Unity Biotechnology (United States)

Background/Objectives: The objective of this study was to determine the treatment effect of foselutoclax in neovascular age-related macular degeneration (AMD) by multifocal electroretinography (mfERG) and evaluate mfERG as a potential clinical endpoint in AMD studies. Methods: A total of five subjects were included in the study who had active choroidal neovascularization and a history of at least two anti-vascular endothelial growth factor (VEGF) injections in the last 6 months. Subjects received a 50 µL intravitreal injection of foselutoclax at the baseline visit and Weeks 4, 24, and 28 of the study period. Results: After foselutoclax treatment, the largest improvement in the mfERG N1-P1 response density occurred at Week 8 as three of five subjects achieved a ≥20% gain. In addition, three of five subjects demonstrated a BCVA improvement of ≥5 ETDRS letters over baseline at Weeks 4, 8, and 24. The mean change in BCVA demonstrated statistical significance in Weeks 4 and 8, showing increases of 5 (p = 0.02) and 6.2 (p = 0.02) letters, respectively. Conclusions: Foselutoclax treatment was shown to have the potential to recover outer retinal function as determined by mfERG and BCVA at approximately Week 8 of treatment.

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics.

We elucidated the molecular cross-talk between cartilage and synovium in osteoarthritis, the most widespread arthritis in the world, using the powerful tool of single-cell RNA-sequencing. Multiple cell types were identified based on profiling of 10,640 synoviocytes and 26,192 chondrocytes: 12 distinct synovial cell types and 7 distinct articular chondrocyte phenotypes from matched tissues. Intact cartilage was enriched for homeostatic and hypertrophic chondrocytes, while damaged cartilage was enriched for prefibro- and fibro-, regulatory, reparative and prehypertrophic chondrocytes. A total of 61 cytokines and growth factors were predicted to regulate the 7 chondrocyte cell phenotypes. Based on production by > 1% of cells, 55% of the cytokines were produced by synovial cells (39% exclusive to synoviocytes and not expressed by chondrocytes) and their presence in osteoarthritic synovial fluid confirmed. The synoviocytes producing IL-1beta (a classic pathogenic cytokine in osteoarthritis), mainly inflammatory macrophages and dendritic cells, were characterized by co-expression of surface proteins corresponding to HLA-DQA1, HLA-DQA2, OLR1 or TLR2. Strategies to deplete these pathogenic intra-articular cell subpopulations could be a therapeutic option for human osteoarthritis.

The restricted expression pattern of B-cell maturation antigen (BCMA) makes it an ideal tumor-associated antigen (TAA) for the treatment of myeloma. BCMA has been targeted by both CD3 bispecific antibody and antibody-drug conjugate (ADC) modalities, but a true comparison of modalities has yet to be performed. Here we utilized a single BCMA antibody to develop and characterize both a CD3 bispecific and 2 ADC formats (cleavable and noncleavable) and compared activity both in vitro and in vivo with the aim of generating an optimal therapeutic. Antibody affinity, but not epitope was influential in drug activity and hence a high-affinity BCMA antibody was selected. Both the bispecific and ADCs were potent in vitro and in vivo, causing dose-dependent cell killing of myeloma cell lines and tumor regression in orthotopic myeloma xenograft models. Primary patient cells were effectively lysed by both CD3 bispecific and ADCs, with the bispecific demonstrating improved potency, maximal cell killing, and consistency across patients. Safety was evaluated in cynomolgus monkey toxicity studies and both modalities were active based on on-target elimination of B lineage cells. Distinct nonclinical toxicity profiles were seen for the bispecific and ADC modalities. When taken together, results from this comparison of BCMA CD3 bispecific and ADC modalities suggest better efficacy and an improved toxicity profile might be achieved with the bispecific modality. This led to the advancement of a bispecific candidate into phase I clinical trials.

The workshop program of the Thirty‐Third AAAI Conference on Artificial Intelligence (AAAI‐19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27 and 28, 2019. There were 16 workshops in the program: Affective Content Analysis: Modeling Affect‐in‐Action; Agile Robotics for Industrial Automation Competition; Artificial Intelligence for Cyber Security; Artificial Intelligence Safety; Dialog System Technology Challenge; Engineering Dependable and Secure Machine Learning Systems; Games and Simulations for Artificial Intelligence; Health Intelligence; Knowledge Extraction from Games; Network Interpretability for Deep Learning; Plan, Activity, and Intent Recognition; Reasoning and Learning for Human‐Machine Dialogues; Reasoning for Complex Question Answering; Recommender Systems Meet Natural Language Processing; Reinforcement Learning in Games; and Reproducible AI. This report contains brief summaries of all the workshops that were held.

It is not possible to make a clear statement whether the ongoing globalisation in the context of system development holds more opportunities or more risks for companies. However, it is undisputed in this context that the system development projects have become and will continue to gain immense complexity in the context of increasing economic interdependencies. Considering the possibilities of the Internet of Things with the multitude of networked, autonomous systems, it quickly becomes clear that the complexity of system development will take on even greater dimensions in the future. The companies have recognized that they can only rise to this challenge with Systems Engineering and that they only can survive on the market of “Systems-of-Systems” by applying interdisciplinary development methods. For this reason the “SE Quick Check” was developed, a pragmatic instrument with which product development in companies can be analysed and tailored in order to lay the foundation for the introduction or targeted optimization of individually coordinated Systems Engineering. As shown in a variety of applications in companies, the approach can be used to determine an SE maturity level and an individual SE target in the shortest possible time. Based on this, measures can be defined and implemented directly to achieve the SE target image.

Bevor wir uns intensiv mit den zwei Megatrends der IT und ihrer Bedeutung fur das heutige Business aus der Perspektive eines mittelstandischen Unternehmens auseinandersetzen, mochten wir den Blick auf einen Prozess richten, ohne den es die Megatrends – wie sie sich uns aktuell darstellen – gar nicht geben wurde: Wir meinen damit das ungeheure Tempo, mit dem die Entwicklung in der Mikroelektronik fortschreitet und aus dem heraus immer neue Innovationen in der IT in anscheinend immer kurzeren Zeitabstanden hervorgebracht werden.

There are number of labor intensive and time consuming tasks in CG such as lighting setup, special effects and image post-process tuning, primarily due to lack of interactive feedback in traditional tools. Recently there are number of solutions which introduce interactivity via static scene relighting or by employing game engines for real-time rendering, however subject of full production pipeline involved is rarely touched.