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This paper focuses on the influence of the crude oil characterization on minimum miscibility pressure (MMP) calculation. It is shown what kind of compounds may be lumped together in order to calculate the MMP with a good accuracy. In a first step, a selected crude oil is modeled with 37 pure compounds using the characterization procedure developed by Avaullée et al. (1997a) in which two parameters of the distillation residue are fitted on the experimental bubble points. In this context, all the experimental data (PVT, swelling test, slim tube test) are calculated with a very good accuracy. In a second step, the heavy intermediate, the middle intermediate and the light compounds are successively lumped. The influence of such lumping procedure on the calculated MMP is discussed. Dans cet article, nous étudions l'influence de la modélisation des coupes d'un pétrole brut sur la valeur calculée de la pression minimale de miscibilité (MMP). En particulier, il est expliqué quels types de constituants peuvent être regroupés sous forme d'un pseudo-constituant sans pour autant dégrader la valeur de la MMP. La procédure suivie dans cet article est simple : à partir d'un pétrole brut donné, nous utilisons dans un premier temps une représentation par 37 composants, exploitant l'information analytique standard disponible sur ce fluide. La modélisation des fractions lourdes utilisée est celle développée par Avaullée et al. (1997a) , dans laquelle nous avons ajusté deux paramètres du résidu de distillation C(20+) sur les pressions de saturation expérimentales. Sur le fluide étudié, nous mettons en évidence le bon niveau de restitution des grandeurs volumétriques, des données expérimentales de gonflement ainsi que de la MMP mesurée lors d'une expérience de déplacement en tube mince. Dans un deuxième temps, les composés intermédiaires lourds, les intermédiaires moyens et les composants légers font successivement l'objet de regroupement en pseudo-constituant. L'influence de tels regroupements sur la valeur calculée de la MMP est alors analysée.

Objectif. – L''objectif principal était de démontrer l''équivalence de l''efficacité clinique de la pristinamycine (1 g × 2/jour) pendant quatre jours vs céfuroxime axétil (250 mg × 2/jour) pendant cinq jours, dans les sinusites maxillaires aiguës de l''adulte.Patients et méthodes. – Cette étude était multicentrique, randomisée, en double insu, double placebo. Le diagnostic reposait sur l''association de douleurs sous-orbitaires spontanées, d''une rhinorrhée purulente et d''un écoulement purulent du méat moyen et était confirmé radiologiquement. Un prélèvement bactériologique sous le méat moyen était réalisé sous rhinofibroscopie. Le critère principal de jugement était le taux de succès clinique dans la population cliniquement évaluable per protocole. Résultats. – Entre janvier 2001 et février 2002, 485 patients étaient inclus (France = 301 ; Tunisie = 48) ; Pologne = 69 ; Argentine = 67). Une bactérie pathogène était isolée chez 46 % des patients, principalement Streptococcus pneumoniae (34,2 %), Haemophilus influenzae (21,5 %), Staphylococcus aureus (15,4 %) et Moraxella catharralis (7,9 %). Les taux de succès clinique à j12–j19 dans la population per protocole clinique, étant respectivement de 91,4 % (201/220) et de 91,1 % (195/214) et l''IC95 % de [–5,1 % ; 5,3 %], la non-infériorité de la pristinamycine vs céfuroxime axétil a été établie. Le maintien de la guérison à distance (j26–j31) était constaté respectivement chez 88,6 et 85,8 % des patients. Les taux de réponse bactériologique satisfaisante à j12–j19 étaient respectivement de 87 % (87/100) et de 87,9 % (87/99).Conclusion. – La pristinamycine 1 g × 2/jour pendant quatre jours est aussi efficace que le céfuroxime axétil 250 mg × 2/jour pendant cinq jours dans le traitement des sinusites maxillaires aiguës non compliquées de l''adulte. [Copyright &y& Elsevier]

The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regression models and mechanistic approaches. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the model's decision-making process, providing insights into decisive input features responsible for the model's prediction. One XAI technique to explain the predictions of neural networks which was proven useful in various domains is Layer-wise Relevance Propagation (LRP). In this work, we extend the LRP technique to a sequence-to-sequence neural network model with GRU layers. The explanation heatmaps provided by LRP allow us to identify important meteorological and temporal features responsible for the accumulation of four major pollutants in the air ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]), and our findings can be backed up with prior knowledge in environmental and pollution research. This illustrates the appropriateness of XAI for understanding pollution forecastings and opens up new avenues for controlling and mitigating the pollutants' load in the air., (© 2023. The Author(s).)

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact., (© The Author(s) 2022; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text's category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications.

Host variation in Toll-like receptors and other innate immune signaling molecules alters infection susceptibility. However, only a portion of the variability observed in the innate immune response is accounted for by known genes in these pathways. Thus, the identification of additional genes that regulate the response to Gram positive bacteria is warranted. Bone marrow-derived macrophages (BMMs) from 43 inbred mouse strains were stimulated with lipotechoic acid (LTA), a major component of the Gram positive bacterial cell wall. Concentrations of the proinflammatory cytokines IL-6, IL-12, and TNF-α were measured. In silico whole genome association (WGA) mapping was performed using cytokine responses followed by network analysis to prioritize candidate genes. To determine which candidate genes could be responsible for regulating the LTA response, candidate genes were inhibited using RNA interference (RNAi) and were overexpressed in RAW264.7 macrophages. BMMs from Bdkrb1-deficient mice were used to assess the effect of Bdkrb1 gene deletion on the response to LTA, heat-killed Streptococcus pneumoniae, and heat-killed Staphylococcus aureus WGA mapping identified 117 loci: IL-6 analysis yielded 20 loci (average locus size = 0.133 Mb; 18 genes), IL-12 analysis produced 5 loci (0.201 Mb average; 7 genes), and TNF-α analysis yielded 92 loci (0.464 Mb average; 186 genes of which 46 were prioritized by network analysis). The follow-up small interfering RNA screen of 71 target genes identified four genes (Bdkrb1, Blnk, Fbxo17, and Nkx6-1) whose inhibition resulted in significantly reduced cytokine production following LTA stimulation. Overexpression of these four genes resulted in significantly increased cytokine production in response to LTA. Bdkrb1-deficient macrophages were less responsive to LTA and heat-killed S. aureus, validating the genetic and RNAi approach to identify novel regulators of the response to LTA. We have identified four innate immune response genes that may contribute to Gram positive bacterial susceptibility., (Copyright © 2016 by the Genetics Society of America.)

The innate immune response plays a key role in fighting infection by activating inflammation and stimulating the adaptive immune response. However, chronic activation of innate immunity can contribute to the pathogenesis of many diseases with an inflammatory component. Thus, various negatively acting factors turn off innate immunity subsequent to its activation to ensure that inflammation is self-limiting and to prevent inflammatory disease. These negatively acting pathways include the production of inhibitory acting alternate proteins encoded by alternative mRNA splice forms of genes in Toll-like receptor (TLR) signaling pathways. We previously found that the SF3a mRNA splicing complex was required for a robust innate immune response; SF3a acts to promote inflammation in part by inhibiting the production of a negatively acting splice form of the TLR signaling adaptor MyD88. Here we inhibit SF3a1 using RNAi and subsequently perform an RNAseq study to identify the full complement of genes and splicing events regulated by SF3a in murine macrophages. Surprisingly, in macrophages, SF3a has significant preference for mRNA splicing events within innate immune signaling pathways compared with other biological pathways, thereby affecting the splicing of specific genes in the TLR signaling pathway to modulate the innate immune response.

Macrophages are key phagocytic innate immune cells. When macrophages encounter a pathogen, they produce antimicrobial proteins and compounds to kill the pathogen, produce various cytokines and chemokines to recruit and stimulate other immune cells, and present antigens to stimulate the adaptive immune response. Thus, being able to efficiently manipulate macrophages with techniques such as RNA-interference (RNAi) is critical to our ability to investigate this important innate immune cell. However, macrophages can be technically challenging to transfect and can exhibit inefficient RNAi-induced gene knockdown. In this protocol, we describe methods to efficiently transfect two mouse macrophage cell lines (RAW264.7 and J774A.1) with siRNA using the Amaxa Nucleofector 96-well Shuttle System and describe procedures to maximize the effect of siRNA on gene knockdown. Moreover, the described methods are adapted to work in 96-well format, allowing for medium and high-throughput studies. To demonstrate the utility of this approach, we describe experiments that utilize RNAi to inhibit genes that regulate lipopolysaccharide (LPS)-induced cytokine production.

The extent of the innate immune response is regulated by many positively and negatively acting signaling proteins. This allows for proper activation of innate immunity to fight infection while ensuring that the response is limited to prevent unwanted complications. Thus mutations in innate immune regulators can lead to immune dysfunction or to inflammatory diseases such as arthritis or atherosclerosis. To identify novel innate immune regulators that could affect infectious or inflammatory disease, we have taken a comparative genomics RNAi screening approach in which we inhibit orthologous genes in the nematode Caenorhabditis elegans and murine macrophages, expecting that genes with evolutionarily conserved function also will regulate innate immunity in humans. Here we report the results of an RNAi screen of approximately half of the C. elegans genome, which led to the identification of many candidate genes that regulate innate immunity in C. elegans and mouse macrophages. One of these novel conserved regulators of innate immunity is the mRNA splicing regulator Eftud2, which we show controls the alternate splicing of the MyD88 innate immunity signaling adaptor to modulate the extent of the innate immune response., (Copyright © 2014 by the Genetics Society of America.)

Controlling infectious disease without inducing unwanted inflammatory disease requires proper regulation of the innate immune response. Thus, innate immunity needs to be activated when needed during an infection, but must be limited to prevent damage. To accomplish this, negative regulators of innate immunity limit the response. Here we investigate one such negative regulator encoded by an alternative splice form of MyD88. MyD88 mRNA exists in two alternative splice forms: MyD88L, a long form that encodes a protein that activates innate immunity by transducing Toll-like receptor (TLR) signals; and a short form that encodes a different protein, MyD88S, that inhibits the response. We find that MyD88S levels regulate the extent of inflammatory cytokine production in murine macrophages. MyD88S mRNA levels are regulated by the SF3A and SF3B mRNA splicing complexes, and these mRNA splicing complexes function with TLR signaling to regulate MyD88S production. Thus, the SF3A mRNA splicing complex controls production of a negative regulator of TLR signaling that limits the extent of innate immune activation., Competing Interests: The authors have declared that no competing interests exist.

Herein we discuss band gap modification of MIL-125, a TiO2/1,4-benzenedicarboxylate (bdc) metal-organic framework (MOF). Through a combination of synthesis and computation, we elucidated the electronic structure of MIL-125 with aminated linkers. The band gap decrease observed when the monoaminated bdc-NH2 linker was used arises from donation of the N 2p electrons to the aromatic linking unit, resulting in a red-shifted band above the valence-band edge of MIL-125. We further explored in silico MIL-125 with the diaminated linker bdc-(NH2)2 and other functional groups (-OH, -CH3, -Cl) as alternative substitutions to control the optical response. The bdc-(NH2)2 linking unit was predicted to lower the band gap of MIL-125 to 1.28 eV, and this was confirmed through the targeted synthesis of the bdc-(NH2)2-based MIL-125. This study illustrates the possibility of tuning the optical response of MOFs through rational functionalization of the linking unit, and the strength of combined synthetic/computational approaches for targeting functionalized hybrid materials.

The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice.

We previously identified Tbc1d23 as a candidate novel regulator of innate immunity using comparative genomics RNA interference screens in Caenorhabditis elegans and mouse macrophages. Using Tbc1d23 knockout mice and macrophages engineered to overexpress Tbc1d23, we now show that Tbc1d23 is a general inhibitor of innate immunity signaling, strongly inhibiting multiple TLR and dectin-signaling pathways. Tbc1d23 likely acts downstream of the TLR-signaling adaptors MyD88 and Trif and upstream of the transcription factor XBP1. Importantly, like XBP1, Tbc1d23 affects the maintenance, but not the initiation, of inflammatory cytokine production induced by LPS. Tbc1d23 acts as a RAB-GAP to regulate innate immunity signaling. Thus, Tbc1d23 exerts its inhibitory effect on innate immunity signaling in a spatiotemporal fashion. The identification of a novel spatiotemporal regulator of innate immunity signaling validates the comparative genomics approach for innate immunity gene discovery.

Toll-like receptors (TLRs) are key receptors in innate immunity and trigger responses following interaction with pathogen-associated molecular patterns (PAMPs). TLR3, TLR4 and TLR9 recognize double stranded RNA, lipopolysaccharide (LPS) and CpG DNA, respectively. These receptors differ importantly in downstream adaptor molecules. TLR4 signals through MyD88 and TRIF; in contrast, the TLR3 pathway involves only TRIF while TLR9 signals solely through MyD88. To determine how differences in downstream signaling could influence gene expression in innate immunity, gene expression patterns were determined for the RAW264.7 macrophage cell line stimulated with LPS, poly (I:C), or CpG DNA. Gene expression profiles 6 and 24h post-stimulation were analyzed to determine genes, pathways and transcriptional networks induced. As these experiments showed, the number and extent of genes expressed varied with stimulus. LPS and poly (I:C) induced an abundant array of genes in RAW264.7 cells at 6h and 24h following treatment while CpG DNA induced many fewer. By analyzing data for networks and pathways, we prioritized differentially expressed genes with respect to those common to the three TLR ligands as well as those shared by LPS and poly (I:C) but not CpG DNA. The importance of changes in gene expression was demonstrated by experiments indicating that RNA interference-mediated inhibition of two genes identified in this analysis, PLEC1 and TPST1, reduced IL-6 production by J774A.1 and RAW264.7 macrophages stimulated with LPS. Together, these findings delineate macrophage gene response patterns induced by different PAMPs and identify new genes that have not previously been implicated in innate immunity., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Objective: The aim of this multicentric, randomized, double blind study was to demonstrate that a 4-day treatment with pristinamycin 1 g bid was as efficient as a 5-day treatment with cefuroxime axetil 250 mg bid in adults presenting with an acute maxillary sinusitis., Design: The clinical diagnosis was based on the association of sub-orbital pain, purulent rhinorrhea and purulent discharge on the middle nasal meatus and was confirmed radiologically. A rhinoscopic bacteriologic sampling was made on the middle nasal meatus., Results: Four hundred and eighty five patients were included in the study: in France (n = 301), Tunisia (n = 48), Poland (n = 69) and Argentina (n = 67) between January 2001 and February 2002. Cultures were positive in 199/434 patients (46%), mainly S. pneumoniae (34.2%), H. influenzae (21.5%), S. aureus (15.4%), and M. catharralis (7.9%). The clinical cure rate at day 12-19 in the per protocol population, the main study criterion, was equal to 91.4% (201/220) and 91.1% (195/214) respectively in the pristinamycin and cefuroxime axetil groups; delta = 0.14%; 95%CI: [-5.1%; 5.3%]. The non-inferiority of 4-day pristinamycin versus 5-day cefuroxime axetil was demonstrated. The efficacy at follow up after treatment (day 26-31) was 88.6% and 85.8% respectively, confirming the non-inferiority. The bacteriological cure rate at day 12-19 was 87% (87/100) and 87.9% (87/99) respectively. Both treatments were well tolerated., Conclusion: A 4-day course with pristinamycin 1 g bid is as effective as a 5-day course of cefuroxime axetil 250 mg bid in the treatment of acute maxillary sinusitis in adults.