Impact of soil warming on the plant metabolome of Icelandic grasslands

Altres ajuts: Scholarly Studies programme of the Smithsonian Institution, projects LM2015061 and LO1415 of the Ministry of Education, Youth and Sports of the Czech Republic, and the Research Foundation-Flanders (FWO aspirant grant to N.L.).Climate change is stronger at high than at temperate and tropical latitudes. The natural geothermal conditions in southern Iceland provide an opportunity to study the impact of warming on plants, because of the geothermal bedrock channels that induce stable gradients of soil temperature. We studied two valleys, one where such gradients have been present for centuries (long-term treatment), and another where new gradients were created in 2008 after a shallow crustal earthquake (short-term treatment). We studied the impact of soil warming (0 to +15 °C) on the foliar metabolomes of two common plant species of high northern latitudes: Agrostis capillaris, a monocotyledon grass; and Ranunculus acris, a dicotyledonous herb, and evaluated the dependence of shifts in their metabolomes on the length of the warming treatment. The two species responded differently to warming, depending on the length of exposure. The grass metabolome clearly shifted at the site of long-term warming, but the herb metabolome did not. The main up-regulated compounds at the highest temperatures at the long-term site were saccharides and amino acids, both involved in heat-shock metabolic pathways. Moreover, some secondary metabolites, such as phenolic acids and terpenes, associated with a wide array of stresses, were also up-regulated. Most current climatic models predict an increase in annual average temperature between 2-8 °C over land masses in the Arctic towards the end of this century. The metabolomes of A. capillaris and R. acris shifted abruptly and nonlinearly to soil warming >5 °C above the control temperature for the coming decades. These results thus suggest that a slight warming increase may not imply substantial changes in plant function, but if the temperature rises more than 5 °C, warming may end up triggering metabolic pathways associated with heat stress in some plant species currently dominant in this region

Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach.Peer ReviewedPostprint (author's final draft

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

Prevalence and genetic characteristics of Staphylococcus aureus CC398 isolates from invasive infections in spanish hospitals, focusing on the livestock-independent CC398-MSSA clade

Background: Livestock-associated (LA)-CC398-MRSA is closely related to pigs, being unfrequently detected in human invasive infections. CC398-MSSA is emerging in human invasive infections in some countries, but genetic and epidemiological characteristics are still scarcely reported. Objectives: To determine the prevalence of Staphylococcus aureus (SA) CC398, both MRSA and MSSA, among blood cultures SA isolates recovered in Spanish hospitals located in regions with different pig-farming densities (PD) and characterize the recovered isolates. Methods: One thousand twenty-two SA isolates (761 MSSA, 261 MRSA) recovered from blood cultures during 6–12 months in 17 Spanish hospitals (2018–2019) were studied. CC398 lineage identification, detection of spa-types, and antibiotic resistance, virulence and human immune evasion cluster (IEC) genes were analyzed by PCR/sequencing. Results: Forty-four CC398-MSSA isolates (4.3% of SA; 5.8% of MSSA) and 10 CC398-MRSA isolates (1% of SA; 3.8% of MRSA) were detected. Eleven spa-types were found among the CC398-MSSA isolates with t571 and t1451 the most frequent spa-types detected (75%). Most of CC398-MSSA isolates were Immune-Evasion-Cluster (IEC)-positive (88.6%), tetracycline-susceptible (95.5%) and erythromycin/clindamycin–inducible-resistant/erm(T)-positive (75%). No statistical significance was detected when the CC398-MSSA/MSSA rate was correlated to PD (pigs/km2) (p = 0.108). On the contrary, CC398-MRSA isolates were all IEC-negative, predominately spa-t011 (70%), and the CC398-MRSA/MRSA rate was significantly associated to PD (p < 0.005). Conclusion: CC398-MSSA is an emerging clade in invasive infections in Spanish hospitals. CC398-MRSA (mostly t011) and CC398-MSSA (mostly t571 and t1451) show important differences, possibly suggesting divergent steps in host-adaptation evolutionary processes. While CC398-MRSA is livestock-associated (lacking IEC-system), CC398-MSSA seems to be mostly livestock-independent, carrying human-adaptation markers.

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Visualizing Logic Explanations for Social Media Moderation

Autonomous artificial moderators can be useful to monitor social media for content that violates platform policies, but such artificial moderators can be confidently wrong about their decisions. While creating an approach that makes no mistakes is effectively impossible, being able to generate explanations for any given decision can simplify the task of detecting when the system is wrong. In this work we present LiveEvents, a neuro-symbolic agent capable of generating explanations based on which rules have lead to its decisions. We deliver these explanations via Cogni-Sketch, which provides users with an interactive visual representation, allowing them to easily understand the explanations given by the system

A Pilot Study on Detecting Violence in Videos Fusing Proxy Models

We propose an approach to detect violence in CCTV feeds that is robust to new datasets and situations. This approach breaks with the traditional assumption of having large amounts of training data that are representative samples. Detecting violence in CCTV feeds is an objectively hard problem that is of paramount importance to solve for effective situational understanding. Violence comprises a large spectrum of activities that can go from abuse, to fighting, to road accidents, that can therefore take place in completely different environments, from public buildings, to underground stations, to roads during the day or the night. This large spectrum of activities and environments makes this a hard classification task for machines. We show that there are specific, detectable, and measurable features of video feeds that correlate with-among other things-violence and, by fusing such features with semantic knowledge, we can in principle provide estimates of sequences of videos that correlate with violence

DeepCEP: Deep complex event processing using distributed multimodal information

Deep learning models typically make inferences over transient features of the latent space, i.e., they learn data representations to make decisions based on the current state of the inputs over short periods of time. Such models would struggle with state-based events, or complex events, that are composed of simple events with complex spatial and temporal dependencies. In this paper, we propose DeepCEP, a framework that integrates the concepts of deep learning models with complex event processing engines to make inferences across distributed, multimodal information streams with complex spatial and temporal dependencies. DeepCEP utilizes deep learning to detect primitive events. A user can define a complex event to be detected as a particular sequence or pattern of primitive events as well as any other logical predicates that constrain the definition of such an event. The integration of human logic not only increases robustness and interpretability, but also greatly reduces the amount of training data required. Further, we demonstrate how the uncertainty of a model can be propagated throughout the complex event detection pipeline. Finally, we enumerate the future directions of research enabled by DeepCEP. In particular, we detail how an end-to-end training model for complex event processing with deep learning may be realized

DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settings

Detecting complex events from subsymbolic data streams (such as images, audio recordings or videos) is a challenging problem, as traditional symbolic approaches cannot be used to process subsymbolic data, and neural-only approaches usually require larger amounts of training data than available. In this paper, we present DeepProbCEP, a Complex Event Processing (CEP) approach designed with four objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining flexibility and modularity in the definition of complex event rules, (iii) limiting the cost of obtaining training data and (iv) being robust against adversarial conditions. DeepProbCEP archives this by using a neuro-symbolic approach, which combines the neural and symbolic approaches to allow training with sparse data. This is made possible through the injection of human knowledge. In this paper, we demonstrate that DeepProbCEP outperforms other state-of-the-art approaches when training using sparse data. We also show that DeepProbCEP is robust in different adversarial settings. Finally, DeepProbCEP's flexibility is demonstrated by showing it can be used to process both images and audio as input

Uncertainty-Aware situational understanding

Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. probabilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data, effective instantiations, to determine the probabilities for unobserved variables, on the basis of the observed variables, is significantly smaller than the size of the training data, the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: This can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts