Bacterial diversity and reductive dehalogenase redundancy in a 1,2-dichloroethane-degrading bacterial consortium enriched from a contaminated aquifer

<p>Abstract</p> <p>Background</p> <p>Bacteria possess a reservoir of metabolic functionalities ready to be exploited for multiple purposes. The use of microorganisms to clean up xenobiotics from polluted ecosystems (e.g. soil and water) represents an eco-sustainable and powerful alternative to traditional remediation processes. Recent developments in molecular-biology-based techniques have led to rapid and accurate strategies for monitoring and identification of bacteria and catabolic genes involved in the degradation of xenobiotics, key processes to follow up the activities <it>in situ</it>.</p> <p>Results</p> <p>We report the characterization of the response of an enriched bacterial community of a 1,2-dichloroethane (1,2-DCA) contaminated aquifer to the spiking with 5 mM lactate as electron donor in microcosm studies. After 15 days of incubation, the microbial community structure was analyzed. The bacterial 16S rRNA gene clone library showed that the most represented phylogenetic group within the consortium was affiliated with the phylum <it>Firmicutes</it>. Among them, known degraders of chlorinated compounds were identified. A reductive dehalogenase genes clone library showed that the community held four phylogenetically-distinct catalytic enzymes, all conserving signature residues previously shown to be linked to 1,2-DCA dehalogenation.</p> <p>Conclusions</p> <p>The overall data indicate that the enriched bacterial consortium shares the metabolic functionality between different members of the microbial community and is characterized by a high functional redundancy. These are fundamental features for the maintenance of the community's functionality, especially under stress conditions and suggest the feasibility of a bioremediation treatment with a potential prompt dehalogenation and a process stability over time.</p

3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction

ObjectiveThe extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility and machine learning prediction of response to neoadjuvant chemotherapy. Materials and methodsThis retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient &gt;0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation. Results1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy. ConclusionCompared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier

Match running performance and physical capacity profiles of U8 and U10 soccer players

Aim This study aimed to characterize match running performance of very young soccer players and evaluate the relationship between these data and physical capacities and technical skills. Methods Distances covered at different speed thresholds were measured during 31 official matches using GPS technology in U10 (n = 12; age 10.1 ± 0.1 years) and U8 (n = 15; age 7.9 ± 0.1 years) national soccer players. Counter movement jump performance (CMJ), 20 m shuttle running (20 m-SR), linear sprint performance (10, 20, 30 m), shuttle (SHDT) and slalom dribble tests (SLDT) were performed to determine the players physical capacities and technical skills. Results Physical capacities and technical skills were higher in U10 versus U8 players [P 0.05, ES: 0.74). The U10 players covered more total (TD) and high-intensity running distance (HIRD) than their younger counterparts did (P 0.05, ES: 0.99). TD and HIRD covered across the three 15 min periods of match play did not decline (P > 0.05, ES: 0.02–0.55). Very large magnitude correlations were observed between the U8 and U10 players performances during the 20 m-SR versus TD (r = 0.79; P < 0.01) and HIRD (r = 0.82; P < 0.01) covered during match play. Conclusions Data demonstrate differences in match running performance and physical capacity between U8 and U10 players, and large magnitude relationships between match play measures and physical test performances. These findings could be useful to sports science staff working within the academies

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Effect of adipose tissue thickness and tissue optical properties on the differential pathlength factor estimation for NIRS studies on human skeletal muscle

We propose a quantitative and systematic investigation of the differential pathlength factor (DPF) behavior for skeletal muscles and its dependence on different factors, such as the subcutaneous adipose tissue thickness (ATT), the variations of the tissue absorption (µa ) and reduced scattering (µ's ) coefficients, and the source-detector distance. A time domain (TD) NIRS simulation study is performed in a two-layer geometry mimicking a human skeletal muscle with an overlying adipose tissue layer. The DPF decreases when µa increases, while it increases when µ's increases. Moreover, a positive correlation between DPF and ATT is found. These results are supported by an in-vivo TD NIRS study on vastus lateralis and biceps brachii muscles of eleven subjects at rest, showing a high inter-subject and inter-muscle variabilit

Internet of Things for Earthquake Early Warning Systems: A Performance Comparison Between Communication Protocols

Earthquake Early Warning Systems (EEWSs) characterize seismic events in real time and estimate the expected ground motion amplitude in specific areas to send alerts before the destructive waves arrive. Together with the reliability of the results, the rapidity with which an EEWS can detect an earthquake becomes a focal point for developing efficient seismic node networks. Internet of Things (IoT) architectures can be used in EEWSs to expand a seismic network and acquire data even from low-cost seismic nodes. However, the latency and the total alert time introduced by the adopted communication protocols should be carefully evaluated. This study proposes an IoT solution based on the message queue-telemetry transport protocol for the waveform transmission acquired by seismic nodes and presents a performance comparison between it and the most widely used standard in current EEWSs. The comparison was performed in evaluation tests where different seismic networks were simulated using a dataset of real earthquakes. This study analyzes the phases preceding the earthquake detection, showing how the proposed solution detects the same events of traditional EEWSs with a total alert time of approximately 1.6 seconds lower

A review of exhaled breath: a key role in lung cancer diagnosis

One of the main causes of the high mortality rate in lung cancer is the late-stage tumor detection. Early diagnosis is therefore essential to increase the chances of obtaining an effective treatment quickly thus increasing the survival rate. Current screening techniques are based on imaging, with low-dose computed tomography (LDCT) as the pivotal approach. Even if LDCT has high accuracy, its invasiveness and high false positive rate limit its application to high-risk population screening. A noninvasive, cost-efficient, and easy-to-use test should instead be designed as an alternative. Exhaled breath contains thousands of volatile organic compounds (VOCs). Since ancient times, it has been understood that changes in the VOCs' mixture maybe directly related to the presence of a disease, and recent studies have quantified the change in the compounds' concentration. Analyzing exhaled breath to achieve lung cancer early diagnosis represents a non-invasive, low-cost, and user-friendly approach, thus being a promising candidate for high-risk lung cancer population screening. This review discusses technological solutions that have been proposed in the literature as tools to analyze exhaled breath for lung cancer diagnosis, together with factors that potentially affect the outcome of the analysis. Even if research on this topic started many years ago, and many different technological approaches have since been adopted, there is still no validated clinical application of this technique. Standard guidelines and protocols should be defined by the medical community in order to translate exhaled breath analysis to clinical practice

Performance Evaluation of a Low-Cost Sensing Unit for Seismic Applications: Field Testing during Seismic Events of 2016-2017 in Central Italy

We propose a low-cost and low-power consumption device for seismic monitoring consisting of three single-axis accelerometers connected to a data logger with acquisition, synchronization, and transmission functionalities. The device was designed to be densely and prolifically deployed in high seismic risk areas, thus strengthening the Italian seismic network and providing a more accurate estimation of shaking maps. Moreover, the availability of such low-cost and high-performance units can allow the widespread diffusion of smart systems for seismic and structural monitoring, finalized to collapses prevention in critical structures, such as schools and hospitals, as well as constitute the founding nucleus of early warning systems based on Internet of Things architectures. The realized station was submitted to a testing phase, placing it contiguously to a high-performance seismic station located in central Italy and responsible for national seismic monitoring. The test station, installed from September 2016 to March 2017, was able to record the significant and numerous earthquakes that devastated central Italy during this period. The simultaneous acquisition of these seismic events by the sensors of the national seismic network, including that co-sited with the device under test, has furnished sufficient data for the device validation and performance quantification. A comparative analysis was performed through waveforms correlations study, strong motion parameters estimation, and spectral analysis. The proposed device demonstrated performances very close to those of more sophisticated and expensive systems. Therefore, it can effectively replace them or be added in engineering and civil protection applications and, finally, be used in earthquake early warning systems