Evolutionarily Optimized Electromagnetic Sensor Measurements for Robust Surgical Navigation

© 2001-2012 IEEE. Miniaturized electromagnetic sensors are increasingly introduced to navigate surgical instruments to anatomical targets during minimally invasive procedures, such as endoscopic surgery. These sensors are usually attached at the distal tips of surgical instruments to track their three-dimensional motion represented by the position and orientation in six degrees of freedom. Unfortunately, these sensors suffer from inaccurate measurements and jitter errors due to the patient movement (e.g., respiratory motion) and magnetic field distortion. This paper proposes an evolutionary computing strategy to optimize the sensor measurements and improve the tracking accuracy of surgical navigation. We modified two evolutionary computation algorithms and proposed adaptive particle swarm optimization (APSO) and observation-boosted differential evolution (OBDE) to enhance the navigation accuracy. The experimental results demonstrate that our modified algorithms to evolutionarily optimize electromagnetic sensor measurements can critically reduce the tracking error from 4.8 to 2.9 mm. In particular, OBDE outperforms APSO for electromagnetic endoscopic navigation

Neutralisation of SARS-CoV-2 by monoclonal antibody through dual targeting powder formulation

Neutralising monoclonal antibody (mAb) is an important weapon in our arsenal for combating respiratory viral infections. However, the effectiveness of neutralising mAb has been impeded by the rapid emergence of mutant variants. Early administration of broad-spectrum mAb with improved delivery efficiency can potentially enhance efficacy and patient outcomes. WKS13 is a humanised mAb which was previously demonstrated to exhibit broad-spectrum activity against SARS-CoV-2 variants. In this study, a dual targeting formulation strategy was designed to deliver WKS13 to both the nasal cavity and lower airways, the two critical sites of infection caused by SARS-CoV-2. Dry powders of WKS13 were first prepared by spray drying, with cyclodextrin used as stabiliser excipient. Two-fluid nozzle (TFN) was used to produce particles below 5 μm for lung deposition (C-TFN formulation) and ultrasonic nozzle (USN) was used to produce particles above 10 μm for nasal deposition (C-USN formulation). Gel electrophoresis and size exclusion chromatography studies showed that the structural integrity of mAb was successfully preserved with no sign of aggregation after spray drying. To achieve dual targeting property, C-TFN and C-USN were mixed at various ratios. The aerosolisation property of the mixed formulations dispersed from a nasal powder device was examined using a Next Generation Impactor (NGI) coupled with a glass expansion chamber. When the ratio of C-TFN in the mixed formulation increased, the fraction of particles deposited in the lung increased proportionally while the fraction of particles deposited in the nasal cavity decreased correspondingly. A customisable aerosol deposition profile could therefore be achieved by manipulating the mixing ratio between C-TFN and C-USN. Dual administration of C-TFN and C-USN powders to the lung and nasal cavity of hamsters, respectively, was effective in offering prophylactic protection against SARS-CoV-2 Delta variant. Viral loads in both the lung tissues and nasal wash were significantly reduced, and the efficacy was comparable to systemic administration of unformulated WKS13. Overall, dual targeting powder formulation of neutralising mAb is a promising approach for prophylaxis of respiratory viral infections. The ease and non-invasive administration of dual targeting nasal powder may facilitate the widespread distribution of neutralising mAb during the early stage of unpredictable outbreaks

Stability of Mine Car Motion in Curves of Invariable and Variable Radii

We discuss our experiences adapting three recent algorithms for maximum common (connected) subgraph problems to exploit multi-core parallelism. These algorithms do not easily lend themselves to parallel search, as the search trees are extremely irregular, making balanced work distribution hard, and runtimes are very sensitive to value-ordering heuristic behaviour. Nonetheless, our results show that each algorithm can be parallelised successfully, with the threaded algorithms we create being clearly better than the sequential ones. We then look in more detail at the results, and discuss how speedups should be measured for this kind of algorithm. Because of the difficulty in quantifying an average speedup when so-called anomalous speedups (superlinear and sublinear) are common, we propose a new measure called aggregate speedup

Dimensionality and dynamics in the behavior of C. elegans

A major challenge in analyzing animal behavior is to discover some underlying simplicity in complex motor actions. Here we show that the space of shapes adopted by the nematode C. elegans is surprisingly low dimensional, with just four dimensions accounting for 95% of the shape variance, and we partially reconstruct "equations of motion" for the dynamics in this space. These dynamics have multiple attractors, and we find that the worm visits these in a rapid and almost completely deterministic response to weak thermal stimuli. Stimulus-dependent correlations among the different modes suggest that one can generate more reliable behaviors by synchronizing stimuli to the state of the worm in shape space. We confirm this prediction, effectively "steering" the worm in real time.Comment: 9 pages, 6 figures, minor correction

Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Evidence for the Accretion of Gas in Star-forming Galaxies: High N/O Abundances in Regions of Anomalously Low Metallicity

While all models for the evolution of galaxies require the accretion of gas to sustain their growth via on-going star formation, it has proven difficult to directly detect this inflowing material. In this paper we use data of nearby star-forming galaxies in the SDSS IV Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey to search for evidence of accretion imprinted in the chemical composition of the interstellar medium. We measure both the O/H and N/O abundance ratios in regions previously identified as having anomalously low values of O/H. We show that the unusual locations of these regions in the N/O vs. O/H plane indicate that they have been created through the mixing of disk gas having higher metallicity with accreted gas having lower metallicity. Taken together with previous analysis on these anomalously low-metallicity regions, these results imply that accretion of metal-poor gas can probably sustain star formation in present-day late-type galaxies.ERC STF