Biogenic Palladium Improved Perchlorate Reduction during Nitrate Co-Reduction by Diverting Electron Flow in a Hydrogenotrophic Biofilm.

Microbial reduction of perchlorate (ClO4-) is emerging as a cost-effective strategy for groundwater remediation. However, the effectiveness of perchlorate reduction can be suppressed by the common co-contamination of nitrate (NO3-). We propose a means to overcome the limitation of ClO4- reduction: depositing palladium nanoparticles (Pd0NPs) within the matrix of a hydrogenotrophic biofilm. Two H2-based membrane biofilm reactors (MBfRs) were operated in parallel in long-term continuous and batch modes: one system had only a biofilm (bio-MBfR), while the other incorporated biogenic Pd0NPs in the biofilm matrix (bioPd-MBfR). For long-term co-reduction, bioPd-MBfR had a distinct advantage of oxyanion reduction fluxes, and it particularly alleviated the competitive advantage of NO3- reduction over ClO4- reduction. Batch tests also demonstrated that bioPd-MBfR gave more rapid reduction rates for ClO4- and ClO3- compared to those of bio-MBfR. Both biofilm communities were dominated by bacteria known to be perchlorate and nitrate reducers. Functional-gene abundances reflecting the intracellular electron flow from H2 to NADH to the reductases were supplanted by extracellular electron flow with the addition of Pd0NPs.

Refractory dissolved organic matters in sludge leachate trigger the combination of anammox and denitratation for advanced nitrogen removal.

The cost-effective treatment of sludge leachate (SL) with high nitrogen content and refractory dissolved organic matter (rDOM) has drawn increasing attention. This study employed, for the first time, a rDOM triggered denitratation-anammox continuous-flow process to treat landfill SL. Moreover, the mechanisms of exploiting rDOM from SL as an inner carbon source for denitratation were systematically analyzed. The results demonstrated outstanding nitrogen and rDOM removal performance without any external carbon source supplement. In this study, effluent concentrations of 4.27 ± 0.45 mgTIN/L and 5.58 ± 1.64 mgTN/L were achieved, coupled with an impressive COD removal rate of 65.17 % ± 1.71 %. The abundance of bacteria belonging to the Anaerolineaceae genus, which were identified as rDOM degradation bacteria, increased from 18.23 % to 35.62 %. As a result, various types of rDOM were utilized to different extents, with proteins being the most notable, except for lignins. Metagenomic analysis revealed a preference for directing electrons towards NO3--N reductase rather than NO2--N reductase, indicating the coupling of denitratation bacteria and anammox bacteria (Candidatus Brocadia). Overall, this study introduced a novel synergy platform for advanced nitrogen removal in treating SL using its inner carbon source. This approach is characterized by low energy consumption and operational costs, coupled with commendable efficiency.

Insights into the membrane biofouling behavior of planktonic anammox bacteria: Effect of solution pH and ionic strength.

Understanding the effect of solution pH and ionic strength on membrane biofouling of anammox bacteria is essential for the widespread application of anammox MBRs. To provide an original elucidation, this study combined interfacial thermodynamics analysis and filtration experiments with an established planktonic anammox MBR to explore the biofouling behavior of anammox bacteria under varying solution pH and ionic strengths. Preliminary results showed that variation in solution pH and ionic strength has critical impacts on the thermodynamic properties of planktonic anammox bacteria and membrane surfaces. The further interfacial thermodynamics analysis and filtration experiments indicated that an increased pH and a decreased ionic strength could reduce membrane fouling by planktonic anammox bacteria. More specifically, a higher pH or lower ionic strength resulted in a stronger repulsive energy barrier due to the larger interaction distance covered by the dominant electrostatic double layer (EL) component compared to the Lewis acid-base (AB) and Lifshitz-van der Waals (LW) components, which corresponded to a reduction in the normalized flux (J/J0) decline and the accumulation of cake resistance (Rc) during the filtration process. Furthermore, the aforementioned effect mechanism was verified by a correlation analysis of the thermodynamic properties and filtration behavior. These findings have generalized significance for understanding the biofouling or aggregation behavior of anammox bacteria.

A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (Differential Regulation Analysis by Bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

Enhancement and mechanisms of iron-assisted anammox process.

Anaerobic ammonium oxidation (anammox) is a sustainable biological nitrogen removal technology that has limited large-scale applications owing to the low cell yield and high sensitivity of anammox bacteria (AnAOB). Fortunately, iron-assisted anammox, being a highly practical method could be an effective solution. This review focused on the iron-assisted anammox process, especially on its performance and mechanisms. In this review, the effects of iron in three different forms (ionic iron, zero-valent iron and iron-containing minerals) on the performance of the anammox process were systematically reviewed and summarized, and the strengthening effects of Fe (II) seem to be more prominent. Moreover, the detailed mechanisms of iron-assisted anammox in previous researches were discussed from macro to micro perspectives. Additionally, applicable iron-assisted methods and unified strengthening mechanisms for improving the stability of nitrogen removal and shortening the start-up time of the system in anammox processes were suggested to explore in future studies. This review was intended to provide helpful information for scientific research and engineering applications of iron-assisted anammox.

Coupled generation.

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest.

Pharmacological Chaperones for GCase that Switch Conformation with pH Enhance Enzyme Levels in Gaucher Animal Models.

Gaucher disease is a lysosomal storage disorder caused by mutations which destabilize the native folded form of GCase, triggering degradation and ultimately resulting in low enzyme activity. Pharmacological chaperones (PCs) which stabilize mutant GCase have been used to increase lysosomal activity through improving trafficking efficiency. By engineering their inherent basicity, we have synthesized PCs that change conformation between the ER and the lysosomal environment, thus weakening binding to GCase after its successful trafficking to the lysosome. NMR studies confirmed the conformational change while X-ray data reveal bound conformations and binding modes. These results were further corroborated by cell studies showing increases in GCase activity when using the pH-switchable probe at low dosing. Preliminary in vivo assays with humanized mouse models of Gaucher showed enhanced GCase activity levels in relevant tissues, including the brain, further supporting their potential.

Significance Tests of Feature Relevance for a Black-Box Learner.

An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with a black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretations of the decision-making process based on a deep learning model. However, testing for a neural network poses a challenge because of its black-box nature and unknown limiting distributions of parameter estimates while existing methods require strong assumptions or excessive computation. In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type, such as an image. The one-split test estimates and evaluates a black-box model based on estimation and inference subsets through sample splitting and data perturbation. The two-split test further splits the inference subset into two but requires no perturbation. Also, we develop their combined versions by aggregating the p -values based on repeated sample splitting. By deflating the bias-sd-ratio, we establish asymptotic null distributions of the test statistics and the consistency in terms of Type 2 error. Numerically, we demonstrate the utility of the proposed tests on seven simulated examples and six real datasets. Accompanying this article is our python library dnn-inference (https://dnn-inference.readthedocs.io/en/latest/) that implements the proposed tests.

Embedding Learning.

Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of U-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.

Complex vectorial optics through gradient index lens cascades.

Graded index (GRIN) lenses are commonly used for compact imaging systems. It is not widely appreciated that the ion-exchange process that creates the rotationally symmetric GRIN lens index profile also causes a symmetric birefringence variation. This property is usually considered a nuisance, such that manufacturing processes are optimized to keep it to a minimum. Here, rather than avoiding this birefringence, we understand and harness it by using GRIN lenses in cascade with other optical components to enable extra functionality in commonplace GRIN lens systems. We show how birefringence in the GRIN cascades can generate vector vortex beams and foci, and how it can be used advantageously to improve axial resolution. Through using the birefringence for analysis, we show that the GRIN cascades form the basis of a new single-shot Müller matrix polarimeter with potential for endoscopic label-free cancer diagnostics. The versatility of these cascades opens up new technological directions.

Probiotic Studies in Neonatal Mice Using Gavage.

Adult mouse models have been widely used to understand the mechanism behind disease progression in humans. The applicability of studies done in adult mouse models to neonatal diseases is limited. To better understand disease progression, host responses and long-term impact of interventions in neonates, a neonatal mouse model likely is a better fit. The sparse use of neonatal mouse models can in part be attributed to the technical difficulties of working with these small animals. A neonatal mouse model was developed to determine the effects of probiotic administration in early life and to specifically assess the ability to establish colonization in the newborn mouse intestinal tract. Specifically, to assess probiotic colonization in the neonatal mouse, Lactobacillus plantarum (LP) was delivered directly into the neonatal mouse gastrointestinal tract. To this end, LP was administered to mice by feeding through intra-esophageal (IE) gavage. A highly reproducible method was developed to standardize the process of IE gavage that allows an accurate administration of probiotic dosages while minimizing trauma, an aspect particularly important given the fragility of newborn mice. Limitations of this process include possibilities of esophageal irritation or damage and aspiration if gavaged incorrectly. This approach represents an improvement on current practices because IE gavage into the distal esophagus reduces the chances of aspiration. Following gavage, the colonization profile of the probiotic was traced using quantitative polymerase chain reaction (qPCR) of the extracted intestinal DNA with LP specific primers. Different litter settings and cage management techniques were used to assess the potential for colonization-spread. The protocol details the intricacies of IE neonatal mouse gavage and subsequent colonization quantification with LP.

Development of a traffic noise prediction model on inland waterway of China using the FHWA.

Based on the local environmental standards, vessels types and traffic conditions, an inland waterway traffic noise prediction model was developed for use in China. This model was modified from the US FHWA model by adding the ground absorption and water surface attenuation correction terms to the governing equations. The parameters that were input into the equations, including traffic flow, vessel speed, distance from the center of the inland waterway to the receiver, position and height of the barriers and buildings, location of the receiver, type of ground, percentage of soft ground cover within the segment, and water surface conditions were re-defined. The model was validated by comparing the measured noise levels obtained at 33 sampling sites from Shugang Channel, Yanhe Channel and Danjinlicaohe Channel in China with the predicted values. The deviation between the predicted and measured noise levels within the range of ±1.5dB(A) was 81.8%. The mean difference between the predicted and measured noise levels was 0.15±1.75dB(A). However, the noise levels predicted developed model are generally higher than the measured levels. Overall, the comparison has proved that the developed method is of a high precision, and that it can be applied to estimate the traffic noise exposure level on inland waterway in China.

[Offshore ecosystem health status assessment: a review].

With the promotion of the concept of sustainable development, the issues of aquatic ecosystem health attract substantial attention, and considerable work has been carried out on the health assessment of waters, e. g. , rivers and lakes. However, the health assessment of offshore ecosystem is still at its exploratory stage. Based on the investigations on the related references at home and abroad, this paper analyzed the concepts of offshore ecosystem health assessment, summarized the main methods for the assessment, the principles for screening related indicators, and the research philosophy, and systematically listed the quantitative indices for the assessment. Aiming at the main existing issues in the researches of offshore ecosystem health, the future research directions about the offshore ecosystem health were suggested. It was considered that the concept and connotation analyses, the screening of assessment indicators, the choice of assessment scale, and the integration of assessment methods should be further strengthened to improve the assessment of offshore ecosystem health.

[Application and development of spectroscopy methodologies in the study on non-covalent interactions].

Spectrophotometric method is widely used in the structure determination of biologic macromolecules and non-covalent interactions study for its convenience and speed. In the present paper, spectroscopy methodologies in the study of non-covalent interactions between small-molecule and biomacromolecule is comprehensively reviewed with 25 references. This review article focuses on the applications and development of common spectroscopy methodologies in the study of non-covalent interactions between small molecule and biomacromolecule,including the UV, fluorescence, CD, IR, Raman, resonance light scattering technique and SPR. The advantages and disadvantages of spectroscopy methodologies are also described. UV-Vis absorption spectrum (UV) method is widely used in the study of non-covalent interactions for its convenience and speed. The binding site number, the apparent binding constant and the interaction mode of non-covalent interactions can be obtained by fluorescence spectrum method. Circular dichroism (CD) method is effective way in the study of non-covalent interactions measure. Spectroscopy information about protein secondary structure and conformation can be acquired by infrared spectrometry (IR) method. Raman spectroscopy method is a better way to investigate the conformation change in macromolecules in solution. Non-covalent interactions can be measured by surface plasma resonance (SPR) method under the natural active condition. X-ray diffraction analysis method is better for non-covalent interactions research, but it is difficult to cultivate crystalline complex.