Soluble carbon source recovery using preconditioning coagulants for applicable short-term fermentation of waste activated sludge in WWTPs.

A huge production of waste activated sludge (WAS) has been a burden for wastewater treatment plants (WWTPs) with high disposal cost and little benefit back to wastewater purification. The short-chain fatty acids (SCFAs) produced by a short-term acidogenic fermentation of WAS before methane production have been proven to be a high-quality carbon source available for microbial denitrification process. The dual purpose of full recovery of fermentation liquid products and facilitating disposal of residual solid waste necessitate an efficient solid-liquid separation process of short-term fermentation liquid. The transformation and loss of various soluble carbon sources between solid and liquid are very important issues for carbon recovery efficiency when combining short-term fermentation and sludge dewatering in WWTPs. Here we testified the three conventional preconditioning coagulants, Polyferric Sulfate (PFS), Poly Aluminum Chloride (PAC) and Polyacrylamide (PAM), to improve the efficiency of subsequent solid-liquid separation. The results show that conversion yield of SCFAs in the liquid phase of sludge after short-term fermentation was 195 mg COD/g VSS, when using the coagulants PFS, PAC, and PAM for recovery, the recovery ratio was 79.5%, 82.0%, and 85.9%, respectively, while the dewaterability could be improved after preconditioning short-term fermentation sludge. The complexation of Al3+/Fe3+ in metal coagulants with carboxyl groups of SCFA demonstrated by Density Functional Theory calculation led to small part of soluble carbons co-migration to the solid phase, mainly a loss of high molecular weight organic compounds (carbohydrate, proteins, humic acids), while the application of PAM had little impact on carbon recovery. Economic calculations further showed PAM preconditioning short-term fermentation liquid of WAS could achieve higher recovery benefits.

Volatile fatty acids bio-production using extracellular polymeric substances disengaged from sludge for carbon source recycling.

Excessive waste-activated sludge (WAS) and insufficient carbon source (CS) for biological nitrogen removal (BNR) often coexist in municipal sewage treatment. Although the production of volatile fatty acids (VFAs) from WAS has been recognized as a promising solution, the development is limited by low VFAs production efficiency and dewatering deterioration of sludge. This study extracted the extracellular polymeric substances (EPS) from sludge by low-temperature thermal-hydrolysis (LTH) and high-speed hydro-cyclone (HSHC) pretreatment and recovered it for high-quality VFAs bio-production in thermophilic fermentation. Microbial mechanism analysis disclosed that interspecific interaction networks composed of functional flora, which accumulate VFAs by bio-converting EPS primarily and supplemented by EPS synthesis, guaranteed the efficient bio-production of VFAs. This process scheme shows promise in providing alternative denitrification CSs and avoiding deterioration of sludge dewaterability.

Effects of alkaline biochar on nitrogen transformation with fertilizer in agricultural soil.

The loss and negative impacts of nitrogen from fertilized soils remain a global challenge in agricultural field. Ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) leaching, together with volatile ammonia loss are the main pathways of nitrogen loss. To improve nitrogen availability, alkaline biochar with improved adsorption capacities is a promising soil amendment. This study was objected to investigate the effects of alkaline biochar (ABC, pH 8.68) on nitrogen mitigation, the effects on nitrogen loss, and the interactions among the mixed soils (biochar, nitrogen fertilizer, and soil) under both pot and field experiments. From pot experiments, ABC addition resulted in the poor reservation of NH4+-N which converted to volatile NH3 under higher alkaline environments, mainly occurring in the first 3 days. But after, NO3--N could be largely retained in surface soil by ABC addition. The reservation of NO3--N by ABC offsets the loss of volatile NH3, and ABC ultimately showed positive reservations of nitrogen with fertilization. In the field experiment, the addition of urea inhibitor (UI) addition could inhibit the volatile NH3 loss caused by ABC mainly in the first week. The long-term operation demonstrated that ABC supported persistent effectiveness in reducing N loss, while UI treatment temporarily delayed the N loss through inhibition of fertilizer hydrolysis. Therefore, the addition of both ABC and UI contributed to reserve soil N in layers (0-50 cm) suitable for crop growth thus improving crops growth.

Direct wavefront sensing with a plenoptic sensor based on deep learning.

Traditional plenoptic wavefront sensors (PWS) suffer from the obvious step change of the slope response which leads to the poor performance of phase retrieval. In this paper, a neural network model combining the transformer architecture with the U-Net model is utilized to restore wavefront directly from the plenoptic image of PWS. The simulation results show that the averaged root mean square error (RMSE) of residual wavefront is less than 1/14λ (Marechal criterion), proving the proposed method successfully breaks through the non-linear problem existed in PWS wavefront sensing. In addition, our model performs better than the recently developed deep learning models and traditional modal approach. Furthermore, the robustness of our model to turbulence strength and signal level is also tested, proving the good generalizability of our model. To the best of our knowledge, it is the first time to perform direct wavefront detection with a deep-learning-based method in PWS-based applications and achieve the state-of-the-art performance.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Deep learning assisted plenoptic wavefront sensor for direct wavefront detection.

Traditional plenoptic wavefront sensors (PWFS) suffer from the obvious step change of the slope response, leading to poor wavefront detection performance. In order to solve this problem, in this paper, a deep learning model is proposed to restore phase maps directly from slope measurements of PWFS. Numerical simulations are employed to demonstrate our approach, and the statistical residual wavefront root mean square error (RMSE) of our method is 0.0810 ± 0.0258λ, which is much superior to those of modal algorithm (0.2511 ± 0.0587λ) and zonal approach (0.3584 ± 0.0487λ). The internal driving force of PWFS-ResUnet is investigated, and the slope response differences between sub-apertures and directions are considered as a probably key role to help our model to accurately restore the phase map. Additionally, the robustness of our model to turbulence strength and signal-to-noise ratio (SNR) level is also tested. The proposed method provides a new direction to solve the nonlinear problem of traditional PWFS.

Carbon source recovery from waste sludge reduces greenhouse gas emissions in a pilot-scale industrial wastewater treatment plant.

Carbon cycle regulation and greenhouse gas (GHG) emission abatement within wastewater treatment plants (WWTPs) can theoretically improve sustainability. Currently, however, large amounts of external carbon sources used for deep nitrogen removal and waste sludge disposal aggravate the carbon footprint of most WWTPs. In this pilot-scale study, considerable carbon was preliminarily recovered from primary sludge (PS) through short-term (five days) acidogenic fermentation and subsequently utilized on-site for denitrification in a wool processing industrial WWTP. The recovered sludge-derived carbon sources were excellent electron donors that could be used as additional carbon supplements for commercial glucose to enhance denitrification. Additionally, improvements in carbon and nitrogen flow further contributed to GHG emission abatement. Overall, a 9.1% reduction in sludge volatile solids was achieved from carbon recovery, which offset 57.4% of external carbon sources, and the indirect GHG emissions of the target industrial WWTP were reduced by 8.05%. This study demonstrates that optimizing the allocation of carbon mass flow within a WWTP has numerous benefits.

Study of natural attenuation after acid in situ leaching of uranium mines using isotope fractionation and geochemical data.

Acid in situ leaching (AISL) is a subsurface mining approach suitable for low-grade ores which does not generate tailings, and has been adopted widely in uranium mining. However, this technique causes an extremely high concentration of contaminants at post-mining sites and in the surroundings soon after the mining ceases. As a potential AISL remediation strategy, natural attenuation has not been studied in detail. To address this problem, groundwater collected from 26 wells located within, adjacent, upgradient, and downgradient of a post-mining site were chosen to analyze the fate of U(VI), SO42-, δ34S, and δ238U, to reveal the main mechanisms governing the migration and attenuation of the dominant contaminants and the spatio-temporal evolutions of contaminants in the confined aquifer of the post-mining site. The δ238U values vary from -0.07 ‰ to 0.09 ‰ in the post-mining site and from -1.43 ‰ to 0.03 ‰ around the post-mining site. The δ34S values were found to vary from 3.3 ‰ to 6.2 ‰ in the post-mining site and from 6.0 ‰ to 11.0 ‰ around the post-mining site. Detailed analysis suggests that there are large differences between the range of isotopic composition variation and the range of pollutants concentration distribution, and the estimated Rayleigh isotope fractionation factor is 0.9994-0.9997 for uranium and 1.0032-1.0061 for sulfur. The isotope ratio of uranium and sulfur can be used to deduce the migration history of the contaminants and the irreversibility of the natural attenuation process in the anoxic confined aquifer. Combining the isotopic fractionation data for U and S with the concentrations of uranium and sulfate improved the accuracy of understanding of reducing conditions along the flow path. The study also indicated that as long as the geological conditions are favorable for redox reactions, natural attenuation could be used as a cost-effective remediation scheme.

A tartrate-EDTA-Fe complex mediates electron transfer and enhances ammonia recovery in a bioelectrochemical-stripping system.

Traditional bioelectrochemical systems (BESs) coupled with stripping units for ammonia recovery suffer from an insufficient supply of electron acceptors due to the low solubility of oxygen. In this study, we proposed a novel strategy to efficiently transport the oxidizing equivalent provided at the stripping unit to the cathode by introducing a highly soluble electron mediator (EM) into the catholyte. To validate this strategy, we developed a new kind of iron complex system (tartrate-EDTA-Fe) as the EM. EDTA-Fe contributed to the redox property with a midpoint potential of -0.075 V (vs. standard hydrogen electrode, SHE) at pH 10, whereas tartrate acted as a stabilizer to avoid iron precipitation under alkaline conditions. At a ratio of the catholyte recirculation rate to the anolyte flow rate (RC-A) of 12, the NH4 +-N recovery rate in the system with 50 mM tartrate-EDTA-Fe complex reached 6.9 ±â€¯0.2 g N m-2 d-1, approximately 3.8 times higher than that in the non-EM control. With the help of the complex, our system showed an NH4 +-N recovery performance comparable to that previously reported but with an extremely low RC-A (0.5 vs. 288). The strategy proposed here may guide the future of ammonia recovery BES scale-up because the introduction of an EM allows aeration to be performed only at the stripping unit instead of at every cathode, which is beneficial for the system design due to its simplicity and reliability.

Untargeted Metabolomics of Slc13a5 Deficiency Reveal Critical Liver-Brain Axis for Lipid Homeostasis.

Though biallelic variants in SLC13A5 are known to cause severe encephalopathy, the mechanism of this disease is poorly understood. SLC13A5 protein deficiency reduces citrate transport into the cell. Downstream abnormalities in fatty acid synthesis and energy generation have been described, though biochemical signs of these perturbations are inconsistent across SLC13A5 deficiency patients. To investigate SLC13A5-related disorders, we performed untargeted metabolic analyses on the liver, brain, and serum from a Slc13a5-deficient mouse model. Metabolomic data were analyzed using the connect-the-dots (CTD) methodology and were compared to plasma and CSF metabolomics from SLC13A5-deficient patients. Mice homozygous for the Slc13a5tm1b/tm1b null allele had perturbations in fatty acids, bile acids, and energy metabolites in all tissues examined. Further analyses demonstrated that for several of these molecules, the ratio of their relative tissue concentrations differed widely in the knockout mouse, suggesting that deficiency of Slc13a5 impacts the biosynthesis and flux of metabolites between tissues. Similar findings were observed in patient biofluids, indicating altered transport and/or flux of molecules involved in energy, fatty acid, nucleotide, and bile acid metabolism. Deficiency of SLC13A5 likely causes a broader state of metabolic dysregulation than previously recognized, particularly regarding lipid synthesis, storage, and metabolism, supporting SLC13A5 deficiency as a lipid disorder.

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data.

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

Integrated constructed wetland and bioelectrochemistry system approach for simultaneous enhancment of p-chloronitrobenzene and nitrogen transformations performance.

Constructed wetlands (CWs) integrated with the bioelectrochemical system (BES-CW) to stimulate bio-refractory compounds removal holds particular promise, owing to its inherent greater scale and well-recognized environmentally benign wastewater advanced purification technology. However, the knowledge regarding the feasibility and removal mechanisms, particularly the potential negative effects of biorefractory compounds on nitrogen removal performance for the CWs is far insufficient. This study performed a critical assessment by using BES-CW (ECW) and conventional CW (CW) to investigate the effects of p-Chloronitrobenzene (pCNB) on nitrogen transformations in CWs. The results showed that low concentration (1 mg·L-1) of pCNB would inhibit the ammonia oxidation in CWs, while ECW could improve its tolerance to pCNB to a certain level (8 mg·L-1) due to the high pCNB degradation efficiencies (2.5 times higher than CWs), accordingly, much higher TN and nitrate removal efficiencies were observed in ECWs, 81.71% - 96.82% (TN) higher than CWs, further leading to a lower N2O emission from ECWs than CWs. The main intermediate of pCNB degradation was p-Chloroaniline (pCAN) and the genera Geobacter and Propionimicrobium were consider to be the responsible pCNB degradation bacteria in the present study. However, too high concentration (20 mg·L-1) of pCNB would have a huge impact on ECW and CW, especially microbial biomass. Nevertheless, ECW could improve the 1.87 times higher microbial biomass than CW on the substrate. Accordingly, considerably higher functional gene abundance was observed in ECW. Therefore, the introduction of BES has great potential to ensure CW stability when treating industrial wastewater containing bio-refractory compounds.

An Information-Theoretic Bound on p-Values for Detecting Communities Shared between Weighted Labeled Graphs.

Extraction of subsets of highly connected nodes ("communities" or modules) is a standard step in the analysis of complex social and biological networks. We here consider the problem of finding a relatively small set of nodes in two labeled weighted graphs that is highly connected in both. While many scoring functions and algorithms tackle the problem, the typically high computational cost of permutation testing required to establish the p-value for the observed pattern presents a major practical obstacle. To address this problem, we here extend the recently proposed CTD ("Connect the Dots") approach to establish information-theoretic upper bounds on the p-values and lower bounds on the size and connectedness of communities that are detectable. This is an innovation on the applicability of CTD, broadening its use to pairs of graphs.

Correction: CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

[This corrects the article DOI: 10.1371/journal.pcbi.1008550.].

Effective Aspirin Treatment of Women at Risk for Preeclampsia Delays the Metabolic Clock of Gestation.

[Figure: see text].

CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.

IS26-mediated amplification of blaOXA-1 and blaCTX-M-15 with concurrent outer membrane porin disruption associated with de novo carbapenem resistance in a recurrent bacteraemia cohort.

BACKGROUND: Approximately half of clinical carbapenem-resistant Enterobacterales (CRE) isolates lack carbapenem-hydrolysing enzymes and develop carbapenem resistance through alternative mechanisms. OBJECTIVES: To elucidate development of carbapenem resistance mechanisms from clonal, recurrent ESBL-positive Enterobacterales (ESBL-E) bacteraemia isolates in a vulnerable patient population. METHODS: This study investigated a cohort of ESBL-E bacteraemia cases in Houston, TX, USA. Oxford Nanopore Technologies long-read and Illumina short-read sequencing data were used for comparative genomic analysis. Serial passaging experiments were performed on a set of clinical ST131 Escherichia coli isolates to recapitulate in vivo observations. Quantitative PCR (qPCR) and qRT-PCR were used to determine copy number and transcript levels of ß-lactamase genes, respectively. RESULTS: Non-carbapenemase-producing CRE (non-CP-CRE) clinical isolates emerged from an ESBL-E background through a concurrence of primarily IS26-mediated amplifications of blaOXA-1 and blaCTX-M-1 group genes coupled with porin inactivation. The discrete, modular translocatable units (TUs) that carried and amplified ß-lactamase genes mobilized intracellularly from a chromosomal, IS26-bound transposon and inserted within porin genes, thereby increasing ß-lactamase gene copy number and inactivating porins concurrently. The carbapenem resistance phenotype and TU-mediated ß-lactamase gene amplification were recapitulated by passaging a clinical ESBL-E isolate in the presence of ertapenem. Clinical non-CP-CRE isolates had stable carbapenem resistance phenotypes in the absence of ertapenem exposure. CONCLUSIONS: These data demonstrate IS26-mediated mechanisms underlying ß-lactamase gene amplification with concurrent outer membrane porin disruption driving emergence of clinical non-CP-CRE. Furthermore, these amplifications were stable in the absence of antimicrobial pressure. Long-read sequencing can be utilized to identify unique mobile genetic element mechanisms that drive antimicrobial resistance.

Staphylococcus epidermidis MSCRAMM SesJ Is Encoded in Composite Islands.

Staphylococcus epidermidis is a leading cause of nosocomial infections in patients with a compromised immune system and/or an implanted medical device. Seventy to 90% of S. epidermidis clinical isolates are methicillin resistant and carry the mecA gene, present in a mobile genetic element (MGE) called the staphylococcal cassette chromosome mec (SCCmec) element. Along with the presence of antibiotic and heavy metal resistance genes, MGEs can also contain genes encoding secreted or cell wall-anchored virulence factors. In our earlier studies of S. epidermidis clinical isolates, we discovered S. epidermidis surface protein J (SesJ), a prototype of a recently discovered subfamily of the microbial surface component recognizing adhesive matrix molecule (MSCRAMM) group. MSCRAMMs are major virulence factors of pathogenic Gram-positive bacteria. Here, we report that the sesJ gene is always accompanied by two glycosyltransferase genes, gtfA and gtfB, and is present in two MGEs, called the arginine catabolic mobile element (ACME) and the staphylococcal cassette chromosome (SCC) element. The presence of the sesJ gene was associated with the left-hand direct repeat DR_B or DR_E. When inserted via DR_E, the sesJ gene was encoded in the SCC element. When inserted via DR_B, the sesJ gene was accompanied by the genes for the type 1 restriction modification system and was encoded in the ACME. Additionally, the SCC element and ACME carry different isoforms of the SesJ protein. To date, the genes encoding MSCRAMMs have been seen to be located in the bacterial core genome. Here, we report the presence of an MSCRAMM in an MGE in S. epidermidis clinical isolates.IMPORTANCES. epidermidis is an opportunistic bacterium that has established itself as a successful nosocomial pathogen. The modern era of novel therapeutics and medical devices has extended the longevity of human life, but at the same time, we also witness the evolution of pathogens to adapt to newly available niches in the host. Increasing antibiotic resistance among pathogens provides an example of such pathogen adaptation. With limited opportunities to modify the core genome, most of the adaptation occurs by acquiring new genes, such as virulence factors and antibiotic resistance determinants present in MGEs. In this study, we describe that the sesJ gene, encoding a recently discovered cell wall-anchored protein in S. epidermidis, is present in both ACME and the SCC element. The presence of virulence factors in MGEs can influence the virulence potential of a specific strain. Therefore, it is critical to study the virulence factors found in MGEs in emerging pathogenic bacteria or strains to understand the mechanisms used by these bacteria to cause infections.

Reinjection oilfield wastewater treatment using bioelectrochemical system and consequent corrosive community evolution on pipe material.

The corrosive issues are comprehensively caused in oilfield rejection system, in which sulfide is one of (bio-)chemical factors leading to high corrosive rate and blocking problem. Generally, aerobic treatment is a well-established and cost-effective unit for sulfide removal before oilfield wastewater reinjection. However, the residual dissolved oxygen (DO), which causes chemical, biological and electrochemical corrosion to water injection pipeline equipment, is still high after multi-stage filtration of DO removal. Here, a novel system to achieve quick and efficient DO removal through a three-electrode (cathode-anode-cathode)-upflow bioelectrochemical reactor (RCAC) was constructed before wastewater reinjection. Bioelectrodes were well established by utilizing organic matters of oilfield wastewater and conducted extracellular electron transport to achieve a steady DO removal from ∼5 mg/L to 0.01 mg/L (HRT 6 h), the DO removal efficiency reached approximately 100%, and the downside biocathode made the largest contribution for DO removal. In the treated wastewater, the corrosion rate of stainless steel N80 ultimately declined over 30 days testing. As a result of DO removal and ammonia conversion to nitrate by bioelectrodes, the corrosive microorganisms were substantially changed. Especially, sulfate-reducing bacteria (SRB) on the surface of N80 immersed in treated wastewater were decreased in abundance; while nitrate-reducing bacteria (NRB) enriched more, which can compete with SRB to prevent biological corrosion.

Bimorph deformable mirror-based adaptive optics scanning laser ophthalmoscope for the clinical design and performance.

We developed a clinical ophthalmic prototype by combining bimorph deformable mirror (DM)-based adaptive optics (AO) with a confocal scanning laser ophthalmoscope. A low-cost bimorph DM with a large stroke of 50 µ m and an aperture of 20 mm was utilized to realize a strategy for successive AO control of aberration correction, which permitted open-loop compensation for low-order aberrations and closed-loop correction of high-order aberrations to acceptable root mean square errors of < 0.08 µ m in all subjects. Spherical mirrors were folded in a nonplanar configuration to minimize off-axis aberrations and provide a compact, cost-effective design, which achieved a diffraction-limited performance capable of imaging individual photoreceptor cells and blood vessels not only in healthy subjects but also in patients suffering from retinitis pigmentosa. The adaptive optics scanning laser ophthalmoscope (AOSLO) images of the diseased retina had much higher resolutions than those captured by the commercial AO fundus camera, and loss of the photoreceptor mosaic could be distinguished more accurately due to the improvement in resolution. The compact design and easy handling of the bimorph DM-based AO control may facilitate the translation of AOSLO into clinical settings, and this prototype development will continue with future device refinement and extensive clinical testing.