Outdoor tubular photobioreactor microalgae-microorganisms biofilm treatment of municipal wastewater: Enhanced heterotrophic assimilation and synergistic aerobic denitrogenation.

This research evaluated a microalgae consortium (MC) in a pilot-scale tubular photobioreactor for municipal wastewater (MWW) treatment, compared with an aeration column photobioreactor. Transitioning from suspended MC to a microalgae-microbial biofilm (MMBF) maintained treatment performance despite increasing influent from 50 L to 150 L in a 260 L system. Carbon and nitrogen removal were effective, but phosphorus removal varied due to biofilm shading and the absence of phosphorus-accumulating organisms. High influent flow caused MMBF detachment due to shear stress. Stabilizing and re-establishing the MMBF showed that a stable phycosphere influenced microbial diversity and interactions, potentially destabilizing the MMBF. Heterotrophic nitrification-aerobic denitrification bacteria were crucial for MC equilibrium. Elevated gene expression related to nitrogen fixation, organic nitrogen metabolism, and nitrate reduction confirmed strong microalgal symbiosis, highlighting MMBF's treatment potential. This study supports the practical application of microalgae in wastewater treatment.

Molecular Dynamics Simulations of the Short-Chain Fluorocarbon Surfactant PFHXA and the Anionic Surfactant SDS at the Air/Water Interface.

The research and development of alternatives to long-chain fluorocarbon surfactants are desperately needed because they are extremely toxic, difficult to break down, seriously harm the environment, and limit the use of conventional aqueous film-forming foam fire extinguishing agents. In this study, mixed surfactant systems containing the short-chain fluorocarbon surfactant perfluorohexanoic acid (PFHXA) and the hydrocarbon surfactant sodium dodecyl sulfate (SDS) were investigated by molecular dynamics simulation to investigate the microscopic properties at the air/water interface at different molar ratios. Some representative parameters, such as surface tension, degree of order, density distribution, radial distribution function, number of hydrogen bonds, and solvent-accessible surface area, were calculated. Molecular dynamics simulations show that compared with a single type of surfactant, mixtures of surfactants provide superior performance in improving the interfacial properties of the gas-liquid interface. A dense monolayer film is formed by the strong synergistic impact of the two surfactants. Compared to the pure SDS system, the addition of PFHXA caused SDS to be more vertically oriented at the air/water interface with a reduced tilt angle, and a more ordered structure of the mixed surfactants was observed. Hydrogen bonding between SDS headgroups and water molecules is enhanced with the increasing PFHXA. The surface activity is arranged in the following order: PFHXA/SDS = 1:1 > PFHXA/SDS = 3:1 > PFHXA/SDS = 1:3. These results indicate that a degree of synergistic relationship exists between PFHXA and SDS at the air/water interface.

Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.

This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.

Insights of microalgal municipal wastewater treatment at low temperatures: Performance, microbiota patterns, and cold-adaptation of tubular and aeration column photobioreactors.

In order to refine the treatment of microalgae consortium (MC) for municipal wastewater (MWW) during the winter, this study investigated the effectiveness of tubular and aeration column photobioreactors (TPBR and APBR) in wastewater treatment plant (WWTP) during winter by two start-up modes: microalgae/microalgae-activated sludge (AS). The operation results showed that under 5.7-13.1 °C, TPBR enhanced the assimilation of N and P pollutant by microalgal accumulation, meeting the Chinese discharge standard within 24 h (NH4+-N, TP, and COD ≤8.0, 0.5, and 50 mg·L-1). The microbial community profiles were identified and showed that inoculating AS under low-temperature still promoted bacterial interspecific association, but influenced by the inhibition of microbial diversity by the homogeneous circulation of TPBR, the nitrogen transfer function of MC was lower than that of APBR at low temperatures, except nitrogen fixation (K02588), nitrosification (K10944, K10945, and K10946), assimilatory nitrate reduction (K00366), and ammonification (K01915 and K05601). And the intermittent aeration in the APBR was still beneficial in increasing microbial diversity, which was more beneficial for reducing COD through microbial collaboration. In the treatment, the cryotolerant MGPM were Delftia, Romboutsia, Rhizobiales, and Bacillus, and the cold stress-related genes that were highly up-regulated were defense signaling molecules (K03671 and K00384), cold shock protein gene (K03704), and cellular protector (K01784) were present in both PBRs. This study provided a reference for the feasibility of the low temperature treatment of MC with the different types of PBR, which improved the application of wastewater treatment in more climatic environments.

High-performance broadband flexible photodetector based on Gd3Fe5O12-assisted double van der Waals heterojunctions.

Flexible photodetectors are fundamental components for developing wearable systems, which can be widely used for medical detection, environmental monitoring and flexible imaging. However, compared with 3D materials, low-dimensional materials have degraded performance, a key challenge for current flexible photodetectors. Here, a high-performance broadband photodetector has been proposed and fabricated. By combining the high mobility of graphene (Gr) with the strong light-matter interactions of single-walled carbon nanotubes (SWCNTs) and molybdenum disulfide (MoS2), the flexible photodetector exhibits a greatly improved photoresponse covering the visible to near-infrared range. Additionally, a thin layer of gadolinium iron garnet (Gd3Fe5O12, GdlG) film is introduced to improve the interface of the double van der Waals heterojunctions to reduce the dark current. The SWCNT/GdIG/Gr/GdIG/MoS2 flexible photodetector exhibits a high photoresponsivity of 47.375 A/W and a high detectivity of 1.952 × 1012 Jones at 450 nm, a high photoresponsivity of 109.311 A/W and a high detectivity of 4.504 × 1012 Jones at 1080 nm, and good mechanical stability at room temperature. This work demonstrates the good capacity of GdIG-assisted double van der Waals heterojunctions on flexible substrates and provides a new solution for constructing high-performance flexible photodetectors.

A novel protein encoded by circRsrc1 regulates mitochondrial ribosome assembly and translation during spermatogenesis.

BACKGROUND: Circular RNAs (circRNAs) are a large class of mammalian RNAs. Several protein products translated by circRNAs have been reported to be involved in the development of various tissues and systems; however, their physiological functions in male reproduction have yet not been explored. RESULTS: Here, we report an endogenous circRNA (circRsrc1) that encodes a novel 161-amino-acid protein which we named Rsrc1-161aa through circRNA sequencing coupled with mass spectrometry analysis on mouse testicular tissues. Deletion of Rsrc1-161aa in mice impaired male fertility with a significant decrease in sperm count and motility due to dysfunctions of mitochondrial energy metabolism. A series of in vitro rescue experiments revealed that circRsrc1 regulates mitochondrial functions via its encoded protein Rsrc1-161aa. Mechanistically, Rsrc1-161aa directly interacts with mitochondrial protein C1qbp and enhances its binding activity to mitochondrial mRNAs, thereby regulating the assembly of mitochondrial ribosomes and affecting the translation of oxidative phosphorylation (OXPHOS) proteins and mitochondrial energy metabolism. CONCLUSIONS: Our studies reveal that Rsrc1-161aa protein encoded by circRsrc1 regulates mitochondrial ribosome assembly and translation during spermatogenesis, thereby affecting male fertility.

Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment.

STUDY OBJECTIVE: To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission. DESIGN: Retrospective cohort study. SETTING: Single university teaching hospital in Taipei, Taiwan. PATIENTS: 6238 critically ill patients from August 2020 to August 2021. MEASUREMENTS: Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance. MAIN RESULTS: Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835-0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780-0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911-0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792-0.886) was the highest for the 24H LR model. CONCLUSION: Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.

Deep Bidirectional Recurrent Neural Networks Ensemble for Remaining Useful Life Prediction of Aircraft Engine.

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

A male germ-cell-specific ribosome controls male fertility.

Ribosomes are highly sophisticated translation machines that have been demonstrated to be heterogeneous in the regulation of protein synthesis1,2. Male germ cell development involves complex translational regulation during sperm formation3. However, it remains unclear whether translation during sperm formation is performed by a specific ribosome. Here we report a ribosome with a specialized nascent polypeptide exit tunnel, RibosomeST, that is assembled with the male germ-cell-specific protein RPL39L, the paralogue of core ribosome (RibosomeCore) protein RPL39. Deletion of RibosomeST in mice causes defective sperm formation, resulting in substantially reduced fertility. Our comparison of single-particle cryo-electron microscopy structures of ribosomes from mouse kidneys and testes indicates that RibosomeST features a ribosomal polypeptide exit tunnel of distinct size and charge states compared with RibosomeCore. RibosomeST predominantly cotranslationally regulates the folding of a subset of male germ-cell-specific proteins that are essential for the formation of sperm. Moreover, we found that specialized functions of RibosomeST were not replaceable by RibosomeCore. Taken together, identification of this sperm-specific ribosome should greatly expand our understanding of ribosome function and tissue-specific regulation of protein expression pattern in mammals.

Proteomic profiling of the endogenous peptides of MRSA and MSSA.

Staphylococcus aureus is a Gram-positive bacterium that can cause diverse skin and soft tissue infections. Methicillin-resistant Staphylococcus aureus (MRSA) can cause more severe infections than methicillin-susceptible Staphylococcus aureus (MSSA). Nevertheless, the physiological and metabolic regulation of MSSA and MRSA has not been well studied. In light of the increased interest in endogenous peptides and recognition of the important roles that they play, we studied the endogenous peptidome of MSSA and MRSA. We identified 1,065 endogenous peptides, among which 435 were differentially expressed (DE), with 292 MSSA-abundant endogenous peptides and 35 MRSA-abundant endogenous peptides. MSSA-abundant endogenous peptides have significantly enriched "VXXXK" motif of at the C-terminus. MSSA-abundant endogenous peptides are involved in penicillin-binding and immune responses, whereas MRSA-abundant endogenous peptides are associated with antibiotic resistance and increased toxicity. Our characterization of the peptidome of MSSA and MRSA provides a rich resource for future studies to explore the functional regulation of drug resistance in S. aureus and may also help elucidate the mechanisms of its pathogenicity and the development of treatments.

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms.

Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.

Appropriate locations of fixed bearings of continuous beams considering rail-bridge thermal interaction.

Due to the rail-bridge thermal interaction, the high additional axial force in continuously welded rails on continuous bridges may lead to rail buckling or breaking. However, there is little research on the influence of the location of the fixed bearing of continuous beam on the additional force of rail. In order to study the influence of bridge bearing arrangement on the additional longitudinal force of CWR, the thermal interaction model is established for rail, and simple and continuous beams considering nonlinear stiffness and the methods are proposed to determine the locations of fixed bearings of continuous beams corresponding to the maximum additional forces in rail reaching minimum values. Multiple continuous beams with several different lengths and simple beams with three types of bearing arrangements are taken into account to find the effect laws of the locations of the fixed bearings of continuous beams on the maximum additional forces in rail. The results show that as long as the same number of continuous beams, the ratios of the distances of adjacent two fixed bearings to the distance between the two fixed bearings of the simple beams neighbour to the first and last continuous beams respectively are approximately equal to each other. Furthermore the appropriate locations of the fixed bearings of continuous beams are recommended. The results can guide designing the location of the fixed bearing of continuous railway bridge while reducing the additional axial force in continuously welded rails due to bridge thermal effect.

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties, such as poor structure and heterogenicity of the raw EMR data, are encountered when introducing AI with ICU data. Labor-intensive work, including manual data entry, personal medical records sorting, and laboratory results interpretation may hinder the progress of AI. In this article, we introduce the developing of an AI algorithm designed for sepsis diagnosis using pre-selected features; and compare the performance of the AI algorithm with SOFA score based diagnostic method. MATERIALS AND METHODS: This is a prospective open-label cohort study. A specialized EMR, named TED_ICU, was implemented for continuous data recording. One hundred six clinical features relevant to sepsis diagnosis were selected prospectively. A labeling work to allocate SEPSIS or NON_SEPSIS status for each ICU patient was performed by the in-charge intensivist according to SEPSIS-3 criteria, along with the automatic recording of selected features every day by TED_ICU. Afterward, we use de-identified data to develop the AI algorithm. Several machine learning methods were evaluated using 5-fold cross-validation, and XGBoost, a decision-tree based algorithm was adopted for our AI algorithm development due to best performance. RESULTS: The study was conducted between August 2018 and December 2018 for the first stage of analysis. We collected 1588 instances, including 444 SEPSIS and 1144 NON-SEPSIS, from 434 patients. The 434 patients included 259 (59.6%) male patients and 175 female patients. The mean age was 67.6-year-old, and the mean APACHE II score was 13.8. The SEPSIS cohort had a higher SOFA score and increased use of organ support treatment. The AI algorithm was developed with a shuffle method using 80% of the instances for training and 20% for testing. The established AI algorithm achieved the following: accuracy = 82% ± 1%; sensitivity = 65% ± 5%; specificity = 88% ± 2%; precision = 67% ± 3%; and F1 = 0.66 ± 0.02. The area under the receiver operating characteristic curve (AUROC) was approximately 0.89. The SOFA score was used on the same 1588 instances for sepsis diagnosis, and the result was inferior to our AI algorithm (AUROC = 0.596). CONCLUSION: Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well.

Phosphoproteomic Analysis of Neonatal Regenerative Myocardium Revealed Important Roles of Checkpoint Kinase 1 via Activating Mammalian Target of Rapamycin C1/Ribosomal Protein S6 Kinase b-1 Pathway.

BACKGROUND: In mammals, regenerative therapy after myocardial infarction is hampered by the limited regenerative capacity of adult heart, whereas a transient regenerative capacity is maintained in the neonatal heart. Systemic phosphorylation signaling analysis on ischemic neonatal myocardium might be helpful to identify key pathways involved in heart regeneration. Our aim was to define the kinase-substrate network in ischemic neonatal myocardium and to identify key pathways involved in heart regeneration after ischemic insult. METHODS: Quantitative phosphoproteomics profiling was performed on infarct border zone of neonatal myocardium, and kinase-substrate network analysis revealed 11 kinases with enriched substrates and upregulated phosphorylation levels, including checkpoint kinase 1 (CHK1) kinase. The effect of CHK1 on cardiac regeneration was tested on Institute of Cancer Research CD1 neonatal and adult mice that underwent apical resection or myocardial infarction. RESULTS: In vitro, CHK1 overexpression promoted whereas CHK1 knockdown blunted cardiomyocyte proliferation. In vivo, inhibition of CHK1 hindered myocardial regeneration on resection border zone in neonatal mice. In adult myocardial infarction mice, CHK1 overexpression on infarct border zone upregulated mammalian target of rapamycin C1/ribosomal protein S6 kinase b-1 pathway, promoted cardiomyocyte proliferation, and improved cardiac function. Inhibiting mammalian target of rapamycin activity by rapamycin blunted the neonatal cardiomyocyte proliferation induced by CHK1 overexpression in vitro. CONCLUSIONS: Our study indicates that phosphoproteome of neonatal regenerative myocardium could help identify important signaling pathways involved in myocardial regeneration. CHK1 is found to be a key signaling responsible for neonatal regeneration. Myocardial overexpression of CHK1 could improve cardiac regeneration in adult hearts by activating the mammalian target of rapamycin C1/ribosomal protein S6 kinase b-1 pathway. Thus, CHK1 might serve as a potential novel target in myocardial repair after myocardial infarction.

Identification of a parasitic symbiosis between respiratory metabolisms in the biogeochemical chlorine cycle.

A key step in the chlorine cycle is the reduction of perchlorate (ClO4-) and chlorate (ClO3-) to chloride by microbial respiratory pathways. Perchlorate-reducing bacteria and chlorate-reducing bacteria differ in that the latter cannot use perchlorate, the most oxidized chlorine compound. However, a recent study identified a bacterium with the chlorate reduction pathway dominating a community provided only perchlorate. Here we confirm a metabolic interaction between perchlorate- and chlorate-reducing bacteria and define its mechanism. Perchlorate-reducing bacteria supported the growth of chlorate-reducing bacteria to up to 90% of total cells in communities and co-cultures. Chlorate-reducing bacteria required the gene for chlorate reductase to grow in co-culture with perchlorate-reducing bacteria, demonstrating that chlorate is responsible for the interaction, not the subsequent intermediates chlorite and oxygen. Modeling of the interaction suggested that cells specialized for chlorate reduction have a competitive advantage for consuming chlorate produced from perchlorate, especially at high concentrations of perchlorate, because perchlorate and chlorate compete for a single enzyme in perchlorate-reducing cells. We conclude that perchlorate-reducing bacteria inadvertently support large populations of chlorate-reducing bacteria in a parasitic relationship through the release of the intermediate chlorate. An implication of these findings is that undetected chlorate-reducing bacteria have likely negatively impacted efforts to bioremediate perchlorate pollution for decades.

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network.

Remaining useful life (RUL) prediction is very important for improving the availability of a system and reducing its life cycle cost. This paper proposes a deep long short-term memory (DLSTM) network-based RUL prediction method using multiple sensor time series signals. The DLSTM model fuses multi-sensor monitoring signals for accurate RUL prediction, which is able to discover the hidden long-term dependencies among sensor time series signals through deep learning structure. By grid search strategy, the network structure and parameters of the DLSTM are efficiently tuned using an adaptive moment estimation algorithm so as to realize an accurate and robust prediction. Two various turbofan engine datasets are adopted to verify the performance of the DLSTM model. The experimental results demonstrate that the DLSTM model has a competitive performance in comparison with state-of-the-arts reported in literatures and other neural network models.

RSBP15 interacts with and stabilizes dRSPH3 during sperm axoneme assembly in Drosophila.

Flagellum in sperm is composed of over 200 different proteins and is essential for sperm motility. In particular, defects in the assembly of the radial spoke in the flagellum result in male infertility due to loss of sperm motility. However, mechanisms regulating radial spoke assembly remain unclear in metazoans. Here, we identified a novel Drosophila protein radial spoke binding protein 15 (RSBP15) which plays an important role in regulating radial spoke assembly. Loss of RSBP15 results in complete lack of mature sperms in seminal vesicles (SVs), asynchronous individualization complex (IC) and defective "9 + 2" structure in flagella. RSBP15 is colocalized with dRSPH3 in sperm flagella, and interacts with dRSPH3 through its DD_R_PKA superfamily domain which is important for the stabilization of dRSPH3. Moreover, loss of dRSPH3, as well as dRSPH1, dRSPH4a and dRSPH9, showed similar phenotypes to rsbp15KO mutant. Together, our results suggest that RSBP15 acts in stabilizing the radial spoke protein complex to anchor and strengthen the radial spoke structures in sperm flagella.

Resistance and Resilience of Sulfidogenic Communities in the Face of the Specific Inhibitor Perchlorate.

Hydrogen sulfide is a toxic and corrosive gas, produced by the activity of sulfate-reducing microorganisms (SRM). Owing to the environmental, economic and human-health consequences of sulfide, there is interest in developing specific inhibitors of SRM. Recent studies have identified perchlorate as a promising emerging inhibitor. The aim of this work is to quantitatively dissect the inhibitory dynamics of perchlorate. Sulfidogenic mixed continuous-flow systems were treated with perchlorate. SRM number, sulfide production and community structure were monitored pre-, during and post-treatment. The data generated was compared to a simple mathematical model, where SRM growth slows as a result of inhibition. The experimental data supports the interpretation that perchlorate largely acts to suppress SRM growth rates, rendering planktonic SRM increasingly susceptible to wash-out. Surface-attachment was identified as an important parameter preventing SRM wash-out and thus governing inhibitory dynamics. Our study confirmed the lesser depletion of surface-attached SRM as compared to planktonic SRM during perchlorate treatment. Indirect effects of perchlorate (bio-competitive exclusion of SRM by dissimilatory perchlorate-reducing bacteria, DPRB) were also assayed by amending reactors with DPRB. Indeed, low concentrations of perchlorate coupled with DRPB amendment can drive sulfide concentrations to zero. Further, inhibition in a complex community was compared to that in a pure culture, highlighting similarities and differences between the two scenarios. Finally, we quantified susceptibility to perchlorate across SRM in various culture conditions, showing that prediction of complex behavior in continuous systems from batch results is possible. This study thus provides an overview of the sensitivity of sulfidogenic communities to perchlorate, as well as mechanisms underlying these patterns.

The Protein Phosphorylation Landscape of Mouse Spermatids during Spermiogenesis.

The characteristic tadpole shape of sperm is formed from round spermatids via spermiogenesis, a process which results in dramatic morphological changes in the final stage of spermatogenesis in the testis. Protein phosphorylation, as one of the most important post-translational modifications, can regulate spermiogenesis; however, the phosphorylation events taking place during this process have not been systematically analyzed. In order to better understand the role of phosphorylation in spermiogenesis, large-scale phosphoproteome profiling is performed using IMAC and TiO2 enrichment. In total, 13 835 phosphorylation sites, in 4196 phosphoproteins, are identified in purified mouse spermatids undergoing spermiogenesis in two biological replicates. Overall, 735 testis-specific proteins are identified to be phosphorylated, and are expressed at high levels during spermiogenesis. Gene ontology analysis shows enrichment of the identified phosphoproteins in terms of histone modification, cilium organization, centrosome and the adherens junction. Further characterization of the kinase-substrate phosphorylation network demonstrates enrichment of phosphorylation substrates related to the regulation of spermiogenesis. This global protein phosphorylation landscape of spermiogenesis shows wide phosphoregulation across a diverse range of processes during spermiogenesis and can help to further characterize the process of sperm generation. All MS data are available via ProteomeXchange with the identifier PXD011890.