Valorization of organic carbon in primary sludge via semi-continuous dark fermentation: First step to establish a wastewater biorefinery.

In this study, lab-scale, bench-scale, and pilot-scale experiments were carried out to optimize short-chain fatty acids production from primary sludge. Batch tests showed the requirement of short retention times and semi-continuous operation mode showed a plateau of maximum daily productivity at 36-hours hydraulic retention time with minimal methanation. Optimization from pH 5 to pH 10 at 36 h-hydraulic retention time under long-term semi-continuous operating mode revealed that production of short-chain fatty acids was pH dependent and highest yields could be achieved at pH 7 by establishing optimum redox conditions for fermentation. Pilot-scale experiments at 32 °C showed that daily productivity (3.1 g∙Lreactor-1∙dHRT-1) and yields (150 mg∙gVS-1; OLR = 21 gVS∙Lreactor-1∙dHRT-1; pH 7) of short-chain fatty acids could be significantly improved, specifically for acetic and propionic acids. From these results, a robust dark fermentation step for recovery of valuable products from the solids treatment step in a biorefinery can be achieved.

A new strategy to map landslides with a generalized convolutional neural network.

Rapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.

Spirometric Assessment of Impact of Complete Dentures on Respiratory Performance: An in vitro Study.

INTRODUCTION: Edentulism is one of the debilitating states affecting a significant portion of the geriatric population. It is often considered as the ultimate marker for predicting the pressure and burden on oral health. Respiration is also a routine vital function running continuously through the human body and most commonly assessed by a spirometer. Hence, we planned the present study to assess the impact of complete dentures on the functioning of the respiratory system. MATERIALS AND METHODS: In the present study, a spirometric assessment of the effect of complete dentures on respiratory performance was done. A total of 100 subjects were included, and diagnostic spirometer was used for carrying out the spiro-metric test at different stages of each subject. The spirometric test was carried out at four different stages: In the absence of both the denture (AODs), with both the dentures (maxillary and mandibular) inserted in the patient's mouth (BDs), with only maxillary denture inserted in the oral cavity (UDs), and finally, by inserting only the mandibular dentures in the oral cavity (LDs). Forced vital capacity (FVC), peak expiratory flow (PEF), forced expiratory volume in 1 second (FEV1), and forced expiratory flow between 25 and 75% (FEF25-75) were evaluated. All the results were compiled and assessed using Statistical Package for the Social Sciences (SPSS) software. RESULTS: Of the total 100 subjects included in the study, 42 were males and 58 were females. The mean FVC values of AOD, BD, LD, and UD group were 3.10, 3.02, 2.90, and 2.93 respectively. The mean PEF values of AOD, BD, LD, and UD group were 5.79, 5.60, 5.40, and 5.48 respectively; 2.39, 2.35, 2.33, and 2.32 were the mean FEV1 values observed in AOD, BD, LD, and UD group respectively. Statistically significant results were obtained while comparing AOD-FVC and BD-FVC and other oral conditions. CONCLUSION: Spirometric values of respiratory functional tests in edentulous patients might be unfavorably affected by wearing complete dentures. CLINICAL SIGNIFICANCE: Since subjects without complete dentures exhibited a maximum value of respiratory functional test, respiratory exercise protocols should also be carried in denture-wearing edentulous patients for increasing the performance of the respiratory system.

Cyclic 3',5'-guanosine monophosphate-dependent protein kinase inhibits colon cancer cell adaptation to hypoxia.

BACKGROUND: Type 1 cyclic 3',5'-guanosine monophosphate-dependent protein kinase (PKG) has recently been reported to inhibit tumor growth and angiogenesis. These effects suggest that PKG activation may have therapeutic value for colon cancer treatment, but the signaling downstream of this enzyme is poorly understood. The present study examined the mechanism underlying the inhibition of angiogenesis by PKG. METHODS: The effect of ectopically expressed PKG on colon cancer cell adaptation to a 1% O(2) (hypoxic) environment was examined in vitro by measuring hypoxic markers, cell death/viability, and hypoxia inducible factor (HIF) activity. RESULTS: Ectopic PKG inhibited angiogenesis in SW620 xenografts and significantly attenuated hypoxia-induced increases in vascular endothelial growth factor at both the mRNA and protein levels. PKG activation also blocked hypoxia-induced hexokinase 2 expression, which corresponded with reduced cellular adenosine triphosphate levels. Moreover, PKG expression significantly reduced cell viability and promoted necrotic cell death after 2 days in a hypoxic environment. To gain some mechanistic insight, the effect of PKG on HIF activation was determined using luciferase reporter assays. PKG activation inhibited HIF transcriptional activity in several colon cancer cell lines, including SW620, HCT116, and HT29. The mechanism by which PKG can inhibit HIF activity is not known, but it does not affect HIF-1α protein accumulation or nuclear translocation. CONCLUSIONS: These findings demonstrate for the first time that PKG can block the adaptation of colon cancer cells to hypoxia and highlights this enzyme for further evaluation as a potential target for colon cancer treatment.