Bacterial community analysis in upflow multilayer anaerobic reactor (UMAR) treating high-solids organic wastes

A novel anaerobic digestion configuration, the upflow multi-layer anaerobic reactor (UMAR), was developed to treat high-solids organic wastes. The UMAR was hypothesized to form multi-layer along depth due to the upflow plug flow; use of a recirculation system and a rotating distributor and baffles aimed to assist treating high-solids influent. The chemical oxygen demand (COD) removal efficiency and methane (CH4) production rate were 89% and 2.10 L CH4/L/day, respectively, at the peak influent COD concentration (110.4 g/L) and organic loading rate (7.5 g COD/L/day). The 454 pyrosequencing results clearly indicated heterogeneous distribution of bacterial communities at different vertical locations (upper, middle, and bottom) of the UMAR. Firmicutes was the dominant (>70%) phylum at the middle and bottom parts, while Deltaproteobacteria and Chloroflexi were only found in the upper part. Potential functions of the bacteria were discussed to speculate on their roles in the anaerobic performance of the UMAR system

Piezo-tolerant natural gas-producing microbes under accumulating pCO2

<p>Background: It is known that a part of natural gas is produced by biogenic degradation of organic matter, but the microbial pathways resulting in the formation of pressurized gas fields remain unknown. Autogeneration of biogas pressure of up to 20 bar has been shown to improve the quality of biogas to the level of biogenic natural gas as the fraction of CO2 decreased. Still, the pCO2 is higher compared to atmospheric digestion and this may affect the process in several ways. In this work, we investigated the effect of elevated pCO2 of up to 0.5 MPa on Gibbs free energy, microbial community composition and substrate utilization kinetics in autogenerative high-pressure digestion. Results: In this study, biogas pressure (up to 2.0 MPa) was batch-wise autogenerated for 268 days at 303 K in an 8-L bioreactor, resulting in a population dominated by archaeal Methanosaeta concilii, Methanobacterium formicicum and Mtb. beijingense and bacterial Kosmotoga-like (31% of total bacterial species), Propioniferax-like (25%) and Treponema-like (12%) species. Related microorganisms have also been detected in gas, oil and abandoned coal-bed reservoirs, where elevated pressure prevails. After 107 days autogeneration of biogas pressure up to 0.50 MPa of pCO2, propionate accumulated whilst CH4 formation declined. Alongside the Propioniferax-like organism, a putative propionate producer, increased in relative abundance in the period of propionate accumulation. Complementary experiments showed that specific propionate conversion rates decreased linearly from 30.3 mg g−1 VSadded day−1 by more than 90% to 2.2 mg g−1 VSadded day−1 after elevating pCO2 from 0.10 to 0.50 MPa. Neither thermodynamic limitations, especially due to elevated pH2, nor pH inhibition could sufficiently explain this phenomenon. The reduced propionate conversion could therefore be attributed to reversible CO2-toxicity. Conclusions: The results of this study suggest a generic role of the detected bacterial and archaeal species in biogenic methane formation at elevated pressure. The propionate conversion rate and subsequent methane production rate were inhibited by up to 90% by the accumulating pCO2 up to 0.5 MPa in the pressure reactor, which opens opportunities for steering carboxylate production using reversible CO2-toxicity in mixed-culture microbial electrosynthesis and fermentation.</p

Assessing the Impact of Temporal Resolution Using BSM1 on the Performance of Machine Learning

Objectives This study aims to establish efficient strategies for data-driven operational management by examining the variations in machine learning modeling outcomes and data characteristics based on data acquisition intervals and methods. Methods The BSM1 was used to simulate wastewater treatment facilities and to generate influent and effluent water quality data at 15-minute intervals. The generated data was processed by volume reduction through down sampling and data characteristic observation via resampling techniques, including up sampling through interpolation. Subsequently, the study involved a comparative analysis of the performance of 30 machine learning models built with the down sampled data. Results and Discussion As data acquisition interval increased (i.e., down sampling progressed), R2 decreased and RMSE increased. When using the mean value as a representation, data accuracy was high, and error loss was minimal. Utilizing the maximum value as a representation helped maintain data characteristics and reduce information loss. Simple interpolation methods did not yield improved data accuracy. Furthermore, with wider data acquisition intervals, the practical predictive performance of machine learning models decreased, and the models experienced a sharp decline in performance when data became insufficient. Conclusion For models requiring the ability to detect changes rather than accuracy, utilizing the maximum value over a specific period proves to be effective. The measurement interval of data emerges as a significant factor affecting the performance of machine learning models, with models developed under different measurement intervals often failing to demonstrate the expected performance. In this study, we have implemented all stages of data preprocessing, classification, training, and validation using LabVIEW, confirming the potential for integrating data analysis processes into LabVIEW, a widely used platform in the fields of control and measurement

Comparison of different machine learning algorithms to estimate liquid level for bioreactor management

Estimating the liquid level in an anaerobic digester can be disturbed by its closedness, bubbles and scum formation, and the inhomogeneity of the digestate. In our previous study, a soft-sensor approach using seven pressure meters has been proposed as an alternative for real-time liquid level estimation. Here, machine learning techniques were used to improve the estimation accuracy and optimize the number of sensors required in this approach. Four algorithms, multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) with radial basis function kernel were compared for this purpose. All models outperformed the cubic model developed in the previous study, among which the ANN and RF models performed the best. Variable importance analysis suggested that the pressure readings from the top (in the headspace) were the most significant, while the other pressure meters showed varying significance levels depending on the model type. The sensor that experienced both headspace and liquid phases depending on the level variation incurred a higher error than other sensors. The results showed that the ML techniques can provide an effective tool to estimate digester liquid levels by optimizing the number of sensors and reducing the error rate

De novo extraction of microbial strains from metagenomes reveals intra-species niche partitioning

Background We introduce DESMAN for De novo Extraction of Strains from MetAgeNomes. Metagenome sequencing generates short reads from throughout the genomes of a microbial community. Increasingly large, multi-sample metagenomes, stratified in space and time are being generated from communities with thousands of species. Repeats result in fragmentary co-assemblies with potentially millions of contigs. Contigs can be binned into metagenome assembled genomes (MAGs) but strain level variation will remain. DESMAN identifies variants on core genes, then uses co-occurrence across samples to link variants into strain sequences and abundance profiles. These strain profiles are then searched for on non-core genes to determine the accessory genes present in each strain. Results We validated DESMAN on a synthetic twenty genome community with 64 samples. We could resolve the five E. coli strains present with 99.58% accuracy across core gene variable sites and their gene complement with 95.7% accuracy. Similarly, on real fecal metagenomes from the 2011 E. coli (STEC) O104:H4 outbreak, the outbreak strain was reconstructed with 99.8% core sequence accuracy. Application to an anaerobic digester metagenome time series reveals that strain level variation is endemic with 16 out of 26 MAGs (61.5%) examined exhibiting two strains. In almost all cases the strain proportions were not statistically different between replicate reactors, suggesting intra-species niche partitioning. The only exception being when the two strains had almost identical gene complement and, hence, functional capability. Conclusions DESMAN will provide a provide a powerful tool for de novo resolution of fine-scale variation in microbial communities. It is available as open source software from https://github.com/chrisquince/DESMAN

Hysteroscopic Resection of the Vaginal Septum in Uterus Didelphys with Obstructed Hemivagina: A Case Report

Uterus didelphys with obstructed hemivagina and ipsilateral renal agenesis is a rare congenital anomaly. Excision of the obstructed vaginal septum is the treatment of choice for symptom relief and the preservation of reproductive capability. A 14-yr-old girl complained of persistent vaginal spotting following each menstruation. Pelvic magnetic resonance imaging revealed a uterus didelphys with left hematocolpos and ipsilateral renal agenesis. Instead of conventional transvaginal excision of the vaginal septum, we used hysteroscopic excision under transabdominal ultrasonographic guidance to preserve the integrity of the hymen. The postoperative course was uneventful, and clinical symptoms were completely resolved after this intervention. Resectoscopic excision of the vaginal septum was found to be easy, safe, effective, and appropriate for young women as it preserved hymen integrity. We believe that this is the first Korean report on the use of a hysteroscopy for vaginal septum resection in a patient with uterus didelphys with obstructed hemivagina

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment

LoWMob: Intra-PAN Mobility Support Schemes for 6LoWPAN

Mobility in 6LoWPAN (IPv6 over Low Power Personal Area Networks) is being utilized in realizing many applications where sensor nodes, while moving, sense and transmit the gathered data to a monitoring server. By employing IEEE802.15.4 as a baseline for the link layer technology, 6LoWPAN implies low data rate and low power consumption with periodic sleep and wakeups for sensor nodes, without requiring them to incorporate complex hardware. Also enabling sensor nodes with IPv6 ensures that the sensor data can be accessed anytime and anywhere from the world. Several existing mobility-related schemes like HMIPv6, MIPv6, HAWAII, and Cellular IP require active participation of mobile nodes in the mobility signaling, thus leading to the mobility-related changes in the protocol stack of mobile nodes. In this paper, we present LoWMob, which is a network-based mobility scheme for mobile 6LoWPAN nodes in which the mobility of 6LoWPAN nodes is handled at the network-side. LoWMob ensures multi-hop communication between gateways and mobile nodes with the help of the static nodes within a 6LoWPAN. In order to reduce the signaling overhead of static nodes for supporting mobile nodes, LoWMob proposes a mobility support packet format at the adaptation layer of 6LoWPAN. Also we present a distributed version of LoWMob, named as DLoWMob (or Distributed LoWMob), which employs Mobility Support Points (MSPs) to distribute the traffic concentration at the gateways and to optimize the multi-hop routing path between source and destination nodes in a 6LoWPAN. Moreover, we have also discussed the security considerations for our proposed mobility schemes. The performance of our proposed schemes is evaluated in terms of mobility signaling costs, end-to-end delay, and packet success ratio

KMT-2016-BLG-1107: A New Hollywood-Planet Close/Wide Degeneracy

We show that microlensing event KMT-2016-BLG-1107 displays a new type of degeneracy between wide-binary and close-binary Hollywood events in which a giant-star source envelops the planetary caustic. The planetary anomaly takes the form of a smooth, two-day "bump" far out on the falling wing of the light curve, which can be interpreted either as the source completely enveloping a minor-image caustic due to a close companion with mass ratio $q=0.036$, or partially enveloping a major-image caustic due to a wide companion with $q=0.004$. The best estimates of the companion masses are both in the planetary regime ($3.3^{+3.5}_{-1.8}\,M_{\rm jup}$ and $0.090^{+0.096}_{-0.037}\,M_{\rm jup}$) but differ by an even larger factor than the mass ratios due to different inferred host masses. We show that the two solutions can be distinguished by high-resolution imaging at first light on next-generation ("30m") telescopes. We provide analytic guidance to understand the conditions under which this new type of degeneracy can appear.Comment: 23 pages, 7 figures, accepted for publication in A