T1 and T2 mapping of articular cartilage and menisci in early osteoarthritis of the knee using 3-Tesla magnetic resonance imaging

Purpose: 3-Tesla magnetic resonance imaging (MRI) T1 and T2 mapping to detect and quantify cartilage matrix and meniscal degeneration between normal healthy volunteers and early osteoarthritis patients. Material and methods: A prospective study including 25 patients and 10 healthy volunteers was done. Patients with symptoms of early osteoarthritis and Kellgren-Lawrence grade I-II on plain radiograph were included for MRI knee. Patients with inflammatory arthritis, infection, trauma, and history of knee surgery were excluded. Healthy, normal adult volunteers (preferably age and sex matched) without symptoms of osteoarthritis of the knee were drawn from patient's relatives/hospital employees/colleagues for MRI knee. Results: T1 and T2 relaxation time values of articular cartilage and menisci were significantly higher in osteoarthritis patients as compared to healthy volunteers. No significant difference was found in morphological thickness of articular cartilage and menisci in early osteoarthritis patients and healthy volunteers. Conclusions: T1 and T2 mapping are noninvasive MRI techniques reflecting changes in the biochemical composition of cartilage and menisci. T1 values reflect changes in proteoglycan content, and T2 values are sensitive to interaction between water molecules and collagen network. Mapping techniques assess early cartilage and meniscal matrix degeneration in osteoarthritis of the knee, and help in initiating treatment and monitoring disease progression. MRI is a sensitive modality for assessment of pathological changes in articular cartilage. With use of T1 and T2 mapping techniques, it is possible to evaluate the collagen network and proteoglycan content in articular cartilage and meniscal matrix

Generating Behaviorally Diverse Policies with Latent Diffusion Models

Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled learning a collection of behaviorally diverse, high performing policies. However, these methods typically involve storing thousands of policies, which results in high space-complexity and poor scaling to additional behaviors. Condensing the archive into a single model while retaining the performance and coverage of the original collection of policies has proved challenging. In this work, we propose using diffusion models to distill the archive into a single generative model over policy parameters. We show that our method achieves a compression ratio of 13x while recovering 98% of the original rewards and 89% of the original coverage. Further, the conditioning mechanism of diffusion models allows for flexibly selecting and sequencing behaviors, including using language. Project website: https://sites.google.com/view/policydiffusion/hom

Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations

Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions. Model-free methods penalize values at all unseen actions, while model-based methods are able to further exploit unseen states via model rollouts. However, such methods are handicapped in their ability to find unseen states far away from the available offline data due to two factors -- (a) very short rollout horizons in models due to cascading model errors, and (b) model rollouts originating solely from states observed in offline data. We relax the second assumption and present a novel unseen state augmentation strategy to allow exploitation of unseen states where the learned model and value estimates generalize. Our strategy finds unseen states by value-informed perturbations of seen states followed by filtering out states with epistemic uncertainty estimates too high (high error) or too low (too similar to seen data). We observe improved performance in several offline RL tasks and find that our augmentation strategy consistently leads to overall lower average dataset Q-value estimates i.e. more conservative Q-value estimates than a baseline

Complexity of rice Hsp100 gene family: lessons from rice genome sequence data

Elucidation of genome sequence provides an excellent platform to understand detailed complexity of the various gene families. Hsp100 is an important family of chaperones in diverse living systems. There are eight putative gene loci encoding for Hsp100 proteins in Arabidopsis genome. In rice, two full-length Hsp100 cDNAs have been isolated and sequenced so far. Analysis of rice genomic sequence by in silico approach showed that two isolated rice Hsp100 cDNAs correspond to Os05g44340 and Os02g32520 genes in the rice genome database. There appears to be three additional proteins (encoded by Os03g31300, Os04g32560 and Os04g33210 gene loci) that are variably homologous to Os05g44340 and Os02g32520 throughout the entire amino acid sequence. The above five rice Hsp100 genes show significant similarities in the signature sequences known to be conserved among Hsp100 proteins. While Os05g44340 encodes cytoplasmic Hsp100 protein, those encoded by the other four genes are predicted to have chloroplast transit peptides

Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

Training generally capable agents that perform well in unseen dynamic environments is a long-term goal of robot learning. Quality Diversity Reinforcement Learning (QD-RL) is an emerging class of reinforcement learning (RL) algorithms that blend insights from Quality Diversity (QD) and RL to produce a collection of high performing and behaviorally diverse policies with respect to a behavioral embedding. Existing QD-RL approaches have thus far taken advantage of sample-efficient off-policy RL algorithms. However, recent advances in high-throughput, massively parallelized robotic simulators have opened the door for algorithms that can take advantage of such parallelism, and it is unclear how to scale existing off-policy QD-RL methods to these new data-rich regimes. In this work, we take the first steps to combine on-policy RL methods, specifically Proximal Policy Optimization (PPO), that can leverage massive parallelism, with QD, and propose a new QD-RL method with these high-throughput simulators and on-policy training in mind. Our proposed Proximal Policy Gradient Arborescence (PPGA) algorithm yields a 4x improvement over baselines on the challenging humanoid domain.Comment: Submitted to Neurips 202

Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning

End-to-end deep reinforcement learning (DRL) for quadrotor control promises many benefits -- easy deployment, task generalization and real-time execution capability. Prior end-to-end DRL-based methods have showcased the ability to deploy learned controllers onto single quadrotors or quadrotor teams maneuvering in simple, obstacle-free environments. However, the addition of obstacles increases the number of possible interactions exponentially, thereby increasing the difficulty of training RL policies. In this work, we propose an end-to-end DRL approach to control quadrotor swarms in environments with obstacles. We provide our agents a curriculum and a replay buffer of the clipped collision episodes to improve performance in obstacle-rich environments. We implement an attention mechanism to attend to the neighbor robots and obstacle interactions - the first successful demonstration of this mechanism on policies for swarm behavior deployed on severely compute-constrained hardware. Our work is the first work that demonstrates the possibility of learning neighbor-avoiding and obstacle-avoiding control policies trained with end-to-end DRL that transfers zero-shot to real quadrotors. Our approach scales to 32 robots with 80% obstacle density in simulation and 8 robots with 20% obstacle density in physical deployment. Video demonstrations are available on the project website at: https://sites.google.com/view/obst-avoid-swarm-rl.Comment: Submitted to ICRA 202

Enhanced cell density cultivation and rapid expression-screening of recombinant Pichia pastoris clones in microscale

Cultivation of yeast Pichia pastoris in the microtiter plate, for optimisation of culture conditions, and expression screening of transformants has gained significance in recent years. However, in the microtiter plate, it has been challenging to attain cell densities similar to well-aerated shake-flask culture, due to the poor mixing resulting in oxygen limitation. To solve this problem, we investigated the influence of multiple cultivation parameters on P. pastoris cell growth, including the architecture of 96-deepwell plate (96-DWP), shaking throw diameter, shaking frequency, culture volume/well, and media composition. In the optimised conditions, a cell density of OD600 ~50 (dry cell weight ~13 g/L) with >99% cell viability was achieved in the casamino acids supplemented buffered-minimal-media in 300 to 1000 μl culture volume/well. We have devised a simplified method for coating of the culture supernatant on the polystyrene surface for immunoassay. Clones for secretory expression of envelope domain III of dengue virus serotype-1 under the control of inducible and constitutive promoter were screened using the developed method. Described microscale cultivation strategy can be used for rapid high-throughput screening of P. pastoris clones, media optimization, and high-throughput recombinant protein production. The knowledge gained through this work may also be applied, to other suspension cultures, with some modifications.</p

Casamino acids facilitate the secretion of recombinant dengue virus serotype-3 envelope domain III in Pichia pastoris

Background: Dengue is a viral disease spread to humans by mosquitoes. Notably, there are four serotypes of Dengue Viruses (DENV) that places &#8764;40% of the global population at risk of infection. However, lack of a suitable drug or a preventive vaccine exacerbates the matter further. Envelope Domain-III (EDIII) antigen of Dengue Virus (DENV) has garnered much attention as a promising vaccine candidate for dengue, in addition to its use as a diagnostic intermediate. Hence developing a method for efficient production of high quality recombinant EDIII is important for research and industrial purpose. Results: In this work, a Pichia pastoris system was optimized for the secretory over-expression of DENV serotype-3 EDIII under the control of methanol inducible AOX1 promoter. Temperature alone had a significant impact upon the amount of secretory EDIII, with 2.5-fold increase upon reducing the induction temperature from 30 to 20 °C. However surprisingly, supplementation of culture media with Casamino Acids (CA), further augmented secretory EDIII titer, with a concomitant drop of intracellular EDIII levels at both temperatures. Though, reduction in intracellular retention of EDIII was more prominent at 20°C than 30°C. This suggests that CA supplementation facilitates overexpressing P. pastoris cells to secrete more EDIII by reducing the proportion retained intracellularly. Moreover, a bell-shaped correlation was observed between CA concentration and secretory EDIII titer. The maximum EDIII expression level of 187 mg/L was achieved under shake flask conditions with induction at 20°C in the presence of 1% CA. The overall increase in EDIII titer was &#8764;9-fold compared to un-optimized conditions. Notably, mouse immune-sera, generated using this purified EDIII antigen, efficiently neutralized the DENV. Conclusions: The strategy described herein could enable fulfilling the mounting demand for recombinant EDIII as well as lay direction to future studies on secretory expression of recombinant proteins in P. pastoris with CA as a media supplement

Ultrasensitive and Robust Point-of-Care Immunoassay for the Detection of Plasmodium falciparum Malaria.

Plasmodium falciparum malaria is widespread in the tropical and subtropical regions of the world. There is ongoing effort to eliminate malaria from endemic regions, and sensitive point-of-care (POC) diagnostic tests are required to support this effort. However, current POC tests are not sufficiently sensitive to detect P. falciparum in asymptomatic individuals. After extensive optimization, we have developed a highly sensitive and robust POC test for the detection of P. falciparum infection. The test is based on upconverting nanophosphor-based lateral flow (UCNP-LF) immunoassay. The developed UCNP-LF test was validated using whole blood reference panels containing samples at different parasite densities covering eight strains of P. falciparum from different geographical areas. The limit of detection was compared to a WHO-prequalified rapid diagnostic test (RDT). The UCNP-LF achieved a detection limit of 0.2-2 parasites/μL, depending on the strain, which is 50- to 250-fold improvement in analytical sensitivity over the conventional RDTs. The developed UCNP-LF is highly stable even at 40 °C for at least 5 months. The extensively optimized UCNP-LF assay is as simple as the conventional malaria RDTs and requires 5 μL of whole blood as sample. Results can be read after 20 min from sample addition, with a simple photoluminescence reader. In the absence of a reader device at the testing site, the strips after running the test can be transported and read at a central location with access to a reader. We have found that the test and control line signals are stable for at least 10 months after running the test. The UCNP-LF has potential for diagnostic testing of both symptomatic and asymptomatic individuals