Objective: COVID19 has caused a global and ongoing pandemic. The need for population seroconversion data is apparent to monitor and respond to the pandemic. Using a lateral flow assay (LFA) testing platform, the seropositivity in 63 New York Blood Center (NYBC) Convelescent Plasma (CP) donor samples were evaluated for the presence of COVID19 specific IgG and IgM. Results: CP donors showed diverse antibody result. Convalescent donor plasma contains SARS-CoV-2 specific antibodies. Weak antibody bands may identify low titer CP donors. LFA tests can identify antibody positive individuals that have recovered from COVID19. Confirming suspected cases using antibody detection could help inform the patient and the community as to the relative risk to future exposure and a better understanding of disease exposure.

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing -data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

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This paper presents Unified Communication X (UCX), a set of network APIs and their implementations for high throughput computing. UCX comes from the combined effort of national laboratories, industry, and academia to design and implement a high-performing and highly-scalable network stack for next generation applications and systems. UCX design provides the ability to tailor its APIs and network functionality to suit a wide variety of application domains and hardware. We envision these APIs to satisfy the networking needs of many programming models such as Message Passing Interface (MPI), OpenSHMEM, Partitioned Global Address Space (PGAS) languages, task-based paradigms and I/O bound applications. To evaluate the design we implement the APIs and protocols, and measure the performance of overhead-critical network primitives fundamental for implementing many parallel programming models and system libraries. Our results show that the latency, bandwidth, and message rate achieved by the portable UCX prototype is very close to that of the underlying driver. With UCX, we achieved a message exchange latency of 0.89 us, a bandwidth of 6138.5 MB/s, and a message rate of 14 million messages per second. As far as we know, this is the highest bandwidth and message rate achieved by any network stack (publicly known) on this hardware.

Long Term Evolution (LTE) is defined by the Third Generation Partnership Project (3GPP) standards as Release 8/9. The LTE supports at max 20 MHz channel bandwidth for a carrier. The number of LTE users and their applications are increasing, which increases the demand on the system BW. A new feature of the LTE-Advanced (LTE-A) which is defined in the 3GPP standards as Release 10/11 is called Carrier Aggregation (CA), this feature allows the network to aggregate more carriers in-order to provide a higher bandwidth. Carrier Aggregation has three main cases: Intra-band contiguous, Intra-band non-contiguous, Inter-band contiguous. The main contribution of this paper was in implementing the Intra-band contiguous case by modifying the LTE-Sim-5, then evaluating the Quality of Service (QoS) performance of the Modified Largest Weighted Delay First (MLWDF), the Exponential Rule (Exp-Rule), and the Logarithmic Rule (Log-Rule) scheduling algorithms over LTE/LTE-A in the Down-Link direction. The QoS performance evaluation is based on the system's average throughput, Packet Loss Rate (PLR), average packet delay, and fairness among users. Simulation results show that the use of CA improved the system's average throughput, and almost doubled the system's maximum throughput. It reduced the PLR values almost by a half. It also reduced the average packet delay by 20-40\% that varied according to the video bit-rate and the number of users. The fairness indicator was improved with the use of CA by a factor of 10-20%.

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Overall health care spending in the United States is equivalent to more than 15% of GDP, yet outcomes rank below the top 25 in most quality categories when compared with other Organization for Economic Cooperation and Development (OECD) countries. The majority of spending is consumed by small patient populations with chronic diseases. Experts believe increased patient‐physician shared decision making (SDM) should result in better overall longitudinal care but understanding the physician's role in facilitating SDM is limited. Structural equation modelling was applied to results of a 2016 questionnaire‐based survey of 330 US physicians who treat approximately 55% of primary immune deficiency requiring immune globulin therapy; it tested the relationship between slow/rational vs fast/intuitive decision‐making styles and SDM as mediated by patient‐centric care and moderated by physician's trust in the patient. The results showed a statistically significant relationship between slow/rational decision making and SDM. The results also suggest differences related to age, gender, education, and race but no differences related to trust.

A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.