The Indian national capital, Delhi, routinely experiences some of the world's highest urban particulate matter concentrations. While fine particulate matter (PM2.5) mass concentrations in Delhi are ...at least an order of magnitude higher than in many western cities, the particle number (PN) concentrations are not similarly elevated. Here we report on 1.25 years of highly time-resolved particle size distribution (PSD) data in the size range of 12–560 nm. We observed that the large number of accumulation mode particles – that constitute most of the PM2.5 mass – also contributed substantially to the PN concentrations. The ultrafine particle (UFP; Dp<100 nm) fraction of PNs was higher during the traffic rush hours and for daytimes of warmer seasons, which is consistent with traffic and nucleation events being major sources of urban UFPs. UFP concentrations were found to be relatively lower during periods with some of the highest mass concentrations. Calculations based on measured PSDs and coagulation theory suggest UFP concentrations are suppressed by a rapid coagulation sink during polluted periods when large concentrations of particles in the accumulation mode result in high surface area concentrations. A smaller accumulation mode for warmer months results in an increased UFP fraction, likely owing to a comparatively smaller coagulation sink. We also see evidence suggestive of nucleation which may also contribute to the increased UFP proportions during the warmer seasons. Even though coagulation does not affect mass concentrations, it can significantly govern PN levels with important health and policy implications. Implications of a strong accumulation mode coagulation sink for future air quality control efforts in Delhi are that a reduction in mass concentration, especially in winter, may not produce a proportional reduction in PN concentrations. Strategies that only target accumulation mode particles (which constitute much of the fine PM2.5 mass) may even lead to an increase in the UFP concentrations as the coagulation sink decreases.
Mitochondria are essential for the viability of eukaryotic cells as they perform crucial functions in bioenergetics, metabolism and signalling and have been associated with numerous diseases. Recent ...functional and proteomic studies have revealed the remarkable complexity of mitochondrial protein organization. Protein machineries with diverse functions such as protein translocation, respiration, metabolite transport, protein quality control and the control of membrane architecture interact with each other in dynamic networks. In this Review, we discuss the emerging role of the mitochondrial protein import machinery as a key organizer of these mitochondrial protein networks. The preprotein translocases that reside on the mitochondrial membranes not only function during organelle biogenesis to deliver newly synthesized proteins to their final mitochondrial destination but also cooperate with numerous other mitochondrial protein complexes that perform a wide range of functions. Moreover, these protein networks form membrane contact sites, for example, with the endoplasmic reticulum, that are key for integration of mitochondria with cellular function, and defects in protein import can lead to diseases.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, ...critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.
NR-CBZ was prepared from a ternary mixture in which Naringenin (NR), carbamazepine (CBZ), and NR-CBZ all exist. In order to control the quality of NR-CBZ, vibrational spectroscopy (ATR-FTIR and Raman ...spectroscopy) in conjunction with PLS and PCR has been successfully applied to the simultaneous quantitation of NR, CBZ, and NR-CBZ in ternary mixtures. Finally, from a comparison of the predictive performance and error analysis, it was found that Raman spectroscopy performs with higher accuracy at the whole spectral range. The PLS model with Savitzky-Golay filter algorithms at the whole spectral range resulted in a better result which predicted the concentration of Naringenin, carbamazepine, and NR-CBZ.
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•The first quantitative method of naringenin-carbamazepine cocrystal by spectrums.•The PLS and PCR were applied to cocrystal’s quantitative, firstly.•Several pre-processing algorithms were evaluated systematically.
Naringenin, commonly found in citrus fruits, is one of the active pharmaceutical ingredient (API) in naringenin-carbamazepine drug-drug cocrystal (NR-CBZ). In the preparation of cocrystal, quantitative determination of NR-CBZ is essential for the quality control. In this paper, ATR-FTIR and Raman spectroscopies combined with partial least squares (PLS) and principal component regression (PCR) were used to quantify NR-CBZ in the mixtures. To improve the accuracy of the prediction models, median, denoising, multiple scattering correction (MSC), first and second order derivatives, Savitzky-Golay filter and SNV were used and their performance was evaluated using prediction errors in combination with different spectral ranges. Raman spectra, PLS combined with Savitzky-Golay filter over the entire spectral range were found to determine the best content prediction result for NR-CBZ. The root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and squared correlation coefficient (R2) of the model were 0.101, 0.132 and 0.870, respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ObjectiveQuality assurance (QA) in neuro-ophthalmology (NOPH) is often lacking. The QA registry, NODE (Neuro-ophthalmology Database), was established and implemented in tertiary NOPH clinics in ...Australia. We developed a consensus on triage categories according to Australian standardised triage categories;P1 (consult<=30 days), P2 (consult<=30-60 days) and P3 (consult>60 days).MethodsData on 410 patients at Alfred Hospital, Melbourne was collected with NODE. We developed a consensus on assignation of NOPH conditions to triage categories using recommendations from a panel of neuro-ophthalmologists with the modified Delphi approach. The average days from referral to triage and triage to the initial consultation were compared to the developed triage category standard.ResultsMost patients presenting to the service were female (n=262, 64%), aged 21 to 30 years. Common diagnoses were Idiopathic Intracranial Hypertension, IIH (24%), Optic Neuropathy, ON (17%), Headaches, (11%) Cranial Nerve Defects, CND (9%) and Eye Movement Disorders, EOMD (9%). The mean time from referral to triage was <2 days for all the common NOPH conditions. The mean time (days, +-standard deviation) from P1 category triage to initial consult for IIH was 26 (±7), ON 27 (±11), and CND was 17 (± 5). The mean time (days) from P2 triage to initial consultant for Headaches was 27 (±12), and EOMD was (±17). The mean time (days) from P3 triage to initial consultant for Myasthenia Gravis was 30 (±10).ConclusionWe have established a consensus agreement on triage categories for neuro-ophthalmological conditions. We established a QA framework for other NOPH clinics in Australia.
Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors ...that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 μg/m3. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.
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IJS, KILJ, NUK, PNG, UL, UM
Peroxisomal biogenesis disorders (PBDs) are genetic disorders of peroxisome biogenesis and metabolism that are characterized by profound developmental and neurological phenotypes. The most severe ...class of PBDs—Zellweger spectrum disorder (ZSD)—is caused by mutations in peroxin genes that result in both non‐functional peroxisomes and mitochondrial dysfunction. It is unclear, however, how defective peroxisomes contribute to mitochondrial impairment. In order to understand the molecular basis of this inter‐organellar relationship, we investigated the fate of peroxisomal mRNAs and proteins in ZSD model systems. We found that peroxins were still expressed and a subset of them accumulated on the mitochondrial membrane, which resulted in gross mitochondrial abnormalities and impaired mitochondrial metabolic function. We showed that overexpression of ATAD1, a mitochondrial quality control factor, was sufficient to rescue several aspects of mitochondrial function in human ZSD fibroblasts. Together, these data suggest that aberrant peroxisomal protein localization is necessary and sufficient for the devastating mitochondrial morphological and metabolic phenotypes in ZSDs.
Synopsis
How peroxisomal biogenesis disorders lead to mitochondrial dysfunction is not well understood. This study reveals that peroxisomal proteins mislocalize to the mitochondria, thereby disrupting mitochondrial function. The mitochondrial quality control protein ATAD1 can rescue this defect.
Peroxin transcription and translation is unperturbed by loss of peroxisomes.
Several peroxins (Pex13, Pex11, Pex14, Pex2, Pex17, Pex25) mislocalize to the mitochondrial outer membrane.
Mislocalized peroxins are able to form a subassembly of the peroxisomal importomer on mitochondria, which interferes with mitochondrial function.
The mitochondrial extractase ATAD1 can remove peroxins from the mitochondrial membrane, which is sufficient to restore mitochondrial morphology, respiration, and metabolism in cells derived from ZSD patients.
How peroxisomal biogenesis disorders lead to mitochondrial dysfunction is not well understood. This study reveals that peroxisomal proteins mislocalize to the mitochondria, thereby disrupting mitochondrial function. The mitochondrial quality control protein ATAD1 can rescue this defect.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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