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Cell-free communication has the potential to significantly improve grant-free transmission in massive machine-type communication, wherein multiple access points jointly serve a large number of user equipments to improve coverage and spectral efficiency. In this paper, we propose a novel framework for joint active user detection (AUD), channel estimation (CE), and data detection (DD) for massive grant-free transmission in cell-free systems. We formulate an optimization problem for joint AUD, CE, and DD by considering both the sparsity of the data matrix, which arises from intermittent user activity, and the sparsity of the effective channel matrix, which arises from intermittent user activity and large-scale fading. We approximately solve this optimization problem with a box-constrained forward-backward splitting algorithm, which significantly improves AUD, CE, and DD performance. We demonstrate the effectiveness of the proposed framework through simulation experiments. 

Particles in biopharmaceutical products present high risks due to their detrimental impacts on product quality and safety. Identification and quantification of particles in drug products are important to understand particle formation mechanisms, which can help develop control strategies for particle formation during the formulation development and manufacturing process. However, existing analytical techniques such as microflow imaging and light obscuration measurement lack the sensitivity and resolution to detect particles with sizes smaller than 2 μm. More importantly, these techniques are not able to provide chemical information to determine particle composition. In this work, we overcome these challenges by applying the stimulated Raman scattering (SRS) microscopy technique to monitor the C−H Raman stretching modes of the proteinaceous particles and silicone oil droplets formed in the prefilled syringe barrel. By comparing the relative signal intensity and spectral features of each component, most particles can be classified as protein−silicone oil aggregates. We further show that morphological features are poor indicators of particle composition. Our method has the capability to quantify aggregation in protein therapeutics with chemical and spatial information in a label-free manner, potentially allowing high throughput screening or investigation of aggregation mechanisms. 

Periodontal diseases are prevalent worldwide and are linked to numerous other health conditions due to dysbiosis and chronic inflammatory state. Most periodontal diseases are caused by pathogenic bacteria that colonize dental tissues in the form of biofilm. Eradication of bacterial biofilms can be difficult to achieve due to the complex architecture of the teeth and gums which complicates the removal. Orthodontic wires and dental devices introduce additional hurdles to the adequate removal of biofilms by traditional methods since mechanical disruption via direct contact with toothbrush bristles, floss, and abrasive toothpaste is limited. Magnetically activated nanoparticles (NPs), specifically iron oxide nanoparticles (IONPs) that can be functionalized as antimicrobial particles and remotely controlled by magnetic fields, are of interest for oral biofilm eradication. We present data in multi-species bacterial cultures, established biofilms, human gingival keratinocytes, and human gingival fibroblast cells alone and in the presence of multispecies biofilm co-cultures to determine the safest, most efficacious IONP size ranges and treatment concentrations of active magnetic NPs for removal of dental biofilms. We report enhanced efficacy for IONPs coated with alginate vs. dextran, and small sizes (~8 nm vs. >20 nm in size) appear to exhibit enhanced antimicrobial efficacy. Human gingival keratinocyte (TIGK) cells in co-culture with treated and untreated multispecies biofilms in an in-vitro periodontitis model also exhibited a trend of reduced inflammatory markers in wells with IONP-treated biofilms.