Review of experimental methods to determine spontaneous combustion susceptibility of coal – Indian context

This paper presents a critical review of the different techniques developed to investigate the susceptibility of coal to spontaneous combustion and fire. These methods may be sub-classified into the two following areas: (1) Basic coal characterisation studies (chemical constituents) and their influence on spontaneous combustion susceptibility. (2) Test methods to assess the susceptibility of a coal sample to spontaneous combustion. This is followed by a critical literature review that summarises previous research with special emphasis given to Indian coals

PPARα downregulates airway inflammation induced by lipopolysaccharide in the mouse

BACKGROUND: Inflammation is a hallmark of acute lung injury and chronic airway diseases. In chronic airway diseases, it is associated with profound tissue remodeling. Peroxisome proliferator-activated receptor-α (PPARα) is a ligand-activated transcription factor, that belongs to the nuclear receptor family. Agonists for PPARα have been recently shown to reduce lipopolysaccharide (LPS)- and cytokine-induced secretion of matrix metalloproteinase-9 (MMP-9) in human monocytes and rat mesangial cells, suggesting that PPARα may play a beneficial role in inflammation and tissue remodeling. METHODS: We have investigated the role of PPARα in a mouse model of LPS-induced airway inflammation characterized by neutrophil and macrophage infiltration, by production of the chemoattractants, tumor necrosis factor-α (TNF-α), keratinocyte derived-chemokine (KC), macrophage inflammatory protein-2 (MIP-2) and monocyte chemoattractant protein-1 (MCP-1), and by increased MMP-2 and MMP-9 activity in bronchoalveolar lavage fluid (BALF). The role of PPARα in this model was studied using both PPARα-deficient mice and mice treated with the PPARα activator, fenofibrate. RESULTS: Upon intranasal exposure to LPS, PPARα(-/- )mice exhibited greater neutrophil and macrophage number in BALF, as well as increased levels of TNF-α, KC, MIP-2 and MCP-1, when compared to PPARα(+/+ )mice. PPARα(-/- )mice also displayed enhanced MMP-9 activity. Conversely, fenofibrate (0.15 to 15 mg/day) dose-dependently reduced the increase in neutrophil and macrophage number induced by LPS in wild-type mice. In animals treated with 15 mg/day fenofibrate, this effect was associated with a reduction in TNF-α, KC, MIP-2 and MCP-1 levels, as well as in MMP-2 and MMP-9 activity. PPARα(-/- )mice treated with 15 mg/day fenofibrate failed to exhibit decreased airway inflammatory cell infiltrate, demonstrating that PPARα mediates the anti-inflammatory effect of fenofibrate. CONCLUSION: Using both genetic and pharmacological approaches, our data clearly show that PPARα downregulates cell infiltration, chemoattractant production and enhanced MMP activity triggered by LPS in mouse lung. This suggests that PPARα activation may have a beneficial effect in acute or chronic inflammatory airway disorders involving neutrophils and macrophages

The plant leaf movement analyzer (PALMA): a simple tool for the analysis of periodic cotyledon and leaf movement in Arabidopsis thaliana

Wagner L, Schmal C, Staiger D, Danisman S. The plant leaf movement analyzer (PALMA): a simple tool for the analysis of periodic cotyledon and leaf movement in Arabidopsis thaliana. Plant Methods. 2017;13(1): 2.Background The analysis of circadian leaf movement rhythms is a simple yet effective method to study effects of treatments or gene mutations on the circadian clock of plants. Currently, leaf movements are analysed using time lapse photography and subsequent bioinformatics analyses of leaf movements. Programs that are used for this purpose either are able to perform one function (i.e. leaf tip detection or rhythm analysis) or their function is limited to specific computational environments. We developed a leaf movement analysis tool—PALMA—that works in command line and combines image extraction with rhythm analysis using Fast Fourier transformation and non-linear least squares fitting. Results We validated PALMA in both simulated time series and in experiments using the known short period mutant sensitivity to red light reduced 1 (srr1-1). We compared PALMA with two established leaf movement analysis tools and found it to perform equally well. Finally, we tested the effect of reduced iron conditions on the leaf movement rhythms of wild type plants. Here, we found that PALMA successfully detected period lengthening under reduced iron conditions. Conclusions PALMA correctly estimated the period of both simulated and real-life leaf movement experiments. As a platform-independent console-program that unites both functions needed for the analysis of circadian leaf movements it is a valid alternative to existing leaf movement analysis tools

Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial.

BACKGROUND: Opioid-related adverse events are a serious problem in hospitalized patients. Little is known about patients who are likely to experience opioid-induced respiratory depression events on the general care floor and may benefit from improved monitoring and early intervention. The trial objective was to derive and validate a risk prediction tool for respiratory depression in patients receiving opioids, as detected by continuous pulse oximetry and capnography monitoring. METHODS: PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) was a prospective, observational trial of blinded continuous capnography and oximetry conducted at 16 sites in the United States, Europe, and Asia. Vital signs were intermittently monitored per standard of care. A total of 1335 patients receiving parenteral opioids and continuously monitored on the general care floor were included in the analysis. A respiratory depression episode was defined as respiratory rate ≤5 breaths/min (bpm), oxygen saturation ≤85%, or end-tidal carbon dioxide ≤15 or ≥60 mm Hg for ≥3 minutes; apnea episode lasting \u3e30 seconds; or any respiratory opioid-related adverse event. A risk prediction tool was derived using a multivariable logistic regression model of 46 a priori defined risk factors with stepwise selection and was internally validated by bootstrapping. RESULTS: One or more respiratory depression episodes were detected in 614 (46%) of 1335 general care floor patients (43% male; mean age, 58 ± 14 years) continuously monitored for a median of 24 hours (interquartile range [IQR], 17-26). A multivariable respiratory depression prediction model with area under the curve of 0.740 was developed using 5 independent variables: age ≥60 (in decades), sex, opioid naivety, sleep disorders, and chronic heart failure. The PRODIGY risk prediction tool showed significant separation between patients with and without respiratory depression (P \u3c .001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30; P \u3c .001) between the high- and low-risk groups. Compared to patients without respiratory depression episodes, mean hospital length of stay was 3 days longer in patients with ≥1 respiratory depression episode (10.5 ± 10.8 vs 7.7 ± 7.8 days; P \u3c .0001) identified using continuous oximetry and capnography monitoring. CONCLUSIONS: A PRODIGY risk prediction model, derived from continuous oximetry and capnography, accurately predicts respiratory depression episodes in patients receiving opioids on the general care floor. Implementation of the PRODIGY score to determine the need for continuous monitoring may be a first step to reduce the incidence and consequences of respiratory compromise in patients receiving opioids on the general care floor