Candida albicans (28.5%), followed by Aspergillus niger (11.4%), and Penicillium chrysogenum (9.5%). Factors independently associated with fungal contamination of students’ mobile phones were: lack of mobile phone cleaning , and usage of mobile phones near patients’ beds . The results of this study confirmed that students who use their mobile phones in hospital wards have a higher rate of fungal contamination. The development of active surveillance and preventive strategies is needed to reduce the risk of cross-contamination and increase awareness of fungal transmission via mobile phones.The study aimed to characterize fungal contamination of medical students’ mobile phones, investigate mobile phones’ usage and cleaning habits, identify independent risk factors for fungal contamination, and awareness of mobile phones as a potential route of infection. In a cross-sectional study, medical students’ mobile phones were sampled for possible fungal contamination. The questionnaire was used to record mobile phone usage, cleaning habits, and awareness of mobile phones as a source of infection. A total of 492 medical students were included and fungal contamination of mobile phones was confirmed in 32.11%. The most frequent fungal isolates on students’ mobile phones were  The usage of mobile phones has spread abruptly throughout the world and dramatically influenced the way of communication, access to information, and lifestyle in general2. The number of mobile users in 2021 is estimated to be 7.1 billion with forecasts suggesting this is likely to rise to 7.49 billion by 20253. It is estimated that 90% of teenagers in Europe and Asia4, and 86–94% of individuals of both genders aged below 65 years, have their mobile phones5.During the last decade, mobile phones (both keypads and smartphone devices) become one of the most common items people carry around in both professional and personal settings6. The medical profession has integrated mobile phones as an important means of communication among healthcare workers7. Besides enhancing clinician communication, mobile phones provide instant access to unlimited resources and have the potential to transform traditional uses of imaging, sensing, and diagnostic systems, especially for point-of-care applications and field settings8.Nowadays, mobile phones have evolved from just a talking medium to a multipurpose device and become one of the most essential accessories, being affordable and easy to handle9. Mobile phones are now used almost everywhere resulting in continuous exposure to different types of microorganisms10. Moreover, moving constantly with its owner mobile phones of health care professionals can be easily contaminated with microorganisms from the hospital environment6. Mobile phones can be reservoirs for many microorganisms and a potential vehicle for the spread of pathogenic microorganisms in hospital settings11. Constant handling, the heat generated from mobile phones, and the cervices of cracked screens and phone covers provide excellent habitats for colonization with different microorganisms2.However, mobile phones come into close contact with their users and carry the personal microbiome of their owners12. Different kinds of microorganisms can be isolated from the surface of mobile phones that can be part of normal skin microbiota, but the isolation of pathogens causing nosocomial infections has also been frequently reported such as Staphylococcus aureus, coagulase-negative staphylococci, Escherichia coli, Candida spp., etc.12. Microorganisms that can be transmitted from mobile phones represent a potential risk, especially for patients in intensive care units, who are at very high risk of nosocomial infections due to a high number of invasive devices  that can facilitate the entry of pathogens12.There is growing evidence that mobile phones in clinical settings are highly contaminated with different microorganisms and could, like other contaminated fomites and surfaces, play a key role in the spread of nosocomial infections12, but contamination of mobile phones by fungi has been poorly investigated. In the present study, we hypothesized that students who use their mobile phones in hospital wards have a higher rate of fungal contamination.Many studies investigate the contamination of mobile phones by bacteriaThis study aimed to characterize the fungal contamination of medical students’ mobile phones. First, we examined which fungi are most frequently isolated from students’ mobile phones. Second, we investigate the details of mobile phone usage, and identified independent risk factors for fungal contamination. Third, we analyzed the extent and mode of mobile phone cleaning habits. Finally, we surveyed students’ awareness of mobile phones as reservoirs as well as potential routes for infection.th-grade students practicing mostly clinical subjects in different clinical wards recruited by the convenience sampling method. Data from students, as well as samples for laboratory diagnosis, were collected in lab rooms at the Institute of Microbiology and Immunology through an anonymous questionnaire administered before the start of compulsory practical lessons in Clinical Microbiology. The required sample size was calculated to be 143, with a test power of 0.8, and a two-sided α level of 0.05.A cross-sectional study was conducted on medical students at the Faculty of the Medicine University of Belgrade from 15th October to 20th December 2019. The study population included 4All the participants were informed about the goals of the research and informed consent was obtained from each participant included in the study. We excluded students who refuse to get their mobile phones sampled for fungal contamination and students who returned the incompletely filled questionnaire. The study was approved by the Ethics Committee of the Faculty of Medicine University of Belgrade (1322/VII-45). All methods were performed following the relevant guidelines and regulations.An anonymous questionnaire was used to record the cleaning habits and mobile phone usage details of participants. The questionnaire was designed for the purpose of the study by two researchers . To ease understanding and interpretation of the items, a pilot study was conducted among 20 medical students at the University of Belgrade, and their suggestions were incorporated into the final version. The questionnaire included basic demographic data , academic data (study year), mobile phone usage data , as well as mobile phone cleaning habits (frequency and way of cleaning).Regarding the frequency of daily mobile phone usage students could choose a couple of times a day – mainly basic use of answering/calling and texting, periodically – if there is time during a day at least a couple of hours without using a device, and constantly – continuously usage of mobile phone for different purposes .Regarding the frequency of cleaning students could choose daily – cleaning at least once daily, weekly – cleaning at least once weekly, and occasionally – irregular cleaning mostly where there is evident filth on a mobile phone.The questionnaire also included questions regarding awareness that mobile phones can serve as a source of infection. Students were asked to declare if they regard mobile phones as a potential reservoir of infection, practice hand hygiene after handling a mobile phone, and have ever superficial fungal infection in a lifetime.Every student included in the study consented to have their mobile phone sampled for possible fungal contamination. A sterile cotton swab immersed in sterile saline was rolled over the front, back and lateral side of the mobile phone including buttons of keypads, mouth and earpiece, volume and lock keys, audio, and battery charger jack, to cover the entire surface of the device. Phones that were in personalized cases were not removed from the casing and the external surface was swabbed. All the surfaces were thoroughly swabbed and swabs were immediately inoculated on Sabouraud dextrose agar . Inoculated media were transported to Medical Mycology Laboratory at the Institute of Microbiology and Immunology, Faculty of the Medicine University of Belgrade where all the laboratory testing was done.13. Plates with no fungal growth even after 4 weeks of incubation were considered negative.Inoculated plates were incubated for 21 days at 30 ± 2 °C, and checked weekly for fungal growth. If fungal growth was observed, the causative agent was identified by colony morphology and microscopic characteristicsThe identification of dermatophytes and non-dermatophyte moulds was performed by macroscopic and microscopic morphological examination of colonies, and by biochemical methods (urease test) or microculture technique application if required. Yeast isolates were identified by growth on CHROM agar  and with the use of the API 20C AUX Commercial System .t-test. The statistical software used was SPSS v15 . P-value ˂0.05 was considered to be statistically significant.A descriptive analysis was used to detail the main characteristics of the study population. Statistical analyses were performed using chi-square and Fisher’s test to compare categorical variables. Mobile phone usage was analyzed by univariate and multivariate logistic regression analysis. In the multivariate analysis, all the variables that correlated with fungal contamination of students’ mobile phones at a significance level of ≤ 0.10 were included. The comparison of means was performed by independent samples The study included 492 out of 516 (response rate 95.35%), medical students, in the 4th academic year. The average age of students was 22.63 ± 1.06 (age range 21–29). The majority of students were females 319/492 (64.84%), while 173/492 (35.16%) were male students. Fungal contamination of students’ mobile phones was found in 158/492 (32.11%), while 334/492 (67.89%) were negative for fungal contamination.p = 0.005) had significantly more often mobile phones contaminated with fungi. There was no difference in fungal contamination of students’ mobile phones concerning the place of residence , while the average frequency of mobile phone replacement was 32.84 ± 12.73 months (frequency of replacement range from 12 to 120 months). The majority of medical students had smartphones 488/492 (99.19%), while 4/492 (0.81%) had keypad mobile phones.p = 0.007), hold their phones in pockets at university (p = 0.038), and keep their mobile phones in random places at home (p < 0.001). Also, students with fungal contamination of their mobile phones significantly less often clean their phones compared to students negative for mobile phones’ fungal contamination (p < 0.001). There was no difference in fungal contamination of students’ mobile phones concerning the duration of mobile phone possession, the average duration of mobile phone possession, frequency of mobile phone replacement, and frequency of daily mobile phone usage , and usage of mobile phones near patients’ beds .In the multivariate logistic regression analysis were included four variables that showed statistical significance by univariate logistic regression analysis: mobile phone cleaning, mobile phone holding place at university, mobile phone holding place at home, and usage of mobile phone near patients’ beds. Factors independently associated with fungal contamination of students’ mobile phones were Table : mobile A total of 351/492 (71.34%) students report that they clean their mobile phones. The majority of students cleaned their mobile phones with alcohol 189/351 (53.85%), followed by cleaning with wet wipes 89/351 (25.34%). Regarding the frequency of mobile phone cleaning 243/351 (69.23%) students clean their mobile phones occasionally.There was no difference in presence of fungal contamination on students’ mobile phones regarding the way and frequency of mobile phone cleaning Table .Table 3DThe majority of students included in the study 412/492 (83.74%) regard mobile phones as a reservoir of infection, but only 167/492 (33.94%) wash their hands after mobile phone usage in the wards, while 107/492 (21.75%) reported having ever superficial fungal infection in a lifetime.p = 0.001). There was no difference in fungal contamination of students’ mobile phones regarding having ever in lifetime superficial fungal infection, and washing hands after phone usage in hospital wards , followed by Aspergillus niger in 18/158 (11.4%), and Penicillium chrysogenum in 15/158 (9.5%). Regarding fungal groups, non-dermatophyte moulds were most frequently isolated from students’ mobile phones in 91/158 (57.6%), followed by yeasts in 60/158 (41.1%), while dermatophytes were isolated from just two mobile phones and identified as Trichophyton rubrum (1.3%). Among non-dermatophyte moulds, the most frequent were Aspergillus spp. isolated in 34/91 (37.4%), of which Aspergillus niger was most frequent in 18/34 (52.9%). The most frequent yeast was Candida spp. isolated in 60/65 (92.3%), with Candida albicans as the most common in 45/60 (75.0%) . On the other hand, we did not include a representative sample of students in the preclinical years (first-third) to see whether the difference in fungal contamination is associated with hospital settings.Our results highlight that the mobile phones of medical students can be contaminated by a wide range of fungi including common agents of fungal infection in humans. Independent risk factors in our study population were: lack of mobile phone cleaning and usage of mobile phones near patients’ beds. The results of this study confirmed that students who use their mobile phones in hospital wards have a higher rate of fungal contamination. The practical implication of the study results can be reflected in the need for the development of active surveillance and the adoption of efficient and suitable decontamination procedures to lessen the chances of cross-contamination via mobile phones. Further research should be conducted on students both from clinical and preclinical years to better assess the impact of the hospital environment on mobile phone contamination. Also, samples should be analyzed separately from different clinical wards as they can interfere with phone contamination. Reporting findings of other microorganisms on mobile phones will provide deeper insight into the extent of microbial burden. Comprehensive knowledge of the effectiveness of decontamination procedures is needed to develop adequate preventive strategies in a hospital environment.Supplementary Information.


Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications. Flash floods are considered as one of the most dangerous natural hazards causing extensive damages to property, and their impact extended to the ecosystem and humans. Communication and transportation systems, infrastructure, crops, social facilities, and service and educational buildings could also be affected by flash floods triggering a considerable economic loss. Based on the reported investigation by the World Meteorological Organization, the property loss due to flash floods was ranked in the top 10 among the other natural hazards in 75% of countries (Ashley and Ashley Machine learning (ML) is part of algorithmic and heuristic approaches that are designed to understand correlations in specific datasets through intuitive training. Various researchers and scientists have reported ML approaches toward analysis and forecast studies for hydrology, floods, and landslides analysis and prediction  occupied the area between North El-Galala to the south and El-Sokhna Road to the north Fig. . GhoweibTherefore, the current study attempts to identify the area with sediment accumulation and erosion evolved by flash floods. Additionally, this study also focused on the new hybrid approach of ML algorithm and hydrologic indices for detecting the impacted and highly vulnerable areas. Moreover, the environmental impacts of accumulation and erosion of the sediment were investigated. The elevation thematic layer ) was used to delineate the watershed, and extract the drainage network and different stream characteristics. Machine learning model (MLM) was successfully created using R environment, while the watershed characteristics were developed in GIS package. Thereafter, the hydrologic indices were calculated and identified of Wadi Ghoweiba and surrounding. Furthermore, the risk map of sediment accumulation and erosion was developed. Finally, the applied hybrid approach in the present work allows scientists and relevant flood authorities to simulate the occurrence of sediment accumulation, erosion, and the expected areas of the impending flood.2. The Ghoweiba and surrounding are situated between 29° 10′ 56′′ N to 29° 56′ 23′′ N latitudes and 31° 38′ 54′′ to 32° 21′ 41′′ E longitudes  are over 1100 m Abdallah . The mosIn this study, coupling the hydrologic indices with the Naïve Bayes (NïB) method to propose a novel hybrid machine learning (HML) model was applied. Within the NïB algorithm, it is easy to predict the class of test dataset, and it is also performing well in multi-class prediction. In simple understanding, NïB classifier assumes that the presence or absence of a particular class feature is unrelated to the presence or absence of any other feature. The HML model is used for flash flood forecast purposes in areas of arid and semi-arid characteristics. An integrative approach of hydrologic indices and Bayesian technique were implemented to develop flash flood risk, sediment accumulation, and erosion assessments. All predictor variables of this study were processed in ArcGIS 10.5) and the R computing environment for the modeling step for naïve Bayes R 3.6). A comprehensive framework developed in this study was constructed including the following steps: (i) creation and preparation of variables; (ii) creation of erosion map using stream transport index (STI), stream power index (SPI), and topographic wetness index (TWI) variables; (iii) develop the probability map by NïB approach; and finally (iv) creation of final hazard map. A schematic diagram of the methodological framework is described in Fig. .6. A com0.5 and tIn order to develop the current hybrid approach, hydrologic indices and NïB were calculated. NïB was used to determine the probability by utilizing Bayes’ theorem (derived from Bayesian statistics). Based on the assumption of features that are independent of class, NïB significantly simplifies learning as the following equation (Eq. ):1\documNïB approach has been previously used to determine the probability of landslides by other authors  with a resolution of 30 m for the Ghoweiba basin. Several studies were reported by other authors  . Determination of the flood controlling factors and/or predictor variables varies greatly from one watershed to another. The controlling factors were selected in susceptibility mapping depending on watershed characteristics  from the field survey were utilized for training and testing the model. The NïB approach exhibited a model accuracy of 87.7%, while the training and testing datasets were classified randomly into 70 and 30%, respectively. However, the applied hybrid-based method in the current work demonstrated a significantly higher accuracy reaches to 90.8%. The proposed model performs well for spatial analysis and prediction for flood sediment accumulation and erosions. The limitation in the obtained hybrid model was the hydraulic data such as the velocity, sediment load, flood inundation, etc. The output maps and thematic layers can help the planner and authorities in semi-arid regions in flood management and unplanned urbanization.


Aspergillus niger as a biosorbent was investigated. We measured physical changes  during the formation and growth of fungus pellets and the pH values that influence the adsorption performance and biosorption mechanism. Three acid anionic dyes containing Acid Orange 56, Acid Blue 40 and Methyl Blue were chosen as model dyes to investigate batch adsorption efficiency. Two adsorption models (in vivo and in vitro) were adopted to decolorize the acid dyes. The results show that fungus pellets have excellent decoloration abilities with a high adsorption efficiency of 98% for 200 mg L−1 of acid dye. The pH value of the dye solution varied with the adsorption time and the dye removal efficiency greatly depended on the pH. The bioadsorption mechanism of nano-scale hyphae was revealed to be mainly due to electrostatic interactions caused by the pH change. Furthermore, the surface morphologies of the fungus after adsorption indicated that the dyes had been adsorbed on the surface of the fungus mycelia. Moreover, prepared 3D fungus/GO aerogels demonstrated superior dye removal abilities compared with fungus aerogels.In this work, the treatment of textile wastewater by a facile and high-efficiency technology using eco-friendly  Aspergillus niger as a biosorbent to treat textile wastewater. Excellent adsorption ability with nearly 98% decoloration was achieved. The bioadsorption technology could be used to fight environmental damage caused by organic or inorganic pollutants.We report a facile and high-efficiency technology using eco-friendly  More than 100 000 types of commercial dyes are produced annually and over 1000 tons of dyes are annually discharged into water systems from the textile industry alone. This causes significant environmental challenges.5 Many materials  have been applied in wastewater treatment.6–8 Various methods have been developed to remove dyes from wastewater and adsorption has become a commonly adopted technology.9–12 Acidic anionic dyes are currently used for dying natural fibers, such as wool, flax and silk, and chemical fibers, such as nylon. Most acid anionic dyes are azo-type dyes and anthraquinone-type dyes, which can pollute aquatic environments and threaten people's health and the lives of microorganisms. Therefore, textile wastewater should be strictly controlled at a permitted concentration. Before dye-containing wastewater is released into the environment, absorbents are typically applied to absorb harmful organic compounds and reduce the concentration of dyes in the wastewater.Dyes are organic chemical reagents that are widely used in plastics, paper, textiles, leather tanning, printing and food-making processes.Aspergillus niger is one of the most widely used fungal microorganisms due to its low cost, fast growth, easy cultivation and eco-friendly properties. In the past decades, fungi have received increasing attention as they are a rich source of degrading enzymes and possess the ability to withstand extremely harsh conditions such as low pH, fluctuating pollution load and low nutrient concentrations.13–16 The unique capacity of fungi to produce specific and non-specific enzymes make them very attractive for the degradation of complex organic pollutants: synthetic dyestuffs, phenols and pharmaceutical compounds.17 Thus, they are commonly employed in biotechnological approaches to wastewater treatment.18–20 There are three forms of fungal morphologies that can be found: suspended mycelia , clumps (aggregated but still dispersed) and pellets .21,22 Fungal pellets are spherical, ellipsoidal or oval masses consisting of intertwined hyphae with sizes ranging from several hundred micrometers to several millimetres.23–25 Fungal pellets can offer a series of operational advantages compared to dispersed mycelia because of the ease of separating fungal biomass from complex dye solutions. Interestingly, fungal biomass can absorb dyes from wastewater regardless of whether the fungus is dry or wet. Moreover, there is no effect during the growth process of pellets if the media is mixed with organic dyes under extreme conditions. Dye decoloration with microorganisms is a cost effective and environmentally friendly method, which can be used for the removal and degradation of dye-containing effluents.Aspergillus niger fungus pellets as biomass adsorbent materials and evaluated the growth activity. Furthermore, we also investigated the biosorption capacity of the pellets for three typical acidic anionic dyes  under various adsorption models (in vivo adsorption and in vitro adsorption). The in vivo adsorption method was achieved by adding dyes into the culture medium with the fungal spores and allowing the growth of fungal pellets and dye decoloration to occur simultaneously. The in vitro strategy involved the cultivation of fungus pellets and a biosorption process that were performed separately. Freeze-dried biosorbents were dispersed into a certain concentration of dyestuff for further adsorption.The aim of this work was to characterize the mechanism of fungal spore biosorption for the sake of obtaining a deeper insight into the potential of fungal spores as a biosorbent. In our work, we selected and cultivated a batch of Aspergillus niger , sucrose , sodium nitrate, potassium dihydrogen phosphate, magnesium sulphate heptahydrate, potassium chlorate, ferrous sulfate, graphite , KMnO4, H2O2, sulfuric acid (98%), hydrochloric acid (HCl), acid dyes Everacid Blue A-2G (C.I. Acid Blue 40) and Everacid Orange N-G (C.I. Acid Orange 56) were obtained from Everlight Chemical Industry, Taiwan. Methylene Blue was purchased from Sinopharm Chemical Reagent Co., Ltd. All chemical reagents were of analytical grade and used as received without further purification.Aspergillus niger fungus hyphae, the Czapek–Dox medium was sterilized at 121 °C under high pressure for 20 min.The Czapek–Dox medium contained 30 g sucrose, 3 g sodium nitrate, 1 g potassium dihydrogen phosphate, 0.5 g magnesium sulphate heptahydrate, 0.5 g potassium chlorate and 0.01 g ferrous sulphate in 1 L of water. Before culturing Aspergillus niger fungus hyphae were incubated in 100 mL culture medium at 37 °C with a shaking rate of 150 rpm for about 3 days. Under these conditions, the fungus mycelia could be formed in large pellet shapes. In order to obtain a fungus mycelium dispersion, vigorous agitation using a magnetic stirrer (1500 rounds per minute) was necessary. The prepared dispersed fungus hyphae were stored in a refrigerator at 4 °C for further use.Several activated −1.5 mL of dispersed fungus hyphae were added into 100 mL of sterilized culture medium. The solution was incubated at 30 °C for 72 h with a shaking rate of 150 rpm. After incubation, a large amount of fungus pellets (the diameter of single pellet is larger than 5 mm) was produced. Subsequently, the prepared fungus pellets were captured by a colander (pore size is less than 5 mm) and washed with deionized water three times so as to remove the culture medium. The collected fungus pellets were frozen and freeze-dried in a lyophilizer  for 48 h. In order to prepare a 3D fungal aerogel, the collected fungus pellets were dispersed in water again. Before usage, the fungus pellets were treated with a blender for 3 minutes to prepare a fungal hyphae suspension. The fungus hyphae suspension was transferred into the desired container and lyophilized for 48 h to obtain a cellulose-based fungus aerogel. The mass concentration of the fungal suspension was 5 mg mL4 was added into the glass bottle gently and the temperature was controlled to below 20 °C. The glass bottle was then transferred into an oil bath and the reaction solution was heated to 34 °C with stirring for 2 h. After that, 50 mL water was carefully added successively to 90 °C and maintained for 10 min. Finally, another 120 mL of water and 5 mL 30% H2O2 were carefully placed into the glass bottle. The mixture quickly turned light yellow. After the reaction, the solution was vacuum filtered to collect the graphite oxide, which was rinsed with 250 mL of HCl (v/v = 1 : 10) several times to remove the metal ions. The graphite oxide solution was further purified via dialysis in water for 7 days. The obtained purified graphite oxide was treated with ultrasonication for 6 h to prepare the GO solution. For further usage, the GO solution was freeze dried in a vacuum dryer and prepared at the desired concentration.GO was obtained by using a modified Hummers method. Briefly, 1 g of graphite was added into 23 mL sulfuric acid (98%) in a 500 mL glass bottle with rigorous stirring. The glass bottle was pre-treated in an ice bath with a temperature below 5 °C. 3 g KMnO−1. The mixture was stirred under vigorous magnetic stirring for several hours and kept at room temperature overnight. Subsequently, the mixture was poured into a 24-well cell culture plate and frozen at −80 °C for 2 h and then transferred into a vacuum drier for 3 days. Finally, the fungus/GO free-standing aerogel was prepared.0.2 g of dried GO pieces was dispersed into a 100 mL fungus suspension solution with a concentration of 5 mg mL−1 acid dyes into individual flasks and added 0.5 g of dry-weight fungus adsorbents. Afterward, the flask was kept in a constant temperature oscillator with a shaking rate of 150 rpm at 30 °C. The solution after adsorption was removed to measure the UV-vis spectra after a certain time period. The adsorption percentage  and adsorption capacity  can be expressed as in Co and Ce (mg L−1) are the initial and equilibrium concentrations after adsorption and Qe (mg g−1) is the equilibrium adsorption capacity. V (L) is the volume of acid dye solution and m (g) is the mass of the adsorbents.The batch adsorption experiments were conducted by adding the adsorbents  into a certain concentration of acid dye solution in a conical flask. In our experiments, we poured 50 mL of 200 mg Lin vivo culture process of fungus hyphae in acid dye solution was the same as that used for the cultivation of fungus pellets, except that 5 mg or 10 mg of acid dyes were added into 100 mL of Czapek–Dox culture medium to form the dye-containing medium. Three acid anionic dyes were applied in the adsorption experiment: Acid Blue 40 (an azo-type dye), Acid Orange 56  and Methylene Blue (a common aromatic dye). For convenience, we defined each dye culture medium like this, for example, B-40(5) represents 5 mg Acid Blue 40 dispersed into 100 mL of Czapek–Dox culture medium. However, in vitro culture was conducted by adding a certain amount of dried fungus aerogel into dye solution. For the preparation of H+-f and HCO3−-f adsorbents, fungus pellets were soaked in 0.1 M HCl or NaHCO3 for 2 h, washed with water several times to remove extra reagents and finally dried in an oven. Then 0.5 g of dried adsorbent was immersed into flasks containing 50 mL 200 mg L−1 of each of the three different dye solutions. Afterward, these flasks were put into a constant temperature oscillator with a shaking rate of 150 rpm at 30 °C. After adsorption for a certain time, about 3 mL of the dye solution was removed to measure the UV-vis spectrum.The −1 using a Nicolet 5700 Fourier transform infrared (FTIR) spectrometer equipped with a single reflection ATR system. The resolution ratio was 4 cm−1. Raman spectra were obtained using a spectrophotometer (LabRAM XploRA) in the range of 4000–500 cm−1. The excitation wavelength was 513 nm and the acquisition time was 10 s. The chemical composition was examined using a Kratos Axis Ultra HAS X-ray photoelectron spectroscope (XPS) with an AlKα X-ray source. The excitation source was driven by 100 W power and the pressure applied in the analysis chamber was 4.0 × 10−9 Pa. The static water contact angles were obtained on an optical contact angle measurement Krüss DSA 100, using 6 μL deionized water droplets as the measuring liquid. UV-vis spectra were recorded on a UV-vis-NIR spectrophotometer  at room temperature with a wavelength range from 200 to 800 nm. The concentration of the dye solution was determined according to the maximum absorbance value (λmax). The pH values of the dye solutions during the whole adsorption process were examined using a Mettler-Toledo portable pH meter.The surface morphologies of the as-prepared aerogels were conducted using a Hitachi S-4800 scanning emission microscope (SEM) at an acceleration voltage of 3 kV. For this, all samples were freeze dried and coated with a gold coating. The samples were blended with KBr and pressed into a disk, which were then examined in range of 400–4000 cm26 In our work, we selected Aspergillus niger as a target and researched the whole growth cycle for about one week. As shown in Aspergillus niger.Generally speaking, three main phases have been identified for the formation and growth of filamentous fungi to pellet fungi during the whole culture process: micro-morphological growth, macro-morphological growth and fungus cell autolysis. Fungus pellets can be formed from either a single spore (non-coagulative), aggregates of spores (coagulative) or agglomerated hyphae.In addition, the influences of the fungus pellet culture time on the fungal size and mass were assessed in this experiment. As shown in Fig. S1,The whole adsorption cycle was 6 days; fungal hyphae grew well and gradually formed fungus pellets despite the addition of dyes. At the same time, colored dyes were adsorbed throughout the formed fungus pellets  and the in vivo culture time. In the first three days, fungus pellets grew slowly and the yield was relatively lower, causing a slow adsorption velocity. Afterward, the yield of fungus pellets increased sharply and the adsorption ability improved significantly. Decoloration rates of above 80% were obtained for all of the various dyes after six days of cultivation. Even >98% adsorption was observed after the in vivo adsorption process, leaving a clean culture medium. However, low adsorption capacities for all dyes were observed, as shown in in vivo adsorption process. The surface morphologies of the fungus pellets after adsorbing the acid dyes are shown in As shown in in vivo adsorption process were also recorded using a portable pH meter every 24 h. With the culture time, all dye solutions showed a decrease of pH from the initial of 4.6 to a minimum of 1.8; a decline of 1.4–2.9 in pH can be observed in Fig. S2.2+, PO42−, K+ and Fe2+ and the formation of the chemical products. The pH of the culture medium may have an effect on dye adsorption. Though high adsorption efficiencies were obtained, the long culture duration and low adsorption capacity could not satisfy the demand to rapidly purify wastewater. Therefore, the in vivo adsorption method does not seem to be an ideal way to adsorb dyes rapidly and efficiently. It is therefore essential to explore the adsorption mechanisms and find a new strategy that can save time and raw materials.The pH changes of the dye solutions during the in vivo biosorption, a lower adsorption capacity was obtained, as we verified before. Therefore, we investigated the adsorption performance of a quantitative controlled fungus pellet via a freeze-drying route. Furthermore, in order to figure out the relationship between the adsorption ability and the pH values of the dye solution, three different forms of dry-weight biomass adsorbents were applied: fungus pellets, H+-f and HCO3−-f. 3 to the three acid dyes. Interestingly, the dried fungal pellets exhibited similar rules during adsorption of Acid Blue 40, Acid Orange 56 and Acid Blue 93 in the first 12 h and then upon desorbing. The mechanism of adsorption and the reason for this phenomenon will be discussed in detail below. However, H+-f pellets showed significant adsorption enhancement and achieved a high decoloration rate above 96% for Acid Blue 40 and Acid Orange 56, compared to untreated fungus pellets. Moreover, a high adsorption capacity of about 48 mg g−1 for these two dyes was obtained. H+-f pellets exhibited a low adsorption rate to Acid Blue 93, though it was better than fungus pellets. For HCO3−-f pellets, inferior adsorption performances for the three acid dyes were observed, as shown in 3−-f pellets for all acid dyes were observed.Since the growth of fungus pellets was varied and uncontrolled during the In order to investigate the reasons behind the adsorption and desorption behavior of the fungus pellets, we conducted a series of experiments. As shown in 27 In this work, we also studied the effect of pH values on the dye solutions. As shown in Fig. S4,The pH of the solution affects the ionization of the dye and the surface charge of the biomass, thus affecting their interaction.+-f and HCO3−-f after biosorption. The above results reveal the high affinity of the fungus pellets to acid anionic dyes and prove them to be effective in removing dyes from wastewater.The surface morphologies of fungal pellets after adsorbing various dyes were also characterized. −1 is attributed to hydroxyl groups. The peaks at about 2800 cm−1  and 1720 cm−1 (carboxyl group) are mostly associated with the IR absorption of lipids and fatty acids. Other absorption peaks at 1620, 1540, 1440 and 1380 cm−1 are ascribed to the vibration of amide groups on proteins and chitin. The sharp peak at about 1100 cm−1 belongs to polysaccharides.19,28 Thus the fungus hyphae mainly consisted of polysaccharides, lipids and proteins. We also conducted EDS measurements to further confirm the chemical elements and element distribution on the hyphae  a passive reaction, which is non-metabolism dependent, and (ii) an active uptake, which is metabolism dependent and comprises an energy driven process.31 Carbon sources could supply the energy needed for active transport, contributing greatly to achieving maximum biosorption. Nutritional supplementation could maintain the production of enzymes, which is a significant factor in biodegradation.32 By comparing the SEM images before and after dye adsorption, we can further deduce that Aspergillus niger as a biosorbent has prominent biosorption properties, especially for acid anionic dyes (azo-type dyes and anthraquinone-type dyes). This biosorbent will greatly decrease the effluent concentration of wastewater from the textile industry, particularly for highly concentrated dye solutions, which is beneficial for aquatic ecosystems and human health.In addition to the electrostatic interactions, other mechanisms may be involved in the biosorption of the dyes. Fungi possess the ability to produce non-specific oxidative enzymes, which allow degradation of pollutions with highly complex structures.33–36 Herein, we also put forward a composite fungus/GO aerogel for dye adsorption. In order to demonstrate the adsorption properties of composite fungus/GO aerogels, fungus aerogels also needed to be investigated. As shown in Three-dimensional monolayer aerogel materials have attracted enormous interest in many applications due to their large specific surface area, porous structure and mechanical stability.−1  and 1720 cm−1 are mainly associated with the IR absorption of lipids and fatty acids. Some peaks located at 1620, 1540, 1440 and 1380 cm−1 are assigned to amide vibrations on the proteins and chitin. In addition, the sharp peaks at around 1100 cm−1 are caused by polysaccharides. For the FTIR spectra of GO, the absorption at peaks at about 3400, 1740, 1610, 1230 and 1000 cm−1 are due to hydroxyl, carbonyl, aromatic, epoxy and alkoxy groups, respectively. We can see that the intensity of oxygen-containing groups in the composite fungus/GO aerogels decreased, especially the peak at 1740 cm−1, indicating that the hydroxyl groups in the polysaccharides of fungus reacted with epoxy groups and formed rGO.37,38 During the cross-linking process between them, the functional groups changed and electron transfer occurred, which is beneficial for the biosorption of the dyes. Moreover, the GO sheet of the composite aerogel provided more surface area compared with the fungus aerogel.This can be explained by the FTIR spectrum in Fig. S6.Before carbonization, fungus and fungus/GO aerogels are insulators, which cannot conduct electricity. As depicted in Fig. S7a,Aspergillus niger have great potential in wastewater treatment because they are easily separated from dye-containing wastewater, and exhibit good decoloration properties and strong acid/base tolerances during the culturing and adsorption processes. A batch adsorption study indicated that the fungus pellets possess efficient decoloration abilities with a high adsorption rate of 98%. An analysis of the mechanism showed that the excellent decoloration performance relies on the active groups on the fungus pellets and electrostatic interactions, including enzymes involved in cell wall degradation. The performance can be further improved by adjusting the pH values of the dye solution. Moreover, incorporating GO sheets into a fungus solution can also increase the dye adsorption. This article provides valuable insight into the management and treatment of textile effluents, especially those containing acid anionic dyes, such as azo-type dyes and anthraquinone-type dyes. We believe that micro/nano-fungus bioadsorption technology could be a potential way to fight environmental damage caused by organic or inorganic pollutions, even when the fungal biomass is inactivated.Fungal biotechnology for wastewater treatment has recently been of great interest in the environment decontamination of organic and inorganic pollutants. Self-assembled fungal pellets containing The authors declare that they have no conflicts of interest.NA-001-C8NA00132D-s001


Lactobacillus population. The results showed that the Lactobacillus population was lower in broilers fed 8% pomegranate peel powder and 4% waste oil in diets. The inclusion of 8% pomegranate peel powder in diets showed lower villus height and crypt depth in the duodenum, jejunum, and ileum. The inclusion of 4% pomegranate peel decreased the peroxide value (PV) of meat. Dietary inclusion of 4% waste oil raised the PV of meat. Alpha-tocopherol supplementation decreased the PV of meat. Finally, the results provide information that 4% of pomegranate peel and 4% waste oil could be used as an alternative feed ingredient in broiler diets without adverse effects.Agricultural residues can be used as alternative feed sources in industrial chicken production. The impacts of different levels of pomegranate peel and waste cooking oil as an agricultural residue on broilers' nutrition were investigated. Results showed that the replacement of 8% pomegranate peel in diets decreased the growth performance of broilers. Supplementing 8% pomegranate peel in diets reduced apparent nutrient digestibility. The highest level of waste oil inclusion in broiler diets indicated negative impacts on apparent zmetabolizable energy and crude fat apparent nutrients digestibility. Broilers fed the diet containing 4% pomegranate peel had a higher  Accordingly, it appears that pomegranate peel is one of the agricultural residues that can be a suitable source of feedstuff for livestock2. Pomegranate has been cultivated widely from ancient times in tropical and subtropical areas. Pomegranate peel remains after pomegranate juice and pastes production. It should be noted that the peel of pomegranate constitutes up to 50% of the whole fruit3. Pomegranate peel contains a large number of high molecular weight phenolic compounds including tannins, proanthocyanidins, and complex polysaccharides such as ellagic acid, and gallic acid, which can have an antioxidant, antimicrobial, and anticancer effects in animals and human4. Researchers stated that the use of pomegranate peel in broiler diets improved the performance of broilers3. Also, Abbas et al.5 indicated that the addition of 4.5 and 7.5% pomegranate peel inclusion in diets improved the growth performance of quails.It should be noted that feeding cost is one of the important factors in poultry industry. Therefore, nutritionists have attempted to improve productivity and decrease feedstuff’s cost. Hence, one way to reduce the prices of feed is to replace them with cheap items such as agricultural and food industry waste5. The researchers indicated that the use of 4 and 7.5% pomegranate peel inclusion could be used as an alternative feed source and antioxidant in bird diets5.The last studies revealed that pomegranate peel has nutritional value, which can be used as a cheap feed source in broiler diets6. Bio-active ingredients of the plants are organic compounds obtained from the secondary metabolism of the plant divided into various groups: polyphenols (tannins), saponins, organ sulfur, and one of their approved significant attributes is their antimicrobial activity against yeasts, bacterium, and fungi7. It had noted that pomegranate peel had an antiviral potential. In this regard, the researchers demonstrated that pomegranate peel could prevent SARS-CoV-2 entry into host cells. Also, the researchers showed that pomegranate peel polyphenols had antiviral impacts against COVID-198. The effectiveness of these compounds and especially polyphenols (tannins) and saponins were investigated for their antimicrobial activity and their effectiveness in gastrointestinal tract improvement9.Recently, phytochemicals or various active ingredients in plants have been isolated from different herb species and have been investigated for their effectiveness in animal health and promoting their products2. Some previous studies demonstrated that the waste oil could be supplemented in diets without any adverse effects for broilers. In contrast, some others observed that utilization of high peroxide value oil (oxidized oil) in diets has disadvantageous impacts on the health and performance of birds10. It seems that the supplementation of waste oil resulted in increased free radicals and oxidative stress in birds. It should be noted that malondialdehyde (MDA) is the second product of peroxidation that has a harmful influence on broiler health11. In general, waste oil generates free radicals which could have harmful consequences on the intestinal performance and health of broilers12. Reactive oxygen species (ROS) resulted in oxidative stress in the body, which harms broiler health and digestibility11. Pomegranate peel contains phenolic compounds such as tannins which can have antioxidant activity in broilers13. Also, it seems that the antioxidant characteristics of pomegranate peel reduced the adverse impacts of oxidative stress caused by oxidized oil4. The improvement in the global populations has caused a global revolution in food consumption models and enhanced requests for livestock products such as broiler chickens14. The higher demands in human life have led to a direct effect on increasing demand and production of livestock feedstuff which has inevitably placed increased pressure on limited resources14. Scientists indicated that pomegranate peel and waste vegetable oil as a by-product can replace in broiler diets. It seems that consuming this waste in poultry nutrition can diminish the disposal of this waste in the environment thus preventing environmental pollution. Pomegranate peel can also be a potential source of raw material for the animal feed industry. Generally, it has been hypothesized that utilizing these products can result in decreasing waste production and broiler industry diet cost. Also, it seems that pomegranate peel is one of the medicinal plants that had a strong antioxidant activity for poultry2. Hence, pomegranate peel and oxidized oil may play an important function as a new resource for broiler diets15. Nevertheless, a general investigation of the effects of these products on intestinal function and digestibility seems important. Therefore, the present study aimed to investigate the effects of using waste oils and pomegranate peel in diets on growth performance, intestinal function, apparent nutrient digestibility, and meat quality in broiler chickens.Waste vegetable oil (oxidized oil), obtained from food processing which is mostly produced due to lifestyle changes and increased consumption of fast food by human beings, may be used as a high-energy, and low-cost feed resource in broiler diets16, The research protocol advises animal rights and welfare by ensuring minimal stress to animals. All procedures were carried out by the relevant research protocol. The experiment was carried out in keeping with the ARRIVE guidelines. The experiment was designed according to 3 × 3 × 2 factorial arrangement, with factors (i) the pomegranate peel powder , (ii) the oxidized oil  and (iii) the α-tocopherol (zero and 200 mg/kg). Hence, a total of 1080-day-old, male Ross 308 strain broiler chickens, were randomly allocated to 90-floor pens in a completely randomized design with 18 treatments and five replicates with twelve chicks in each replicate. The chicks were fed commercial diets during the first 10 days of the experiment. At the end of 10 days, the chicks were weighed and housed in 1.5 × 1.5 m floor pens each equipped with a pan feeder and manual drinker. Stocking density is based on the EU broiler welfare Directive (2007), 33 kg/m2. Birds received 22L:2D throughout the experiment period. Feed and water were available ad libitum. In this study, chicks received mash diets. All chicks were kept under management conditions according to the Ross 308 strain catalog. All chicks in the treatment groups were fed a grower (11–24 days) and finisher diet (25–49 days). All diets were formulated based on nutritional requirements proposed by the National Research Council17 (Tables 18 (Table ® (Info@Sclabs.ir), Iran, and is a powder α-tocopherol supplement that consists of 5500 IU α-tocopherol.The research protocol has been approved by the Animal Care and Use Committee of the Islamic Azad University 93/987-2014) 4 16, The18 methods. Furthermore, hydrolysable tannins and condensed tannins in pomegranate peel were obtained15. The essential amino acids in pomegranate peel such as methionine, lysine, and threonine were analyzed according to the method defined by the AOAC18 (2005) and Cooperative19 using HPLC and the modification of the PICO-TAG process. The chemical composition of pomegranate peel is shown in Table 120. The Sib bald method was used to determine the metabolizable energy of pomegranate peel21 , crude protein (CP), Ether extract (EE), crude fiber (CF), nitrogen-free extract (NFE), and ash) was measured according to AOACe AOAC18 005 and C21 Table .Table 4N22. Also, the peroxide value (PV) and MDA of samples were analyzed23. The chemical compositions of experimental soybean oils (oxidized and fresh oil) are shown in . Oxidized soybean oil was stored at −20 °C without any additives to avoid additional oxidation. The fatty acid profile of oxidized and fresh soybean oil was examined by using the method described by the American Oil Chemists Societyin Table .Table 5AThe performance of broilers in terms of body weight gain (BWG), feed intake (FI), and feed conversion ratio (FCR) during the experimental period (11–49 days) were determined.2O3) at 46 days old for three days. At the end of the experiment, two broilers from each pen were slaughtered via cervical dislocation, and ileal contents were collected for nutrient digestibility measurement. Each sample  was homogenized, pre-dried at 55 °C, ground to 1 mm screen, and examined. Concentrations of chromium oxide in the samples, as well as the indigestibility factor, were calculated according to formulas defined by Sakomura and Rostagno24. The digestibility of DM, crude protein, crude fat, gross energy, and chromium oxide concentration, was determined by the method as described. Also, the AME digestibility was measured by the methods of Scott and Boldaji25.Broilers received the experimental diets with the inclusion of 0.3% of indicator chromium oxide  were counted by Reinforced Clostridial agar. It should be stated that all the phases continued near the flame until environmental pollution was eliminated. Bacterial colony forming units (CFU) were counted utilizing a colony counter. Finally, the bacteria counts were displayed as log10 colony-forming units per g of digestal (log10 CFU g−1)16.At the end of the experiment, two broilers from each pen were randomly selected for the determination of intestinal microbial population. For intestinal microflora estimation, the primary one gram of the sample of the intestinal content from each broiler was diluted with 9 ml of 0.9% saline solution and combined on a vortex. In the following phase, 0.1 ml of proper dilutions of ileum were cultured in Violet Red Bile Agar (VRBA) for investigating coliform bacteria. Also, Rogossa Agar (MRSA) was utilized for 6.On the last day of the experiment, and before slaughtering the birds, all of them starved for five hours and the feeding troughs were removed from the pens. Two broilers from each pen was slaughtered and subsequently, 5 cm length of intestine was sampled from the duodenum, jejunum, and ileum of it. The intestine samples were washed with sodium phosphate buffer and fixed by Clark stabilizer solution. All of the intestine samples were prepared for microscopic investigation later staining with Periodic Acid Schiff Solution by separating the muscular layer and making the lamella. Ultimately, the villi height and the crypt depth were estimated according to the process of Ghasemi-Sadabadi et al.6. Peroxidation value was calculated based on the amount of peroxide (meq/kg extracted fat), according to the American Oil Chemists Society22. The fatty acid profile of the meat was analyzed by gas chromatography of the method as described by Ghasemi-Sadabadi27.At the end of the experiment, two broilers from each pen were slaughtered and the meat was trimmed and prepared for fatty acid profile and peroxide value measurement. The breast muscle was trimmed and stored at − 20 °C. Next, the meat sample (50 g) was mixed in a blender with 150 ml of Chloroform-methanol (Merck) in a 1:2 ratio for a minute. Then, 50 mL of sodium chloride (0.88%) was added to the mixture. Potassium chloride was used for dewatering, the aqueous phase was collected and methanol- potassium chloride 0.88% (v/v: 1/1) was mixed with the aqueous phase. The final solution was dried at 35 °C, after that, the residual solvent was evaporated under nitrogen gas pressure to obtain pure oil. Eventually, the fat was obtained for the measurement of peroxidation. To measure meat peroxidation, chloroform–acetic acid (25 ml) was added to one g of extracted fat in a ratio of 2:3. Then, one mL of potassium iodide was added to the mixture, which was stored for five min in the dark. The mixture was tittered with normal 1% potassium thiosulfateStatistical analyses were performed using SAS (2003) software. The data were analyzed using a 3 × 3 × 2 factorial arrangement design. Significant differences between treatments were separated using the Tukey range test at (p < 0.05).ijk is the dependent variable; μ is the overall mean; Pi is the effect of different pomegranate peel; Oj is the effect of waste oil; Vk of is the effect of α-tocopherol; PiOj is the interaction of pomegranate peel and waste oil; PiVk is the interaction of pomegranate peel and α-tocopherol; OjVk is the interaction of waste oil and α- tocopherol; PiOjVk is the interaction of pomegranate peel, waste oil, and α-tocopherol; Eijk is the random error.Data following model:15. Similarly, the researchers stated that the addition of three g/kg pomegranate peel in broilers' diets decreased the growth performance15. One of the results of our research was that the inclusion of 4% pomegranate peel in diets had not significantly affected the growth performance of broilers. Also, Abbas et al.5 indicated that the addition of 4.5 and 7.5% pomegranate peel inclusion in diets did not affect the growth performance of Japanese quail. Generally, it seems that high amounts of tannins and CF of pomegranate peel impaired the growth performance of the birds4. Furthermore, Ghasemi-Sadabadi et al.2 stated that the pomegranate peel compounds such as CF and tannins may reduce gastrointestinal motility and consequently decrease the growth performance of birds. It has already been determined that the high amount of tannic acid reduces BWG and FI in poultry28. Past studies have shown that tannins can affect the physiological and biochemical systems of the body. In addition, tannins in the body lead to adverse nitrogen balances, growth inhibition, diminished intestinal absorption of sugars and amino acids, reduced immune response, and improved protein catabolism29. In this study, α-tocopherol did not affect growth performance. Contrary to this result, researchers indicated that vitamin E may be used in diets containing up to 6% of Distilled fatty acids for male quails because of the resulting betterment in nutrient utilization, growth performance, and meat quality30.The effects of using different levels of pomegranate peel, waste soybean cooking oil, and α-tocopherol in the diet on BWG, FI, and FCR in broiler chickens are shown in Table 15; Ghasem-Sadabadi et al.2. Also, our results agree with Akuru et al.31, who stated that the addition of pomegranate peel in diets reduced DM, CP, and CF digestibility in broilers. Similar effects were discussed by Saleh et al.32 for the nutrient digestibility of broilers fed high dietary levels of pomegranate peel. Consequently, it appears that the lower digestibility in the 8% pomegranate peel group in this study could be related to higher tannins and fiber contents32. Furthermore, previous examinations confirmed that the pomegranate peel contains a high amount of CF and tannin, which adversely affected absorption and digestion33. The high amount of tannins had destructive influences on the physiological and biochemical systems, which resulted in adverse nitrogen balances, lower intestinal absorption of sugars and amino acids, diminished immune response, and increased protein catabolism29. It has been mentioned that phenolic compounds such as tannins could be bonded to dietary and endogenous proteins such as digestive enzymes and proteins located in the gastrointestinal tract that reduced the apparent nutrient digestibility . The reaction between the hydroxyl groups of polyphenol and the carbonyl groups of protein can explain the lower digestibility of protein in this study35. Similarly, researchers concluded that the use of high-tannin sorghum grain in broiler diets reduced protein digestibility34. The earlier reports had shown that the use of high polyphenol compounds in diets decreased fat absorption in rats and birds. Generally, tannins could bind to bile salts and cholesterol and reduce the digestibility of fat. Furthermore, trypsin, amylase, and lipase are the main digestive enzymes in birds that can be bonded by tannins and reduced digestibility of nutrients32. The results reveal a clear significant correlation between the waste oil and digestibility. According to the results, the supplementation of 4% waste oil decreased fat and AME digestibility. Our findings are in agreement with those of Kamran et al.36, who indicated that the addition of 4% waste oil in diets reduced digestibility. Tavárez et al.10 stated that the supplementation of waste oil in the diet diminished the growth performance of broilers. Moreover, feeding of waste oil results in lower digestibility of fat and energy in birds which is determined by the decreased capability of digestion37. It seems that parallel reduction of apparent CF digestibility, a reduced AME has been observed, indicating extensive fatty acid polymerization and a negative impact on the gastrointestinal tract38.The results Table  indicatelactobacillus and coliform bacteria populations from 11 to 49 days (Table lactobacillus count was observed in 4% pomegranate peel powder groups (p < 0.05). Although, broilers fed on diets including 4% of pomegranate peel powder had significantly lower coliform populations than other groups (p < 0.05). Although coliforms and the total bacteria population were not affected by dietary waste oil, significant differences were determined in the lactobacilli population of waste oil by linear contrast (p < 0.05) (Table lactobacilli population in broilers (p < 0.05). The lactobacilli bacteria populations were influenced by α-tocopherol supplementation within diets (p < 0.05) (Table lactobacilli bacteria population was found in the group containing 200 mg/kg α-tocopherol (p < 0.05). Besides, the lowest lactobacilli bacteria population was observed in the non-supplemented group. Further, an interaction was not found between dietary treatments on the intestinal microbial population in this experiment. The present study concluded the optimal level of pomegranate peel in diets required to achieve the best intestinal health in broilers at the end of the experiment. The use of 4% pomegranate peel powder in diets increased lactobacilli bacteria count in broilers, whereas broilers fed with 4% pomegranate peel powder had lower coliform bacteria count. In similar studies, researchers have reported that pomegranate peel increased beneficial microorganisms of the intestine such as lactobacilli bacteria by restraining the pathogens bacteria, which improved digestion and absorption of broilers39. Overall, researchers suggested that phenolic compounds have antimicrobial characteristics. It was mentioned that phenolic compounds through the adhesion of pathogenic microorganisms by the ‘lectin–receptor’ mechanism and inhibition of the incorporation of pathogenic bacteria to the mucosal layer decreased gut pathogenic bacteria counts40. The phenolic compounds played an antimicrobial role against pathogenic microorganisms such as E.coli bacteria41. In general, it has been noted that higher lactobacillus counts in the intestine were well correlated with lower coliforms and salmonella bacteria count, which improved broiler performance and health16. The development in the lactobacilli bacteria count in this study seems to be related to the reduction in pathogenic bacteria in the gut16. Similarly, the tannins reduce pathogenic bacteria; hinder harmful gut microbe's metabolism and the activities of harmful microbial enzymes by preventing oxidative phosphorylation42. The results showed that the use of 4% waste oil in diets decreased the lactobacilli bacteria counts in broilers. Also, the inclusion of the different levels of waste oil in diets did not affect coliform bacteria and the total bacteria population. In the same case, it has been demonstrated that oxidative stress harmed intestinal microflora, which increased pathogenic bacteria in the intestine43. Oxidative stress influences the gut epithelial cells and stimulates intestinal bacteria and lipopolysaccharide (LPS). It seems that LPS is understood to cause apoptosis and damage in different cells44. Also, the researchers stated that oxidative stress could have a destructive effect on the intestinal immune barrier that increased pathogenic bacteria and inflammatory infiltrate12. In the present study, the supplementation of 200 mg/kg α-tocopherol was shown to increase the lactobacilli population in broilers. In contrast, Dalia et al.45 reported that dietary 100 mg/kg α-tocopherol in broilers could not affect the caecal lactobacilli bacteria population. Results on the influence of α-tocopherol on microbial population are limited. Nevertheless, the researcher indicated that the decline in Salmonella spp. population due to α-tocopherol use unclear45. Hernken et al.46 concluded that α-tocopherol could affect the antioxidant and immunity response of broilers. The use of vitamin E has been shown to decrease some of the adverse effects of oxidative stress47. Therefore, it is well known that the antioxidants substances improved intestinal health and functioning due to a reduction in oxidation stress in the intestine45.The effect of dietary pomegranate peel powder was significant (p < 0.05) on the ys Table . The hig5) Table . Hence, 43. It mentioned that the intestine is largely responsible for digestion and absorption of all the principal nutrients, consequently, lower villi height may be related to low digestive capacity5. In contrast with our results, researchers indicated that dietary pomegranate peel in quail diets had increased villus height in the jejunum of the intestine5.These results Table  indicate6. The use of high peroxide value oil on intestine morphology was examined by other researchers43, who have shown that dietary oxidized oil decreases intestinal mucosa of birds in response to oxidative stress, which can result in low villus height. Similarly, Marchini et al.48 observed that oxidative stress in the intestine reduced the villus height on broilers. It has been shown that birds under oxidative stress present smaller crypt depth, mucous area, and villus height of the small intestine, leading to an adverse influence on the intestine function43. On the other hand, the inclusion of oxidized oils in broiler diets reduced antioxidants and immune response within the intestinal mucosa. Increasing the amount of ROS in the intestine, results in inflammation and reduces the absorption capacity43.In this study, 4% waste oil inclusion in diets decreased the broilers' villus height and villi/crypts ratio at 30 and 60% parts of the intestine. This conclusion was comparable with the finding that high peroxide value oil decreased the villus high in birds2 and Abdel Baset et al.49. It seems that the inhibitory influence of pomegranate peel on meat oxidation could be due to phenolic compounds, which have an antioxidant influence on lipid oxidation49. Similarly, Kishawy et al.50 indicated that the use of pomegranate peel extract in diets increased the total phenol and total flavonoid contents of breast muscle in broilers.The effects of using different levels of pomegranate peel, waste oil, and α-tocopherol in the diet on the peroxide value of meat in broiler chickens are shown in Fig. 51. Thus, the higher PV of meat has been indicated to be positively correlated with oxidized oil in diets. It had been stated that oxidative damage reduced the quality of poultry meat, including its PV11.It can be concluded that the inclusion of 4% waste oil in diets improved the PV of meat. Researchers also confirmed that dietary waste oil enhanced the PV in the meat of broiler chickensIn conclusion, the dietary inclusion of 8% pomegranate peel reduced the growth performance of birds in comparison with the inclusion of 4% pomegranate peel and the control group. The inclusion of 4% pomegranate peel into broiler diets showed no significant effect in comparison with the control group.lactobacilli population. 4% pomegranate peel in the diet improved the gut morphology of broilers compared with the inclusion of 8% pomegranate peel and the control group. Whereas, the supplementation of 4% waste oil showed lower villus height and crypt depth compared to the other groups. In general, interaction effects among pomegranate peel powder, waste oil, and α-tocopherol have not been observed in this study. The effect of 4% pomegranate peel was decreased on the PV of meat. Also, the inclusion of 4% waste oil into diets reduced meat quality compared with other groups. alpha-tocopherol (200 mg/kg) resulted in improved meat quality.The use of 8% pomegranate peel decreased apparent nutrient digestibility in this experiment. Also, the supplementation of 4% waste oil in the diet decreased CF and AME digestibility in broilers. The 4% pomegranate peel and 200 mg/kg α-tocopherol dietary inclusion improved the intestinal population in this experiment. The presence of waste oil in diets reduced the


Understanding resource demands and tradeoffs among energy, water, and land socioeconomic sectors requires an explicit consideration of spatial scale. However, incorporation of land dynamics within the energy-water nexus has been limited due inconsistent spatial units of observation from disparate data sources. Herein we describe the development of a National Water and Energy Land Dataset (NWELD) for the conterminous United States. NWELD is a 30-m, 86-layer rasterized dataset depicting the land use of mappable components of the United States energy sector life cycles (and related water used for energy), specifically the extraction, development, production, storage, distribution, and operation of eight renewable and non-renewable technologies. Through geospatial processing and programming, the final products were assembled using four different methodologies, each depending upon the nature and availability of raw data sources. For validation, NWELD provided a relatively accurate portrayal of the spatial extent of energy life cycles yet displayed low measures of association  with mainstream land cover and land use datasets, indicating the provision of new land use information for the energy-water nexus.  Through the lens of EWL, MSD research examines natural human-environmental system coupling, measures dependencies, and evaluates tradeoffs in resources amongst the various economic sectors1. Considerable attention has been devoted solely to evaluating critical interdependencies or tradeoffs within the energy-water nexus, such as studies on thermoelectric water use2, drinking water supply and distribution3 and wastewater treatment4. Although energy and water are critical components of the economy and society, both elements are highly dependent upon land5.Multi-sector dynamics is a growing field of research aimed at understanding the vulnerabilities and risks of stressors and shocks to interdependencies converging at the intersection of energy, water, and land (EWL) systems6. Beyond the agricultural sector, however, explicit consideration of spatial scales, particularly land dynamics within the energy-water nexus, has seen far less attention5. Incorporation of land dynamics within the energy-water nexus engenders a common spatial scale within multi-sector evaluations, such as quantifying land competition among sectors7, evaluating resource burdens of alternative socioeconomic choices by way of life cycle analysis10, or understanding distal teleconnections imbedded within energy life cycles resources11.Indeed, consideration of food production in sustainability studies has tied landscapes to the energy-water nexus12 by breaking down the energy types into their respective life cycle steps; production, processing, power generation, and transmission13. One challenge and necessity in linking energy and water to the land is differentiating land cover from land use. Traditionally, land cover mapping has relied on remotely sensed multi-spectral imagery to understand the extent of natural ecosystems and human activities in the landscape as seen by the cover of the Earth’s surface, whereas land use describes the “arrangements, activities, and inputs people undertake within a particular land cover type”14.Understanding the broader environmental impacts among energy technologies requires translating energy life cycle components into landscape-relevant measures. Specifically, mapping land use of energy technologies is imperative, as it provides an estimation of the environmental footprint of electricity consumption15. For instance, the national land use dataset (NLUD) is a 30 m product comprised of 79 anthropogenic land use classes and provides an extensive categorization of sectors for land use mapping; however, layers pertaining to energy life cycles and water sectors are limited16. The national wall-to-wall anthropogenic land use trends (NWALT) dataset (60 m) provides 19 classes of anthropogenic land use17. Although it lacks the specificity of NLUD, it spans five time periods (1976 to 2012) and contains a critical layer (mining) that portrays a key component of energy life cycles17. The national land cover dataset (NLCD) is a high-resolution (30 m) depiction of the land surface cover modeled from Landsat imagery and released every five years between 2001 and 201918. NLCD depicts 28 classes of land cover, characterized to show complex land cover change19. While these land cover and land use datasets offer broad classification systems that include elements of energy use, they do not represent the majority of the water and land components of energy production life cycles.Although products that represent broad land components of energy sectors  are available for the conterminous United States, current land use data products are limited in their ability to capture all (or at least the majority) of life cycle components of energy sector relevant to landscape analysisHerein, we describe the development of a National Water and Energy Land Dataset (NWELD) depicting the land use of energy production life cycles and water sources pertaining to energy production in the lower 48 states. Although the NLCD extends to Alaska and Hawaii, our exclusion of these states are due to the lack of other land cover/use rasterized data supporting our analysis, particularly the NLUD and NWALT. The various classes of land use in NWELD are, in part, inspired by missing elements shared by NLUD and NWALT, but are extended to provide an operable template for MSD research. Depending on the availability and accuracy of source information, four methodologies involving geospatial techniques were used to create an 86-layer, 30m-resolution gridded product.20; some manufacturing is included where it can be linked to an energy production service . However, NWELD does not include the material acquisition aspects of construction and also excludes decommissioning phases21. This is due to the lack of decommissioning geospatial data from OSM and the inability to allocate upstream goods and services to a single energy production source. The inclusion of energy-water life cycles in NWELD are based on relatively simple criteria: the life cycle must have a spatial or mappable component supported by available data or reliable estimation methods. Only life cycles that occur on the surface of the earth are depicted; for example, only surface mines are represented, not underground mines or fields. Aside from extraction of essential fuels or elements that can be strictly tied to a sub-category, sourcing of other raw materials and manufacturing is not included.NWELD’s classification scheme is organized by the energy life cycles of major renewable and non-renewable energy sources: coal, hydropower, natural gas, oil, nuclear, bioenergy, solar, and wind Table . These e22. OSM has become a very popular resource since it is user friendly and created by contributors that describe the attributes of the objects created24. The database provides both local and worldwide vectors24 and is edited and improved by thousands of registered users so that their edits are stored and made available for others25. OSM provides a valuable resource for developing land use maps of energy and water infrastructures.Based on the types of raw datasets available for mapping energy and water features, we conceptualized two general paths for developing land-use models, where they could be used interchangeably: 1) a downscaling model process whereby a combination of rasterized land-cover products (i.e. surrogates) are used in conjunction with information at coarser boundaries to approximate land use for energy in a given area, or 2) direct allocation of land use based on proximate, highly accurate, and mutually exclusive vector boundaries that necessarily identify land use. Part of the problem with the second path lies with the completeness of open-access sources and technological limits to harness those sources that provide high spatial fidelity and accuracy. Developing energy and water land sector mapping is difficult and data intensive; it requires high-resolution depictions of power plants, transmission, and detailed infrastructure in a heavy data format. An example of an accessible data source that provides these types of footprints is Open Street Map (OSM)Four methodologies were used depending upon the availability, geometry, granularity, and type of raw data used to represent each energy life cycle Fig. . Essenti26 the R programming environment. Search terms varied depending upon the subclass in question. For example, search terms for coal power plants, gas power plants, hydro power plants, nuclear power plants, oil power plants, and solar farms were structured after that of Dunnet et al.22. Examples of R code for various states and subclasses are provided in the SI text.Data products used to develop each of the land use layers are also provided in Supplementary Table In cases where the geographic boundaries of a given energy life cycle or water footprint were readily available and accurately represented the final geometry , we extracted rasterized land cover within those known geographic boundaries. This methodology was commonly used for datasets with polygon or line geometry, but also occasionally used with point data if underlying infrastructure had a very small footprint  (Supplementary File A = πr2). Instances of missing OSM data are summarized in Supporting Information 2. Additionally, regression equations for buffer radii and any buffer modifications for energy life cycles are provided in Supporting Information 2.The second methodology used OSM polygons in two main ways. First, OSM polygons, when available and accurate, were used to directly allocate all land surfaces falling within the geographic boundary of a given energy life cycle at a location , theissen polygonsFinally, there were situations in which required datasets were either unavailable or lacked the specificity to utilize the previous three methodologies. This generally arose if source datasets had very few observations or there was a complete lack of OSM polygons for a life cycle component. In these cases, we manually digitized spatial footprints using aerial imagery, after which subsequent raster processing followed one of the first two methods  has an associated gridded integer raster product projected within the North American Datum 83 Conus Albers coordinate system. In total, NWELD has 86 layers for coal, hydropower, natural gas, petroleum, solar, wind, biomass, general renewable, infrastructure, and water sectors as they apply to energy production  of buffer radii linear regressions used to estimate spatial footprints – this information can be used in future evaluations of spatial footprint assessments in addition to examining potential sources of error in NWELD. Second, we compare NWELD classes to those of NLCD, NWALT and NLUD to differentiate our depiction of energy life cycles from standard land cover and land use products. Third, we used a random stratification process to spot check instances of each energy life cycle layer to determine how well the final product accurately depicts the observed energy use in aerial images. Finally, we compared the aerial footprint of energy technologies in NWELD to comprehensive reviews conducted by Fthenakis and Kim2 < 0.5. Models with higher performance included natural gas storage facilities (R2 = 0.61), solar farms (R2 = 0.62), and flood control dams (R2 = 0.55). Examples of layers with moderate explanatory power include petroleum refineries (R2 = 0.22), several of the layers depicting dams used for various purposes (R2 = 0.24–0.46), and substations (R2 = 0.39).The explanatory power of linear regressions for buffer radii varied widely according to energy life cycles; the majority of linear regressions had R30. Our results indicate that NWELD has little association with previous land use and land cover datasets. Cramer’s V values were 0.210, 0.203, and 0.174 for comparisons between NWELD with NLCD, NWALT, and NLUD, respectively. A visual depiction of this discontinuity between NWELD and contemporary land use-land cover datasets can be seen for nine of the NWELD layers  to conduct a visual comparison of the accuracy in the raster layer’s representation of edges and extent of a given energy life cycle compared to aerial imagery . Each sample was assigned a score from 1 to 3, indicating poor to excellent representation, respectively. Average scores are reported by layer Fig. .Fig. 10AGeneral renewable metal processing plants are very well represented by NWELD and ranked highest. Nuclear was the second best represented (2.97), followed by wind (2.8), transmission (2.75), natural gas/oil (2.5), water sources (2.37), oil (2.3), biomass (2.26), natural gas (2.1), infrastructure (1.93), coal (1.8), hydropower (1.77), and solar (1.6). The above process included 50 of the 86 NWELD layers. The remaining layers, such as underground mines/pipes, biomass crops, and biomass forests/mills were excluded because they were unsuitable for aerial assessment .29  per Terawatt-hour: coal, natural gas, nuclear, hydropower, wind, and solar. To calculate land intensity values for NWELD, we obtained electricity consumption estimates for the conterminous US from the Energy Information Administration . See Supplementary File 3 for more details on calculations. Finally, to examine the robustness of our geospatial methods, we compared applicable NWELD layers to the mathematical calculations of previous reviews of land used for electricity29 Fig. . First, 2 TWh−1), NWELD’s estimates are larger than the literature, with wind as the exception  or raster math , NWELD’s raster surfaces can be translated into measures of flux or risk surfaces.NWELD offers a rasterized dataset depicting the land use of energy resource extraction, transportation, production, and operations, as well as the land use of water sources related to energy production. Each raster layer is provided at 30-m resolution and depicts the spatial distribution of a respective energy life cycle category, where the spatial extent only maps a life cycle’s presence, denoted as 1. Raster layers can be summarized using a number of spatial statistics, such as agglomerating grid results to coarser regions  emissions21, studying the energy sector’s socioeconomic impacts33, or investigating the most compatible energy types for specific urban areas34. NWELD also provides a resource for land use planning. Specifically, land management organizations can utilize NWELD to create innovative land use plans that consider the effect of energy consumption, production, and resource extraction on local ecosystem integrity and habitat fragmentation. NWELD provides a means to explore regional heterogeneity in the land-use efficiencies of energy technologies to cross-compare energy resource assessments with land consumption and minimize future deployment impacts by avoiding regions where land-use efficiency is low.NWELD is a dramatic improvement in understanding land use for energy production and transmission, whereas previous land use/cover rasterized datasets such as NLCD, NWALT, and NLUD provide rather vague land uses/covers that do not differentiate amongst these detailed uses. The granularity and specificity of land use data afforded by NWELD could be used to compare and validate previous spatial footprint calculation methods used to compare energy technologiesAmong the limitations of NWELD are that it approximates area coverage. Therefore, NWELD is not intended to be used as a site-by-site assessment of the precise layout of the US’s energy infrastructure, but rather, a spatial product for regional assessment, modeling, and nexus applications. Approximations of the energy life cycle footprints are based on the layer’s methodology, underlying datasets, including the accuracy of rasterized datasets used to refine the final product. As indicated, we used linear regressions to estimate the area of polygon buffers, i.e., the footprint of a given energy life cycle. While this is undoubtedly a source of error, these models could also be useful for predicting land area requirements or projecting future land use development related to energy resource deployment.Supplementary File 2Supplementary File 3Supplementary Table 1Supplementary Table 2Supplementary File 1