Abstract: The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency. Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system.

Abstract: The Promoting Action on Research Implementation in Health Services, or PARIHS framework, was first published in 1998. Since this time, work has been ongoing to further develop, refine and test it. Widely used as an organising or conceptual framework to help both explain and predict why the implementation of evidence into practice is or is not successful, PARIHS was one of the first frameworks to make explicit the multi-dimensional and complex nature of implementation as well as highlighting the central importance of context. Several critiques of the framework have also pointed out its limitations and suggested areas for improvement. Building on the published critiques and a number of empirical studies, this paper introduces a revised version of the framework, called the integrated or i-PARIHS framework. The theoretical antecedents of the framework are described as well as outlining the revised and new elements, notably, the revision of how evidence is described; how the individual and teams are incorporated; and how context is further delineated. We describe how the framework can be operationalised and draw on case study data to demonstrate the preliminary testing of the face and content validity of the revised framework. This paper is presented for deliberation and discussion within the implementation science community. Responding to a series of critiques and helpful feedback on the utility of the original PARIHS framework, we seek feedback on the proposed improvements to the framework. We believe that the i-PARIHS framework creates a more integrated approach to understand the theoretical complexity from which implementation science draws its propositions and working hypotheses; that the new framework is more coherent and comprehensive and at the same time maintains it intuitive appeal; and that the models of facilitation described enable its more effective operationalisation.

Abstract: Zakljucno, potrebno je i nadalje svakodnevno raditi na prevenciji demencije, osiguravati resurse i osmisljavati strategije borbe protiv demencije, jer ce broj osoba s demencijom i u Hrvatskoj u buducnosti biti veci. No, s obzirom da se u svijetu ulažu znatna sredstva u podrucju istraživanja demencije, za ocekivati je pozitivnih pomaka, kako na podrucju terapije, rane dijagnostike, tako i na podrucju rehabilitacije osoba s demencijom. Stoga - vrijeme je na nasoj strani!

Abstract: We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Abstract: A majority of mammalian genes exhibit daily fluctuations in expression levels, making circadian expression rhythms the largest known regulatory network in normal physiology. Cell-autonomous circadian clocks interact with daily light-dark and feeding-fasting cycles to generate approximately 24-hour oscillations in the function of thousands of genes. Circadian expression of secreted molecules and signaling components transmits timing information between cells and tissues. Such intra- and intercellular daily rhythms optimize physiology both by managing energy use and by temporally segregating incompatible processes. Experimental animal models and epidemiological data indicate that chronic circadian rhythm disruption increases the risk of metabolic diseases. Conversely, time-restricted feeding, which imposes daily cycles of feeding and fasting without caloric reduction, sustains robust diurnal rhythms and can alleviate metabolic diseases. These findings highlight an integrative role of circadian rhythms in physiology and offer a new perspective for treating chronic diseases in which metabolic disruption is a hallmark.

Abstract: These guidelines are intended for use by infectious disease specialists, orthopedic surgeons, neurosurgeons, radiologists, and other healthcare professionals who care for patients with native vertebral osteomyelitis (NVO). They include evidence and opinion-based recommendations for the diagnosis and management of patients with NVO treated with antimicrobial therapy, with or without surgical intervention.

Abstract: Climate change enhances root exudation of organic compounds into soils and can lead to loss of soil carbon. Research now shows that oxalic acid (a common exudate) releases organic compounds from protective mineral associations.

Abstract: Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.

Abstract: This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales.

Abstract: There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

Abstract: Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.

Abstract: Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. Despite advances in treatment modalities it remains largely incurable. The objective of our review is to provide a holistic picture of GBM epidemiology, etiology, pathogenesis, clinical findings and treatment. A literature search was conducted for GBM at PubMed and Google Scholar, with relevant key words like glioblastoma multiforme, pathogenesis, signs and symptoms, treatment etc., and papers published until 2015 were reviewed. It was found that radiation and certain genetic syndromes are the only risk factors identified to date for GBM. Depending on the tumor site patients may present to the clinic with varying symptoms. To confirm the presence and the extent of tumor, various invasive and non-invasive imaging techniques require employment. The literature survey revealed the pathogenesis to involve aberrations of multiple signaling pathways through multiple genetic mutations and altered gene expression. Although several treatment options are available, including surgery, along with adjuvant chemo- and radio-therapy, the disease has a poor prognosis and patients generally succumb within 14 months of diagnosis.

Abstract: In his monumental 1687 work "Philosophiae Naturalis Principia Mathematica", known familiarly as the "Principia", Isaac Newton laid out in mathematical terms the principles of time, force, and motion that have guided the development of modern physical science. Even after more than three centuries and the revolutions of Einsteinian relativity and quantum mechanics, Newtonian physics continues to account for many of the phenomena of the observed world, and Newtonian celestial dynamics is used to determine the orbits of our space vehicles. This completely new translation, the first in 270 years, is based on the third (1726) edition, the final revised version approved by Newton; it includes extracts from the earlier editions, corrects errors found in earlier versions, and replaces archaic English with contemporary prose and up-to-date mathematical forms. Newton's principles describe acceleration, deceleration, and inertial movement; fluid dynamics; and, the motions of the earth, moon, planets, and comets. A great work in itself, the "Principia" also revolutionized the methods of scientific investigation. It set forth the fundamental three laws of motion and the law of universal gravity, the physical principles that account for the Copernican system of the world as emended by Kepler, thus effectively ending controversy concerning the Copernican planetary system. The illuminating "Guide to the Principia" by I. Bernard Cohen, along with his and Anne Whitman's translation, will make this preeminent work truly accessible for today's scientists, scholars, and students.

Abstract: This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP.

Abstract: Finding a viable electrolyte for next-generation 5 V-class lithium-ion batteries is of primary importance. A long-standing obstacle has been metal-ion dissolution at high voltages. The LiPF6 salt in conventional electrolytes is chemically unstable, which accelerates transition metal dissolution of the electrode material, yet beneficially suppresses oxidative dissolution of the aluminium current collector; replacing LiPF6 with more stable lithium salts may diminish transition metal dissolution but unfortunately encounters severe aluminium oxidation. Here we report an electrolyte design that can solve this dilemma. By mixing a stable lithium salt LiN(SO2F)2 with dimethyl carbonate solvent at extremely high concentrations, we obtain an unusual liquid showing a three-dimensional network of anions and solvent molecules that coordinate strongly to Li(+) ions. This simple formulation of superconcentrated LiN(SO2F)2/dimethyl carbonate electrolyte inhibits the dissolution of both aluminium and transition metal at around 5 V, and realizes a high-voltage LiNi0.5Mn1.5O4/graphite battery that exhibits excellent cycling durability, high rate capability and enhanced safety.

Abstract: COVID-19 can be understood by the region of the lung that is infected. Mild disease will be confined to the conducting airways and severe disease will involve the gas exchange portion of the lung.

Abstract: Our herein described combined analysis of the latest neutrino oscillation data presented at the Neutrino2020 conference shows that previous hints for the neutrino mass ordering have significantly decreased, and normal ordering (NO) is favored only at the 1.6σ level. Combined with the χ2 map provided by Super-Kamiokande for their atmospheric neutrino data analysis the hint for NO is at 2.7σ. The CP conserving value δCP = 180° is within 0.6σ of the global best fit point. Only if we restrict to inverted mass ordering, CP violation is favored at the ∼ 3σ level. We discuss the origin of these results — which are driven by the new data from the T2K and NOvA long-baseline experiments —, and the relevance of the LBL-reactor oscillation frequency complementarity. The previous 2.2σ tension in ∆m221 preferred by KamLAND and solar experiments is also reduced to the 1.1σ level after the inclusion of the latest Super-Kamiokande solar neutrino results. Finally we present updated allowed ranges for the oscillation parameters and for the leptonic Jarlskog determinant from the global analysis.

Abstract: A 28-year-old woman presents with a 7-month history of recurrent, crampy pain in the left lower abdominal quadrant, bloating with abdominal distention, and frequent, loose stools. She reports having had similar but milder symptoms since childhood. She spends long times in the bathroom because she is worried about uncontrollable discomfort and fecal soiling if she does not completely empty her bowels before leaving the house. She feels anxious and fatigued and is frustrated that her previous physician did not seem to take her distress seriously. Physical examination is unremarkable except for tenderness over the left lower quadrant. How should her case be evaluated and treated?

Abstract: Expanding the range of healable materials is an important challenge for sustainable societies. Noncrystalline, high-molecular-weight polymers generally form mechanically robust materials, which, however, are difficult to repair once they are fractured. This is because their polymer chains are heavily entangled and diffuse too sluggishly to unite fractured surfaces within reasonable time scales. Here we report that low-molecular-weight polymers, when cross-linked by dense hydrogen bonds, yield mechanically robust yet readily repairable materials, despite their extremely slow diffusion dynamics. A key was to use thiourea, which anomalously forms a zigzag hydrogen-bonded array that does not induce unfavorable crystallization. Another key was to incorporate a structural element for activating the exchange of hydrogen-bonded pairs, which enables the fractured portions to rejoin readily upon compression.

Abstract: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

Abstract: Generation of thick vascularized tissues that fully match the patient still remains an unmet challenge in cardiac tissue engineering. Here, a simple approach to 3D-print thick, vascularized, and perfusable cardiac patches that completely match the immunological, cellular, biochemical, and anatomical properties of the patient is reported. To this end, a biopsy of an omental tissue is taken from patients. While the cells are reprogrammed to become pluripotent stem cells, and differentiated to cardiomyocytes and endothelial cells, the extracellular matrix is processed into a personalized hydrogel. Following, the two cell types are separately combined with hydrogels to form bioinks for the parenchymal cardiac tissue and blood vessels. The ability to print functional vascularized patches according to the patient's anatomy is demonstrated. Blood vessel architecture is further improved by mathematical modeling of oxygen transfer. The structure and function of the patches are studied in vitro, and cardiac cell morphology is assessed after transplantation, revealing elongated cardiomyocytes with massive actinin striation. Finally, as a proof of concept, cellularized human hearts with a natural architecture are printed. These results demonstrate the potential of the approach for engineering personalized tissues and organs, or for drug screening in an appropriate anatomical structure and patient-specific biochemical microenvironment.

Abstract: Wearable sensors are increasingly finding their way into applications of kinesthetic sensing, personal health monitoring, and smart prosthetics/robotics. A graphene-based composite fiber sensor with a "compression spring" structure is fabricated, featuring the ability of detecting multiple kinds of deformation. This fiber sensor is integrated into wearable sensors for monitoring human activities and intricate movements of robotics successfully.

Abstract: Episodic memories initially require rapid synaptic plasticity within the hippocampus for their formation and are gradually consolidated in neocortical networks for permanent storage. However, the engrams and circuits that support neocortical memory consolidation have thus far been unknown. We found that neocortical prefrontal memory engram cells, which are critical for remote contextual fear memory, were rapidly generated during initial learning through inputs from both the hippocampal–entorhinal cortex network and the basolateral amygdala. After their generation, the prefrontal engram cells, with support from hippocampal memory engram cells, became functionally mature with time. Whereas hippocampal engram cells gradually became silent with time, engram cells in the basolateral amygdala, which were necessary for fear memory, were maintained. Our data provide new insights into the functional reorganization of engrams and circuits underlying systems consolidation of memory.

Abstract: RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new methods to handle the high error rate of long reads and offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of short-read assemblies. StringTie2 is more accurate and faster and uses less memory than all comparable short-read and long-read analysis tools.

Abstract: Scene text detection methods based on neural networks have emerged recently and have shown promising results. Previous methods trained with rigid word-level bounding boxes exhibit limitations in representing the text region in an arbitrary shape. In this paper, we propose a new scene text detection method to effectively detect text area by exploring each character and affinity between characters. To overcome the lack of individual character level annotations, our proposed framework exploits both the given character-level annotations for synthetic images and the estimated character-level ground-truths for real images acquired by the learned interim model. In order to estimate affinity between characters, the network is trained with the newly proposed representation for affinity. Extensive experiments on six benchmarks, including the TotalText and CTW-1500 datasets which contain highly curved texts in natural images, demonstrate that our character-level text detection significantly outperforms the state-of-the-art detectors. According to the results, our proposed method guarantees high flexibility in detecting complicated scene text images, such as arbitrarily-oriented, curved, or deformed texts.

Abstract: There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA). In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined—at all scales—in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing. The criteria and approach we propose, for a paradigm shift towards sustainable intensification of agriculture, integrates the dual and interdependent goals of using sustainable practices to meet rising human needs while contributing to resilience and sustainability of landscapes, the biosphere, and the Earth system. Both of these, in turn, are required to sustain the future viability of agriculture. This paradigm shift aims at repositioning world agriculture from its current role as the world’s single largest driver of global environmental change, to becoming a key contributor of a global transition to a sustainable world within a safe operating space on Earth.

Abstract: The emergence of a novel, highly pathogenic coronavirus, 2019-nCoV, in China, and its rapid national and international spread pose a global health emergency. Coronaviruses use their spike proteins to select and enter target cells and insights into nCoV-2019 spike (S)-driven entry might facilitate assessment of pandemic potential and reveal therapeutic targets. Here, we demonstrate that 2019-nCoV-S uses the SARS-coronavirus receptor, ACE2, for entry and the cellular protease TMPRSS2 for 2019-nCoV-S priming. A TMPRSS2 inhibitor blocked entry and might constitute a treatment option. Finally, we show that the serum form a convalescent SARS patient neutralized 2019-nCoV-S-driven entry. Our results reveal important commonalities between 2019-nCoV and SARS-coronavirus infection, which might translate into similar transmissibility and disease pathogenesis. Moreover, they identify a target for antiviral intervention. One sentence summary The novel 2019 coronavirus and the SARS-coronavirus share central biological properties which can guide risk assessment and intervention.