bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining data assimilation and machine learning to infer unresolved scale parametrization
Authors: Brajard Julien , Carrassi Alberto , Bocquet Marc , Bertino Laurent 
Published: February 2021

In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data, in the realistic scenario of noisy and sparse observations. The algorithm proposed in this work is a two-step process. First, data assimilation (DA) techniques are applied to estimate the full state of the system from a truncated model. The unresolved part of the truncated model is viewed as a model error in the DA system. In a second step, ML is used to emulate the unresolved part, a predictor of model error given the state of the system. Finally, the ML-based parametrization model is added to the physical core truncated model to produce a hybrid model. The DA component of the proposed method relies on an ensemble Kalman filter while the ML parametrization is represented by a neural network. The approach is applied to the two-scale Lorenz model and to MAOOAM, a reduced-order coupled ocean-atmosphere model. We show that in both cases, the hybrid model yields forecasts with better skill than the truncated model. Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Learning earth system models from observations: machine learning or data assimilation?
Authors: Geer A. J. 
Published: February 2021

Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth system models directly from the observations. Earth sciences already use data assimilation (DA), which underpins decades of progress in weather forecasting. DA and ML have many similarities: they are both inverse methods that can be united under a Bayesian (probabilistic) framework. ML could benefit from approaches used in DA, which has evolved to deal with real observations—these are uncertain, sparsely sampled, and only indirectly sensitive to the processes of interest. DA could also become more like ML and start learning improved models of the earth system, using parameter estimation, or by directly incorporating machine-learnable models. DA follows the Bayesian approach more exactly in terms of representing uncertainty, and in retaining existing physical knowledge, which helps to better constrain the learnt aspects of models. This article makes equivalences between DA and ML in the unifying framework of Bayesian networks. These help illustrate the equivalences between four-dimensional variational (4D-Var) DA and a recurrent neural network (RNN), for example. More broadly, Bayesian networks are graphical representations of the knowledge and processes embodied in earth system models, giving a framework for organising modelling components and knowledge, whether coming from physical equations or learnt from observations. Their full Bayesian solution is not computationally feasible but these networks can be solved with approximate methods already used in DA and ML, so they could provide a practical framework for the unification of the two. Development of all these approaches could address the grand challenge of making better use of observations to improve physical models of earth system processes.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Physics-informed machine learning: case studies for weather and climate modelling
Authors: Kashinath K. , Mustafa M. , Albert A. , Wu J-L. , Jiang C. , Esmaeilzadeh S. , Azizzadenesheli K. , Wang R. , Chattopadhyay A. , Singh A. , Manepalli A. , Chirila D. , Yu R. , Walters R. , White B. , Xiao H. , Tchelepi H. A. , Marcus P. , Anandkumar A. , Hassanzadeh P. , Prabhat  
Published: February 2021

Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories. Through 10 case studies, we show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes. The accomplishments of these studies include greater physical consistency, reduced training time, improved data efficiency, and better generalization. Finally, we synthesize the lessons learned and identify scientific, diagnostic, computational, and resource challenges for developing truly robust and reliable physics-informed ML models for weather and climate processes.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Can deep learning beat numerical weather prediction?
Authors: Schultz M. G. , Betancourt C. , Gong B. , Kleinert F. , Langguth M. , Leufen L. H. , Mozaffari A. , Stadtler S. 
Published: February 2021

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach.
This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Machine learning for weather and climate are worlds apart
Authors: Watson-Parris D. 
Published: February 2021

Modern weather and climate models share a common heritage and often even components; however, they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour, there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem, which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. To emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make climate a domain which is rich in interesting machine learning challenges. Here, I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
Authors: Kirkwood Charlie , Economou Theo , Odbert Henry , Pugeault Nicolas 
Published: February 2021

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the ‘ideal’ forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Relation of inflammatory marker trajectories with frailty and aging in a 20-year longitudinal study
Authors: Samson Leonard Daniël , Buisman Anne-Marie , Ferreira José A. , Picavet H. Susan J. , Verschuren W. M. Monique , Boots A. Mieke H. , Engelfriet Peter 
Published: February 2021

AbstractLittle is known about the development of low-grade inflammation with age and its relationship with the onset of frailty. In this exploratory study, we investigated 18 inflammatory markers measured in blood of 144 individuals aged 65-75 years at study endpoint, collected over 20 years at five-year intervals. IFNγ-induced markers and platelet activation markers changed in synchrony over time. Chronically elevated levels of IL-6-related markers, such as CRP and sIL-6R, were associated with frailty and becoming frail over time, poorer lung function, or less physical strength. Overweight was a possible driver of these associations. More and stronger associations were detected in women, such as between increasing sCD14 levels and frailty, indicating possible monocyte overactivation. Multivariate prediction of frailty showed low accuracy but confirmed the main results. In summary, we documented 20-year temporal changes of inflammatory markers in an aging population, and related these to clinically relevant health outcomes.
Potential Association of Mitochondrial Haplogroups and A8860G Mutation with Breast Cancer Risk
Authors: Fadhl Han N. Mohammed , Abdulkarim Farhad M. 
Published: February 2021

AbstractThe last decade has witnessed great progresses regarding the molecular basis of breast cancer with discovery of different nuclear susceptibility genes; in addition investigations and researches regarding mitochondrial DNA (mtDNA) mutations in breast cancer have been started. Mitochondrial haplogroup determinants (single nucleotide polymorphism SNP) and somatic mitochondrial mutations have recently been studied as possible risk factors for carcinogenic processes in different tissues, hence in order to identify breast cancer related SNPs and haplogroups among the population of Sulaimaniyah city/Iraq, the entire mitochondrial genome of 20-breast cancer samples and comparable controls were sequenced. Haplogrep 2.0 was used for haplogroup identification; Chi-square and Fishers exact test were applied to assess relational significance. HV haplogroup in the cancer samples appeared to be a risk factor for breast cancer compared to the most common H haplogroup in control samples with a p-values of 0.002 and 0.006 respectively and an Odd Ratio (OR) = 28.00. Besides, SNP (A8860G) was also identified as a risk factor for breast cancer as compared to other randomly selected SNPs (A750G, A1438G and C7028T) with p values □0.05 and OR >1.
Comparison of SARS-CoV-2 viral load in saliva samples in symptomatic and asymptomatic cases
Authors: Bhatta Anuja , Henkhaus Rebecca , Fehling Heather L. 
Published: February 2021

AbstractSARS-CoV-2 infections can be symptomatic as well as asymptomatic. In this study, we analyzed 460,814 saliva samples collected from July 2020 to January 2021 for a SARS-CoV-2-specific gene target using the FDA EUA test, CRL Rapid Response™, based on reverse transcription polymerase chain reaction (RT-PCR). We measured SARS-CoV-2 viral loads using cycle threshold (Ct) values. A total of 17,813 samples tested positive for COVID-19 using self-collected saliva samples. The Ct values ranged from 11 to 40, 91.3% distributed between 22 to 38 Ct. We then compared Ct values for symptomatic and asymptomatic cases for all positive saliva samples. A total of 8,706 cases were symptomatic with an average Ct value of 29.24, and 9,107 cases were asymptomatic with an average Ct value of 30.99. Hence, SARS-CoV-2 viral loads (Ct) in saliva samples for both symptomatic and asymptomatic cases are similar.
Detection of Antibiotic-Resistant Bacteria, Resistance Determinants, and Mobile Elements in Surface Waters in Lebanon
Authors: Moussa Jennifer , Abboud Edmond , Tokajian Sima 
Published: February 2021

AbstractThe prevalence of antibiotic-resistant bacteria in surface water in Lebanon is a growing concern and understanding the mechanisms of the spread of resistance determinants is essential. We aimed at studying the occurrence of resistant organisms and determinants in surface water sources in Lebanon and understanding their mobilization and transmission. Water samples were collected from five major rivers in Lebanon. 91 isolates were recovered out of which 25 were multidrug-resistant (MDR) and accordingly were further characterized. Escherichia coli and Klebsiella pneumoniae were the most commonly identified MDR isolates. Conjugation assays coupled with in silico plasmid analysis were performed and validated using PCR-based replicon typing (PBRT) to identify and confirm incompatibility groups and the localization of β-lactamase encoding genes. E. coli EC23 carried a blaNDM-5 gene on a conjugative, multireplicon plasmid, while blaCTX-M-15 and blaTEM-1B were detected in the majority of the MDR isolates. Different ST types were identified including the highly virulent E. coli ST131. Our results showed a common occurrence of bacterial contaminants in surface water and an increase in the risk for the dissemination of resistance determinants exacerbated with the ongoing intensified population mobility in Lebanon and the widespread lack of wastewater treatment.
The impact of armed conflict on the epidemiological situation of Coronavirus disease (COVID-19) in Libya, Syria, and Yemen
Authors: Daw Mohamed A 
Published: February 2021

BackgroundSince the Arab uprising 2011, Libya, Syria, and Yemen have gone through a major armed conflict. This resulted in a high rate of mortality, injury, and population displacement with a collapse of the health care system. Furthermore, it was complicated by the emergence of, COVID-19 as a global pandemic which made the population of these countries strive under unusual conditions to tackle both the pandemic and the ongoing wars. The objectives of this study were to determine the impacts and influence of armed conflicts on the epidemiology of Novel Coronavirus (SARS-CoV-2) within these war-torn countries and outline the needed strategies to combat the spread of the pandemic and its upcoming consequences.MethodsThe official and public data regarding the dynamics of armed conflict and the spread, of SARS-COV-19 in Libya, Syria, and Yemen were collected from all available sources. Starting from the early emergence of the COVID-19 in each country until the end of December 2020. Datasets were analyzed through a set of statistical techniques and the weekly resolved data were used to probe the link between the intensity levels of the armed conflict and the spread of the pandemic.ResultsData indicated that there is an increase in the intensity of violence levels at an early stage from March to August reached up to two folds in the three countries particularly in Libya. In this violent period, few cases of COVID-19 were reported ranging from 5-53 cases/day. From September to December, a significant decline in the level of the armed conflict was accompanied by steep upsurges in the number of reported COVID-19 cases reached up to 500 cases/day. The highest accumulative cases of COVID-19 were reported in Libya, Syria, and Yemen respectively.ConclusionsOur analysis demonstrates that the armed conflict has provided an opportunity for SARS-COV-19 to spread. At the early weeks of the pandemic that coincided with high levels of the armed conflict few cases were officially reported indicating a vast undercount, which may suggest a hidden mitigating spread at an early stage. Then the pandemic increased immensely as the armed conflict decline to reach the highest by December. A full-blown transmission of the COVID-19 pandemic in these countries is expected. Therefore, urgent national and international strategies should be implemented to combat the pandemic and its upcoming consequences.
Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm
Authors: van den Boom Max , Miller Kai J. , Gregg Nicholas M. , Ojeda Gabriela , Lee Kendall H. , Richner Thomas J. , Ramsey Nick F. , Worrell Greg A. , Hermes Dora 
Published: February 2021

AbstractElectrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI).Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand.The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.
Trade-offs between antiseptic cytotoxicity and efficacy in a humanex vivowound contamination model
Authors: Cheong J. Z. Alex , Liu Aiping , Rust Clayton J. , Kalan Lindsay R. , Gibson Angela L. F. 
Published: February 2021

ABSTRACTWound cleansing agents are routine in wound care, even in the absence of signs of infection. Antiseptic activity prevents contaminating microbes from establishing an infection while also raising concerns of cytotoxicity and delayed wound healing. Here, we used anex vivohuman skin excisional wound model to evaluate the cytotoxicity of five clinically-used wound cleaning agents (saline, povidone iodine, Dove® soap, Dial® soap, and chlorhexidine gluconate). We established a wound contamination model using ∼100 cells ofPseudomonas aeruginosaper wound to evaluate antiseptic efficacy and microbial biofilm spatial organization. We found that Dial® soap and chlorhexidine gluconate significantly reduced metabolic activity of the biopsies, while all treatments except saline affected local cellular viability. Within the contamination model, only chlorhexidine gluconate treatment resulted in significantly lowerP. aeruginosacounts at 24 hours post-treatment, driven by sub-limit-of-detection counts immediately post-treatment. Later applications of chlorhexidine gluconate had no effects on microbial growth, with microscopy showing extensive surface colonization of the wound bed. We present a clinically-relevant model for evaluating antiseptic cytotoxicity and efficacy, with the ability to resolve spatial localization and temporal dynamics of tissue viability and microbial growth.