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We report the construction of two Bacillus subtilis expression vectors, pBNS1/pBNS2. Both vectors are based on the strong promoter P43 and the ampicillin resistance gene expression cassette. Additionally, a fragment with the Shine-Dalgarno sequence and a multiple cloning site (BamHI, SalI, SacI, XhoI, PstI, SphI) were inserted. The coding region for the amyQ (encoding an amylase) signal peptide was fused to the promoter P43 of pBNS1 to construct the secreted expression vector pBNS2. The applicability of vectors was tested by first generating the expression vectors pBNS1-GFP/pBNS2-GFP and then detecting for green fluorescent protein gene expression. Next, the mannanase gene from B. pumilus Nsic-2 was fused to vector pBNS2 and we measured the mannanase activity in the supernatant. The mannanase total enzyme activity was 8.65 U/ml, which was 6 times higher than that of the parent strain. Our work provides a feasible way to achieve an effective transformation system for gene expression in B. subtilis and is the first report to achieve B. pumilus mannanase secretory expression in B. subtilis.

Currently, most implanted visual prosthetic systems generate vision by translating sensor data from a headworn camera into electrical stimulation of the human vision system. Unfortunately, the resulting bionic vision has low spatial resolution and limited dynamic range. This dramatically reduces the usefulness of bionic vision in many real world scenarios. Historically, this problem is treated as immutable pathology. Recently, image processing has been proposed as a potential remedy to improve the useability of bionic vision. We explore another alternative: Combining multiple sensing modalities and robotic sensing algorithms. This paper gives a top level summary of ongoing research exploring this alternative.

ADCC can be essential for anti-viral immunity and supports many tumor immunotherapies. It is a challenge to quantify and one wants a fast assay that uses few lymphocytes when patient blood is limited. Three considerations affected our new assay design: 1) normal B cells within PBMCs introduce ‘cold target’ competition of radiolabeled tumor B cells if anti-B cell antibodies are present in the ADCC assays; 2) type 1 antibodies to CD20 B cell epitopes are internalized and cleared from the cell surface while type 2 anti-CD20 antibodies remain external; and 3) target cells with MHC I initiate KIR-regulation of lysis that will vary among donors. To address these issues, we used Daudi B leukemia cells that lack MHC I and pretreated the Daudi cells with the humanized nonfucosylated/glycoengineered monoclonal type 2 anti-CD20 antibody obinutuzumab (GA101 G2). Target cell pretreatment prevented ‘cold target’ competition. We used 4 hr 51Cr-release for its sensitivity and varied the PBMC to target (E:T) cell ratios to obtain lytic units. Flow cytometric counts of CD16A Fc-receptor positive cells with TrueCountR beads determined the exact number of ADCC effectors within the unfractionated PBMCs. There was low NK activity to Daudi cells, which required subtraction of simultaneous controls without antibody. We plotted ADCC activity as a log function of the E:T ratios and observed that ADCC was dependent upon interactions of multiple CD16A-positive effectors per target cell, which was expressed as lytic units per 106 CD16A cells, that varied 50 fold among donors. In addition, the linear slopes of CD16A cell lytic cooperativity varied 8 fold. In summary, we have established a fast quantitative assay for comparison of human ADCC that uses as few as 8 million PBMCs.

This paper presents a 6 degrees of freedom egomotion estimation method using Iterative Closest Point (ICP) for low cost and low accuracy range cameras such as the Microsoft Kinect. Instead of Euclidean coordinates, the method uses inverse depth coordinates which better conforms to the error characteristics of raw sensor data. Novel inverse depth formulations of point-to-point and point-to-plane error metrics are derived as part of our implementation. The implemented system runs in real time at an average of 28 frames per second (fps) on a standard computer. Extensive experiments were performed to evaluate different combinations of error metrics and parameters. Results show that our system is accurate and robust across a variety of motion trajectories. The point-to-plane error metric was found to be the best at coping with large inter-frame motion while remaining accurate and maintaining real time performance.