Impact of waste separation on the biological nitrogen removal in a MSW incineration leachate treatment plant: Performance and microbial community shift.

After waste separation program was launched in China in 2019, incineration leachate treatment plants are facing a challenge of effective removal of nitrogen from leachate due to lack of sufficient carbon source. In this study, the performance of a biological incineration leachate treatment process (anaerobic digestion (AD) - two-stage anoxic/aerobic (A/O) process) was evaluated after adopting the waste separation program, and the changes in the microbial community and function was analyzed using 16S rRNA amplicon sequencing technology. Results showed that after the waste separation, the influent chemical oxygen demand (COD) concentration reduced by 90% (from 19,300 to 1780 mg L-1) with the COD/N ratio decreased from 12.3 to 1.4, which led to a decreased nitrogen removal efficiency (NRE) of <65% and a high effluent NO3- accumulation (445.8-986.5 mg N·L-1). By bypassing approximately 60% of the influent to the two-stage A/O process and adding external carbon source (glucose), the mean NRE increased to 86.3 ± 7.4%. Spearman's analysis revealed that refractory compounds in the bypassed leachate were closely related to the variations in bacterial community composition and nitrogen removal function in the two-stage A/O, leading to a weakened correlation of microbial network. KEGG functional pathway predictions based on Tax4Fun also confirmed that the bypassed leachate induced xenobiotic compounds to the two-stage A/O process, the relative abundance of nitrogen metabolism was reduced by 32%, and more external carbon source was required to ensure the satisfactory nitrogen removal of >80%. The findings provide a good guide for regulation of incineration leachate treatment processes after the waste separation.

The microbial synergy and response mechanisms of hydrolysis-acidification combined microbial electrolysis cell system with stainless-steel cathode for textile-dyeing wastewater treatment.

Microbial electrolysis cell (MEC) has been existing problems such as poor applicability to real wastewater and lack of cost-effective electrode materials in the practical application of refractory wastewater. A hydrolysis-acidification combined MEC system (HAR-MECs) with four inexpensive stainless-steel and conventional carbon cloth cathodes for the treatment of real textile-dyeing wastewater, which was fully evaluated the technical feasibility in terms of parameter optimization, spectral analysis, succession and cooperative/competition effect of microbial. Results showed that the optimum performance was achieved with a 12 h hydraulic retention time (HRT) and an applied voltage of 0.7 V in the HAR-MEC system with a 100 µm aperture stainless-steel mesh cathode (SSM-100 µm), and the associated optimum BOD5/COD improvement efficiency (74.75 ± 4.32 %) and current density (5.94 ± 0.03 A·m-2) were increased by 30.36 % and 22.36 % compared to a conventional carbon cloth cathode. The optimal system had effective removal of refractory organics and produced small molecules by electrical stimulation. The HAR segment could greatly alleviate the imbalance between electron donors and electron acceptors in the real refractory wastewater and reduce the treatment difficulty of the MEC segment, while the MEC system improved wastewater biodegradability, amplified the positive and specific interactions between degraders, fermenters and electroactive bacteria due to the substrate complexity. The SSM-100 µm-based system constructed by phylogenetic molecular ecological network (pMEN) exhibited moderate complexity and significantly strong positive correlation between electroactive bacteria and fermenters. It is highly feasible to use HAR-MEC with inexpensive stainless-steel cathode for textile-dyeing wastewater treatment.

Deciphering physicochemical properties and enhanced microbial electron transfer capacity by magnetic biochar.

Magnetic biochar is important for improving the electron transfer capacity (ETC) of microorganisms in wastewater treatment. In this study, three magnetic biochar under different pyrolysis temperatures (300, 500 and 700 °C) were prepared by co-precipitation, and their characteristics and impacts on mediating microbial ETC were investigated. Results indicated that magnetic biochar had a higher capacitance and conductivity than pyrolytic biochar, with the largest specific capacitance of 14.7F/g for FCS700 (magnetic biochar prepared at 700 °C). The addition of magnetic biochar could improve the nitrogen removal efficiency of a sludge-biochar system. The electron transfer resistance (Rct) of magnetic biochar was lower than pyrolytic biochar by 25.5 % (300 °C), 19.7 % (500 °C), and 11.6 % (700 °C), respectively. The structure of the microbial community in the sludge-biochar system differed significantly. Spearman correlation suggested that the electrochemical properties of biochar were an important factor affecting the structure of the microbial community.

Small biochar addition enhanced anammox granular sludge system for practical wastewater treatment: Performance and microbial community.

Anaerobic ammonium oxidation (Anammox) granular sludge (AnGS) has poor strength and is prone to disintegration under complex environmental conditions, especially in the presence of complex organic carbon, which renders the Anammox process instable. Herein, with a mixture of landfill leachate and domestic sewage as wastewater, the effect on the properties of AnGS with two small particle size (0.1-0.2 mm) biochars (coconut and peach biochars) addition were investigated at different COD concentrations (150 mg·L-1, 200 mg·L-1, and 250 mg·L-1), as well as at different BOD/TN (B/N) (0.3 and 0.5). Results showed that the nitrogen removal efficiencies decreased from 89 % to 72 % as the COD concentration increased by 100 mg·L-1, while peach biochar reactor had better nitrogen removal performance. Excessive organic carbon supply inhibits AnAOB proliferation and B/N had the most significant effect on AnAOB (p < 0.05). The Polymerase Chain Reaction (PCR) indicated peach biochar reactor get higher activity of anammox-related functional genes (hzsA, hdh).

A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions.

Background: We aimed to establish and validate a deep learning-based hybrid artificial intelligence (AI) model for the objective morphometric and colorimetric assessment of vitiligo lesions. Methods: Two main datasets containing curated images of vitiligo lesions from Chinese patients (Fitzpatrick skin types III or IV) were established, including one with 2,720 images for lesion localization study and the other with 1,262 images for lesion segmentation study. Besides, an additional test set containing 145 images of vitiligo lesions from other Fitzpatrick skin types (I, II, or V) was also generated. A 3-stage hybrid model was constructed. YOLO v3 (You Only Look Once, v3) architecture was trained and validated to classify and localize vitiligo lesions, with sensitivity and error rate as primary performance outcomes. Then a segmentation study comparing 3 deep convolutional neural networks (DCNNs), Pyramid Scene Parsing Network (PSPNet), UNet, and UNet++, was carried out based on the Jaccard index (JI). The architecture with the best performance was integrated into the model. Three add-on metrics, namely VAreaA, VAreaR, and VColor were finally developed to measure absolute, relative size changes and pigmentation, respectively. Agreement between the AI model and dermatologist evaluators were assessed. Results: The sensitivity of the YOLO v3 architecture to detect vitiligo lesions was 92.91% with an error rate of 14.98%. The UNet++ architecture outperformed the others in the segmentation study (JI, 0.79) and was integrated into the model. On the additional test set, however, the model achieved a lower detection sensitivity (72.41%) and a lower segmentation score (JI, 0.69). With respect to size changes, no difference was observed between the AI model, trained dermatologists (W=0.812, P<0.05), and Photoshop analysis (P=0.075, P=0.212 respectively), which all displayed good concordance. Conclusions: We developed a novel, convenient, objective, and quantitative deep learning-based hybrid model which simultaneously evaluated both morphometric and colorimetric vitiligo lesions from patients with Fitzpatrick skin types III or IV, rendering it suitable for the assessment of severity of vitiligo lesions in Asians in both clinic and research scenarios. More work is also warranted for its use in other ethnic skin groups.

Bamboo charcoal addition enhanced the nitrogen removal of anammox granular sludge with COD: Performance, physicochemical characteristics and microbial community.

The effects of different chemical oxygen demand (COD) concentrations on the anammox granular sludge with Bamboo Charcoal (BC) addition were evaluated in UASB reactor. The results showed that the average total nitrogen (TN) removal efficiency was reduced from 85.9% to 81.4% when COD concentration was increased from 50 to 150 mg/L. However, the TN removal efficiency of BC addition reactors was dramatically 3.1%-6.4% higher than that without BC under different COD concentrations. The average diameter of granular sludge was 0.13 mm higher than that without BC. The settling velocity was increased by elevated COD concentration, while the EPS and VSS/SS were increased with BC addition. The high-throughput Miseq sequencing analyses revealed that the bacterial diversity and richness were decreased under COD addition, and the Planctomycetes related to anammox bacteria were Candidatus Brocadia and Candidatus Kuenenia. The Metagenomic sequencing indicated that the abundance of denitrification related functional genes all increased with elevated COD, while the abundance of anammox related functional genes of decreased. The functional genes related to anammox was hydrazine synthase encoding genes (hzsA, hzsB and hzsB). The average relative abundance of hzs genes in the reactor with BC addition was higher than the control at COD concentrations of 50 mg/L and 150 mg/L. The functional genes of denitrification mediated by BC were higher than those without BC throughout the operation phase. It is interesting to note that BC addition greatly enriched the related functional genes of denitrification and anammox.

Start-up of Anammox systems with different biochar amendment: Process characteristics and microbial community.

As the 'go-to' process when it comes to biological nitrogen removal from wastewaters in recent years, the Anammox process has undergone lots of investigations in order to optimize its performance. In evaluating the effect of distinct biochar types at different concentrations on the Anammox startup process, as well as analyze their corresponding influence on the microbial community structure, three additives (coconut, peach, and bamboo) at either 5%, 10%, or 15% respectively were amended in various Anammox EGSB setups. (i). The 5% coconut biochar amendment resulted in the fastest startup of 46 days with an average ammonium removal efficiency of 96% whereas the control setup took 69 days. Thus, a more robust and cost effective Anammox process could be realized on an industrial scale. (ii) The Illumina high-throughput sequencing of the collected sludge samples indicated that the amendment with distinct biochar resulted in varied prevailing microbial communities in the respective setups. (iii) Proteobacteria was the dominant microbial community. (iv) However, two Anammox bacteria species, Candidatus Brocadia and Candidatus Jettenia were identified, with relative abundances of 0-4.72% and 0-6.23% respectively. The results from this study illustrate the correlation between Anammox reactor performance (startup and nitrogen removal efficiency), type and concentration of biochar amendment employed, as well as microbial community succession.

Construction and Evaluation of a Deep Learning Model for Assessing Acne Vulgaris Using Clinical Images.

INTRODUCTION: Accurate assessment is the basis for the effective treatment of acne vulgaris. The goal of this study was to achieve standardised diagnosis and treatment based on a deep learning model that was developed according to the current Chinese Guidelines for the Management of Acne Vulgaris. METHODS: The first step was to divide each image of acne vulgaris into four regions. Each of these four regions of the same patient was then combined to form a complete facial region. The second step was to classify the images based lesion type, in accordance with the current Chinese guidelines, and by treatment strategy adopted by experienced dermatologists. The final step was to evaluate the performance of the deep learning model in patients with acne vulgaris. RESULTS: The results showed that the average F1 value of the assessment model is 0.8 (optimum value = 1). The weighted kappa coefficient between the evaluation according to the artificial intelligence model and the evaluation by the attending dermatologists was 0.791 (95% confidence interval 0.671-0.910, P < 0.001), indicating a high degree of consistency. CONCLUSIONS: The assessment model based on deep learning and according to the Chinese guidelines had a slightly higher overall performance is comparable to that of the attending dermatologist.

Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions.

OBJECTIVE: This study used deep learning for diagnosing common, benign hyperpigmentation. METHOD: In this study, two convolutional neural networks were used to identify six pigmentary diseases, and a disease diagnosis model was established. Because the distribution of lesions in the original training picture is very complex, we cropped the image around the lesions, trained the network on the extracted lesion images, and fused the verification results of the overall picture and the extracted picture to assess the model performance in identifying hyperpigmented dermatitis pictures. Finally, we evaluated the image recognition performance of the two convolutional neural networks and the converged networks in the test set through a comparison of the converged network and the physicians' assessments. RESULTS: The AUC of DenseNet-96 for the overall picture was 0.98, whereas the AUC of ResNet-152 was 0.96; therefore, we concluded that DenseNet-96 performed better than ResNet-152. From the AUC, the converged network has the best performance. The converged network model achieved a comprehensive classification performance comparable to that of the doctors. CONCLUSIONS: The diagnostic model for benign, pigmented skin lesions based on convolutional neural networks had a slightly higher overall performance than the skin specialists.

[Characteristics of ANAMMOX Granular Sludge and Differences in Microbial Community Structure Under Different Culture Conditions].

Anaerobic ammonium oxidation (ANAMMOX) granular sludge was cultured during different operating conditions by an expanded granular sludge bed (EGSB) reactor and up-flow anaerobic sludge bed (UASB) reactors, and the characteristics of the granular sludge and microbial community were compared. The results showed that the flocculent ANAMMOX sludge can be granulated after being operated for 384 days by the EGSB and UASB reactors. The average particle size reached 1.17 mm and 1.21 mm, respectively. The particle size ratio of each range (<0.2, 0.2-1.5, 1.5-3, and>3 mm) was 6.06%, 60.05%, 25.25%, and 8.64% in the EGSB reactor, and 7.40%, 58.90%, 32.04%, and 1.66% in the UASB reactor, respectively. The results of scanning electron microscopy showed that the bacterial flora during different operating conditions were mainly Brevibacterium and Cocci aggregates. High-throughput sequencing results showed that the Shannon index of the EGSB reactor was 7.52, higher than the 7.18 of the UASB reactor on day 384; Proteobacteria was the main phylum of the sludge at each stage, and Planctomycetes increased from 3.30% to 12.30% in the EGSB reactor and 13.30% in the UASB reactor on day 384. The main ANAMMOX genera in the EGSB reactor were Candidatus Brocadia, accounting for 7.53%, followed by Candidatus Kuenenia accounting for 1.61%, whereas in the UASB reactor, Candidatus Kuenenia was the dominant anaerobic ammonia genus, accounting for 7.54%, followed by Candidatus Brocadia, which accounted for 3.69%. The proportion of dominant species was related to the change in environmental factors. The proportion of Candidatus Brocadia was positively correlated with the up-flow rate and nitrogen removal rate (NRR), but negatively correlated with hydraulic retention time (HRT). Candidatus Kuenenia was positively correlated with nitrogen removal efficiency (NRE), NRR, and HRT, but negatively correlated with the up-flow rate.

[Process Control and Operation Optimization of PN-SAD Coupling Process Based on SBR-ABR].

This study uses three different operating phases for a sequencing batch reactor (SBR) combined with an anaerobic baffled reactor (ABR) to determine the effect of deep nitrogen and carbon removal by the "partial nitrification-anaerobic ammonium oxidation combined denitrification" (termed PN-SAD) reaction. The effluent of the SBR (NO2--N/NH4+-N ratio range of 1-1.32) was accessed directly to the single compartment ABR anammox system in phase Ⅰ. The results showed that although the anammox reaction was stable, the combined process total nitrogen (TN) removal efficiency was<80%, and the TN concentration of effluent was~20 mg·L-1. In order to increase the denitrification function in the ABR, denitrifying sludge was added to the third compartment of the ABR in phase Ⅱ. We found that the TN removal efficiency of the coupling reaction was still low. An organic carbon source should be supplied in the latter stage of anammox if deep nitrogen removal is required. Therefore, in phase Ⅲ, the effluent of the SBR (NO2--N/NH4+-N ratio of ~5) was mixed with the partial raw water (mixed water NO2--N/NH4+-N ratio of ~1.4; C/N ratio of 2.5). The mixed water was connected to the single compartment of the ABR. The PN-SAD system not only achieved a good matrix ratio at the anammox stage, but also provided a good carbon source for denitrification. The chemical oxygen demand (COD) concentration of the effluent in the whole process was 50 mg·L-1, the TN concentration of the effluent was<6 mg·L-1, and the TN removal efficiency was 95%. We conclude that the stable operation of the combined PN-SAD reaction provides the basis for deep nitrogen and carbon removal using the combined SBR-ABR process.