Heuristic and Hierarchical-Based Population Mining of Salmonella enterica Lineage I Pan-Genomes as a Platform to Enhance Food Safety

The recent incorporation of bacterial whole-genome sequencing (WGS) into Public Health laboratories has enhanced foodborne outbreak detection and source attribution. As a result, large volumes of publicly available datasets can be used to study the biology of foodborne pathogen populations at an unprecedented scale. To demonstrate the application of a heuristic and agnostic hierarchical population structure guided pan-genome enrichment analysis (PANGEA), we used populations of S. enterica lineage I to achieve two main objectives: (i) show how hierarchical population inquiry at different scales of resolution can enhance ecological and epidemiological inquiries; and (ii) identify population-specific inferable traits that could provide selective advantages in food production environments. Publicly available WGS data were obtained from NCBI database for three serovars of Salmonella enterica subsp. enterica lineage I (S. Typhimurium, S. Newport, and S. Infantis). Using the hierarchical genotypic classifications (Serovar, BAPS1, ST, cgMLST), datasets from each of the three serovars showed varying degrees of clonal structuring. When the accessory genome (PANGEA) was mapped onto these hierarchical structures, accessory loci could be linked with specific genotypes. A large heavy-metal resistance mobile element was found in the Monophasic ST34 lineage of S. Typhimurium, and laboratory testing showed that Monophasic isolates have on average a higher degree of copper resistance than the Biphasic ones. In S. Newport, an extra sugEgene copy was found among most isolates of the ST45 lineage, and laboratory testing of multiple isolates confirmed that isolates of S. Newport ST45 were on average less sensitive to the disinfectant cetylpyridimium chloride than non-ST45 isolates. Lastly, data-mining of the accessory genomic content of S. Infantis revealed two cryptic Ecotypes with distinct accessory genomic content and distinct ecological patterns. Poultry appears to be themajor reservoir for Ecotype 1, and temporal analysis further suggested a recent ecological succession, with Ecotype 2 apparently being displaced by Ecotype 1. Altogether, the use of a heuristic hierarchical-based population structure analysis that includes bacterial pan-genomes (core and accessory genomes) can (1) improve genomic resolution for mapping populations and accessing epidemiological patterns; and (2) define lineage-specific informative loci that may be associated with survival in the food chain

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain

TiBi-3D - a Guide through the World of Epigenetics

In the last two decades the study of changes in the genome function that are not induced by changes in DNA has consolidated a strong research field called ”epigenetics”. Chromatin state changes play an essential role in the regulation of transcription of many genes, thus controlling cell differentiation. A large part of these changes is due to histone modifications that alter the accessibility of the DNA. Current state of the art visualization methods for the analysis of epigenetic data sets are not suited to represent the relationship between the combinatorial pattern of histone modifications and their regulatory effects

Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings

The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers

A high-resolution 6.0-megabase transcript map of the type 2 diabetes susceptibility region on human chromosome 20

Recent linkage studies and association analyses indicate the presence of at least one type 2 diabetes susceptibility gene in human chromosome region 20q12-q13.1. We have constructed a high-resolution 6.0-megabase (Mb) transcript map of this interval using two parallel, complementary strategies to construct the map. We assembled a series of bacterial artificial chromosome (BAC) contigs from 56 overlapping BAC clones, using STS/marker screening of 42 genes, 43 ESTs, 38 STSs, 22 polymorphic, and 3 BAC end sequence markers. We performed map assembly with GraphMap, a software program that uses a greedy path searching algorithm, supplemented with local heuristics. We anchored the resulting BAC contigs and oriented them within a yeast artificial chromosome (YAC) scaffold by observing the retention patterns of shared markers in a panel of 21 YAC clones. Concurrently, we assembled a sequence-based map from genomic sequence data released by the Human Genome Project, using a seed-and-walk approach. The map currently provides near-continuous coverage between SGC32867 and WI-17676 (∼ 6.0 Mb). EST database searches and genomic sequence alignments of ESTs, mRNAs, and UniGene clusters enabled the annotation of the sequence interval with experimentally confirmed and putative transcripts. We have begun to systematically evaluate candidate genes and novel ESTs within the transcript map framework. So far, however, we have found no statistically significant evidence of functional allelic variants associated with type 2 diabetes. The combination of the BAC transcript map, YAC-to-BAC scaffold, and reference Human Genome Project sequence provides a powerful integrated resource for future genomic analysis of this region

On Organization of Information: Approach and Early Work

In this report we describe an approach for organizing information for presentation and display. "e approach stems from the observation that there is a stepwise progression in the way signals (from the environment and the system under consideration) are extracted and transformed into data, and then analyzed and abstracted to form representations (e.g., indications and icons) on the user interface. In physical environments such as aerospace and process control, many system components and their corresponding data and information are interrelated (e.g., an increase in a chamber s temperature results in an increase in its pressure). "ese interrelationships, when presented clearly, allow users to understand linkages among system components and how they may affect one another. Organization of these interrelationships by means of an orderly structure provides for the so-called "big picture" that pilots, astronauts, and operators strive for

Scanning tunneling spectroscopy of high-temperature superconductors

Tunneling spectroscopy played a central role in the experimental verification of the microscopic theory of superconductivity in the classical superconductors. Initial attempts to apply the same approach to high-temperature superconductors were hampered by various problems related to the complexity of these materials. The use of scanning tunneling microscopy/spectroscopy (STM/STS) on these compounds allowed to overcome the main difficulties. This success motivated a rapidly growing scientific community to apply this technique to high-temperature superconductors. This paper reviews the experimental highlights obtained over the last decade. We first recall the crucial efforts to gain control over the technique and to obtain reproducible results. We then discuss how the STM/STS technique has contributed to the study of some of the most unusual and remarkable properties of high-temperature superconductors: the unusual large gap values and the absence of scaling with the critical temperature; the pseudogap and its relation to superconductivity; the unprecedented small size of the vortex cores and its influence on vortex matter; the unexpected electronic properties of the vortex cores; the combination of atomic resolution and spectroscopy leading to the observation of periodic local density of states modulations in the superconducting and pseudogap states, and in the vortex cores.Comment: To appear in RMP; 65 pages, 62 figure

Brain connectivity studied by fMRI: homologous network organization in the rat, monkey, and human

The mammalian brain is composed of functional networks operating at different spatial and temporal scales — characterized by patterns of interconnections linking sensory, motor, and cognitive systems. Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires homologous animal models that would allow neurophysiological recordings and experimental manipulations. My current dissertation presents a comprehensive assessment and comparison of rat, macaque, and human brain networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent (BOLD) fMRI signal. The signal fluctuations, recorded in the absence of any task paradigm, have been shown to reflect anatomical connectivity and are presumed to be a hemodynamic manifestation of slow fluctuations in neuronal activity. Importantly, the technique circumvents many practical limitations of other methodologies and can be compared directly between multiple species. Networks of all species were found underlying multiple levels of sensory, motor, and cognitive processing. Remarkable homologous functional connectivity was found across all species, however network complexity was dramatically increased in primate compared to rodent species. Spontaneous temporal dynamics of the resting-state networks were also preserved across species. The results demonstrate that rats and macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks

Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging

Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental data and connecting the results to theory, with feedback flow in both directions. However, state-of-the-art analysis remains selective and manual, prone to human error and unable to handle large quantities of data generated by modern equipment. Recent advances in scanning transmission electron and scanning tunneling microscopies have allowed imaging and manipulation of materials on the atomic level, and these capabilities require development of automated, robust, reproducible methods.Artificial intelligence and machine learning have dealt with similar issues in applications to image and speech recognition, autonomous vehicles, and other projects that are beginning to change the world around us. However, materials science faces significant challenges preventing direct application of the such models without taking physical constraints and domain expertise into account.Atomic resolution imaging can generate data that can lead to better understanding of materials and their properties through using artificial intelligence methods. Machine learning, in particular combinations of deep learning and probabilistic modeling, can learn to recognize physical features in imaging, making this process automated and speeding up characterization. By incorporating the knowledge from theory and simulations with such frameworks, it is possible to create the foundation for the automated atomic scale manufacturing

Attention allocation during the observation of biological motion: an EEG study

The processing of observed biological motion that is the movement of biological organisms has an important role in animals’ vigilance and survival. For humans, it is also implicated in the development of social cognition and communication, with infants showing preferential attention towards motion from an early age. Further, adults can extract a broad range of social information from the biological motion of human figures represented by dots of light (point-light displays), that contain kinematic, structural and dynamic information. From this information, humans can identify individual actors, their sex, emotional state (angry, happy, and sad) and walking direction even when obfuscated by additional noise. The processing of biological motion draws on different cognitive systems such as working memory, selective attention and sensorimotor processing. Humans demonstrate an attentional bias towards human forms and biological motion, compared to other non-biological stimuli, and the observation of biological movement activates sensorimotor cortical regions. Previous research has used EEG to measure mu frequency (~ 8-13 Hz) changes and to infer the activation of sensorimotor regions during biological movement observation. This sensorimotor activation is thought to be an indication of online movement simulation. It has been demonstrated that top-down attentional processes modulate the engagement of sensorimotor simulation during movement observation. What remains unknown is whether biological motion exogenously captures spatial attention and, in turn, modulates sensorimotor simulation; the current study sought to explore this question. In the current study, I used an attentional bias paradigm where movement and control point-light displays are presented laterally and simultaneously as irrelevant cues. Relatively decreased reaction times to subsequent targets that appear in the same location as a cue reflects preferential processing of that preceding cue. I simultaneously recorded EEG and calculated mu frequency changes at both central and occipital electrode locations. I find decreased reaction times and an increase in correct responses to targets that replace the scrambled point light display (PLD), which represents non-biological motion, compared to targets that replaced the coherent PLD representing biological movement. In addition, EEG analysis revealed a left hemisphere bias, with post hoc analysis revealing this bias is driven by the central electrodes; with a larger desynchronisation in the left central electrode compared to the right central electrode, whereas, occipital alpha was desynchronised symmetrically. Together, the behavioural and EEG findings suggest an inhibition of return (IOR) effect