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Abstract Background Skeletal Stem Cells (SSCs) are required for skeletal development, homeostasis, and repair. The perspective of their wide application in regenerative medicine approaches has supported research in this field, even though so far results in the clinic have not reached expectations, possibly due also to partial knowledge of intrinsic, potentially actionable SSC regulatory factors. Among them, the pleiotropic cytokine RANKL, with essential roles also in bone biology, is a candidate deserving deep investigation. Methods To dissect the role of the RANKL cytokine in SSC biology, we performed ex vivo characterization of SSCs and downstream progenitors (SSPCs) in mice lacking Rankl (Rankl −/− ) by means of cytofluorimetric sorting and analysis of SSC populations from different skeletal compartments, gene expression analysis, and in vitro osteogenic differentiation. In addition, we assessed the effect of the pharmacological treatment with the anti-RANKL blocking antibody Denosumab (approved for therapy in patients with pathological bone loss) on the osteogenic potential of bone marrow-derived stromal cells from human healthy subjects (hBMSCs). Results We found that, regardless of the ossification type of bone, osteochondral SSCs had a higher frequency and impaired differentiation along the osteochondrogenic lineage in Rankl −/− mice as compared to wild-type. Rankl −/− mice also had increased frequency of committed osteochondrogenic and adipogenic progenitor cells deriving from perivascular SSCs. These changes were not due to the peculiar bone phenotype of increased density caused by lack of osteoclast resorption (defined osteopetrosis); indeed, they were not found in another osteopetrotic mouse model, i.e., the oc/oc mouse, and were therefore not due to osteopetrosis per se. In addition, Rankl −/− SSCs and primary osteoblasts showed reduced mineralization capacity. Of note, hBMSCs treated in vitro with Denosumab had reduced osteogenic capacity compared to control cultures. Conclusions We provide for the first time the characterization of SSPCs from mouse models of severe recessive osteopetrosis. We demonstrate that Rankl genetic deficiency in murine SSCs and functional blockade in hBMSCs reduce their osteogenic potential. Therefore, we propose that RANKL is an important regulatory factor of SSC features with translational relevance.

IntroductionAutosomal recessive osteopetrosis (ARO) is a rare genetic disease, characterized by increased bone density due to defective osteoclast function. Most of the cases are due to TCIRG1 gene mutation, leading to severe bone phenotype and death in the first years of life. The standard therapy is the hematopoietic stem cell transplantation (HSCT), but its success is limited by several constraints. Conversely, gene therapy (GT) could minimize the immune-mediated complications of allogeneic HSCT and offer a prompt treatment to these patients.MethodsThe Tcirg1-defective oc/oc mouse model displays a short lifespan and high bone density, closely mirroring the human condition. In this work, we exploited the oc/oc neonate mice to optimize the critical steps for a successful therapy.ResultsFirst, we showed that lentiviral vector GT can revert the osteopetrotic bone phenotype, allowing long-term survival and reducing extramedullary haematopoiesis. Then, we demonstrated that plerixafor-induced mobilization can further increase the high number of HSPCs circulating in peripheral blood, facilitating the collection of adequate numbers of cells for therapeutic purposes. Finally, pre-transplant non-genotoxic conditioning allowed the stable engraftment of HSPCs, albeit at lower level than conventional total body irradiation, and led to long-term survival and correction of bone phenotype, in the absence of acute toxicity.ConclusionThese results will pave the way to the implementation of an effective GT protocol, reducing the transplant-related complication risks in the very young and severely affected ARO patients.

Heterotrophic microalgae cultivation has been suggested to reduce conventional photo-autotrophic microalgal biomass production costs. In heterotrophic cultivation, the most relevant operational costs are constituted by the supply of pure substrates used as carbon source (e.g., glucose), and the high energy request for culture aeration. In addition, suboptimal conditions of temperature and pH reduce the algal productivity, further increasing production costs. In this work, an attempt was made to define more sustainable and cost-effective strategies for the heterotrophic cultivation of Chlorellaceae and Scenedesmaceae. Several by-products from a local confectionery industry were thus screened as alternative carbon sources. Manufacturing residues from peppermint and liquorice candies production allowed to achieve comparable maximum growth rates (1.44 d-1), biomass yields (0.33 g COD·g COD-1) and biomass productivities (370 mg COD·L-1·d-1) as those achieved using glucose. A preliminary economic evaluation showed that the operational costs could be lowered of up to 85.6% by substituting glucose with the selected industrial by-products. As for fermentation conditions, high growth rates could be maintained at relatively low dissolved oxygen (DO) concentrations, and in a large range of temperature and pH values. In addition, optimal temperatures (37.0 – 37.2°C), pH values (6.8 – 7.4), and DO concentrations (> 0.5 – 1 mg O2·L-1) were identified. On the overall, the study demonstrated the possibility of achieving the reduction of operational costs for heterotrophic microalgae cultivation, while implementing circular economy principles in the framework of resource recovery during the bioremediation of organic waste.

My aim is to reconstruct the basic steps and the fundamental idea of Kant’s transcendental deduction of categories as well as Hegel’s interpretation and reframing of Kant’s idea. Hegel’s reading is crucial for two reasons: first, for fixing the basic form of the Kant­ian argument and secondly, for understanding its metaphilosophical relevance. For Hegel, philosophical proof has a specific nature, which distinguishes it from scientific proof and brings it closer to a juridical one. In this perspective the transcendental deduction, which is universally considered one of the most difficult chapters in the history of philosophy, reveals itself as the genuine clarification of specifically philosophical proof. I first present the idea of Kant’s transcendental deduction in the Critique of Pure Reason as well as its Hegelian reading in the Science of Logic and reformulation as the very method of philosophy in the Philo­sophy of Right. I show what in the Kantian argumentation constituted the basis for Hegel’s own interpretation and transformation. In so doing, I highlight a ‘red thread’ between the two ideas of the transcendental deduction. I conclude by proposing a formal account of Kant’s and Hegel’s ideas and by summing up the main metaphilosophical insights we can gain from Kant’s idea and its Hegelian interpretation.

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

Pre-B cell leukemia factor 1 (PBX1) is a Three Aminoacid Loop Extension (TALE) homeodomain-containing transcription factor playing crucial roles in organ pattering during embryogenesis, through the formation of nuclear complexes with other TALE class and/or homeobox proteins to regulate target genes. Its contribution to the development of several organs has been elucidated mainly through the study of murine knockout models. A crucial role for human development has been recently highlighted through the discovery of different de novo pathogenic PBX1 variants in children affected by developmental defects. In the adult, PBX1 is expressed in selected tissues such as in the brain, in the gastro-intestinal and urinary systems, or in hematopoietic stem and progenitor cells, while in other organs is barely detectable. When involved in the t(1;19) chromosomal translocation it acts as an oncogene, since the resulting fusion protein drives pre-B cell leukemia, due to the induction of target genes not normally targeted by the native protein. Its aberrant expression has been associated to tumor development, progression, or therapy-resistance as in breast cancer, ovarian cancer or myeloproliferative neoplasm (MPN). On the other hand, in colorectal cancer PBX1 functions as a tumor suppressor, highlighting its context-dependent role. We here discuss differences and analogies of PBX1 roles during embryonic development and in cancer, focusing mainly on the most recent discoveries.

The integration of microalgae cultivation in anaerobic digestion (AD) plants can take advantage of relevant nutrients (ammonium and ortho-phosphate) and CO2 loads. The proposed scheme of microalgae integration in existing biogas plants aims at producing approximately 250 t·y−1 of microalgal biomass, targeting the biostimulants market that is currently under rapid expansion. A full-scale biorefinery was designed to treat 50 kt·y−1 of raw liquid digestate from AD and 0.45 kt·y−1 of CO2 from biogas upgrading, and 0.40 kt·y−1 of sugar-rich solid by-products from a local confectionery industry. An innovative three-stage cultivation process was designed, modelled, and verified, including: i) microalgae inoculation in tubular PBRs to select the desired algal strains, ii) microalgae cultivation in raceway ponds under greenhouses, and iii) heterotrophic microalgae cultivation in fermenters. A detailed economic assessment of the proposed biorefinery allowed to compute a biomass production cost of 2.8 ± 0.3 €·kg DW−1, that is compatible with current downstream process costs to produce biostimulants, suggesting that the proposed nutrient recovery route is feasible from the technical and economic perspective. Based on the case study analysis, a discussion of process, bioproducts and policy barriers that currently hinder the development of microalgae-based biorefineries is presented.

In this work, the nutrients and eutrophication problem are integrated into a nature-based solution by incorporating microalgae-based nutrient removal from wastewater and collecting the residue in an anaerobic digestion plant to produce biogas that is directly exported to an existing gas-fired power plant and closes the bioresource loop. El Burullus lake in Egypt was selected as a case study because it is rich in nutrients and suitable for the integrated system. The theoretical results were promising as for one-hectare, nutrient pollution could be reduced with a total nitrogen removal rate of 4 kg d-1, a total phosphorus removal rate of 1.1 kg d-1, and a total COD removal rate of 9.3 kg d-1. The digester volume corresponding to the biomass produced was 120 m3 per hectare of algae pond and the methane yield (Y) from anaerobic digestion was 73 m^3 d^(-1).

This work provides insights into the possibility of integrating recovered volatile fatty acids (VFAs) into biological nitrogen removal processes. VFAs are the main products of the acidogenic fermentation of waste sludge and are an effective carbon source for denitrification in activated sludge processes. The assessment of denitrification rates and the utilisation hierarchy of different VFAs are relevant to evaluating the possibility of replacing external carbon sources with the fermented liquid, FL, from acidogenic fermentation. To this scope, single VFAs, FL collected from a full-scale waste sludge fermenter, and commercial hydroalcoholic solutions have been tested with manometric lab-scale tests. Regarding single acids, acetic acid showed the highest denitrification rates, up to 4 mg N-NO3 g VSS−1 h−1, while more complex acids usually showed a lower denitrification rate. The synthetic VFA mixture and FL showed a higher denitrification rate than the sole acetate (up to 134% of the acetate denitrification rate). Mass balances across the full-scale wastewater treatment plant demonstrated the positive role of FL dosage in enhancing the denitrification process in the activated sludge treatment, with an average nitrogen removal equal to 57% and 78% without and with FL dosage, respectively. Batch manometric tests proved to be an efficient and reliable tool to assess the quality of the carbon sources as well as the activity of denitrifying bacteria in activated sludge samples.

Introduction: Autosomal recessive osteopetrosis (ARO) is a rare genetic disease, characterized by increased bone density due to defective osteoclast function. Most of the cases are due to TCIRG1 gene mutation, leading to severe bone phenotype and death in the first years of life. The standard therapy is the hematopoietic stem cell transplantation (HSCT), but its success is limited by several constraints. Conversely, gene therapy (GT) could minimize the immune-mediated complications of allogeneic HSCT and offer a prompt treatment to these patients. Methods: The Tcirgl-defective oc/oc mouse model displays a short lifespan and high bone density, closely mirroring the human condition. In this work, we exploited the oc/oc neonate mice to optimize the critical steps for a successful therapy. Results: First, we showed that lentiviral vector GT can revert the osteopetrotic bone phenotype, allowing long-term survival and reducing extramedullary haematopoiesis. Then, we demonstrated that plerixafor-induced mobilization can further increase the high number of HSPCs circulating in peripheral blood, facilitating the collection of adequate numbers of cells for therapeutic purposes. Finally, pre-transplant non-genotoxic conditioning allowed the stable engraftment of HSPCs, albeit at lower level than conventional total body irradiation, and led to long-term survival and correction of bone phenotype, in the absence of acute toxicity. Conclusion: These results will pave the way to the implementation of an effective GT protocol, reducing the transplant-related complication risks in the very young and severely affected ARO patients. [ABSTRACT FROM AUTHOR]

Inflammation impacts human hematopoiesis across physiologic and pathologic conditions, as signals derived from the bone marrow microenvironment, such as pro-inflammatory cytokines and chemokines, have been shown to alter hematopoietic stem cell (HSCs) homeostasis. Dysregulated inflammation can skew HSC fate-related decisions, leading to aberrant hematopoiesis and potentially contributing to the pathogenesis of hematological disorders such as myelodysplastic syndromes (MDS). Recently, emerging studies have used single-cell sequencing and muti-omic approaches to investigate HSC cellular heterogeneity and gene expression in normal hematopoiesis as well as in myeloid malignancies. This review summarizes recent reports mechanistically dissecting the role of inflammatory signaling and innate immune response activation due to MDS progression. Furthermore, we highlight the growing importance of using multi-omic techniques, such as single-cell profiling and deconvolution methods, to unravel MDSs’ heterogeneity. These approaches have provided valuable insights into the patterns of clonal evolution that drive MDS progression and have elucidated the impact of inflammation on the composition of the bone marrow immune microenvironment in MDS.

Research data produced within the CARIPLO IMAP and Perform Water 2030 projects were processed using the SimaPro software to carry out the Life Cycle Assessment according to ISO 14040-44 of an innovative process of treated effluents’ polishing. The study aims to evaluate the integration of a microalgae culture as a side-stream process into the baseline layout of a wastewater treatment plant to remove nitrogen from the supernatant of sludge centrifugation from an environmental perspective. In particular, the investigated system focuses on using the algal biomass produced as an organic matrix for encapsulating zero-valent iron nanoparticles to be used for the final refinement of the effluent. Zero-valent iron (ZVI) is a reactive metal and an effective reducing agent. It can be used to remove organic and inorganic pollutants (e.g., chlorinated organics, pharmaceuticals, metals, textile dyestuffs). The encapsulation of ZVI by hydrothermal carbonization (HTC) in a carbonaceous matrix allows for overcoming the problems related to its lack of stability, easy aggregation, and difficulty in separating the ZVI nanoparticles from the treated solution. The case study refers to Bresso wastewater treatment plant (Milan province, Northern Italy). The environmental performances of the study were assessed following the Life Cycle Impact Assessment methods IMPACT 2002+. According to the results, the new process integration does not affect the environmental performance of the WWTP, still implying a significant improvement in the removal of metals and micropollutants. In fact, due to the ability of ZVI nanoparticles to remove organic and inorganic pollutants, the outflowing load will be significantly reduced, which will improve the environmental performance of the entire Bresso wastewater treatment plant.

The results of a first campaign of sampling and analyses of emerging micropollutants in the influent (municipal centrate) and effluent of a pilot MBP raceway are reported. The algal population was chiefly made of Chlorella spp. and the pilot worked satisfactorily for the removal of nitrogen. 14 emerging micropollutants were analysed. Average removal efficiencies exceeding 80 % were observed for diclofenac, lamotrigine, ketoprofene, clarithromycin. For such compounds the variability of removal efficiency was also reduced, with respect to the other tested molecules, and was particularly low for diclofenac and lamotrigine. Removal efficiencies over 50 % were measured for azithromycin, metoprolol and irbesartan but with strong variability. Lower removal efficiencies were observed for amisulpride and 5-methylbenzotriazole, while for the remaining compounds the concentrations in the effluent were higher than in the influent.

Widespread prevalence of multidrug and pandrug-resistant bacteria has prompted substantial concern over the global dissemination of antibiotic resistance genes (ARGs). Environmental compartments can behave as genetic reservoirs and hotspots, wherein resistance genes can accumulate and be laterally transferred to clinically relevant pathogens. In this work, we explore the ARG copy quantities in three environmental media distributed across four cities in California and demonstrate that there exist city-to-city disparities in soil and drinking water ARGs. Statistically significant differences in ARGs were identified in soil, where differences in blaSHV gene copies were the most striking; the highest copy numbers were observed in Bakersfield (6.0 × 10-2 copies/16S-rRNA gene copies and 2.6 × 106 copies/g of soil), followed by San Diego (1.8 × 10-3 copies/16S-rRNA gene copies and 3.0 × 104 copies/g of soil), Fresno (1.8 × 10-5 copies/16S-rRNA gene copies and 8.5 × 102 copies/g of soil), and Los Angeles (5.8 × 10-6 copies/16S-rRNA gene copies and 5.6 × 102 copies/g of soil). In addition, ARG copy numbers in the air, water, and soil of each city are contextualized in relation to globally reported quantities and illustrate that individual genes are not necessarily predictors for the environmental resistome as a whole.

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor. [ABSTRACT FROM AUTHOR]

The bone marrow (BM) niche is the spatial structure within the intra-trabecular spaces of spongious bones and of the cavity of long bones where adult haematopoietic stem cells (HSCs) maintain their undifferentiated and cellular self-renewal state through the intervention of vascular and nervous networks, metabolic pathways, transcriptional and epigenetic regulators, and humoral signals. Within the niche, HSCs interact with various cell types such as osteoblasts, endothelial cells, macrophages, and mesenchymal stromal cells (MSCs), which maintain HSCs in a quiescent state or sustain their proliferation, differentiation, and trafficking, depending on body needs. In physiological conditions, the BM niche permits the daily production of all the blood and immune cells and their admittance/ingress/progression into the bloodstream. However, disruption of this delicate microenvironment promotes the initiation and progression of malignancies such as those included in the spectrum of myeloid neoplasms, also favouring resistance to pharmacological therapies. Alterations in the MSC population and in the crosstalk with HSCs owing to tumour-derived factors contribute to the formation of a malignant niche. On the other hand, cells of the BM microenvironment cooperate in creating a unique milieu favouring metastasization of distant tumours into the bone. In this framework, the pro-tumorigenic role of MSCs is well-documented, and few evidence suggest also an anti-tumorigenic effect. Here we will review recent advances regarding the BM niche composition and functionality in normal and in malignant conditions, as well as the therapeutic implications of the interplay between its diverse cellular components and malignant cells.

The role of P content on the treatment and valorization of the liquid fraction of digestate, namely centrate, through microalgae-based technologies was evaluated in this study. The performance of four column photobioreactors, which were fed on diluted centrate with corrected (10 mg N/ mg P) and not modified (129 mg N/ mg P) N:P ratio, were monitored and compared. The results demonstrated that P shortage in the centrate affected neither the total nitrogen and COD removal rate nor the volumetric biomass productivity, suggesting that expensive addition of P salts is not necessary to maximize the efficiency of the process. On the contrary, the addition of P to the centrate promoted the ammonia oxidation process as higher nitrite production was observed in the photobioreactors with adjusted N:P ratio than in the ones fed with the non-adjusted N:P ratio. These findings were confirmed by fluorescence in-situ hybridization and quantitative PCR assays, which revealed a higher number of ammonia-oxidizing bacteria in the microalgal suspensions cultivated on centrate with P addition. In conclusion, the N:P ratio in the centrate seems to have a role in controlling the nitrification process rather than in the overall nutrient removal rate and biomass productivity of the microalgae-based system.

Implementation of onsite bioremediation technologies is essential for textile industries due to rising concerns in terms of water resources and quality. Partial nitritation-anaerobic ammonium oxidation (PN/A) processes emerged as a valid, but unexplored, solution. In this study, the performance of a PN/A pilot-scale (9 m3) sequencing batch reactor treating digital textile printing wastewater (10–40 m3 d−1) was monitored by computing nitrogen (N) removal rate and efficiencies. Moreover, the structure of the bacterial community was assessed by next generation sequencing and quantitative polymerase chain reaction (qPCR) analyses of several genes, which are involved in the N cycle. Although anaerobic ammonium oxidation activity was inhibited and denitrification occurred, N removal rate increased from 16 to 61 mg N g VSS−1 d−1 reaching satisfactory removal efficiency (up to 70%). Ammonium (18–70 mg L−1) and nitrite (16–82 mg L−1) were detected in the effluent demonstrating an unbalance between the aerobic and anaerobic ammonia oxidation activity, while constant organic N was attributed to recalcitrant azo dyes. Ratio between nitrification and anammox genes remained stable reflecting a constant ammonia oxidation activity. A prevalence of ammonium oxidizing bacteria and denitrifiers suggested the presence of alternative pathways. PN/A resulted a promising cost-effective alternative for textile wastewater N treatment as shown by the technical-economic assessment. However, operational conditions and design need further tailoring to promote the activity of the anammox bacteria.

Covert networks refer to criminal organizations that operate outside the boundaries of the law; they can be mainly classified as terrorist networks and criminal networks. We consider how Social Network Analysis (SNA) is used to analyze such networks in order to attain a greater knowledge of criminal behavior. In fact, SNA allows examining the network structure and functioning by computing relevant metrics and parameters to identify roles, positions, features, and other network functioning that are not otherwise easily discovered at first glance. This is why Law Enforcement Agencies (LEAs) are showing growing interest in SNA, which is also used to identify weak spots and disrupt criminal groups. This paper provides a literature review and a classification of methods and real-case applications of disruption techniques. It considers covert network adaptability to such dismantling attempts, herein referred to as resilience. Critical problems of SNA in criminal studies are discussed, including data collection techniques and the inevitable incompleteness and biases of real-world datasets, with the aim of promoting a new research stream for both dismantling techniques and data collection issues.

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.

The treatability of seven wastewater samples generated by a textile digital printing industry was evaluated by employing 1) anammox-based processes for nitrogen removal 2) microalgae (Chlorella vulgaris) for nutrient uptake and biomass production 3) white-rot fungi (Pleurotus ostreatus and Phanerochaete chrysosporium) for decolorization and laccase activity. The biodegradative potential of each type of organism was determined in batch tests and correlated with the main characteristics of the textile wastewaters through statistical analyses. The maximum specific anammox activity ranged between 0.1 and 0.2 g N g VSS-1 d-1 depending on the sample of wastewater; the photosynthetic efficiency of the microalgae decreased up to 50% during the first 24 hours of contact with the textile wastewaters, but it improved from then on; Pleurotus ostreatus synthetized laccases and removed between 20-62% of the colour after 14 days, while the enzymatic activity of Phanerochaete chrysosporium was inhibited. Overall, the findings suggest that all microbes have great potential for the treatment and valorisation of textile wastewater after tailored adaptation phases. Yet, the depurative efficiency can be probably enhanced by combining the different processes in sequence.