Spatial Distribution Modelling of Prothonotary Warbler (Protonotaria citrea) on Breeding Grounds

Ecological niche modeling is used to predict a species’ distribution in a geographic area based on abiotic and biotic variables. Understanding a species’ range is important for conservation and restoration efforts. As anthropogenic forces may alter or deplete habitat, it is important to know the ecological requirements of a species to understand how and what habitat to protect. With the increasing threat of climate change and rising temperature and precipitation, the suitable habitat and the distribution for many species is expected to shift. Migratory species are particularly at risk of these changes as they require suitable habitat not only on their wintering and stopover grounds, but on their breeding grounds. Without suitable breeding grounds, reproductive success is guaranteed to decline for a species. Understanding how these changes affect the range and distribution of a species allows researchers and conservationist to better formulate effective species management plan

Traité d'arithmétique théoripratique, 1844

Livro com 215 páginas. Disponível no seguinte link: https://gallica.bnf.fr/ark:/12148/bpt6k1192364g/f9.item.r=Cours%20d%20'%20arithm%C3%A9tiqu

Interactions between U and V sex chromosomes during the life cycle of Ectocarpus

In many animals and flowering plants, sex determination occurs in the diploid phase of the life cycle with XX/XY or ZW/ZZ sex chromosomes. However, in early diverging plants and most macroalgae, sex is determined by female (U) or male (V) sex chromosomes in a haploid phase called the gametophyte. Once the U and V chromosomes unite at fertilization to produce a diploid sporophyte, sex determination no longer occurs, raising key questions about the fate of the U and V sex chromosomes in the sporophyte phase. Here, we investigate genetic and molecular interactions of the UV sex chromosomes in both the haploid and diploid phases of the brown alga Ectocarpus. We reveal extensive developmental regulation of sex chromosome genes across its life cycle and implicate the TALE-HD transcription factor OUROBOROS in suppressing sex determination in the diploid phase. Small RNAs may also play a role in the repression of a female sex-linked gene, and transition to the diploid sporophyte coincides with major reconfiguration of histone H3K79me2, suggesting a more intricate role for this histone mark in Ectocarpus development than previously appreciated.</p

Interactions between U and V sex chromosomes during the life cycle of Ectocarpus

In many animals and flowering plants, sex determination occurs in the diploid phase of the life cycle with XX/XY or ZW/ZZ sex chromosomes. However, in early diverging plants and most macroalgae, sex is determined by female (U) or male (V) sex chromosomes in a haploid phase called the gametophyte. Once the U and V chromosomes unite at fertilization to produce a diploid sporophyte, sex determination no longer occurs, raising key questions about the fate of the U and V sex chromosomes in the sporophyte phase. Here, we investigate genetic and molecular interactions of the UV sex chromosomes in both the haploid and diploid phases of the brown alga Ectocarpus. We reveal extensive developmental regulation of sex chromosome genes across its life cycle and implicate the TALE-HD transcription factor OUROBOROS in suppressing sex determination in the diploid phase. Small RNAs may also play a role in the repression of a female sex-linked gene, and transition to the diploid sporophyte coincides with major reconfiguration of histone H3K79me2, suggesting a more intricate role for this histone mark in Ectocarpus development than previously appreciated.</p

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Modeling Sustainability Reporting with Ternary Attractor Neural Networks

International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02

Chromatin Signature Identifies Monoallelic Gene Expression Across Mammalian Cell Types

Monoallelic expression of autosomal genes (MAE) is a widespread epigenetic phenomenon which is poorly understood, due in part to current limitations of genome-wide approaches for assessing it. Recently, we reported that a specific histone modification signature is strongly associated with MAE and demonstrated that it can serve as a proxy of MAE in human lymphoblastoid cells. Here, we use murine cells to establish that this chromatin signature is conserved between mouse and human and is associated with MAE in multiple cell types. Our analyses reveal extensive conservation in the identity of MAE genes between the two species. By analyzing MAE chromatin signature in a large number of cell and tissue types, we show that it remains consistent during terminal cell differentiation and is predominant among cell-type specific genes, suggesting a link between MAE and specification of cell identity

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies