New Kinds Of Data Research Articles

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

BackgroundThe largest sequence-based models of transcription control to date are obtained by predicting genome-wide gene regulatory assays across the human genome. This setting is fundamentally correlative, as those models are exposed during training solely to the sequence variation between human genes that arose through evolution, questioning the extent to which those models capture genuine causal signals.ResultsHere we confront predictions of state-of-the-art models of transcription regulation against data from two large-scale observational studies and five deep perturbation assays. The most advanced of these sequence-based models, Enformer, by and large, captures causal determinants of human promoters. However, models fail to capture the causal effects of enhancers on expression, notably in medium to long distances and particularly for highly expressed promoters. More generally, the predicted impact of distal elements on gene expression predictions is small and the ability to correctly integrate long-range information is significantly more limited than the receptive fields of the models suggest. This is likely caused by the escalating class imbalance between actual and candidate regulatory elements as distance increases.ConclusionsOur results suggest that sequence-based models have advanced to the point that in silico study of promoter regions and promoter variants can provide meaningful insights and we provide practical guidance on how to use them. Moreover, we foresee that it will require significantly more and particularly new kinds of data to train models accurately accounting for distal elements.

Methodological progress note: Machine learning methods in healthcare research.

Methodological progress note: Machine learning methods in healthcare research.

Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas

Spaceborne imaging spectroscopy is a highly promising data source for all agricultural management and research disciplines that require spatio-temporal information on crop properties. Recently launched science-driven missions, such as the Environmental Mapping and Analysis Program (EnMAP), deliver unprecedented data from the Earth’s surface. This new kind of data should be explored to develop robust retrieval schemes for deriving crucial variables from future routine missions. Therefore, we present a workflow for inferring crop carbon content (Carea), and aboveground dry and wet biomass (AGBdry, AGBfresh) from EnMAP data. To achieve this, a hybrid workflow was generated, combining radiative transfer modeling (RTM) with machine learning regression algorithms. The key concept involves: (1) coupling the RTMs PROSPECT-PRO and 4SAIL for simulation of a wide range of vegetation states, (2) using dimensionality reduction to deal with collinearity, (3) applying a semi-supervised active learning technique against a 4-years campaign dataset, followed by (4) training of a Gaussian process regression (GPR) machine learning algorithm and (5) validation with an independent in situ dataset acquired during the ESA Hypersense experiment campaign at a German test site. Internal validation of the GPR-Careaand GPR-AGB models achieved coefficients of determination (R2) of 0.80 for Carea and 0.80, 0.71 for AGBdry and AGBfresh, respectively. The mapping capability of these models was successfully demonstrated using airborne AVIRIS-NG hyperspectral imagery, which was spectrally resampled to EnMAP spectral properties. Plausible estimates were achieved over both bare and green fields after adding bare soil spectra to the training data. Validation over green winter wheat fields generated reliable estimates as suggested by low associated model uncertainties (<40%). These results suggest a high degree of model reliability for cultivated areas during active growth phases at canopy closure. Overall, our proposed carbon and biomass models based on EnMAP spectral sampling demonstrate a promising path toward the inference of these crucial variables over cultivated areas from future spaceborne operational hyperspectral missions.

TextFormats: Simplifying the definition and parsing of text formats in bioinformatics.

Text formats are common in bioinformatics, as they allow for editing and filtering using standard tools, as well as, since text formats are often human readable, manual inspection and evaluation of the data. Bioinformatics is a rapidly evolving field, hence, new techniques, new software tools, new kinds of data often require the definition of new formats. Often new formats are not formally described in a standard or specification document. Although software libraries are available for accessing the most common formats, writing parsers for text formats, for which no library is currently available, is a very common though tedious task, utilized by many researchers in the field. This manuscript presents the open source software library and toolset TextFormats (available at https://github.com/ggonnella/textformats), which aims at simplifying the definition and parsing of text formats. Formats specifications are written in a simple data description format using an interactive wizard. Automatic generation of data examples and automatic testing of specifications allow for checking for correctness. Given the specification for a text format, TextFormats allows parsing and writing data in that format, using several programming languages (Nim, Python, C/C++) or the provided command line and graphical user interface tools. Although designed as a general purpose software, the main target application field, for the above mentioned reasons, is expected to be in bioinformatics: Thus, the specifications of several common existing bioinformatics formats are included.

An Annotated Checklist of Recent Opossums (Mammalia: Didelphidae)

Living opossums (Didelphidae) comprise 125 species in 18 genera and 4 subfamilies. This synopsis lists all the didelphid taxa (subfamilies, tribes, genera, subgenera, and species) currently recognised as valid, summarizes information about typification, synonyms, and geographic distributions, remarks noteworthy recent changes in usage, and comments on still outstanding problems. A concluding discussion rejects the notion that the almost twofold increase in opossum species from 1993 to the present is “taxonomic inflation” and considers the impact of new kinds of data and new methods of data analysis on species delimitation.

Climate Change, Human Health, and Health Informatics: A New View of Connected and Sustainable Digital Health.

The connection between human health and climate change has had a scientific basis for many decades. However, little attention has been directed to applying the science of health informatics to this aspect of health and healthcare until recently. This paper briefly reviews examples of recent international work on two fronts: to consider how health informatics can reduce the carbon footprint of healthcare, and to consider how it can integrate new kinds of data for insights into the human health impacts of climate change. Health informatics has two principles of fundamental relevance to this work - connectedness, in other words linking and integrating health data from multiple sources; and sustainability, in other words making healthcare overall more efficient and effective. Deepening its commitment to these principles will position health informatics as a discipline and a profession to support and guide technological advances that respond to the world's climate health challenges.

Machine learning and algorithmic fairness in public and population health

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

Disease Analysis Using Medicines’ Sales Data

The medical industry is one of the vast organized industries, which deals with various kinds of health-related products and services. Additionally, this industry also contributes in Research and Development of Medicines to cure diseases. Hence, they also have their sales data for a particular area, using some kind of aggregation, predication and clustering we can use the sales, association data for finding the outbreak of a daisies or we can check the sales of that particular area and demand for a particular medicine. Using this data, we can make some precautionary measures on that area plus we can analyze the effectiveness of the particular medicine. Plus, this data helps in the Research and Development of new medicine. The medical industry is one of the vast organized industries, which deals with various kinds of health-related products and services. Additionally, this industry also contributes in Research and Development of Medicines to cure diseases. One of the important aspects of every organization is the Sales, this industry too has different sales data for every particular area. With the help of aggregation, prediction and clusters we can use the sales data associated with medicines to find outbreak of any disease or we can verify sales of any particular area and look at the demand for a particular medicine. Using this data, we can take early precautionary measures in those areas, also we can analyze the effectiveness of that particular medicine. This new kind of data will be beneficial for Research and Development of new medicines.

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

Enhancing Interdisciplinary and Systems Thinking with an Integrative Plant Chemistry Module Applied in Diverse Undergraduate Course Settings

To meet the complex global challenges that workers in STEM fields face, it is critical that today’s students develop integrative technical skills and cognitive competencies. As a highly interdisciplinary field, medicinal plant chemistry provides an exceptionally rich environment for such training. Here, we describe a hands-on medicinal plant chemistry laboratory module (Phytochemical Laboratory Activities for iNtegrative Thinking and Enhanced Competencies; PLANTEC) for undergraduates that targets the development of core competencies in (i) logical thinking and analysis of text and data, (ii) interdisciplinary and systems thinking, (iii) oral and written communication of science, and (iv) teamwork and collaboration. Each student determines the natural product profile of a plant species using thin layer chromatography and gas chromatography–mass spectrometry. Students work in pairs and small groups to analyze their data and interpret their findings in chemical, biochemical, and biological contexts. PLANTEC is scalable and so can be offered in laboratory or lecture courses, and even partially or entirely online. We implemented this module in an undergraduate biology lecture course over six 50 min lessons in the fall semesters of both 2018 and 2019. We also experimented with modifications of PLANTEC to tailor learning objectives and thereby emphasize different disciplines during data interpretation (e.g., plant chemistry, ecology, evolution). Students consistently responded that PLANTEC increased not only their confidence in analyzing, interpreting, discussing, and writing about new kinds of data and complex ideas but also their interest in medicinal plant chemistry. Interdisciplinary laboratory modules of this type will be particularly useful in developing an innovative and versatile STEM workforce of the future.

Load Passenger Forecasting Towards Future Bus Transportation Network

Knowing the load of a transport system is one of the critical information of the operators and allows them to take strategic moves to optimize the line, involving more buses or changing the missions of the buses. Having this information in real-time and in the near future opens a new dimension in the management possibilities of the line. The paper describes a prototype that demonstrates this possibility and exposes the methodology used and some examples of the results. The study here presented was possible thanks to a new kind of data: the counting of passengers boarding and dropping out at each stop provide by sensors installed above the doors.

Lane Detection and Lane-Changing Identification with High-Resolution Data from a Swarm of Drones

In the era of big data, new transportation-related concepts and methodologies need to be proposed to understand how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment using a swarm of drones over a dense city center, has uncovered new opportunities for revisiting and evaluating existing concepts, and new ways to describe significant traffic-related phenomena. This dataset is part of an open science initiative shared with the research community and consists of more than half a million detailed trajectories of almost every vehicle that was present in the study area. The aim of this paper is to describe the first methodological approach to how such information can be utilized to extract lane-specific information from this new kind of data and set the benchmark for possible future approaches. Specifically, we describe the methodological framework of two related algorithms: lane detection and lane-changing maneuver identification. Azimuth was the main concept utilized in this methodological framework to overcome existing issues in the literature related to identifying lane-changing maneuvers. The combination of high-quality data, clustering techniques, and detailed spatial information in the lane-detection algorithm indicated it was an effective tool without the need for complex computational effort. Moreover, high-resolution data together with modern time-series analysis tools for lane-changing identification, showed that high-accuracy predictive algorithms can be obtained. The accuracy of both tools was over 95%. Challenging scenarios are identified for future studies and to further improve the tools.

A 2020 perspective on “The building of online trust in e-business relationships”

Abstract This research commentary discusses the perspectives offered in the article “The building of trust in online relationships.” The update argues that the creation of online trust is essential to support effective e-commerce transactions and interorganizational relationships. This goes beyond the advantages that buyers and sellers perceive to have become available through the digitalization of transaction-making. For this reason, we call for new kinds of research that goes beyond what the literature has presented before, to include new kinds of data, such as the digital traces of speech and communication. We also advocate more experimental research on consumer perceptions of organizational trustworthiness, including the use of functional magnetic resonance imaging (FMRI) of the brain to gain a more complete understanding of consumer information and perception processing, and their effects on their decision-making.

Investigation on human factors and key aspects involved in Autonomous Vehicles -AVs- acceptance: new instruments and perspectives

The enormous audience that will be impacted by the spread of autonomous vehicles (AVs) will count individuals with different ethnicity, culture and understanding; many disciplines are involved in order to intercept key-aspects of their introduction. According to a “post-disciplinary” approach, training all of them and helping them accept and take advantage of this new technology will be a huge challenge for the years to come. Human factors influence all the process too: which are the most common obstacles to the AVs technology acceptance? What are the main causes? What are the best ways to overcome these barriers?An analysis based on the demographic differences -such as age, gender, culture, personal experience- and abilities of the users can give a huge contribution to the matter; in the paper, an investigation and a possible application of new instruments and techniques is also provided. The aim is understanding if an innovative approach like gamification can help in collecting the new kind of data and in analyzing user fears, limits and needs. With the support of cognitive ergonomics principles to formulate new synergic solutions, it could be possible to create a virtuous circle composed of preventive profiling -understanding how people perceive the AVs and what they want from them- and targeted training -to grant the necessary knowledge everyone need through emotional involvement-.

Technical Perspective for

The main processing paradigm in data management is bulk processing. As introduced by Codd in the early 70's, under this paradigm relations are processed in bulk, one operator at a time. When applied to relations, this paradigm leads to relational algebra, and its variants, relational calculus, and SQL. Over the years, data management was faced with the challenge of extending bulk processing operators to new kinds of data, and/or new kinds of queries: nested relations, semistructured data, recursive queries. Each such extension requires significant systems development, which should be accompanied, in fact preceded, by a careful study of the expressive power of the new language. Is it as expressive, more expressive, or less expressive than relational algebra? The answer to this question has profound implications on the ability of data processing engines to optimize, compute, distribute, reuse queries in that language. For example, extending relational algebra with nested relations does not increase its expressive power, while extending it with fixpoint does, explaining why modern query engines have an easier time supporting JSON than recursion.

Beyond Calories: The New Economics of Nutrition

The economics of human nutrition has changed greatly in recent years as researchers have moved beyond supply and demand of specific foods and total calories to functional aspects of diet quality, such as nutrient composition, sustainability, and a variety of credence attributes. New kinds of data and methods allow researchers to focus on beneficial or harmful attributes of dietary patterns and the cost-effectiveness of interventions aimed at improving health through diet. This review describes some of the recent literature in nutrition economics and its implications for food policy around the world. The new economics of nutrition is benefiting from a strong foundation in the behavioral and social sciences, building on evidence from the natural and health sciences to address fundamental aspects of human well-being and sustainable development.

Digital audiences and the deconstruction of the collective

This paper aims at characterizing the change that occurred in audience conception with the advent of big data technologies. We argue that a good place to analyze this change is in the marketing techniques geared to capturing the characteristics of consumers of contents and goods. Some of these techniques are existing statistical tools applied to new kinds of data, others, like predictive analytics, are radically new. Our contention is that online individual actions are now studied, predicted, and managed in the way macroeconomic parameters were analyzed in the past. By changing the perspective on the individual and the group, these new technologies further transform the manner in which an audience is imagined. The conceptions of modern collectives once defined by top-down, broadly defined demographic categories, are therefore transformed or, rather, deconstructed.

Spatiotemporal tourist behaviour in urban destinations: a framework of analysis

Empirical research focusing on time–space tourist activity has increased in recent years, since new technological tools make it possible as never before to accurately track tourists’ movements. Nonetheless, the published studies are inconsistent when assessing spatiotemporal tourist behaviour (STTB), hindering the development of a solid research base that permits replication of and comparison amongst studies. The incipient conceptualization of STTB leads to the present inconsistency of assessment indicators used in empirical research, which will probably be stressed in the future. Continuous technological evolution will allow the collection of new kinds of data, whose analysis will certainly result in a better understanding of STTB, especially if structured by a consistent taxonomy. Aiming to address this gap, the theoretical underpinnings of the multidimensional concept of STTB are reviewed and a conceptual model is proposed, based on a behavioural perspective and for the urban intra-destination context, as a comprehensive framework of analysis. Further, the systematization of the indicators used in previous research to assess STTB, its influential factors, and other methodological aspects regarding its assessment, as well as practical implications and future research directions are discussed.

Spike Coding for Dynamic Vision Sensor in Intelligent Driving

Dynamic vision sensor (DVS) as a bio-inspired camera, has shown great advantages in wide dynamic range and high temporal resolution imaging in contrast to conventional frame-based cameras. Its ability to capture high speed moving objects enables fast and accurate detection which plays a significant role in the emerging intelligent driving applications. The pixels in DVS independently respond to the luminance changes with output spikes. Thus, the spike stream conveying the ${x}$ -, ${y}$ -addresses, the firing time, and the polarity (ON/OFF), is quite different from conventional video frames. How to compress this kind of new data for efficient transmission and storage remains a big challenge, especially for on-board detection, monitoring and recording. To address this challenge, this paper first analyzes the spike firing mechanism and the spatiotemporal characteristics of the spike data, then introduces a cube-based spike coding framework for DVS. In the framework, an octree-based structure is proposed to adaptively partition the spike stream into coding cubes in both spatial and temporal dimensions, then several prediction modes are designed to exploit the spatial and temporal characteristics of spikes for compression, including address-prior mode and time-prior mode. To explore more flexibility, the intercube prediction is discussed extensively involving motion estimation and motion compensation. Finally, the experimental results demonstrate that our approach achieves an impressive coding performance with the average compression ratio of 2.6536 against the raw spike data, which is much higher than the results of conventional lossless coding algorithms.

Mapping the structure of perceptions in helping networks of Alaska Natives.

This paper introduces a new method for acquiring and interpreting data on cognitive (or perceptual) networks. The proposed method involves the collection of multiple reports on randomly chosen pairs of individuals, and statistical means for aggregating these reports into data of conventional sociometric form. We refer to the method as “perceptual tomography” to emphasize that it aggregates multiple 3rd-party data on the perceived presence or absence of individual properties and pairwise relationships. Key features of the method include its low respondent burden, flexible interpretation, as well as its ability to find “robust intransitive” ties in the form of perceived non-edges. This latter feature, in turn, allows for the application of conventional balance clustering routines to perceptual tomography data. In what follows, we will describe both the method and an example of the implementation of the method from a recent community study among Alaska Natives. Interview data from 170 community residents is used to ascribe 4446 perceived relationships (2146 perceived edges, 2300 perceived non-edges) among 393 community members, and to assert the perceived presence (or absence) of 16 community-oriented helping behaviors to each individual in the community. Using balance theory-based partitioning of the perceptual network, we show that people in the community perceive distinct helping roles as structural associations among community members. The fact that role classes can be detected in network renderings of “tomographic” perceptual information lends support to the suggestion that this method is capable of producing meaningful new kinds of data about perceptual networks.