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Laser particles providing bright, spectrally narrowband emission renders them suitable for use as cellular barcodes. Here, we demonstrate a microfluidic platform integrated with a high-speed spectrometer, capable of reading the emission from laser particles in fluidic channels and routing cells based on their optical barcodes. The sub-nanometer spectral emission of each laser particle enables us to distinguish individual cells labeled with hundreds of different laser colors in the near infrared. Furthermore, cells tagged with laser particles are sorted based on their spectral barcodes at a kilohertz rate by using a real-time field programmable gate array and 2-way electric field switch. We demonstrate several different flavors of sorting, including isolation of barcoded cells, and cells tagged with a specific laser color. We term this novel sorting technique laser particle activated cell sorting (LACS). This flow reading and sorting technology adds to the arsenal of single-cell analysis tools using laser particles.

Laser particles are an emerging technology with unique stimulated emission properties. The ability to produce laser particles over a wide spectral range is critical for massive wavelength-division ...

With the rise of new age media, social media marketing has become one of the most powerful tools in all marketers’ arsenal, and brands’ social media pages have become a way for companies to expand their awareness and customer base. In this paper, factors that attract a social media user to “follow” and engage with brands have been studied, namely, the content and tone used by brands on their social media, online brand communities for the brand, and the brand’s online activism have been identified as some of the important factors. For determination of results, factor analysis through principle axis factoring and varimax rotation has been done on SPSS v23.0. The results indicate that users care about whether brands take a stand on social, economic and political causes, and brand activism is therefore the most important factors for Gen-Z and Millennials in their decision to turn into brand followers. At the same time, users also want to feel like they belong to the brand’s ecosystem and that the brands look at them as important stakeholders, which is achieved through brand communities. This study will help marketers plan their social media marketing activities more effectively so as to give users what they want, and in turn increase their online reach.

Photodynamic therapy (PDT) is an established treatment modality using light-activatable drugs. Despite its unique cytotoxic mechanism, the shallow penetration of light has been a serious drawback limiting the applications of PDT. Here, we report bioluminescence-activated PDT (BL-PDT) using efficient bioluminescence resonance energy transfer (BRET) conjugates of clinically approved photosensitizers, Ce6, and luciferase proteins. A high photon-to-Ce6 conversion efficiency (80%), along with intracellular delivery by membrane-fusion liposomes, enabled effective cancer cell killing in vitro. In a syngeneic mouse model of aggressive 4T1 triple-negative breast cancer, as well as a xenograft model of MDA-MB-231 tumors, BL-PDT resulted in complete tumor remission and prevention of metastasis, as well as neo-adjuvant effects. Our result shows the promise of molecularly activable, clinically viable, depth-unlimited phototherapy.

Training deep neural networks often requires careful hyper-parameter tuning and significant computational resources. In this paper, we propose ConvTimeNet (CTN): an off-the-shelf deep convolutional neural network (CNN) trained on diverse univariate time series classification (TSC) source tasks. Once trained, CTN can be easily adapted to new TSC target tasks via a small amount of fine-tuning using labeled instances from the target tasks. We note that the length of convolutional filters is a key aspect when building a pre-trained model that can generalize to time series of different lengths across datasets. To achieve this, we incorporate filters of multiple lengths in all convolutional layers of CTN to capture temporal features at multiple time scales. We consider all 65 datasets with time series of lengths up to 512 points from the UCR TSC Benchmark for training and testing transferability of CTN: We train CTN on a randomly chosen subset of 24 datasets using a multi-head approach with a different softmax layer for each training dataset, and study generalizability and transferability of the learned filters on the remaining 41 TSC datasets. We observe significant gains in classification accuracy as well as computational efficiency when using pre-trained CTN as a starting point for subsequent task-specific fine-tuning compared to existing state-of-the-art TSC approaches. We also provide qualitative insights into the working of CTN by: i) analyzing the activations and filters of first convolution layer suggesting the filters in CTN are generically useful, ii) analyzing the impact of the design decision to incorporate multiple length decisions, and iii) finding regions of time series that affect the final classification decision via occlusion sensitivity analysis., Accepted at IJCNN 2019