Research status and prospect of purification technology of sulfur-containing odor gas.

Catalytic purification of sulphur-containing malodorous gases has attracted wide attention because of its advantages of high purification efficiency, low energy consumption and lack of secondary pollution. The selection of efficient catalysts is the key to the problem, while the preparation and optimisation of catalysts depend on the analysis of experimental results and in-depth mechanistic analysis. By analysing the published literature, bibliometric analysis can identify existing research hotspots, the areas of interest and predict development trends, which can help to identify hot catalysts in the catalytic purification of sulphur-containing odours and to investigate their catalytic purification mechanisms. Therefore, this paper uses bibliometric analysis, based on Web Of Science and CNKI databases, CiteSpace and VOS viewer software to collate and analyse the literature on the purification of sulphur-containing odour pollutants, to identify the current research hotspots, to summarise the progress of research on the catalytic purification of different types of sulphur-containing odours, and to analyse their reaction mechanisms and kinetics. On this basis, the research progress of catalytic purification of different kinds of sulfur odour is summarized, and the reaction mechanism and dynamics are summarized.

Enhancing ozone catalytic decomposition through acid treatment of α-MnO2 for improved activity and humidity resistance.

Post-etching method using dilute acid solutions is an effective technology to modulate the surface compositions of metal-oxide catalysts. Here the α-MnO2 catalyst treated with 0.1 mol/L nitric acid exhibits higher ozone decomposition activity at high relative humidity than the counterpart treated with acetic acid. Besides the increases in surface area and lattice dislocation, the improved activity can be due to relatively higher Mn valence on the surface and newly-formed Brønsted acid sites adjacent to oxygen vacancies. The remnant nitro species deposited on the catalyst by nitric acid treatment is ideal hydrophobic groups at ambient conditions. The decomposition route is also proposed based on the DRIFTS and DFT calculations: ozone is facile to adsorb on the oxygen vacancy, and the protonic H of Brønsted acid sites bonds to the terminal oxygen of ozone to accelerate its cleavage to O2, reducing the reaction energy barrier of O2 desorption.

Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Co-doped cryptomelane-type manganese oxide in situ grown on a nickel foam substrate for high humidity ozone decomposition.

Monolithic catalysts with excellent O3 catalytic decomposition performance were prepared by in situ loading of Co-doped KMn8O16 on the surface of nickel foam. The triple-layer structure with Co-doped KMn8O16/Ni6MnO8/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO4 to Co(NO3)2·6H2O precursors. Importantly, the formed Ni6MnO8 structure between KMn8O16 and nickel foam during in situ synthesis process effectively protected nickel foam from further etching, which significantly enhanced the reaction stability of catalyst. The optimum amount of Co doping in KMn8O16 was available when the molar ratio of Mn to Co species in the precursor solution was 2:1. And the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity, thus creating outstanding O3 decomposition activity. The O3 conversion under dry conditions and relative humidity of 65%, 90% over a period of 5 hr was 100%, 94% and 80% with the space velocity of 28,000 hr-1, respectively. The in situ constructed Co-doped KMn8O16/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process, which provided an opportunity for the design of monolithic catalyst for O3 catalytic decomposition.

A new insight into the straw decomposition associated with minerals: Promoting straw humification and Cd immobilization.

Organic matter (OM) derived from the decomposition of crop residues plays a key role as a sorbent for cadmium (Cd) immobilization. Few studies have explored the straw decomposition processes with the presence of minerals, and the effect of newly generated organo-mineral complexes on heavy metal adsorption. In this study, we investigated the variations in structure and composition during the rice straw decomposition with or without minerals (goethite and kaolinite), as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization. The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix (EEM) fluorescence and Ultraviolet-visible spectroscopy (UV-vis), while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms. The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components, indicating that the degree of straw decomposition and humification were further intensified during incubation. Additionally, the existence of goethite (SG) accelerated the humification of OM. Sorption experiments revealed that the straw humification increased Cd adsorption capacity. Notably, SG exhibited significantly higher adsorption performance compared to the organic matter without minerals (RS) and the existence of kaolinite (SK). Further analysis using FT-IR spectroscopy and XPS verified that the primary mechanisms involved in Cd immobilization were complexion with -OH and -COOH, as well as the formation of Cd-π binds with aromatic C=C on the surface of solid OMs. These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Atomically Engineered Chlorine Coordination of Iron in Active Centers for Selectively Catalytic H2O2 Decomposition Toward Efficient Antitumor-Specific Therapy.

The intervention of endogenous H2O2 via nanozymes provides a potential antitumor-specific therapy; however, the role of the nanozyme structure in relation to the selective decomposition of H2O2 to hydroxyl radicals (•OH) is yet to be fully understood, which limits the development of this therapeutic approaches. Herein, an iron single-atom nanozyme (Fe─N2Cl2─C SAzyme) is reported, which is prepared through precise Fe─Cl coordination based on the construction of a characteristic Fe-containing molecule. Fe─N2Cl2─C exhibits efficient catalytic H2O2 decomposition (2.19 × 106 mm-1 s-1), which is the highest among reported SAzymes. More importantly, it is found that H2O2 selectively decomposed into •OH on the Fe─N2Cl2─C surface, which is attributable to the d orbitals of the Fe active center matching the O-2p electrons of the adsorbed hydroxide (*OH) intermediate. Fe─N2Cl2─C is strongly cytotoxic toward a variety of cancer-cell lines in vitro but not to normal cells. Furthermore, Fe─N2Cl2─C shows an outstanding specific therapeutic effect in vivo; it efficiently destroys solid malignant tumors without injuring normal tissue. Altogether, these findings highlight the selective catalytic decomposition of H2O2 to •OH, which is achieved by engineering the active center on the atomic level, thereby providing an avenue for the development of specific nanomedicines with efficient antitumor activities.

Ecological drivers of carrion beetle (Staphylinidae: Silphinae) diversity on small to large mammals.

Silphinae (Staphylinidae; carrion beetles) are important contributors to the efficient decomposition and recycling of carrion necromass. Their community composition is important for the provision of this ecosystem function and can be affected by abiotic and biotic factors. However, investigations are lacking on the effects of carrion characteristics on Silphinae diversity. Carrion body mass may affect Silphinae diversity following the more individuals hypothesis (MIH). The MIH predicts a higher number of species at larger carrion because higher numbers of individuals can be supported on the resource patch. Additionally, biotic factors like carrion species identity or decomposition stage, and the abiotic factors elevation, season and temperature could affect Silphinae diversity. To test the hypotheses, we collected Silphinae throughout the decomposition of 100 carcasses representing 10 mammal species ranging from 0.04 to 124 kg. Experimental carcasses were exposed in a mountain forest landscape in Germany during spring and summer of 2021. We analysed Silphinae diversity using recently developed transformation models that considered the difficult data distribution we obtained. We found no consistent effect of carrion body mass on Silphinae species richness and, therefore, rejected the MIH. Carrion decomposition stage, in contrast, strongly influenced Silphinae diversity. Abundance and species richness increased with the decomposition process. Silphinae abundance increased with temperature and decreased with elevation. Furthermore, Silphinae abundance was lower in summer compared to spring, likely due to increased co-occurrence and competition with dipteran larvae in summer. Neither carrion species identity nor any abiotic factor affected Silphinae species richness following a pattern consistent throughout the seasons. Our approach combining a broad study design with an improved method for data analysis, transformation models, revealed new insights into mechanisms driving carrion beetle diversity during carrion decomposition. Overall, our study illustrates the complexity and multifactorial nature of biotic and abiotic factors affecting diversity.

Aluminum Corrosion Chemistry in High-Voltage Lithium Metal Batteries with LiFSI-Based Ether Electrolytes.

High-voltage Li metal batteries (LMBs) based on ether electrolytes hold potential for achieving high energy densities exceeding 500 Wh kg-1, but face challenges with electrolyte oxidative stability, particularly concerning aluminum (Al) current collector corrosion. However, the specific chemistry behind Al corrosion and its effect on electrolyte components remains unexplored. Here, our study delves into Al corrosion in the representative LiFSI-DME electrolyte system, revealing that low-concentration electrolytes exacerbate Al current collector corrosion and solvent decomposition. In contrast, high-concentration electrolytes mitigate these issues, enhancing long-term stability. Remarkably, LiFSI-0.7DME electrolyte demonstrates exceptional stability with up to 1000 cycles at high voltage without significant capacity decay. These findings offer crucial insights into Al corrosion mechanisms in ether-based electrolytes, advancing our comprehension of high-voltage LMBs and facilitating their development for practical applications.

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), named as VCBA, for spatio-temporal fusion of multi-site data to forecast daily PM2.5 concentrations in a city. The approach involves integrating air quality data from the target site with data from neighboring sites, applying mathematical techniques for dimensionality reduction, decomposing PM2.5 concentration data using VMD, and utilizing Causal CNN and BiLSTM models with an attention mechanism to enhance performance. The final prediction results are obtained through linear aggregation. Experimental results demonstrate that the VCBA model performs exceptionally well in predicting daily PM2.5 concentrations at various stations in Taiyuan City, Shanxi Province, China. Evaluation metrics such as RMSE, MAE, and R2 are reported as 2.556, 1.998, and 0.973, respectively. Compared to traditional methods, this approach offers higher prediction accuracy and stronger spatio-temporal modeling capabilities, providing an effective solution for accurate PM2.5 daily concentration prediction.

In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis.

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass ß3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

Understanding Socioeconomic Inequalities in Zero-Dose Children for Vaccination in Underserved Settings of Ethiopia: Decomposition Analysis Approach.

Despite considerable global efforts to enhance vaccine distribution in low-income countries, a significant number of children remain unvaccinated, particularly in Ethiopia. The underlying socioeconomic challenges in these regions are recognized as primary contributors to the low vaccination rates. However, the reasons for this persistent disparity in Ethiopia's remote and underserved regions need further analysis. The study employed a cross-sectional design and was conducted as part of the Project HOPE Zero-Dose Evaluation from 1 February to 31 July 2022. Concentration indices were utilized to quantify the extent of inequality, with further decomposition aimed at identifying contributing factors to this disparity. The findings underscored that populations with lower socioeconomic status encounter high numbers of children receiving no vaccinations. Key factors influencing the number of zero-dose children included distance from healthcare facilities (61.03%), economic status of the household (38.93%), absence of skilled birth assistance (20.36%), underutilization of antenatal care services (

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

A Bayesian Tensor Decomposition Method for Joint Estimation of Channel and Interference Parameters.

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input-multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time-frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation.

Temporal assessment of Gross alpha emissions from the petroleum industry in Tenerife, Canary Islands (2001-2022).

A ca. 76% decrease in gross alpha activity levels, measured in surface aerosols collected in the city of Santa Cruz de Tenerife (Spain), has been explained in the present study in connection with the reduction of activities, and eventual closure, of an oil refinery in the city. Gross Alpha in surface aerosols, collected at weekly intervals over a period of 22 years (2001-2022), was used for the analysis. The dynamic behaviour of the gross alpha time series was studied using statistical wavelet, multifractal analysis, empirical decomposition method, multivariate analysis, principal component, and cluster analyses approaches. This was performed to separate the impact of other sources of alpha emitting radionuclides influencing the gross alpha levels at this site. These in-depth analyses revealed a noteworthy shift in the dynamic behaviour of the gross alpha levels following the refinery's closure in 2013. This analysis also attributed fluctuations and trends in the gross alpha levels to factors such as the 2008 global economic crisis and the refinery's gradual reduction of activity leading up to its closure. The mixed-model approach, incorporating multivariate regression and autoregressive integrated moving average methods, explained approximately 84% of the variance of the gross alpha levels. Finally, this work underscored the marked reduction in alpha activity levels following the refinery's closure, alongside the decline of other pollutants (CO, SO2, NO, NO2, Benzene, Toluene and Xylene) linked to the primary industrial activity in the municipality of Santa Cruz de Tenerife.

A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference.

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.

Feasibility study of photon-counting CT for material identification based on YSO/SiPM detector: A proof of concept.

BACKGROUND: Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain. OBJECTIVE: This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification. METHODS: We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies. RESULTS: In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%. CONCLUSIONS: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

Trend, and multivariate decomposition of perinatal mortality in Ethiopia using further analysis of EDHS 2005-2016.

BACKGROUND: Perinatal mortality is a global health problem, especially in Ethiopia, which has the highest perinatal mortality rate. Studies about perinatal mortality were conducted in Ethiopia, but which factors specifically contribute to the change in perinatal mortality across time is unknown. OBJECTIVES: To assess the trend and multivariate decomposition of perinatal mortality in Ethiopia using EDHS 2005-2016. METHODS: A community-based, cross-sectional study design was used. EDHS 2005-2016 data was used, and weighting has been applied to adjust the difference in the probability of selection. Logit-based multivariate decomposition analysis was used using STATA version 14.1. The best model was selected using the lowest AIC value, and variables were selected with a p-value less than 0.05 at 95% CI. RESULT: The trend of perinatal mortality in Ethiopia decreased from 37 per 1000 births in 2005 to 33 per 1000 births in 2016. About 83.3% of the decrease in perinatal mortality in the survey was attributed to the difference in the endowment (composition) of the women. Among the differences in the endowment, the difference in the composition of ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and birth attendant significantly decreased perinatal mortality in the last 10 years. Among the differences in coefficients, skilled birth attendants significantly decreased perinatal mortality. CONCLUSION AND RECOMMENDATION: The perinatal mortality rate in Ethiopia has declined over time. Variables like ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and skilled birth attendants reduce perinatal mortality. To reduce perinatal mortality more, scaling up maternal and newborn health services has a critical role.

Nitrogen Fixation and Microbial Communities Associated with Decomposing Seagrass Leaves in Temperate Coastal Waters.

Seagrass meadows play pivotal roles in coastal biochemical cycles, with nitrogen fixation being a well-established process associated with living seagrass. Here, we tested the hypothesis that nitrogen fixation is also associated with seagrass debris in Danish coastal waters. We conducted a 52-day in situ experiment to investigate nitrogen fixation (proxied by acetylene reduction) and dynamics of the microbial community (16S rRNA gene amplicon sequencing) and the nitrogen fixing community (nifH DNA/RNA amplicon sequencing) associated with decomposing Zostera marina leaves. The leaves harboured distinct microbial communities, including distinct nitrogen fixers, relative to the surrounding seawater and sediment throughout the experiment. Nitrogen fixation rates were measurable on most days, but highest on days 3 (dark, 334.8 nmol N g-1 dw h-1) and 15 (light, 194.6 nmol N g-1 dw h-1). Nitrogen fixation rates were not correlated with the concentration of inorganic nutrients in the surrounding seawater or with carbon:nitrogen ratios in the leaves. The composition of nitrogen fixers shifted from cyanobacterial Sphaerospermopsis to heterotrophic genera like Desulfopila over the decomposition period. On the days with highest fixation, nifH RNA gene transcripts were mainly accounted for by cyanobacteria, in particular by Sphaerospermopsis and an unknown taxon (order Nostocales), alongside Proteobacteria. Our study shows that seagrass debris in temperate coastal waters harbours substantial nitrogen fixation carried out by cyanobacteria and heterotrophic bacteria that are distinct relative to the surrounding seawater and sediments. This suggests that seagrass debris constitutes a selective environment where degradation is affected by the import of nitrogen via nitrogen fixation.