Comprehensive Quantitative Evaluation Research Articles

Attention Network with Information Distillation for Super-Resolution.

Resolution is an intuitive assessment for the visual quality of images, which is limited by physical devices. Recently, image super-resolution (SR) models based on deep convolutional neural networks (CNNs) have made significant progress. However, most existing SR models require high computational costs with network depth, hindering practical application. In addition, these models treat intermediate features equally and rarely explore the discriminative capacity hidden in their abundant features. To tackle these issues, we propose an attention network with information distillation(AIDN) for efficient and accurate image super-resolution, which adaptively modulates the feature responses by modeling the interactions between channel dimension and spatial features. Specifically, gated channel transformation (GCT) is introduced to gather global contextual information among different channels to modulate intermediate high-level features. Moreover, a recalibrated attention module (RAM) is proposed to rescale these feature responses, and RAM concentrates the essential contents around spatial locations. Benefiting from the gated channel transformation and spatial information masks working jointly, our proposed AIDN can obtain a more powerful ability to identify information. It effectively improves computational efficiency while improving reconstruction accuracy. Comprehensive quantitative and qualitative evaluations demonstrate that our AIDN outperforms state-of-the-art models in terms of reconstruction performance and visual quality.

Deconstruction of the Optimal Design of Urban Road Interchange Based on the Integration of Smart Transportation and Big Data.

Urban interchange is the core hub connecting various regions, and it is of great significance for alleviating the problem of traffic congestion. In the process of urban interchange design, it is impossible to strictly control the traffic volume, interchange types, and standards by relying on traditional technologies. Smart transportation and big data are emerging technologies based on data, which can provide technical support for design and decision making. Based on this, this paper first uses smart transportation and big data technology to predict the traffic volume of Nancheng New District, so as to calculate the future development trend of the target area. Then, on the basis of traffic volume, the article uses smart transportation and big data technology to optimize the original urban interchange design scheme from the aspects of traffic capacity, safety, economic benefits, and environmental benefits. Finally, the article evaluates the optimized urban interchange scheme by means of comprehensive quantitative indicators and evaluation methods. Experiments show that the traffic capacity of the interchange on the outer ring road optimized by smart transportation and big data has increased to 72.6%, and the environmental coordination has increased from 45.2% to 55.2%. Moreover, the design aesthetics of the urban interchange after optimized design based on smart transportation and big data has increased to 65.9%. In addition, the comprehensive evaluation value of the urban interchange after optimization of smart transportation and big data reached 82.6. This fully shows that the optimal design of urban interchange based on the integration of smart transportation and big data can greatly improve the traffic capacity of urban roads.

Quantitative evaluation of runoff variation and its driving forces based on multi-scale separation framework

Study regionThe Xiangjiang River (XJR) basin, China. Study focusOn multiple time scales (annual, quarterly, and monthly), there are few comprehensive and comprehensive quantitative evaluation frameworks for the factors that drive changes in watershed runoff. The focus of this study is to use the Range of Variability Approach (RVA) to quantitatively analyze the changes in the overall hydrological regime of the basin. The Budyko model and the deterministic hydrological process model (ABCD model) were then used to quantitatively separate the factors affecting runoff in the monthly window, and to clarify the differences in the runoff driving force at different time scales. New hydrological insights for the regionAfter the mutation, the runoff of the XJR basin increased by 11%, and the changes in the five groups of hydrological and ecological indicators indicated that the ecological response process of the basin was undergoing negative changes. The contributions of the driving forces on different time scales show significant differences: the climatic factor is the dominant factor of the runoff change on the interannual scale. However, on the seasonal scale, human activities have a greater weight in spring, accounting for 82 % of the total. On a monthly scale, it presents a more complicated situation. Human activities contribute significantly in February, May, and October (60 %, 52 %, and 54 %), while climate factors are the main driving factor in the remaining months (51–170 %).

Analysis and Evaluation of Factors Influencing the Low-Carbon Effect of Urban High-Rise Settlement Planning Schemes Based on AHP-Fuzzy Comprehensive Evaluation Method

With the rise of low-carbon concept and the trend of low-carbon planning, the evaluation of low-carbon planning has been paid more and more attention by the academic and planning circles. Carbon evaluation has become a hot topic after “environmental evaluation” and “green evaluation.” In order to promote the important content of energy conservation and sustainable development in the construction of low-carbon cities, this paper constructs and plans the content of low-carbon city planning and construction. On the basis of the effectiveness evaluation index system, this paper discusses the linkage between analytic hierarchy process and fuzzy comprehensive evaluation method and makes a comprehensive quantitative evaluation on the effectiveness of case planning EIA. Combining the advantages of analytic hierarchy process and fuzzy comprehensive evaluation method, the subobjectives and weights of each index are determined by the analytic hierarchy process. The multi-level fuzzy comprehensive evaluation method is used to evaluate the factors that affect the low-carbon effect of urban high-rise residential district planning. This study leads the new trend of future urban construction, and the construction of low-carbon residential areas, as an important part of promoting urban sustainable development, is the core component of future low-carbon urban planning and construction.

Comprehensive quantitative evaluation of the water resource carrying capacity in Wuhan City based on the “human–water–city” framework: Past, present and future

Given China's rapid urbanization process, the study of water resource carrying capacity (WRCC) has important theoretical and practical significance for urban sustainable development. In this paper, a “human–water–city” (HWC) framework was proposed that offers a comprehensive consideration of the development of human society, water quality and quantity, and the progress of urban construction. Based on this framework, we established an HWC evaluation indicator system and constructed the WRCC-HWC approach, to assess, compare, and forecast the WRCC. Taking Wuhan, China, as an example, principal component analysis (PCA) was adopted to evaluate the current (2009–2019) and future (2020–2030) WRCC-HWC. Specifically, global climate models (GCMs) and a grey model, GM (1,1), were used to forecast the value of the future index system. The results show that the WRCC-HWC in Wuhan gradually increased from −3.04 to 3.23 during 2009–2019, being mainly affected by the income of urban residents, per capita GDP, reuse ratio of water used by industry, built-up area, and the output value of new and high-tech industry. With a rapid growth rate from 2011 to 2016, the WRCC-HWC reached the Grade I (excellent) state in 2016. In addition, the forecast results show that, with the exceptions of precipitation and good air quality rate, all HWC indicators show upward trends from 2020 to 2030 in Wuhan. In particular, the per capita disposable income of urban residents (N1), per capita GDP (N2), and the output value of new and high-tech industry (N11) have faster growth rates, with relative growth rates from 2020 to 2030 exceeding 100%. Correspondingly, the WRCC-HWC from 2020 to 2030 were predicted to grow steadily each year, with a higher absolute growth rate starting in 2025. This study supports a comprehensive understanding of the current and future situation for WRCC-HWC in Wuhan and can provide a reference for the formulation of scientific and sustainable development strategies.

Phenotypic and mutational spectrum of ROR2-related Robinow syndrome.

Robinow syndrome is characterized by a triad of craniofacial dysmorphisms, disproportionate-limb short stature, and genital hypoplasia. A significant degree of phenotypic variability seems to correlate with different genes/loci. Disturbances of the noncanonical WNT-pathway have been identified as the main cause of the syndrome. Biallelic variants in ROR2 cause an autosomal recessive form of the syndrome with distinctive skeletal findings. Twenty-two patients with a clinical diagnosis of autosomal recessive Robinow syndrome were screened for variants in ROR2 using multiple molecular approaches. We identified 25 putatively pathogenic ROR2 variants, 16novel, including single nucleotide variants and exonic deletions. Detailed phenotypic analyses revealed that all subjects presented with a prominent forehead, hypertelorism, short nose, abnormality of the nasal tip, brachydactyly, mesomelic limb shortening, short stature, and genital hypoplasia in male patients. A total of 19 clinical features were present in more than 75% of the subjects, thus pointing to an overall uniformity of the phenotype. Disease-causing variants in ROR2, contribute to a clinically recognizable autosomal recessive trait phenotype with multiple skeletal defects. A comprehensive quantitative clinical evaluation of this cohort delineated the phenotypic spectrum of ROR2-related Robinow syndrome. The identification of exonic deletion variant alleles further supports the contention of a loss-of-function mechanism in the etiology of the syndrome.

Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.

ChildGAN: Face aging and rejuvenation to find missing children

Child-face aging and rejuvenation have amassed considerable active research interest, owing to their immense impact on a broad range of social and security applications, e.g., digital entertainment, fashion and wellness, and searching for long-lost children using childhood photos. All current face aging approaches based on generative adversarial networks (GANs) focus on adult images or long-term aging. We present a new large-scale longitudinal Indian child (ICD) benchmark dataset to facilitate face age progression and regression, cross-age face recognition, age estimation, gender prediction, and kinship face recognition to alleviate these issues. Furthermore, we propose an automatic child-face age progression and regression model, namely, ChildGAN, that generates visually realistic images for enhanced face-identification accuracy while preserving the identity. Consequently, we have trained state-of-the-art (SOTA) face aging models on ICD for comprehensive qualitative and quantitative evaluations. We also present a multi-racial experiments dataset named Multi-Racial Child Dataset (MRCD) containing 64,965 child face images. The images are selected from publicly available datasets and web crawling. Finally, we investigate the generalization of ChildGAN by experimenting with White, Black, Asian, and Indian races. The experimental results suggest that the proposed ChildGAN and SOTA models can aid in reconnecting young children, who were lost at a young age as victims of child trafficking or abduction, with their families. The model and the MRCD web crawled images are available at https://github.com/praveenkumarchandaliya/ChildGAN_Tamp1/.

SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation

In the last decade, numerous supervised deep learning approaches have been proposed for visual–inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning has emerged as a promising alternative that exploits constraints such as geometric and photometric consistency in the scene. In this study, we present a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual–inertial sensor fusion. SelfVIO learns the joint estimation of 6 degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from unlabelled monocular RGB image sequences and inertial measurement unit (IMU) readings. The proposed approach is able to perform VIO without requiring IMU intrinsic parameters and/or extrinsic calibration between IMU and the camera. We provide comprehensive quantitative and qualitative evaluations of the proposed framework and compare its performance with state-of-the-art VIO, VO, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature.

Generalized Gaussian decomposition for full waveform LiDAR processing

Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. Since the shape of a real LiDAR waveform varies for different systems, the conventional models (e.g. the Gaussian function, lognormal function, and generalized normal function) cannot be universally used. In this paper, we present a generalized Gaussian decomposition (GGD) algorithm, which considers the received waveform as the convolution of an arbitrary system waveform with the target response assumed as a Gaussian mixture model. The proposed method was validated using the experimental waveforms sampled from our self-designed LiDAR system with two different system responses. Metrics, including the mean absolute error (MAE) for range retrieval and the root-mean-squared error (RMSE) for waveform fitting, were used to provide a comprehensive quantitative evaluation of the performance. Three classical models for waveform decomposition—the Gaussian, lognormal, and generalized normal functions—were introduced and studied for the comparison. As for the system waveform with a right-skewed profile, the experimental results showed that the GGD algorithm provided the lowest RMSE for waveform fitting, and the most accurate range estimates with an MAE of . The Gaussian decomposition (GD), lognormal decomposition (LND), and generalized normal decomposition (GND) algorithms produced much worse results with MAEs of 0.362, 1.091, and , respectively. As for the system waveform with a negative tail, the GGD algorithm also performed best with an MAE of , while the GD, LND and GND algorithms provided much larger MAEs of 0.457, 0.489, and , respectively. Therefore, the proposed method has the potential to extract more accurate model parameters from a variety of LiDAR waveforms regardless of the shape of the system waveform.

Four-dimensional flow CMR in tetralogy of fallot: current perspectives.

Tetralogy of Fallot is the most common cyanotic congenital heart defect, accounting for 10% of all CHD. Despite most patients now surviving well into adulthood, morbidity and mortality rates continue to be high. Surgical and percutaneous pulmonary valve replacement are procedures that are performed to prevent long-term complications from occurring. Unfortunately, pulmonary valve replacement based on current CMR criteria does not prevent postoperative ventricular arrhythmia, heart failure, and sudden cardiac death. Thus, a more advanced and comprehensive hemodynamic evaluation is needed to better understand right ventricular (dys)function in tetralogy of Fallot patients and to optimize the timing of valve replacement. Recently, four-dimensional flow CMR has emerged as a promising and non-invasive imaging technique that can provide comprehensive quantitative evaluation of flow in an entire volume within the chest in a single imaging session. With velocity-encoding in all three spatial directions throughout the complete cardiac cycle, it can provide analysis of cardiac, pulmonary artery and aortic flow volumes, flow velocities, flow patterns, as well as more advanced hemodynamic parameters. Four-dimensional flow CMR could therefore provide insights into the complex hemodynamics of tetralogy of Fallot and could potentially provide novel criteria for pulmonary valve replacement in these patients. The aim of this review is to provide an overview of available research on four-dimensional flow CMR research in tetralogy of Fallot patients.

Comprehensive Quantitative and Calibration-Free Evaluation of Hyperpolarized Xenon–Host Interaction by Multiparametric NMR

The probing of microscopic environments by hyperpolarized xenon NMR has spurred investigations in supramolecular chemistry as well as important biosensing and molecular imaging applications. While xenon exchange with host structures at micromolar concentrations and below can be readily detected, a quantitative analysis is limited, requiring complementary experimentation by different methodologies and thus lacking completeness and compromising the validity and comparability of numerical results. Here, a new NMR measurement and data analysis approach is introduced for the comprehensive characterization of the host-xenon binding dynamics. The application of chemical exchange saturation transfer of hyperpolarized 129Xe under parametric modulation of the saturation RF amplitude and xenon gas saturation of the solution enables a delineation of exchange mechanisms and, through modeling, a numerical estimation of the various reaction rate constants (and thus magnetization exchange rate constants), the xenon affinity, and the total host molecule concentration. Only the numerical xenon solubility is additionally required for input, a quantity that has a low impact on the measurement uncertainty and is derivable from metrological data collections. Signal calibration by a reference material may thus be avoided, qualifying the method as calibration-free. For demonstration a xenon exchange with the host cucurbit[6]uril at low concentration is investigated, with the numerical results being validated by standard quantitative NMR data obtained at high concentration. The readiness to evaluate xenon exchange for the one sample at hand and in a single experimental attempt by the proposed method may allow comprehensive quantitative studies in supramolecular chemistry, biomacromolecular structure and dynamics, and sensing.

Research on Comprehensive Quantitative Evaluation System of Occupational Stress of Civil Aviation Pilots

Through theoretical analysis, expert survey and issuance of the questionnaire to all age civil aviation pilots this paper summarized several influential factors for occupational stress of civil aviation pilots and established an occupational stress risk comprehensive valuating index system. According to the comprehensive analysis of occupational stress influential factors we took 7 factors, i. e. working load factor, physiological factor, organization factor, psychological factor, environmental factor, equipment factor and incident factor as the criterion. Then an occupational stress risk comprehensive valuating index system was established. Order relation analysis method (G1 method) was used to calculate the weights of indexes. Thus occupational stress risk three grade comprehensive evaluation index system was established. To quantify the level occupational stress index by four levels each occupational stress index was respectively corresponded to different degrees of occupational stress. Civil aviation pilots occupational stress questionnaires are compiled according to the comprehensive evaluation index system. Through the questionnaire statistical analysis results of occupational stress index distribution value interval of occupational stress index corresponding to the degree of occupational stress for civil aviation pilots was divided reasonably which reflected the comprehensive quantitative evaluation of occupational stress. The results showed that the quantitative evaluation system provided a practical way for the evaluation of occupational stress level and risk for civil aviation pilots.

Incorporating the Completeness and Difficulty of Proposals Into Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) in remote sensing images (RSI) only require image-level labels to detect various objects. Most of the WSOD methods incline to capture the most discriminative parts of object rather than the entire object, and the number of easy and hard samples is imbalanced. To address the first problem, a novel metric named objectness score (OS) is proposed and incorporated into the training loss of our WSOD model. The OS is consisted of the traditional class confidence score (CCS) and the object completeness prior score (OCPS). The CCS can provide the probability that a proposal belongs to a certain class, and the OCPS can quantify the completeness that a proposal covers the entire object. Therefore, the samples which cover the entire object with high class confidences will be assigned large weight in the training loss through OS. To handle the second problem, a novel metric named difficulty evaluation score (DES) is proposed and also incorporated into the training loss. The DES is calculated by using the entropy of confidence score vector of each proposal and is used to quantify how difficult a proposal can be identified correctly, consequently, the hard samples will also be assigned large weight in the training loss through DES. The ablation experiments on two RSI datasets verify the effectiveness of the proposed OS and DES. The comprehensive quantitative and subjective evaluations demonstrate that our method inclines to detect the entire object accurately, and surpasses seven state-of-the-art WSOD methods.

Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration.

High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which separately focuses on texture feature transfer over saturated regions and multi-exposure tonal and texture feature fusion. A neural feature transfer mechanism is proposed which establishes spatial correspondence between different exposures based on multi-scale VGG features in the masked saturated HDR domain for discriminative contextual clues over the ambiguous image areas. A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner. In addition, we introduce several novel attention mechanisms, i.e., the motion attention module detects and suppresses the content discrepancies among the reference images; the saturation attention module facilitates differentiating the misalignment caused by saturation from those caused by motion; and the scale attention module ensures texture blending consistency between different coder/decoder scales. We carry out comprehensive qualitative and quantitative evaluations and ablation studies, which validate that these novel modules work coherently under the same framework and outperform state-of-the-art methods.

О КОЛИЧЕСТВЕННОЙ ОЦЕНКЕ РИСКОВ ИНФРАСТРУКТУРНЫХ ПРОЕКТОВ С ГОСУДАРСТВЕННЫМ УЧАСТИЕМ

International experience shows that creating large infrastructure objects (as a rule, with state participation) is often accompanied by significant cost overruns. Importance of this issue is especially high due to responsibility for the use of public resources. The article reviews most recognized methods of the project risks evaluation that play a key role in assessing the total cost of large and long-term projects. Approaches of international organizations, official documents of selected developed countries and requirements for Russian infrastructure projects with state funding are compared. Particular attention is paid to a comprehensive quantitative risk evaluation, which is not sufficiently represented in the Russian official methodology, based on Monte-Carlo simulation modelling. The study considers some practical aspects of applying this method for conditional cost estimation. As an illustration, a model calculation of the impact of risks on the indicators of one of the latest PPP infrastructure projects (CRR-1) is given.

A Driving Simulation to Analysis and Quantitative Comparison of Driving Behavior of Guide Signs at Intersections

Guide signs are an important source for drivers to obtain road information. However, the evaluation methods for the effectiveness of guide signs are not unified. The quantitative model for evaluating guide signs needs to be constructed to unify the current system of guide signs. This study aims to take the commonly used guide signs in China as the research object to explore the evaluation method of guide signs at intersections. Eight kinds of guide signs were designed and made based on the common layout (layout 1 and layout 2) and the amount of information on signs (3–6). Thirty-four drivers were recruited to organize a driving simulation based on the visual cognitive tasks. Drivers’ legibility time and driver behavior were obtained by using the driving simulator and E-Prime program. A comprehensive quantitative evaluation model of guide signs was established based on the factor analysis method and grey correlation analysis method from the perspective of safe driving. The results show that there is no significant difference in the SD of speed and the SD of acceleration under the influence of various guide signs. The average vehicle speed and acceleration decrease, and the lateral offset distance of the vehicle increases with the amount of information on guide signs increasing. The quantitative evaluation results of guide signs show that the visual security decreases with the increase of the amount of information on guide signs. And layout 2 has better performance than layout 1 when the amount of information on guide signs is the same. This study not only explores the change rule of driving behavior under the influence of guide signs, but also provides a reference for the selection of guide signs.

Ancillary service quantitative evaluation for primary frequency regulation of pumped storage units considering refined hydraulic characteristics

With the deepening of global energy transformation process, a higher proportion of variable renewable energy (VRE) is connected to power grid, and it is urgent to improve regulation capacity and operation efficiency of power grid. Pumped storage plants (PSPs) could provide important auxiliary services for power grids, and frequency regulation is a crucial function. Quantitative evaluation of primary frequency regulation (PFR) performance is a key issue for benefits of auxiliary services of PSPs and operation and management of power grid. In this paper, a quantitative evaluation method for PFR performance of pumped storage units (PSUs) is constructed based on fuzzy analytic hierarchy process (FAHP). Five important evaluation indexes are selected based on the auxiliary service compensation in electricity markets in China and the USA. The PFR performance score is calculated through this evaluation method. A refined numerical model of PSUs is established taking a PSP in China as an example. Different operation schemes are set up by inputting different frequency disturbance signals, changing control system characteristics (governor parameters Kp, Ki, bp, and frequency dead zone) and hydraulic characteristics (time constants Tw, Ts, Ty), and the evaluation method is used to quantify PFR performance of PSUs under each scheme. This study reveals the influence mechanism of governor parameters, matching rules among time constants, and frequency dead zone on dynamic response of PFR and auxiliary service performance. The results show that the method provides analytical means and intuitive reference for comprehensive quantitative evaluation for PFR performance of PSUs, and contributes to the guidance of multi-parameter matching and promotion of the coordination between power plants and power grids.

Effects of influent immigration and environmental factors on bacterial assembly of activated sludge microbial communities

The functional mechanism of microbial assembly of activated sludge (AS) in urban wastewater treatment plants (UWTPs) remains unclear. A comprehensive quantitative evaluation of the contribution of influent immigration and environmental factors to AS community composition requires investigation. In this study, the microbial characteristics of six full-scale UWTPs with different influent compositions and environmental factors (altitude, temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH4+-N), and total phosphorus (TP)) were analyzed to determine the main forces affecting the bacterial assembly of AS microbial communities. Abundant and core taxa were screened out based on the abundance and frequency of operational taxonomic units (OTUs) occurrence in all samples. Abundant OTUs (18.7% occurrence) accounted for 87.7% of the total 16S rRNA sequences, while rare OTUs (71.7% occurrence) accounted for only 7.8% of the total 16S rRNA sequences. A total of 135 OTUs were identified as core taxa, accounting for 14.6–26.2% of the total reads, of which 83 OTUs belonged to abundant taxa. The richness and uniformity of the influent community were significantly lower than those of the AS system. The community composition in influent varied from that in AS. Moreover, about 89.7% (86.5% of 16S rRNA sequences) OTUs in AS samples showed positive growth rates, indicating that immigration of influent communities had a limited effect on the microbial composition of AS. Redundancy analysis (RDA) combined with co-occurrence network showed that the bacterial assembly of microbial communities was significantly correlated with altitude, pH, and TN (P < 0.05), and these three parameters could explain 23.3%, 21.1%, and 17.7% of the bacterial assembly of AS microbial communities in UWTPs, respectively.

Estimating the predictive power of silent mutations on cancer classification and prognosis

In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.