of basis partitions of n which are graphical approaches the same limit as the fraction of all partitions of n which are graphical