Aerial additive manufacturing with multiple autonomous robots.

Additive manufacturing methods1-4 using static and mobile robots are being developed for both on-site construction5-8 and off-site prefabrication9,10. Here we introduce a method of additive manufacturing, referred to as aerial additive manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders11 such as wasps who use collective building methods12,13. We present a scalable multi-robot three-dimensional (3D) printing and path-planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous three-dimensional printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring the print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta manipulator with the BuilDrone to further improve the manufacturing accuracy to five millimetres for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder 2.05 metres high consisting of 72 layers of a rapid-curing insulation foam material and a cylinder 0.18 metres high consisting of 28 layers of structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations. Aerial-AM allows manufacturing in-flight and offers future possibilities for building in unbounded, at-height or hard-to-access locations.

Design and synthesis of novel N-[3-(benzimidazol-2-ylamino)phenyl]amine and N-[3-(benzoxazol-2-ylamino)phenyl]amine derivatives as potential anticancer agents.

In this contribution, we report the design, synthesis and cytotoxicity studies of a series of N-[3-(benzimidazol-2-yl-amino)phenyl]amine and N-[3-(benzoxazol-2-ylamino)phenyl]amine derivatives. In vitro cytotoxicity assay of 26 selected compounds was carried out at National Cancer Institute (NCI), USA. Out of them, compounds 10e (NSC D-762842/1) and 11s (NSC D-764942/1) have shown remarkable cytotoxicity with GI50 values ranging between "0.589-14.3 µM" and "0.276-12.3 µM," respectively, in the representative nine subpanels of human tumor cell lines. Further, flow cytometry analysis demonstrated that compound 10e exerted cell cycle arrest at G2/M phase and showed dose-dependent enhancement in apoptosis in K-562 leukemia cancer cells.

Expert-level automated malaria diagnosis on routine blood films with deep neural networks.

Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban healthcare providers in the holoendemic malaria sub-Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub-Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state-of-the-art deep-learning object detectors requires the human-expert labor-intensive process of labeling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach. It leverages routine clinical-microscopy labels from our quality-controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/µL, as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert-level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90, which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan. It is located within the most populous African country (Nigeria) and with one of the largest burdens of Plasmodium falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.

Saliency Detection as a Reactive Process: Unexpected Sensory Events Evoke Corticomuscular Coupling.

Survival in a fast-changing environment requires animals not only to detect unexpected sensory events, but also to react. In humans, these salient sensory events generate large electrocortical responses, which have been traditionally interpreted within the sensory domain. Here we describe a basic physiological mechanism coupling saliency-related cortical responses with motor output. In four experiments conducted on 70 healthy participants, we show that salient substartle sensory stimuli modulate isometric force exertion by human participants, and that this modulation is tightly coupled with electrocortical activity elicited by the same stimuli. We obtained four main results. First, the force modulation follows a complex triphasic pattern consisting of alternating decreases and increases of force, time-locked to stimulus onset. Second, this modulation occurs regardless of the sensory modality of the eliciting stimulus. Third, the magnitude of the force modulation is predicted by the amplitude of the electrocortical activity elicited by the same stimuli. Fourth, both neural and motor effects are not reflexive but depend on contextual factors. Together, these results indicate that sudden environmental stimuli have an immediate effect on motor processing, through a tight corticomuscular coupling. These observations suggest that saliency detection is not merely perceptive but reactive, preparing the animal for subsequent appropriate actions.SIGNIFICANCE STATEMENT Salient events occurring in the environment, regardless of their modalities, elicit large electrical brain responses, dominated by a widespread "vertex" negative-positive potential. This response is the largest synchronization of neural activity that can be recorded from a healthy human being. Current interpretations assume that this vertex potential reflects sensory processes. Contrary to this general assumption, we show that the vertex potential is strongly coupled with a modulation of muscular activity that follows the same pattern. Both the vertex potential and its motor effects are not reflexive but strongly depend on contextual factors. These results reconceptualize the significance of these evoked electrocortical responses, suggesting that saliency detection is not merely perceptive but reactive, preparing the animal for subsequent appropriate actions.

The effect of salient stimuli on neural oscillations, isometric force, and their coupling.

Survival in a suddenly-changing environment requires animals not only to detect salient stimuli, but also to promptly respond to them by initiating or revising ongoing motor processes. We recently discovered that the large vertex brain potentials elicited by sudden supramodal stimuli are strongly coupled with a multiphasic modulation of isometric force, a phenomenon that we named cortico-muscular resonance (CMR). Here, we extend our investigation of the CMR to the time-frequency domain. We show that (i) both somatosensory and auditory stimuli evoke a number of phase-locked and non-phase-locked modulations of EEG spectral power. Remarkably, (ii) some of these phase-locked and non-phase-locked modulations are also present in the Force spectral power. Finally, (iii) EEG and Force time-frequency responses are correlated in two distinct regions of the power spectrum. An early, low-frequency region (∼4 Hz) reflects the previously-described coupling between the phase-locked EEG vertex potential and force modulations. A late, higher-frequency region (beta-band, ∼20 Hz) reflects a second coupling between the non-phase-locked increase of power observed in both EEG and Force. In both time-frequency regions, coupling was maximal over the sensorimotor cortex contralateral to the hand exerting the force, suggesting an effect of the stimuli on the tonic corticospinal drive. Thus, stimulus-induced CMR occurs across at least two different types of cortical activities, whose functional significance in relation to the motor system should be investigated further. We propose that these different types of corticomuscular coupling are important to alter motor behaviour in response to salient environmental events.

In-vivo high resolution AFM topographic imaging of Caenorhabditis elegans reveals previously unreported surface structures of cuticle mutants.

Atomic force microscopy (AFM) is a powerful method for topographic imaging of surfaces with nanometer resolution. AFM offers significant advantages over scanning electron microscopy (SEM) including the acquisition of quantitative 3D-images and biomechanical information. More importantly, for in-vivo biological imaging, AFM does not require sample dehydration/labeling. We show for the first time high-resolution topographical images of the cuticle of the model organism C. elegans under physiological conditions using AFM. C. elegans is used extensively for drug screening and to study pathogen adherence in innate immunity; both applications highly depend on the integrity of the nematode's cuticle. Mutations affecting both drug adsorption and pathogen clearance have been proposed to relate to changes in the cuticle structure, but never visually examined in high resolution. In this study we use AFM to visualize the topography of wild-type adult C. elegans as well as several cuticle collagen mutants and describe previously unseen anatomical differences.

Potential of Piperazinylalkylester Prodrugs of 6-Methoxy-2-Naphthylacetic Acid (6-MNA) for Percutaneous Drug Delivery.

Piperazinylalkyl ester prodrugs (4a-5d) of 6-methoxy-2-naphthylacetic acid (6-MNA) (1) were synthesized and evaluated in vitro for the purpose of percutaneous drug delivery. These ionizable prodrugs exhibited varying aqueous solubilities and lipophilicities depending on the pH of the medium. The prodrugs (4a-5c) showed higher aqueous solubility and similar lipophilicity at pH 5.0 and lower aqueous solubility and higher lipophilicity at pH 7.4 in comparison to 6-MNA. The chemical and enzymatic hydrolyses of the prodrugs was investigated in aqueous buffer solutions (pH 5.0 and 7.4) and in 80% human serum (pH 7.4) at 37°C. The prodrugs showed moderate chemical stability (t 1/2 = 6-60 h) but got readily hydrolyzed enzymatically to 6-MNA with half-life ranging from 10-60 min. In the in vitro permeation study using rat skin, the flux of 6-MNA and the prodrugs was determined in aqueous buffers of pH 5.0 and 7.4. The prodrug (5b) showed 7.9- and 11.2-fold enhancement in skin permeation compared to 6-MNA (1) at pH 5.0 and 7.4, respectively. It was concluded that the parent NSAIDs having favorable pharmacokinetic and pharmacodynamic properties coupled with increased skin permeability of their prodrugs could give better options for the treatment of rheumatic diseases.

The influence of internal pressure and neuromuscular agents on C. elegans biomechanics: an empirical and multi-compartmental in silico modelling study.

The function of a specific tissue and its biomechanics are interdependent, with pathologies or ageing often being intertwined with structural decline. The biomechanics of Caenorhabditis elegans, a model organism widely used in pharmacological and ageing research, has been established as biomarker for healthy ageing. However, the properties of the constituent tissues, and their contribution to the overall mechanical characteristics of the organism, remain relatively unknown. In this study we investigated the biomechanics of healthy C. elegans cuticle, muscle tissue, and pseudocoelom using a combination of indentation experiments and in silico modelling. We performed stiffness measurements using an atomic force microscope. To approximate the nematode's cylindrical body we used a novel three-compartment nonlinear finite element model, enabling us to analyse of how changes in the elasticity of individual compartments affect the bulk stiffness. We then fine-tuned the parameters of the model to match the simulation force-indentation output to the experimental data. To test the finite element model, we modified distinct compartments experimentally. Our in silico results, in agreement with previous studies, suggest that hyperosmotic shock reduces stiffness by decreasing the internal pressure. Unexpectedly, treatment with the neuromuscular agent aldicarb, traditionally associated with muscle contraction, reduced stiffness by decreasing the internal pressure. Furthermore, our finite element model can offer insights into how drugs, mutations, or processes such as ageing target individual tissues.

Minimum resolution requirements of digital pathology images for accurate classification.

Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy.

Exploring Effects of Information Filtering With a VR Interface for Multi-Robot Supervision.

Supervising and controlling remote robot systems currently requires many specialised operators to have knowledge of the internal state of the system in addition to the environment. For applications such as remote maintenance of future nuclear fusion reactors, the number of robots (and hence supervisors) required to maintain or decommission a facility is too large to be financially feasible. To address this issue, this work explores the idea of intelligently filtering information so that a single user can supervise multiple robots safely. We gathered feedback from participants using five methods for teleoperating a semi-autonomous multi-robot system via Virtual Reality (VR). We present a novel 3D interaction method to filter the displayed information to allow the user to read information from the environment without being overwhelmed. The novelty of the interface design is the link between Semantic and Spatial filtering and the hierarchical information contained within the multi robot system. We conducted a user study including a cohort of expert robot teleoperators comparing these methods; highlighting the significant effects of 3D interface design on the performance and perceived workload of a user teleoperating many robot agents in complex environments. The results from this experiment and subjective user feedback will inform future investigations that build upon this initial work.

Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.

Intraoperative frozen section analysis can be used to improve the accuracy of tumour margin estimation during cancer resection surgery through rapid processing and pathological assessment of excised tissue. Its applicability is limited in some cases due to the additional risks associated with prolonged surgery, largely from the time-consuming staining procedure. Our work uses a measurable property of bulk tissue to bypass the staining process: as tumour cells proliferate, they influence the surrounding extra-cellular matrix, and the resulting change in elastic modulus provides a signature of the underlying pathology. In this work we accurately localise atomic force microscopy measurements of human liver tissue samples and train a generative adversarial network to infer elastic modulus from low-resolution images of unstained tissue sections. Pathology is predicted through unsupervised clustering of parameters characterizing the distributions of inferred values, achieving 89% accuracy for all samples based on the nominal assessment (n = 28), and 95% for samples that have been validated by two independent pathologists through post hoc staining (n = 20). Our results demonstrate that this technique could increase the feasibility of intraoperative frozen section analysis for use during resection surgery and improve patient outcomes.

Digital refocusing and extended depth of field reconstruction in Fourier ptychographic microscopy.

Fourier ptychography microscopy (FPM) is a recently developed microscopic imaging method that allows the recovery of a high-resolution complex image by combining a sequence of bright and darkfield images acquired under inclined illumination. The capacity of FPM for high resolution imaging at low magnification makes it particularly attractive for applications in digital pathology which require imaging of large specimens such as tissue sections and blood films. To date most applications of FPM have been limited to imaging thin samples, simplifying both image reconstruction and analysis. In this work we show that, for samples of intermediate thickness (defined here as less than the depth of field of a raw captured image), numerical propagation of the reconstructed complex field allows effective digital refocusing of FPM images. The results are validated by comparison against images obtained with an equivalent high numerical aperture objective lens. We find that post reconstruction refocusing (PRR) yields images comparable in quality to adding a defocus term to the pupil function within the reconstruction algorithm, while reducing computing time by several orders of magnitude. We apply PRR to visualize FPM images of Giemsa-stained peripheral blood films and present a novel image processing pipeline to construct an effective extended depth of field image which optimally displays the 3D sample structure in a 2D image. We also show how digital refocusing allows effective correction of the chromatic focus shifts inherent to the low magnification objective lenses used in FPM setups, improving the overall quality of color FPM images.

Mechanical properties measured by atomic force microscopy define health biomarkers in ageing C. elegans.

Genetic and environmental factors are key drivers regulating organismal lifespan but how these impact healthspan is less well understood. Techniques capturing biomechanical properties of tissues on a nano-scale level are providing new insights into disease mechanisms. Here, we apply Atomic Force Microscopy (AFM) to quantitatively measure the change in biomechanical properties associated with ageing Caenorhabditis elegans in addition to capturing high-resolution topographical images of cuticle senescence. We show that distinct dietary restriction regimes and genetic pathways that increase lifespan lead to radically different healthspan outcomes. Hence, our data support the view that prolonged lifespan does not always coincide with extended healthspan. Importantly, we identify the insulin signalling pathway in C. elegans and interventions altering bacterial physiology as increasing both lifespan and healthspan. Overall, AFM provides a highly sensitive technique to measure organismal biomechanical fitness and delivers an approach to screen for health-improving conditions, an essential step towards healthy ageing.

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal "monolithic" models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 104 participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10-2, MSE ≤ 7 × 10-3, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to - 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways.

Site specific chemical delivery of NSAIDs to inflamed joints: synthesis, biological activity and gamma-imaging studies of quaternary ammonium salts of tropinol esters of some NSAIDs or their active metabolites.

Quaternized tropinol ester derivatives of some commonly used non-steroidal anti-inflammatory drugs (NSAIDs) or their active metabolites, were prepared and studied for their anti-inflammatory activity in a chronic inflammation model and for inflamed tissue tropism. The quaternized esters were radiolabeled with 99mTechnetium (99mTc) and their selective localization in the inflamed tissue was traced using scintigraphy. In the chronic arthritis rodent model, most of the quaternized esters exhibited anti-inflammatory effect comparable to their respective parent drugs. In the gamma-imaging studies only the quaternary derivatives exhibited selective accumulation into the inflamed tissue unlike the parent NSAIDs or the unquaternized tropinol esters. This work is a step ahead in the direction of use of quaternary ammonium ester derivatives for site specific chemical delivery of commonly used NSAIDs to the inflamed tissues to minimize their GIT side effect or other systemic toxicities.

Three-dimensional behavioural phenotyping of freely moving C. elegans using quantitative light field microscopy.

Behavioural phenotyping of model organisms is widely used to investigate fundamental aspects of organism biology, from the functioning of the nervous system to the effects of genetic mutations, as well as for screening new drug compounds. However, our capacity to observe and quantify the full range and complexity of behavioural responses is limited by the inability of conventional microscopy techniques to capture volumetric image information at sufficient speed. In this article we describe how combining light field microscopy with computational depth estimation provides a new method for fast, quantitative assessment of 3D posture and movement of the model organism Caenorhabditis elegans (C. elegans). We apply this technique to compare the behaviour of cuticle collagen mutants, finding significant differences in 3D posture and locomotion. We demonstrate the ability of quantitative light field microscopy to provide new fundamental insights into C. elegans locomotion by analysing the 3D postural modes of a freely swimming worm. Finally, we consider relative merits of the method and its broader application for phenotypic imaging of other organisms and for other volumetric bioimaging applications.

Adjoint Transformation Algorithm for Hand-Eye Calibration with Applications in Robotic Assisted Surgery.

Hand-eye calibration aims at determining the unknown rigid transformation between the coordinate systems of a robot arm and a camera. Existing hand-eye algorithms using closed-form solutions followed by iterative non-linear refinement provide accurate calibration results within a broad range of robotic applications. However, in the context of surgical robotics hand-eye calibration is still a challenging problem due to the required accuracy within the millimetre range, coupled with a large displacement between endoscopic cameras and the robot end-effector. This paper presents a new method for hand-eye calibration based on the adjoint transformation of twist motions that solves the problem iteratively through alternating estimations of rotation and translation. We show that this approach converges to a solution with a higher accuracy than closed form initializations within a broad range of synthetic and real experiments. We also propose a stereo hand-eye formulation that can be used in the context of both our proposed method and previous state-of-the-art closed form solutions. Experiments with real data are conducted with a stereo laparoscope, the KUKA robot arm manipulator, and the da Vinci surgical robot, showing that both our new alternating solution and the explicit representation of stereo camera hand-eye relations contribute to a higher calibration accuracy.