An amendment to this paper has been published and can be accessed via a link at the top of the paper.Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image.  https://www.selleckchem.com/btk.html  Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.The fabrication of nanomaterials from the top-down gives precise structures but it is costly, whereas bottom-up assembly methods are found by trial and error. Nature evolves materials discovery by refining and transmitting the blueprints using DNA mutations autonomously. Genetically inspired optimisation has been used in a range of applications, from catalysis to light emitting materials, but these are not autonomous, and do not use physical mutations. Here we present an autonomously driven materials-evolution robotic platform that can reliably optimise the conditions to produce gold-nanoparticles over many cycles, discovering new synthetic conditions for known nanoparticle shapes using the opto-electronic properties as a driver. Not only can we reliably discover a method, encoded digitally to synthesise these materials, we can seed in materials from preceding generations to engineer more sophisticated architectures. Over three independent cycles of evolution we show our autonomous system can produce spherical nanoparticles, rods, and finally octahedral nanoparticles by using our optimized rods as seeds.Whitefly infestation of cotton crop imparts enormous damage to cotton yield by severely affecting plant health, vigour and transmitting Cotton Leaf Curl Virus (CLCuV). Genetic modification of cotton helps to overcome both the direct whitefly infestation as well as CLCuV based cotton yield losses. We have constitutively overexpressed asparaginase (ZmASN) gene in Gossypium hirsutum to overcome the cotton yield losses imparted by whitefly infestation. We achieved 2.54% transformation efficiency in CIM-482 by Agrobacterium-mediated shoot apex transformation method. The relative qRT-PCR revealed 40-fold higher transcripts of asparaginase in transgenic cotton line vs. non-transgenic cotton lines. Metabolic analysis showed higher contents of aspartic acid and glutamic acid in seeds and phloem sap of the transgenic cotton lines. Phenotypically, the transgenic cotton lines showed vigorous growth and height, greater number of bolls, and yield. Among six representative transgenic cotton lines, line 14 had higher photosynthetic rate, stomatal conductance, smooth fiber surface, increased fiber convolutions (SEM analysis) and 95% whitefly mortality as compared to non-transgenic cotton line. The gene integration analysis by fluorescence in situ hybridization showed single copy gene integration at chromosome number 1. Collectively, asparaginase gene demonstrated potential to control whitefly infestation, post-infestation damages and improve cotton plant health and yield a pre-requisite for farmer's community.Radiative heat transfer between two bodies saturates at very short separation distances due to the nonlocal optical response of the materials. In this work, we show that the presence of radiative interactions with a third body or external bath can also induce a saturation of the heat transfer, even at separation distances for which the optical response of the materials is purely local. We demonstrate that this saturation mechanism is a direct consequence of a thermalization process resulting from many-body interactions in the system. This effect could have an important impact in the field of nanoscale thermal management of complex systems and in the interpretation of measured signals in thermal metrology at the nanoscale.Humane endpoint determination is fundamental in animal experimentation. Despite commonly accepted endpoint criteria for intracranial tumour models (20% body weight loss and deteriorated clinical score) some animals still die before being euthanized in current research. We here systematically evaluated other measures as surrogates for a more reliable humane endpoint determination. Adult male BDIX rats (n = 119) with intracranial glioma formation after BT4Ca cell-injection were used. Clinical score and body weight were assessed daily. One subgroup (n = 14) was assessed daily for species-specific (nesting, burrowing), motor (distance, coordination) and social behaviour. Another subgroup (n = 8) was implanted with a telemetric device for monitoring heart rate (variability), temperature and activity. Body weight and clinical score of all other rats were used for training (n = 34) and validation (n = 63) of an elaborate body weight course analysis algorithm for endpoint detection. BT4Ca cell-injection reliably induced fast-growing tumours. No behavioural or physiological parameter detected deteriorations of the clinical state earlier or more reliable than clinical scoring by experienced observers. However, the body weight course analysis algorithm predicted endpoints in 97% of animals without confounding observer-dependent factors. Clinical scoring together with the novel algorithm enables highly reliable and observer-independent endpoint determination in a rodent intracranial tumour model.