MicroMotility: State of the art, recent accomplishments and perspectives on the mathematical modeling of bio-motility at microscopic scales

Mathematical modeling and quantitative study of biological motility (in particular, of motility at microscopic scales) is producing new biophysical insight and is offering opportunities for new discoveries at the level of both fundamental science and technology. These range from the explanation of how complex behavior at the level of a single organism emerges from body architecture, to the understanding of collective phenomena in groups of organisms and tissues, and of how these forms of swarm intelligence can be controlled and harnessed in engineering applications, to the elucidation of processes of fundamental biological relevance at the cellular and sub-cellular level. In this paper, some of the most exciting new developments in the fields of locomotion of unicellular organisms, of soft adhesive locomotion across scales, of the study of pore translocation properties of knotted DNA, of the development of synthetic active solid sheets, of the mechanics of the unjamming transition in dense cell collectives, of the mechanics of cell sheet folding in volvocalean algae, and of the self-propulsion of topological defects in active matter are discussed. For each of these topics, we provide a brief state of the art, an example of recent achievements, and some directions for future research

Maintaining safe lung cancer surgery during the COVID-19 pandemic in a global city.

Background: SARS-CoV-2 has challenged health service provision worldwide. This work evaluates safe surgical pathways and standard operating procedures implemented in the high volume, global city of London during the first wave of SARS-CoV-2 infection. We also assess the safety of minimally invasive surgery(MIS) for anatomical lung resection. Methods: This multicentre cohort study was conducted across all London thoracic surgical units, covering a catchment area of approximately 14.8 Million. A Pan-London Collaborative was created for data sharing and dissemination of protocols. All patients undergoing anatomical lung resection 1st March-1st June 2020 were included. Primary outcomes were SARS-CoV-2 infection, access to minimally invasive surgery, post-operative complication, length of intensive care and hospital stay (LOS), and death during follow up. Findings: 352 patients underwent anatomical lung resection with a median age of 69 (IQR: 35-86) years. Self-isolation and pre-operative screening were implemented following the UK national lockdown. Pre-operative SARS-CoV-2 swabs were performed in 63.1% and CT imaging in 54.8%. 61.7% of cases were performed minimally invasively (MIS), compared to 59.9% pre pandemic. Median LOS was 6 days with a 30-day survival of 98.3% (comparable to a median LOS of 6 days and 30-day survival of 98.4% pre-pandemic). Significant complications developed in 7.3% of patients (Clavien-Dindo Grade 3-4) and 12 there were re-admissions(3.4%). Seven patients(2.0%) were diagnosed with SARS-CoV-2 infection, two of whom died (28.5%). Interpretation: SARS-CoV-2 infection significantly increases morbidity and mortality in patients undergoing elective anatomical pulmonary resection. However, surgery can be safely undertaken via open and MIS approaches at the peak of a viral pandemic if precautionary measures are implemented. High volume surgery should continue during further viral peaks to minimise health service burden and potential harm to cancer patients. Funding: This work did not receive funding

Zinc Coordination Is Required for and Regulates Transcription Activation by Epstein-Barr Nuclear Antigen 1

Epstein-Barr Nuclear Antigen 1 (EBNA1) is essential for Epstein-Barr virus to immortalize naïve B-cells. Upon binding a cluster of 20 cognate binding-sites termed the family of repeats, EBNA1 transactivates promoters for EBV genes that are required for immortalization. A small domain, termed UR1, that is 25 amino-acids in length, has been identified previously as essential for EBNA1 to activate transcription. In this study, we have elucidated how UR1 contributes to EBNA1's ability to transactivate. We show that zinc is necessary for EBNA1 to activate transcription, and that UR1 coordinates zinc through a pair of essential cysteines contained within it. UR1 dimerizes upon coordinating zinc, indicating that EBNA1 contains a second dimerization interface in its amino-terminus. There is a strong correlation between UR1-mediated dimerization and EBNA1's ability to transactivate cooperatively. Point mutants of EBNA1 that disrupt zinc coordination also prevent self-association, and do not activate transcription cooperatively. Further, we demonstrate that UR1 acts as a molecular sensor that regulates the ability of EBNA1 to activate transcription in response to changes in redox and oxygen partial pressure (pO2). Mild oxidative stress mimicking such environmental changes decreases EBNA1-dependent transcription in a lymphoblastoid cell-line. Coincident with a reduction in EBNA1-dependent transcription, reductions are observed in EBNA2 and LMP1 protein levels. Although these changes do not affect LCL survival, treated cells accumulate in G0/G1. These findings are discussed in the context of EBV latency in body compartments that differ strikingly in their pO2 and redox potential

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods

The effect of root confinement and calcium stress on the physiology, morphology and cation nutrition in tomatoes (Lycopersicon esculentum Mill.)

Five pot experiments using nutrient solution culture were conducted in a glasshouse where the day and night temperatures were 25±5°C and l8±3°C respectively. Tomato (Lycopersicon esculentum Mill.) plants were trained as a single main stem except those in experiment IV where the lateral shoots were left to grow. For the purpose of this study different glasshouse tomato cu1tivars (Eurovite, Nemarex, Lincross and Angela) were used. To provide different degrees of confinement to the root system, plants were range of container sizes (0.025, 0.05, 0.1, 2.0 l) and compared with nonconfined plants (4.5 l). Other treatments involved releasing the roots after a period of confinement in experiment II, and calcium deficient treatments in combination with root confinement treatments in experiment V using radioactive calcium. The dry weights of roots, stems, leaves and fruits, total leaf surface area, leaf number, root length and root number were measured as well as uptake of potassium, calcium and magnesium. Growing plants under root confinement conditions retarded the growth of vegetative parts in all cultivars. The dry weight of leaves, stems and roots was reduced while that of fruits was not affected. Root confinement was also associated with an increase in the shoot:root and top:root ratios compared with the non confined plants. Releasing the roots after a period of confinement increased the dry weight of the vegetative parts with no effect on the dry weight of fruits. The shoot:root ratio in confined-released plants remained as high as the continuously confined plants although the roots were not under physical confinement particularly when they were released to 2.0 l container. It appears that the effect of root confinement is not a simple physical stress to the roots but also involves some physiological effects through the changes in pattern of the distribution of dry matter between the root and the shoot. During the vegetative stage of growth root confinement increased the proportion of dry weight of stems and reduced that of leaves compared with the non confined plants. After the transition to the reproductive phase, plants showed a new pattern of dry weight distribution mainly brought about by the growth of fruits. The increase in the proportion of dry weight allocated to the top of confined plants was not equally shared by all top components. The proportion of the stems and fruits increased while that of leaves decreased. Physical confinement of the roots reduced the dry weight of lateral shoots but had no effect on the relative partitioning of dry weight between the main stem and the lateral shoots. Root confinement changed the morphology of the leaves. The total and average leaf surface area of confined plants were smaller than those of non confined plants. The concentration of chlorophylls a and b in the leaves of confined plants, in particular Eurovite increased, and the leaves became thicker or denser. These changes in the leaf morphology may have increased the photosynthetic rate of the unit leaf surf ace area and ultimately increased its production of dry matter. Root morphology also changed in response to physical confinement of the root. Confined root systems were more branched, and when the confined roots were released to larger containers the number of lateral roots increased rapidly resulting in a greater ratio of root number:root length compared with those of continuously confined and non confined roots. Confined plants took up smaller amounts of K, Ca or Mg compared with the non confined plants. But when the mineral(s) uptake was expressed on the basis of the amount of mineral(s) absorbed by unit root length or root branch, confined plants showed a greater ability to absorb mineral(s) than non confined plants. The concentration of K in the leaves was not affected by root confinement treatments whereas the concentrations of Ca and Mg were lower in the leaves of confined plants than those whose roots were not confined. However, the lower concentrations of Ca and Mg in leaves of confined plants were higher than the optimum concentration suggested for tomato leaves by Ward (1963). The concentration of minerals in the fruits was the same in all treatments. Root confinement interfered with the translocation of Ca from the root to plant tops. Fractionation of root Ca showed that the majority of Ca was in the acid soluble form (Ca-oxalate, phosphate and carbonate). The possible mechanisms for this accumulation of Ca in the confined roots were suggested. Angela plants were more efficient in utilizing K while those of Eurovite were efficient in utilizing Ca. Root confinement increased the efficiency of utilization of K and Ca in both cultivars. It is concluded therefore that efficiency of mineral utilization is sensitive to environmental as well as to genetic factors. Both calcium treatments (+Ca and -Ca) resulted in a remobilization of radioactive calcium previously deposited in the plant tissues. However, the amounts of Ca remobilized were very small and could not prevent the development of calcium deficiency symptoms in different plant organs, particularly in the non confined plants. Confined plants under calcium deficiency conditions delayed the initiation and development of calcium deficiency symptoms on the leaves and fruits by at least five days compared with the corresponding plants with non confined roots. It is concluded that root confinement could be used as a technique to reduce the risk of sudden shortage of calcium supply in the growing medium. Blossom end rot (BER) in tomato fruits is directly related to the lower concentration of Ca in the fruits, and any factor(s) which may directly or indirectly affect the Ca uptake and/or translocation could enhance the development of this disorder. Angela plants under calcium deficiency conditions showed the symptoms of BER earlier and more severely than similar plants of Eurovite. These variations between the two cultivars were related to the ability of the cultivar to remobilize the previously deposited Ca from different plant tissues

Two-Tier genetic programming: towards raw pixel-based image classification

Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately

Extracting image features for classification by two-tier genetic programming

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process