N-(2-methoxybenzyl)phenethylamine (25I-NBOMe) is a new psychoactive substance with strong hallucinogenic properties. Our previous data reported increased release of dopamine, serotonin, and glutamate after acute injections and a tolerance development in the neurotransmitters release and rats’ behavior after chronic treatment with 25I-NBOMe. The recreational use of 25I-NBOMe is associated with severe intoxication and deaths in humans. There is no data about 25I-NBOMe in vivo toxicity towards the brain tissue. In this article 25I-NBOMe-crossing through the blood–brain barrier (BBB), the impact on DNA damage, apoptosis induction, and changes in the number of cortical and hippocampal cells were studied. The presence of 25I-NBOMe in several brain regions shortly after the drug administration and its accumulation after multiple injections was found. The DNA damage was detected 72 h after the chronic treatment. On the contrary, at the same time point apoptotic signal was not identified. A decrease in the number of glial but not in neural cells in the frontal (FC) and medial prefrontal cortex (mPFC) was observed. The obtained data indicate that 25I-NBOMe passes easily across the BBB and accumulates in the brain tissue. Observed oxidative DNA damage may lead to the glial cells’ death.4-Iodo-2,5-dimethoxy- The most popular classic psychedelics representative, i.e., lysergic acid diethylamide (LSD), does not seem to evoke direct toxicity in humans2. However, hallucinogens belonging to the so-called new psychoactive substances (NPS) are related to many severe intoxications and deaths3. In 2003, phenethylamine derivatives from the NBOMe series were synthesized4 for research purposes as potent serotonin 5-HT2A receptor agonists, and in 2010 they appeared on the illicit drug market5. NBOMe compounds are N-2-methoxy-benzyl substituted 2C hallucinogens that exhibit a nanomolar binding affinity mainly to the serotonin 5-HT2A (0.5–1.6 nM) and 5-HT2C (4.6–130 nM) receptors6. Up to date studies confirmed the impact of 25I-NBOMe on several neurotransmitters’ pathways. The drug affects the dopamine system pointing out its possible addictive properties7. What is more, 25I-NBOMe impacts the serotonin system and causes the characteristic for hallucinogens to increase in the cortical glutamate release8. Our recent study confirmed the contribution of 5-HT2A and 5-HT2C receptors in effect on both brain neurotransmission and hallucinogenic activity in rats9. Moreover, the tolerance development in the neurotransmitters’ release and animals behavior was observed after 7-days treatment with 25I-NBOMe10. Due to their similar pharmacological effects on a human, NBOMe series were sold as LSD11. Available case studies confirm NBOMe-linked toxicities at low doses, with the dose of 50 μg being sufficient to produce psychoactive effects. 4-Iodo-2,5-dimethoxy-N-(2-methoxybenzyl)phenethylamine (25I-NBOMe), the most commonly encountered of the NBOMe group, has been identified in plasma, urine, and postmortem heart blood. Typical physiological effects after NBOMe intake include hypertension, tachycardia, diaphoresis, and dilated pupils. Moreover, NBOMe toxidrome, apart from hallucination induction, also causes neuropsychiatric effects, such as delirium, agitation, aggression, seizures, and perceptual disturbances5. According to some reports, the occurrence of NBOMe compounds was confirmed in the same blotters marked as LSD. Moreover, NBOMe series were found in tablets sold as MDMA12. Injections with 25I-NBOMe (encoded as 25I-NBMeO) at doses 0.1, 0.3, and 1 mg/kg significantly decreased the number of surviving BrdU+ cells and BrdU/NeuN+ neurons in the hippocampus in comparison to control rats suggesting decreased neurogenesis in this brain region13. In the in vitro studies, 25I-NBOMe decreased the viability of H9c2 cells and primary mice cardiomyocytes. Moreover, in vitro activity of p21 (CDC42/RAC)-activated kinase 1 (PAK1) was also reduced, and the QT interval measured by electrocardiography in rats was prolonged. Thus, it may be stated that the drug produces cardiotoxicity14. Furthermore, in vitro studies with a close congener of this class, 25C-NBOMe (25–400 µM), showed a potent reduction of SN4741, SH-SY5Y, and PC12 cells viability indicating 50 times higher potency to reduce SH-SY5Y cells in respect to methamphetamine15. The incubation of the rat primary cortical cultures with 25B-NBOMe (30 μM) decreased neuronal activity that did not recover after 19 h of washout period16. Our in vivo studies indicated that the low dose of 25B-NBOMe (0.3 mg/kg) was potent in damaging DNA in the rat frontal cortex17.From the physiological point of view, hallucinogens are one of the safest classes of psychoactive substances. They powerfully alter perception and mood but do not produce dependence and addiction18, intoxications, and deaths, aspects of the in vivo 25I-NBOMe-mechanism of neurotoxicity has to be explored in depth. Thus, the present study aimed to assess the induction of in vivo neurotoxicity in the rat brain after single and repeated 25I-NBOMe treatment. The distribution of the drug in brain regions was determined using mass spectrometry. DNA damage of 25I-NBOMe was evaluated using a comet assay test, and the apoptotic signal was detected using TUNEL assay. Moreover, an immunohistochemical assessment of cells number in the rat brain was studied.Although many studies report adverse effects like seizuresThe performed analysis confirmed the presence of 25I-NBOMe with the precursor 428.14 m/z ion and two fragmentation ions 121.62 and 272.28 m/z  and IBA-1-immunopositive cells  was significantly decreased in the FC  was observed in the mPFC of animals treated chronically with 25I-NBOMe  and mPFC  , frontal cortex (FC), and hippocampus  Fig. b,d,f. Th2,29 = 237, p < 0.0001) and hippocampus , presented as a percent of tail moment measured at 72 h after the drug administration . There was a significant increase in the tail moment value in the hippocampus after acute and chronic treatment with LSD . Similarly to the frontal cortex, the repeated injections caused a higher DNA damage than the single dose (p < 0.02). MDMA, as a positive control in this test, given acutely and chronically at the dose of 5 mg/kg exhibited DNA damaging properties in the frontal cortex  and hippocampus . There was a stronger increase in the tail moment value in the frontal cortex (p < 0.001) and hippocampus (p < 0.001) after four injections of MDMA in comparison to the acute one. Typical microscopic images of nuclei from control, 25I-NBOMe, LSD and MDMA-treated rats were shown in Fig. Single and repeated treatment with 0.3 mg/kg of 25I-NBOMe produced DNA damage by reactive oxygen species (ROS) in the rat frontal cortex , the performed assay did not indicate apoptotic signal in brain slices from rats treated acutely or chronically with 25I-NBOMe given at the dose of 0.3 mg/kg. Since qualitative examination of sliced material did not provide any positive signal from the rat frontal cortex and anterior and posterior hippocampus (only a few isolated cells), the quantitative examination was not continued. The representative data are presented in Fig. Our study indicated that 25I-NBOMe easily crosses the blood–brain barrier (BBB) since it was detected both in the blood serum and several brain regions 15 min after the injection. Repeated administration of 25I-NBOMe caused drug accumulation in the brain tissue. Seven-days treatment with 0.3 mg/kg of 25I-NBOMe decreased the number of astrocytes in the FC and mPFC and the number of microglia cells in the FC. On the other hand, the chronic treatment did not affect the number of cells in the hippocampus. Furthermore, the drug produced double- and single-strand DNA breaks by ROS after single and multiple injections in rats’ frontal cortex and hippocampus. However, these changes did not lead to cell death by apoptosis. Our data, for the first time, presents the in vivo neurotoxicity of 25I-NBOMe.19 study, carbon-11 labelled 25I-NBOMe known as 11C-CIMBI-5 was located in the cortex and the lower range in the cerebellum shortly after the intravenous injection. In our study, among other brain regions, 25I-NBOMe accumulated mostly in the frontal cortex and hippocampus. This effect may be explained by the significant density of the 5-HT2A receptors to which 25I-NBOMe exhibits a high binding affinity and which are located on both cortical pyramidal cells and GABA interneurons21 as well as pyramidal cells in CA1, CA2, and CA3 regions of the hippocampus22.25I-NBOMe was detected in the blood serum shortly after subcutaneous injections. The calculated spectral intensity was an order of magnitude higher after the 10 mg/kg dose than 1 mg/kg. Similarly, 15 min after injections, the drug was detected in the brain structures, mainly in the nucleus accumbens. It suggests fast and efficient penetration of 25I-NBOMe to the brain. The high intensity of 25I-NBOMe spectra from the nucleus accumbens and striatum could be explained by the first pass of the drug through more ventral subcortical brain regions and then crossing to more distant dorsal regions, such as cortex and hippocampus. Repeated administration with 0.3 mg/kg for 7 days of 25I-NBOMe resulted in the drug accumulation in all studied brain regions since the precursor ion intensity was increased in this experimental group compared to the single-injected one. In Ettrup et al.15. Another congener 25B-NBOMe was also cytotoxic and attenuated neuronal activity of primary cortical cultures16. Our in vivo experiments indicated astrocytic cells loss as labeled with S100β after repeated treatment with 25I-NBOMe in the FC and mPFC and decreased the number of microglia cells as labeled with IBA-1 in the FC. Presented results do not indicate neurons loss in both cortical regions and the rats' hippocampus. S100β is a monomeric protein primarily synthesized by the end feet process of the astrocytes. It was found mainly in the nucleus and cytoplasm of astrocytes. S100β released from the cell may have trophic or toxic effects depending on its concentration. It has a neuroprotective effect at low concentrations, while at higher doses, it inhibits neuronal proliferation and differentiation and induces neurodegeneration and apoptosis23. In our immunohistochemical studies, we observed a lower number of S100β-positive cells in the FC and mPFC that may correspond to a decreased number of astrocytes in response to 25I-NBOMe treatment. Furthermore, astrocytes may release S100β protein extracellularly, affecting both astrocytes itself and neurons24. It is believed that a high S100β protein level is observed in a wide variety of pathological conditions and is negatively correlated with BBB integrity25. On the other hand, IBA-1 is a cytoplasmic protein specifically expressed in microglia. The role of microglia is mainly related to phagocytosis and removing damaged and apoptotic cells, neurofibrillary tangles, and plaques, but also DNA fragments26. Moreover, apart from the phagocytosis of apoptotic cells, microglia are also a key molecules in proinflammatory activity, maintaining homeostasis and controlling the fate of neurons and their progenitors27. It could be speculated that decreased number of microglia and astrocytes observed in our experiments may be related to the weakened repair mechanisms and defense functions of these cell types. Thus, 25I-NBOMe may pass easily and quickly through the damaged BBB and accumulate after chronic administration in the brain tissue, as demonstrated in our study.In vitro studies with the NBOMe series suggest toxic properties of these drugs. Twenty-four hour incubation with a wide range of 25C-NBOMe doses potently reduced the viability of cells28 suggests the genotoxic capacity of phenethylamines in high concentration and hypothesizes a possible involvement of oxidative stress that does not always minimize cell viability. Our data is in vivo confirmation of this study showing the DNA damaging effect of phenethylamine congener 25I-NBOMe. Furthermore, our findings show the regional difference in neurotoxicity of 25I-NBOMe, indicating its damaging impact on cortical microglia and astrocytes being the important defense line in the brain. The lack of apoptotic signal detection in the TUNEL assay may be related to the time point of 72 h as insufficient to induce programmed cell death. For better understanding of a time of the apoptosis induction, the research in different time points is needed. Nevertheless, our data indicate the decrease in markers of glial cells, which corresponds to the appearance of double- and single-strand DNA breaks and points to the defective defense system of these cells but not neurons. Notably, the difference between brain regions in cells resistance against damage is noticed. Interestingly, LSD was weaker in its damaging effect on DNA than 25I-NBOMe due to its different receptor profiles6 and its different impact on glutamatergic and dopaminergic neurotransmission29. Although, LSD is considered to be a safe substance, there are some older reports pointing out the possibility of causing chromosomal aberrations in vitro and in vivo, especially in the higher doses and in the long-term use31. Moreover, it was proposed that LSD may act directly on DNA by intercalation within DNA helix producing conformational changes that were not sufficient to decrease internal stability32. Thus, our data are the first that assess LSD effect on DNA damage studied in the comet assay. MDMA, known as oxidative stress inductor34 and used in our study as a comparator, was nearly equal to 25I-NBOMe in oxidative damage of DNA in both brain regions.Observed DNA damage at 72 h after single and repeated administration suggests neurotoxic properties of 25I-NBOMe. Cortical cells seem to be less resistant to the influence of 25I-NBOMe since our immunohistochemical studies indicated changes in cells number only in the FC and mPFC but not in the hippocampus. What is more, mass spectrometry analyses showed a higher accumulation of the drug in the frontal cortex with respect to the hippocampus. The in vitro work of Cocchi et al.Our data clearly indicate that phenethylamine hallucinogen 25I-NBOMe sold as a replacement for LSD and acting with similar potency as LSD may cause severe neurotoxicity by inducing oxidative stress and suppressing the defense role of astrocytic and microglial cells in rat frontal cortex and hippocampus.All experiments were performed on male Wistar–Han rats , weighting 280–320 g. The animals were initially acclimatized and housed in groups of 5 each in temperature (23 ± 1 °C) and humidity (55 ± 10%) controlled rooms under a 12 h light/dark cycle (light was turned on at 6 a.m.), with free access to tap water and standard laboratory food . The experiments were conducted in accordance with the European regulations for animal experimentation . The 2nd Local Institutional Animal Care and Use Committee (IACUC) in Kraków, Poland (Permit number: 189/2017) approved the experimental protocols. The study was carried out in compliance with the ARRIVE guidelines. This article does not contain any studies with human participants by any of the Authors.N-(2-methoxybenzyl)phenethylamine hydrochloride (25I-NBOMe) was purchased from Chiron AS . The chemicals used for the alkaline comet assay were from Trevigen  and Merck . TUNEL assay was performed with the usage of Click-iT™ Plus TUNEL Assay of Invitrogen, Thermo Fisher Scientific . The reagents used in immunohistochemistry came from Sigma Aldrich , Vector Laboratories , and Proteintech  and the one for mass spectrometry were from Merck . Animals were anesthetized with the usage of sodium pentobarbital .4-Iodo-2,5-dimethoxy-Animals received single or multiple (once per day for seven days) subcutaneous injections of 25I-NBOMe dissolved in 0.9% NaCl at a dose of 0.3 mg/kg/2 ml. The control group was treated with 0.9% NaCl solution in the same way. All procedures were performed 72 h after the last injection. The time point of 72 h was chosen due to the possible generation of neurotoxicity via oxidative stress caused by reactive oxygen species (ROS). Exceptionally, for initial 25I-NBOMe brain distribution studies, rats received the drug in the dose of 1 and 10 mg/kg and were sacrificed 15 min after the drug administration. The rat blood plasma and brain tissues were collected immediately after rats’ decapitation. LSD  was given daily for 7 days in a volume of 1 ml/kg, while MDMA 5 mg/kg, ip was injected in a volume of 2 ml/kg every second day .4, and were centrifuged at 10,000×g for 10 min at 4 °C. Blood was incubated for 30 min and then centrifuged for 15 min, 3000 rpm at 4 °C. The collected blood serum and tissue supernatants were frozen at − 80 °C until the time of mass spectrometry analysis. The value pH of samples was about 2. To purify the samples, solid phase extraction (SPE) columns MacroSpin Columns Silica C18  were used. Initially, columns were activated with 100% methanol (MeOH) solution by double centrifugation at 110×g for 1 min. Next, to remove MeOH, 2% acetonitrile (ACN) with 0.1% HCOOH was used. Subsequently, the columns were washed twice with the blood and tissue samples. To remove adenosine-5'-diphosphate (ADP) that has the same precursor ion as 25I-NBOMe the columns were washed twice with 2% ACN. Finally, to wash out 25I-NBOMe, the column was washed twice with 80% ACN with 0.1% HCOOH. Samples were left to dry in SpeedVac Vacuum Concentrator  overnight. The following day, samples were dissolved in 10% ACN with 0.1% HCOOH and injected to the LC–MS/MS system. The nanoLC-MS/MS analysis used to separate compounds was performed using the Ultimate 3000 nanocapillary chromatography system . The separations were performed using a PepMap column . The gradient was formed using 0.1% HCOOH in H2O (solvent A) and 0.1% HCOOH in ACN (solvent B), at a flow rate of 300 nl/min. The system was controlled by the Hystar software . A gradient was produced from 5 to 80% B in 100 min, then kept at 90% of phase B for 10 min. The mass spectrometry detection was performed in positive ion mode by electrospray ion trap mass spectrometry . The retention time of 25I-NBOMe was about 30.3 min. The acquired mass spectra were analyzed using the Bruker Data Analysis v. 4.0 software . The data presented in tables are calculated as intensity per 1 mg of wet tissue or 1 ml of blood serum, while mass spectra represent the real intensity of 25I-NBOMe-precursor ion from tissue samples with different mass.The presence of 25I-NBOMe in the rat brain regions and blood serum was analyzed with electrospray ion trap mass spectrometry . At 72 h after the last injection with 25I-NBOMe or 15 min after injection of single low and high (1 and 10 mg/kg) dose, animals were sacrificed by decapitation. Brains were removed and the frontal cortex, striatum, nucleus accumbens, and hippocampus were dissected. The blood samples were also collected after 25I-NBOMe injections. Tissue samples were homogenized in an ice-cold 0.1 M HClO2O2) solution to give a brown color to NeuN, S100β and IBA-1-immunoreactive cells. For data presentation, digital images were captured using a digital camera CX 9000  attached to a Leica microscope (CTR 6000) with 2.5 and 5.0 dry or 63 × and 100 × oil objectives (Leica) that was controlled by Stereo Investigator software . The final photomicrographs were composed using the Adobe Photoshop program. The numbers of NeuN-, S100β- and IBA-1-immunopositive cells in the analyzed brain regions were estimated using unbiased stereological methods36. Briefly, every sixth section from the systematic random sampling along the rostrocaudal axis was analyzed with a 63 ×/1.4–0.7 lens using the Stereo Investigator stereology system software. The cells appearing in the upper focal plane were omitted to prevent the counting of cell caps (− 5 μm of the topmost surface of the section). Immunopositive cells were marked when they were within the optical dissectors, which comprised a focal plane of 1600 μm2 × 15 μm. The total numbers of NeuN, S100β- and IBA-1-immunopositive cells in the FC, mPFC, and hippocampus were automatically calculated by the Stereo Investigator software. In addition, the volume of the cortical regions and hippocampus was calculated using the Cavalieri Estimator option of the Stereo Investigator software.Animals were deeply anesthetized and transcardially perfused with 0.9% NaCl followed by 4% paraformaldehyde (PFA) in 0.1 M phosphate-buffered saline (PBS). After 24 h of fixation in 4% PFA (4 °C), 300 µm sections were cut through the frontal cortex (FC) and medial prefrontal cortex (mPFC) as well as hippocampus using a VT-1000S vibratome . Free-floating sections were processed for single staining of neurons with neuronal-specific nuclear protein (NeuN), glia with specific calcium-binding protein B (S100β), and microglia cells with ionized calcium-binding adaptor molecule 1 (IBA-1) antibodies. Subsequently, brain sections were rinsed and incubated for 1 h in a blocking buffer: 0.01 M PBS containing 0.3% Triton X-100 and 5% average horse, goat, or rabbit serum. After that, the sections were incubated for 48 h at 4 °C with one of the following primary antibodies: monoclonal anti-NeuN (1:1000), monoclonal anti-S100β-subunit (1:1000) or polyclonal IBA-1 (1:500) diluted in 0.01 M PBS containing 0.3% Triton X-100 (PBST) and 3% average horse or goat serum. Primary antibody binding was visualized with biotinylated secondary antibodies, the Avidin/Biotin Complex (Vectastain Elite ABC Kit) according to recommended by manufacturer concentration and 3,3′-diaminobenzidine tetrahydrochloride , LSD  or MDMA  animals were sacrificed by decapitation and the frontal cortex (that consist of the sum of FC and mPFC) and hippocampus were dissected. After homogenization and several purification and centrifugation stages (as described previously in17), the nuclear suspension was obtained using a sucrose gradient . The nuclear fraction was mixed with low melting point agarose and transferred immediately onto CometSlides™. The following steps, including membrane lysis, DNA unwinding, alkaline electrophoresis, and staining (SYBR® Gold), were carried out according to Trevigen CometAssay® protocol. Stained sections were acquired and analyzed under a fluorescence microscope  equipped with a camera and NIS Elements software. The data was analyzed using OpenComet software v.1.3 (cometbio.org), a plugin of ImageJ program v.1.47 . DNA damage was presented as a tail moment. Tail moment incorporates a measure of both the smallest detectable size of migrating DNA (reflected by the comet tail length) and the number of damaged pieces (represented by the intensity of DNA in the tail).The alkaline comet assay was performed with the use of CometAssayAs described above, animals were perfused transcardially and brains were fixed overnight in 4% PFA, then gradually dehydrated and stored in 70% ethanol (EtOH). Paraffin-embedded brain tissue was sectioned on a microtome  for 7-µm-thick sections. For further experiments following sections from the region of the frontal cortex, anterior and posterior hippocampus were collected. Next, chosen microscope slides with tissue sections were deparaffinized in xylene and declining concentrations of EtOH and remained in 0.01 M PBS. The apoptotic signal was assessed using Click-iT™ Plus TUNEL Assay for in situ apoptosis detection with Alexa Fluor™ 488 picolyl azide dye. For positive control, brain slices were incubated with 1 unit of DNAse I  as recommended by manufacturer. Vectashield Mounting Medium (Vector Laboratories) with DAPI  was used for slipping and counterstaining the slides. Stained slides were examined under a fluorescence microscope  equipped with a camera and NIS Elements software.p-value was smaller than 0.05. All statistical analyses were carried out using STATISTICA v.10 StatSoft Inc. 1984–2011 , and the figures were prepared in the GraphPad Prism v.9.00  and Adobe Photoshop program v. 19.0.Presented results were analyzed with one-way ANOVA followed by Tukey’s post hoc test. The differences were considered significant if the Supplementary Figure S1.


The influence of the rGO content on the physico-mechanical properties of the oxide composite was revealed. Graphene oxide was obtained using a modified Hummers method. Well-dispersed ZTA-GO nanopowders were produced using the colloidal processing method. Using spark plasma sintering technology (SPS), theoretically dense composites were obtained, which also reduced GO during SPS. The microstructure, phase composition, and physico-mechanical properties of the sintered composites were studied. The sintered ZTA composite with an in situ reduced graphene content of 0.28 wt.% after the characterization showed improved mechanical properties: bending strength was 876 ± 43 MPa, fracture toughness—6.8 ± 0.3 MPa·m1/2 and hardness—17.6 ± 0.3 GPa. Microstructure studies showed a uniform zirconia distribution in the ZTA ceramics. The study of the electrical conductivity of reduced graphene oxide-containing composites showed electrical conductivity above the percolation threshold with a small content of graphene oxide (0.28 wt.%). This electrical conductivity makes it possible to produce sintered ceramics by electrical discharge machining (EDM), which significantly reduces the cost of manufacturing complex-shaped products. Besides improved mechanical properties and EDM machinability, 0.28 wt.% rGO composites demonstrated high resistance to hydrothermal degradation.This paper presents a study of Al However, this method is only possible for materials with an electrical resistance below 100–300 Ω·cm ,26,27. TThe aim of this work was to prepare homogeneous mixtures of ceramic powders with the addition of GO by colloidal processing and freeze-drying to obtain alumina-based composites with 16 vol.% 3Y-TZP and various concentrations of GO and to study the microstructure, physico-mechanical properties, and aging resistance of these materials.50 = 3 mm was used.A modified Hammers method was used to obtain graphene oxide (GO) from graphite powder. This process was described in more detail in previous papers ,36,37. I2 , and alumina  powders with particle size d50 = 0.20 µm and d50 = 0.30 µm, respectively were used in this work. The content of the powder mixture was 84 vol.% of Al2O3 and 16 vol.% ZrO2. The necessary amount of powders was placed in a plastic container with Al2O3 balls (diameter 3 mm), distilled water, and Dolapix CE 64 as dispersant. Obtained mixture was wet mixed in a multi-directional mixer for 24 h at 150 rpm and subsequently dried by FreeZone2.5 freeze-drying system . Distilled water with the pH value of 10 was added to the produced mixture, which then was dispersed under mixing for 30 min. To obtain compositions with the required content of graphene oxide, it was added drop by drop to the suspensions and stirred for 1 h. To obtain the powder mixtures for further consolidation by SPS, resulting suspensions were dried at 110 °C in a Lab spray dryer . Thereby, ZTA powders with GO content of 0, 0.28 wt.% (0.5 vol%), 0.52 wt.% (1 vol.%), and 6.1 wt.% (5 vol.%) were prepared and labeled as 0-G, 0.5-G, 1-G, and 5-G, respectively. All powder mixtures were sintered in an H-HP D 25 SD Spark Plasma Sintering machine  in vacuum at 1500 °C applying heating rate and pressure 100 °C/min and 80 MPa, respectively. The isothermal hold at final temperature was 3 min. The produced samples had diameter of 20 mm and a height of 3 mm. During sintering, the in situ thermal reduction of the GO takes place. In this work, the obtained carbon material after consolidation will be referred to as reduced graphene oxide (rGO).Commercial t-ZrOA field emission scanning electron microscopy LYRA3  equipped with an X-Act energy dispersive spectroscopy detector  was used to study the microstructure. Before the studies, the samples were polished using diamond polishing slurries with grit sizes ranging from 9 micrometers to one micrometer. After polishing, the samples were washed in ultrasound bath in ethanol for 15 min and dried using compressed air.θ angle range of 5–70°. The analysis was carried out with a scanning speed of 0.06/min and a step size of 0.05. Raman analysis was achieved in a Raman analyzer DXRTM2,  using a 532 nm laser with a power of 2.0 mW for the control of the graphene-based mixtures and composites. A 50× optical microscope objective was applied to focus the laser beam on the studied area into a 2 μm spot with an accumulation time of about 10 s [Phase identification of sintered samples and raw powders was carried out by X-ray Diffraction in an Empyrean diffractometer  with radiation source Cu–Kα (λ = 1.5405981 Å) operated with an intensity of 30 mA at 40 kV  in the 2out 10 s .2 was completed by X-ray diffraction on the sample surface by means of aging experiment interruptions at given times. The amount of m-ZrO2 and its volume fraction were estimated according to the Garvie and Nicholson [Aging experiments were performed at 134 °C for 30 h with a pressure of 200 kPa in an autoclave . The t → m transformation control of ZrOicholson  as well icholson  methods,2 density of 6.05 g/cm3, Al2O3 density of 3.98 g/cm3, and rGO density of 2.2 g/cm3.The density of the composites was measured by Archimedes’ method in distilled water. The theoretical density was calculated using a ZrOvH) was measured from 10 footprints  per sample under load and loading time 98 N and 10 s, respectively. To estimate the average of hardness values the following equation was used:P was the set load (N); and d is the average length of two diagonals (mm).Their Vickers hardness  and (3) proposed by Miranzo and Moya [vH is Vickers hardness of material (GPa), P is the applied load (N), d is the average length of the two diagonals (mm), c average length of cracks (mm), and E is the Young’s modulus of material (GPa).The values of Vickers indentation fracture toughness, and Moya .(2)KC=0fσ) was evaluated through a biaxial bending test (ISO 6872). Each sample was placed onto a device with three balls of 3 mm in diameter that were made of hardened steel and disposed on a holder (10 mm in diameter) at 120° to each other. The load was applied with an AutoGraph AG-X  universal testing machine by means of a plain head of 1.6 mm in diameter at a speed of 1 mm·min−1 up the failure. The specimen thickness was measured at the breakage point. The strength value was calculated by averaging the data after testing 12 sintered disks. The equation for strength calculations was described in a previous work [The flexural strength  in the frequency range from 20 Hz to 5 MHz. To ensure identical electrical contact between the samples and the device electrodes, silver-based conductive adhesive Rexant Kontaktol-Avto  with a specific electrical resistance of 0.0 Ohm·mmRepresentative X-ray diffraction patterns corresponding to powder mixtures and sintered composites are shown in 2θ = 26.5° for the fabricated graphene oxide, moved to its most intense peak located at 2θ ~ 11.2°. Graphite is not presented in the GO curve, which confirms its complete oxidation due to the formation of various oxygen functional groups. A well-defined peak (002) centered at ~25° on the diffractogram of rGO is observed after the thermal reduction of graphene oxide during sintering  shows structural flaws, such as the lattice deformation [The wide G peak and slight second-order area are features of spgraphene  and the ormation . The redD/IG) It was found that this intensity in the raw powder mixtures reached 1.01, 0.98, and 0.99 for 0.5, 1, and 5 vol.% of GO, respectively. On the other hand, in the sintered composites, ID/IG diminished to 0.38 and 0.51 for 0.28 and 0.52 wt.% rGO, respectively, which proves less defectivity of the rGOs into the thermally treated samples. As for the SPSed composites with 6.1 wt.% rGO, this ratio was 0.85, highlighting the presence of many defects. Perhaps thermal reduction of the composition with a high content of graphene oxide requires more dwelling time at the maximum temperature. However, an increase in sintering time may promote grain growth of the ceramic matrix and, consequently, deterioration of the mechanical properties. In addition, a well-resolved two-dimensional symmetric peak at ∼2700 cm−1 appears. An increase in the I2D/IG ratio to 0.66 compared to the raw powder mixtures (~0.12) was found in SPSed composites, regardless of the composition. It also confirms the restoration of the graphene structure after sintering. The results confirmed the thermal reduction (including the reduction of large sp2 regions) of the graphene oxide during sintering at 1500 °C. These spectra indicate that GO was in situ reduced (rGO) during SPS. This is confirmed by the decrease in the intensity ratio between the D- and G-bands .Obviously, the presence of GO leads to a decrease in grain size. As for the 5-G composition, the high content of graphene oxide in the raw mixture did not allow a homogeneous distribution of ZrOThe fracture surfaces of the specimens broken in the flexural loading test are shown in 2 in the ceramic matrix. Only in the 5-G composition were agglomerates of ZrO2 observed. The micrograph shows that the ZrO2 grains are embedded around the Al2O3 grain. These images confirm the uniform distribution of ZrO1/2 for strength and fracture toughness, respectively. The dependence of the Vickers hardness (HV), fracture toughness (Kc), and flexural strength (σf) of the studied composites on the percentage of graphene oxide in the initial mixture is presented in 2 agglomerates at a higher rGO content in the composite matrix.Suarez et al. reported that the mechanical behavior of alumina can be improved by the presence of a small amount of graphene . The cle−1), which indicates that these samples are suitable for electrical discharge machining. This proves that an addition of 0.5% rGO is already sufficient to form conductive pathways in the ceramic matrix. As the rGO content continues to increase, the electrical resistance of the samples continues to decrease.This can be explained by the fact that with increasing rGO content there is an increase in inter-sheet bonds, which cause an improvement in conductivity along the planes a–b (orientation: perpendicular to the direction of pressure applied during sintering of the spark plasma). A serious drawback of using carbon nanotubes to fabricate composites is that they tend to form agglomerates and which inhibits contact between points. Such heterogeneity leads to a decrease in the electrical conductivity behavior of the composites. At the same time, the presence of rGO in the material leads to the emergence of contacts between lamellated graphene particles such as “area to area”. As a consequence, tightly adjacent and interconnected highly conductive carbon networks lead to lower electrical resistivity. In addition, the removal of oxygen-containing groups during thermal reduction of GO increases its specific surface area, which leads to high electron mobility and the formation of conductive pathways resulting in improved electrical conductivity.2 transformed into m-ZrO2 in the composites with 0, 0.28, and 0.52 wt.% GO, respectively. The resulting monoclinal phase values in the newly developed composites are below the ISO 13356 limit of 25% for yttrium-stabilized tetragonal ZrO2-based ceramics [2 in the Al2O3 matrix.At the beginning of the low-temperature degradation process, the monocline phase content on the polished surfaces was 1%, 2%, 4%, and 10% for composites with 0, 0.28, 0.52, and 6.1 wt.%. GO, respectively. The significant difference between these values appears after 10 h in the autoclave at 134 °C. For composites with 6.1%, the amount of monoclinal phase is already 27%, which is almost three times higher than at the beginning of the experiment. While in other composites, this difference is not significant and is 5%, 6%, and 8% for 0, 0.28, and 0.52 wt.%. After 30 h in the autoclave at 134 °C, the composite with the highest content of the graphene oxide monoclinal phase is already 78%, which is more than half of the tetragonal phase that has been transformed. Whereas only 18%, 20%, and 22% of the initial t-ZrOceramics . Therefo1/2), and hardness (17.6 GPa) were obtained for the composites with the lowest GO content. Additionally, these composites also demonstrated increased resistance to low-temperature degradation. Thanks to their combination of mechanical performance and phase stability, we feel that this composition is a suitable alternative for medical implants. Moreover, the presence of carbon structures leads to sufficient conductivity of the studied composites for electrical discharge machining. The compositions with graphene oxide of more than 0.28 wt.% demonstrate the loss of mechanical properties by the agglomerates of the graphene structures.ZTA-GO composites  were manufactured through a wet processing route and spark plasma sintering. The use of low GO content as an additive to the ZTA ceramic matrix enhances fracture toughness and hardness at the same time. The highest strength (876 MPa), fracture toughness (6.8 MPa·m


During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance. So, it is important to define the geographical patches based on actual mobility of individuals in order to implement preventative policies with greater precision. In most cases, quarantine policies and data related to the COVID-19 outbreak are based on arbitrary borders such as state or county boundary lines [The emergence and spread of the 2019 novel coronavirus (SARS-CoV-2 or COVID-19) has caused a global health emergency. With a high level of observed contagiousness . It is cry lines . In the For the purposes of examining the spread of COVID-19 in the US, mobility patterns can be characterized in three overarching concepts: short distance , medium distance (e.g. travel for job or fun) and long distance . While short-distance movements lead to the local spread of the virus, medium and large-distance movements distribute the virus across larger scales to other cities and states. Travel can be thought of as occurring in ‘bubbles’ of progressively larger geographical scales. National travel bubbles include the collective movement of individuals travelling long distances from one region of the country to another. This type of mobility pattern was quickly identified as risky and attempts to limit it within the country were put in place. For example, at the very beginning of the North American outbreak in March 2020, a group of university students from around the country gathered on Florida beaches for uninhibited socialization during spring vacation, also known as ‘Spring Break’. During this gathering, local transmission of COVID-19 was detected and the disease subsequently spread to other regions of the country long distances away from the original outbreak . Soon afThe recent availability of large-scale human activity datasets has greatly improved our ability to study social systems –12. Geo-Borders of patches are subject to vary during global events such as the COVID-19 pandemic. In this work, we study the dynamics of the modular structure of individual movements within the US in order to quantify effectiveness of policies that seek to lower transmission by reducing the amount of social movement and distance of travel within the population. We use anonymized cellphone data collected by SafeGraph company from February to May 2020 to build mobility networks based on the movement of mobile device users between census block groups (CBGs). SafeGraph data have been widely used in detecting mobility patterns during the current pandemic. By applying the Louvain method as a community detection algorithm, fragmentation patterns of the networks are extracted. Patches represent the areas in which residents spend the most time. Applying the algorithm at multiple scales reveals the multi-scale modular structure of society and the features of that structure during the pandemic. We carefully study the properties and statistics of these patches and their dynamics in the presence of lockdown restrictions using the location of COVID-19 cases to define the risk of disease diffusion in mobility patches. Fusion of mobility and disease vectors has the potential to optimize future public health policies by restricting movements to and from infected patches, potentially reducing or eliminating the need for large-scale lockdowns.. 2. 2.1Mobility datasets: In March 2020, technology companies that gather geo-located information on individuals started to share anonymized mobility data to help researchers stop the spread of COVID-19. Here, we used the aggregated mobility datasets of the US by SafeGraph company to construct mobility networks from where individuals go. SafeGraph provides cellphone data and, for security of the users, anonymizes the data and aggregates them in CBGs. Each file describes individual CBGs and lists links with weights (number of links) to other CBGs that occurred on a specific day. First, we separate all these relationships and describe them as individual objects. Each relationship has a source, target, date and weight of interaction. Daily dataframes are combined into weekly dataframes; they are grouped and their relationships are summed. Each CBG in each relationship is augmented with central points derived from CBG polygons. See COVID-19 datasets: We use daily confirmed cases time-series data from Johns Hopkins University COVID-19 Data Repository. This dataset provides cumulative counts of confirmed cases at county level for the US. By adding the number of active confirmed COVID-19 cases to the map, we define risk exposure for the communities.. 2.2To extract mobility fragmentation patterns, we first build mobility networks representing the connectivity of areas based on the movement of individuals. Applying a community detection algorithm shows us which geographical areas in the mobility networks are highly connected at multiple scales from mega-communities, to communities and sub-communities. To quantify changes in the network structures, we measure distributions of degree (number of movements from/to each CBG) and movement distances in communities.Mobility network: In the mobility network, nodes represent a lattice with cells overlaid on a map of the US. Cells are CBGs used by SafeGraph and are the nodes in the mobility network. Edges represent the movement of an individual from one CBG (node) to another one. Edge's weight represents the number of people who travel between the two CBGs. This network aggregates the heterogeneities of human mobilities in a large-scale representation of social collective behaviours .Community detection algorithm: We analyse social fragmentation by applying a community detection method to the mobility network. The term network fragmentation is often used to describe network dismantling in the literature ,25. In cm represents the total number of links in the network, ijA counts link weight between nodes i and j, and ik and jk are the sum of links to and from nodes i and j. The second term in the equation indicates the expected number of links between the two nodes. δ is equal to 1 if there is a link between the communities of node i and j, otherwise it is 0. Modularity is a scalar value −1 < M < 1 that quantifies how distant the number of edges inside a community is from those of a random distribution. Values closer to 1 represent better detected communities. Due to the existence of multiple local minima in the Louvain algorithm, some variation in the assignment of nodes may occur between algorithm runs [In this work, we use the Louvain algorithm  which wothm runs ,36. To qthm runs ,21.Each CBG corresponds to a polygon and belongs to a community. We merge and dissolve polygons of CBGs that belong to the same community to represent communities as polygons. Rendering polygons with the same colour allows us to better visualize the communities and their borders on a map.We define inter-community distances by creating a network that consists of communities as nodes and aggregate mobilities between them as links. Using this network structure we can further define clusters of communities with stronger connections. These clusters describe higher level aggregate behaviour enabling policy decisions about travel restrictions at the larger scale. We preserve colour hues for mega-communities: purple hues were consistently assigned to northeastern states, blues to southeastern, greys to midwest, pink–magentas to southern communities and yellow–brown to the western communities. By applying the Louvain algorithm to the network inside communities, we define sub-communities in high population areas that mostly represent cities and surrounding metropolitan regions. Thus, our analysis shows three different scales of communities in the US.A = 1, 2, 3, … , Amax, the survival function counts the frequency of nodes/links that have more than A degrees/lengths, Degree and distance distributions: We examined the distribution of the number of nodes versus degree (number of in and out links to the nodes) and number of links versus the length of the links using a survival function. If the degree of nodes/length of links per node/link is in range 2 Power-law distributions can be described by N(A) ∼ Aα−, in which α quantifies how heavy-tailed the distribution is. On a log–log plot, a power-law distribution appears linear, and its slope is equal to α. Power-law behaviour is also termed ‘scale-free’ because it follows the same relationship at all scales [If communities include a few nodes/links with large degrees/lengths and many nodes/links with a few degrees/lengths, the distribution will be skewed. An extreme skewed distribution is the power-law distribution, in which the frequency of events decreases as a power of size of the events .2 Power-l scales .. 33 as well as statistics of weekly aggregated mobility data. The statistics count the number of relations, number of source and target CBGs, sum of weights and the average weights of links in the networks. The first cases of COVID-19 were announced in Wuhan, China, in December 2019, and confirmation of its transmissibility put Wuhan on lockdown. Soon after, in January, additional early cases were identified around the globe, and the US government declared a public health emergency. In February of 2020, a surge in infections led to travel restrictions between countries. In 4 On 19 March 2020, California was the first state to issue shelter-in-place orders. Residents were asked to stay home except for essential or emergency needs [In cy needs . New Yora) and 5–11 April (panel b). Areas with the same colour belong to the same community and communities with the same colour hue represent clusters of communities with stronger connections. The US has five clusters in the following regions: west, north, northeast, southeast and south. Panels (c) and (d) show the degree distribution of the nodes, and panels (e) and (f) show the edges' length distribution for the communities in panels (a) and (b) . Linear behaviour of the distributions in log–log axes represents the power-law nature of these distributions. National and local lockdowns in the US came into effect in March 2020 and led to dramatic reductions in movements compared with mobility patterns in February. c) and (d), degree of movements for all CBGs in all communities from greater than 200 movements reduced to greater than 100 movements. The highest degree of the CBGs in the communities reduced from 1500 > degree > 75 000 to 800 > degree > 7500. Movement frequencies from 106 > F > 107 decreased to 105 > F > 4 × 106, and the frequencies of CBG movements in all communities decreased distance due to lockdowns. For example, Florida was connected to the northeastern US cluster of communities during 23–29 February. After lockdowns were implemented in the northeast, connections became more localized and formed clusters with proximate neighbouring states. See electronic supplementary material for more details and community patterns in other weeks. In panels (g) and (h), we compare the deviations of the partitions in the first community detection level on 23–29 February from other weeks using two measures of cluster similarity: adjusted rank [5 scores. These measurements evaluate the similarity of communities, with values ranging between 0 (no intersection) and 1 (perfect coherence). Our analysis shows a decrease in scores from 0.91 to 0.76 for adjusted rank and 0.81 for completeness, representing deviation from pre-existing community patterns following the implementation of lockdowns. We find the largest deviation occurred in the first week of April, when most of the country was in hard lockdown, and social distancing and mask-wearing behaviours were strongly encouraged.In ted rank  and compa,b, US mobility patterns during week 5–11 April are shown, with sub-communities separated from each other with black lines. Yellow lines represent state borders in the upper panel and county borders in the bottom panel. Although community borders align with administrative borders in some areas, in most areas community borders deviate significantly from administrative borders, see values less than 1 for adjusted rank and completeness scores, shown in panels (c) and (d), as evidence of this deviation. This indicates that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. Some states appear as a single community such as Michigan (MI), West Virginia (WV) and Louisiana (LA). Meanwhile, while some others merge together and appear as a large community, for example, the six-state region of New England (Maine (ME), Massachusetts (MA), New Hampshire (NH), Vermont (VT), Rhode Island (RI) and Connecticut (CT)). The North and South states of Carolina (NC and SC), Nebraska (NE), Iowa (IA) and South Dakota (SD) are two other examples of merged states. Some states split into small communities representing a dismantled mobility pattern within those states, such as, California (CA), Texas (TX) and Florida (FL). More intelligent contact tracing and quarantine programmes in suspected areas at the right time can dramatically slow the acceleration of the pandemic to connected areas and reduce severe impacts. Thus, it would be better to carefully define the borders of connected areas based on individuals’ movements and the scale that policies are applicable and appropriate for. It is not enough to implement contact tracing that simply traces the locations where a confirmed case has been. Rather, we also need to know the locations and movements of persons who were in contact with the infected person.Applying a community detection algorithm on the network of nodes inside each of the communities reveals the sub-structure of the communities, exposing the mobility patterns with more detail. In figure 3Unfortunately, due to the delays in applying preventative policies across the US, many areas have seen a large number of cases. By tracking the location of recent active cases and adding them on top of the map of the communities, we can define the risk exposure for the communities. In a), interesting facts are observable: —6, figure 5b.Areas with no mobility data: There are some urban areas that do not share mobility data, like a Native American community in New York state — oc, are two Universities that are located in central New York State yet are isolated sub-communities for New York City. This corresponds with a 2014 investigation,7 which estimated that 65% of all students at Cornell from New York State came from that region of the state.Universities: New York State is the home to many universities. These universities attract people from different areas. The examples of Cornell University and SUNY Cortland, figure 5 o9 In New York City, the Catskill Mountains are one of these escapes, figure 5d, while for Philadelphia the Poconos serve the same purpose, figure 5e.Vacationers: There are vacation spots that individuals from metropolitan regions of one community go to yet are in the middle of different communities. These regions are known for their nice outdoor spaces and somewhat close proximity to the city they are connected to. This phenomenon creates isolated communities in the middle of other communities. Multiple reports have mentioned this pattern occurring.Isolated communities: Some parts of a community can be geographically disconnected from the rest of the community. This, for example, occurs in university and vacation areas for larger cities. —f, within the community of upstate/western New York there are specific sub-communities with connections to university campuses. On the left side of the figure, the smaller shape south of Rochester is Rochester Institute of Technology, while the right side of the figure has two smaller areas in Syracuse, both associated with Syracuse University. These universities are large and attract many students from the upstate/western New York region.Sub-communities within other sub-communities: All around the US, there are some areas in which people mostly interact with those immediately around them rather than their nearby urban areas. University campuses are good examples of such sub-communities. As shown in figure 5 —g. The light purple region highlights the Philadelphia community. This community is multi-state and includes parts of northeastern Maryland, northern Delaware, southern New Jersey and southeastern Pennsylvania. The southern part of the Jersey Shore is a popular travel destination for people from Philadelphia, and the areas in Delaware and Maryland appear to be extensions of the great Philadelphia area.Communities that cross state borders: Examples include the area of Philadelphia and southern New Jersey, figure 5 —h. Brooklyn and Queens have defined sub-communities that are necessary to investigate. Sub-community 1 includes areas of Queens . Sub-community 2 includes parts of northern Brooklyn . Sub-community 3 includes central Brooklyn . Sub-community 4 includes areas around Prospect Park . The public transportation that supports them is different for each area (within Brooklyn division as well). The Prospect Park area of Brooklyn is the most wealthy . The border between sub-community 4 and 3 on the map can basically be the wealth divide.10 The racial divide between sub-community 3 and 4 can be striking on this map as well [Sub-communities in city areas: Racial and income differences, city infrastructure and transportation can be reasons for community formation in city areas. In New York City, these communities are shown by light green in figure 5 as well .By zooming into the map (for example northeast of the US on 5–11 April, figure 5. 4In conclusion, mobility patterns are one of the signs that not only reveal the effectiveness of lockdown policies, but also define areas that are in high risk with regard to the severity of COVID-19 exposure and need for more restriction actions. Mobility networks represent patches that people mostly stay within. This is important because they are also mostly in contact with individuals inside those patches. Lockdown and quarantine policies should attempt to change the mobility patterns and adapt to and strengthen borders of the patches. These policies should eliminate most of the long-distance movements and make them more localized. Patches in the city areas are mostly smaller and in the suburban or rural areas, they become larger. Quantifying movements from and to the patches and restricting commutes between low and high risk patches can be used to control the spread of coronavirus across various areas and help policymakers and governments to control the pandemic.Our mobility and COVID case data have some limitations in the populations covered and geographical resolutions. For the privacy and security of users, SafeGraph aggregates its cell phone data into CBGs, and so these data cannot capture sub-CBG heterogeneity. Infected cases are further aggregated to the county level. This mismatch between geographical aggregations of mobility and infected cases creates some problems measuring risk of exposure in communities. To solve this, we split the infected cases of the counties that crossed several mobility patches. While these aggregations prohibit analysis at scales smaller than a certain resolution threshold, on larger scales these limitations pose no problems to our analysis. Despite these limitations, mobility data and SafeGraph data, in particular, have been used in many research projects during the recent SARS-CoV-2 pandemic –46. In fClick here for additional data file.


Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work. In the past 20 years, advances in DNA sequencing have made it possible to quantify the taxonomic and metagenomic composition of microbial communities in high throughput. This has vastly improved our understanding of the assembly process of natural and semi-natural communities, from host-associated microbiomes to host-free communities in natural and constructed environments . The comex situ in a defined growth medium under well-controlled conditions , present significant challenges for theory development. All of these challenges are circumvented in enrichment communities, which consist of natural microbiomes cultivated nditions . In enrinditions . Therefonditions . LikewisImportantly, the recent development of robot-assisted culturing platforms for large numbers of microbial communities under well-controlled conditions, both in batch and in continuous culture , has madThis review aims not to provide a comprehensive overview of the use of enrichment cultures in microbial ecology throughout history. This is an old technique, dating back over a century ago to Winogradsky, Beijerinck, and other pioneers in microbiology. Other authors have recently written excellent overviews that provide newcomers with an introduction to their use , and alsUnderstanding how reproducible community assembly is under different ecological conditions is essential if we wish to predict the assembly process and the response of microbial communities to perturbations. If assembly is not reproducible, then we cannot predict it. If it is reproducible, it may then become predictable as long as we learn the rules that govern it. Enrichment communities represent an ideal model system to address this question ,b. FirstTo understand how reproducible microbial community assembly is, we have recently carried out experiments that monitored the assembly of hundreds of enrichment communities in a minimal synthetic medium containing a single growth limiting resource . These cOne salient finding of these experiments was that repeating the same enrichment experiment multiple different times with the same inoculum often led to different community compositions at the species level, yet to strongly convergent compositions at the family and higher levels of taxonomy . Family-Because our enrichment communities had been assembled under well-understood and well-controlled ecological and nutritional conditions, we were able to investigate the ultimate causes for these patterns of reproducibility and divergence at different levels of taxonomy. We found that the family-level structure reflects an underlying metabolic structure . In glucD, whose elements ijD capture the number of molecules of resource i that are secreted to the environment per molecule of resource j uptaken. Every species is defined in the model by a specific vector of uptake rates for the different environmental resources, including those that have been released by other taxa as well as those that were exogenously supplied.The controlled nature of high-throughput assembly experiments make them ideal to be contrasted with theory . To thisFor instance, consumer-resource models show that many of these experimental findings are generic properties of large consumer-resource systems seeded with random consumption vectors . These gIn particular, the observation that communities converge functionally despite variation in taxonomy (particularly at the species level) has been made in a wide range of empirical systems, from plant associated communities to the human microbiome or the oceans ,b, 2018.What drives some habitats to have a higher diversity than others? Ecological theory indicates that the number of species that can be found in a given habitat cannot exceed the number of growth-limiting resources present . It stanBacillus strains in enrichment cultures with soil bacteria in M9 medium , which can then recruit other taxa, paving the way for a further increase in diversity. The diversity-begets-diversity hypothesis predicts a positive relationship between the number of species within a single focal taxonomic level , and the number of those higher taxonomic levels in the community. Analyses of a number of natural microbiomes have lent support to this hypothesis  grow in the laboratory . To overAn example is Dissimilarity-Overlap Analysis (DOA), a recently developed technique to examine the degree of variation in population dynamics and species interactions across a given sample of habitats, such as the same body site for various different hosts . DOA quaOur hope in writing this review was to motivate the reader to think of new ways in which the combination of multi-replicated enrichment communities and mathematical models may help us to develop a quantitative understanding of how communities assemble, how different factors govern their diversity and function, and other open questions in microbial ecology. Throughout this piece, we have highlighted some of those open questions and hinted how they may be addressed. Because this piece is focused on the combination of theory and ecologically motivated studies on multi-replicated enrichment communities, we have not commented on the very large number of fascinating studies that have used enrichment communities in microbial ecology throughout its history. An entire review piece could be devoted to that topic. Just in the past couple of years, enrichment communities have been used as a means to investigate assembly mechanisms and species interactions in the gut , soil Z, and marUnderstanding the ecological dynamics and the repeatability of microbial community structure and function is important for human-health, bioremediation, biotechnology, and to understand the consequences of anthropogenic changes in ecosystem function. Unlike engineered communities from the bottom-up, microbial enrichment experiments allow us to control and manipulate the environment while avoiding making assumptions about the relevant ecological interactions and trait distributions. Thus, we can use these communities to investigate how environmental changes like increased temperatures and drought affect ecological interactions and use these results to develop predictive models of changes in metabolic structure and ecosystem function. Furthermore, and given the high overlap of ecological and evolutionary timescales for microbes, we can use multi-replicated enrichment communities to understand the effects of evolutionary change in community assembly, and how, as communities change, they affect evolution. Recent work with self-assembled communities has already shown that community context can affect the dynamics of evolution, with high diversity limiting adaptation . The tigAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


GPS navigation is commonplace in everyday life. While it has the capacity to make our lives easier, it is often used to automate functions that were once exclusively performed by our brain. Staying mentally active is key to healthy brain aging. Therefore, is GPS navigation causing more harm than good? Here we demonstrate that traditional turn-by-turn navigation promotes passive spatial navigation and ultimately, poor spatial learning of the surrounding environment. We propose an alternative form of GPS navigation based on sensory augmentation, that has the potential to fundamentally alter the way we navigate with GPS. By implementing a 3D spatial audio system similar to an auditory compass, users are directed towards their destination without explicit directions. Rather than being led passively through verbal directions, users are encouraged to take an active role in their own spatial navigation, leading to more accurate cognitive maps of space. Technology will always play a significant role in everyday life; however, it is important that we actively engage with the world around us. By simply rethinking the way we interact with GPS navigation, we can engage users in their own spatial navigation, leading to a better spatial understanding of the explored environment. In fact, knowing where to go and where we came from are such fundamental processes that they were once critical to our own ancestral survival2. But with the arrival of GPS and navigation apps, is this a thing of the past? And more importantly, if these apps are now responsible for a function that is performed by our brain and was once critical to our survival, is it doing more harm than good?Navigation is a fundamental human behavior that is critical for everyday life. Exploring new environments, understanding the current location, and remembering how to get back are essential for successful navigation8. While spatial cognition is not solely attributed to one brain region, the hippocampus is well known to play a critical role in spatial learning, memory and navigation, and has long been thought to contain the “cognitive map”9. In fact, the creation of cognitive maps, development of spatial expertise, and exploration of novel environments can have a positive impact on hippocampal structure and function15. As we age, however, our hippocampus and spatial abilities decline21, even predicting the conversion from mild cognitive impairment to Alzheimer’s disease24. Therefore, creating mental maps is not only important for the navigation of daily life, but is critically important as we age.Navigation requires the coordination of numerous perceptual and sensory processes that are further supported by several brain regions, including the hippocampus, retrosplenial cortex, striatum and entorhinal cortex, to create successful representations of space28. While the animal work has described the formation of these spatial neural networks in detail, studies have demonstrated that humans are quite capable of learning about space in other ways such as with the use of (paper) maps30. Most significantly, the literature describes two main types of navigation strategies: egocentric and allocentric. Egocentric navigation is considered striatal and describes how cues within the environment relate to the individual (a set of directions) whereas allocentric navigation is hippocampal and describes how cues within the environment relate to one another (a map). While both navigation strategies have their advantages, they are thought to operate both independently and in parallel31. A true cognitive map requires an allocentric perspective of space that the hippocampus provides, so that the spatial information is flexible and can be used from any location9. Furthermore, a successful creation of cognitive maps requires active engagement in the navigation process as spatial decision making is the primary component of active learning for the acquisition of map based knowledge32.At the most fundamental level, cognitive maps are formed through exploration33. Turn-by-turn navigation, is a passive navigation system based on an egocentric perspective. The user does not make any decisions about the navigation process, they simply input a desired location and follow the directions on the app.Is it possible to find a balance between our internal navigation system and modern technology? Certainly, new technology has allowed us to go further and reach unexplored places in ways we would have only imagined prior to GPS access on our mobile phones. One can get lost in the streets of Tokyo and still find their way back without fear. Hence, we have reached a modern paradox for navigation: navigation apps allow us to explore more places, while at the same time making us worse explorers. Here, we argue that current GPS apps (based on turn-by-turn navigation) promote a passive form of navigation that does not support learning or the formation of cognitive maps37. Several studies have observed a link between GPS navigation and poor spatial awareness. Recently, a study found that greater lifetime GPS experience, based on GPS habits and reliance on GPS in various navigation situations, correlated with worse spatial performance34. In addition, greater GPS habits were tied to lower cognitive mapping abilities and less dependence on spatial strategies associated with the hippocampus. In a study directly comparing the wayfinding abilities of GPS users, map users, and direct experience users , GPS users traveled longer distances, took longer to complete the navigation task, and had a worse topological understanding of the routes when compared to the map users or direct experience users37. From an anthropological perspective, Inuit hunters in northern Canada have noted their extraordinary ability to navigate the arctic tundra, despite the harsh, desolate terrain and the lack of reliable landmarks across large spaces. While the use of traditional navigation tools, like maps and compasses, has empowered their extraordinary perceptual skills, that were developed and passed down from one generation to the next; GPS technologies replaced the need for such skills altogether. Younger generations of Inuit hunters have traded in these deep-rooted wayfinding skills for modern GPS technology. While easy to use, GPS technology could not help the young Inuit hunters adapt to the extreme weather or hazards of the arctic tundra. An over dependence on GPS technology led to an increase in serious accidents and death, as these young Inuit hunters were essentially navigating “blindfolded”2. Even in modern cities, in-car GPS navigation “inhibits the process of experiencing the physical world”33. Rather than aiding our ability to navigate, GPS navigation is designed to relieve us of our involvement and disengage us from our surrounding environment. After all, GPS navigation is a form of destination-oriented transport in which humans become a “passenger of their own body”, as opposed to wayfaring, which embeds us into the environment as the driver, in order to find the destination38.While traditional turn-by-turn navigation is effective in its ability to lead us to a desired location, this passive form of navigation does not support spatial learning, thus having a detrimental impact on humans navigation skills and spatial cognitionHere we suggest an alternative form of GPS navigation, that is possible with the use of 3D spatialized audio. We position a virtual auditory beacon, which is essentially a continuous sound virtually positioned in 3D, to always be emanating from the direction straight from the destination it is associated with. By the means of this virtual auditory beacon, users of GPS apps can regain their active role, and navigate in the space like they used to: without delegating decisions . Users navigate in the direction from which the beacon sounds, constrained only by the available paths traveling in that direction. In this scenario, the mobile app acts as a compass that directs the user towards the destination in an allocentric manner. The goal of our research is to demonstrate that by simply rethinking the way we interact with technology and introducing sensory augmentation into the equation (in the form of 3D audio) we can have a real impact on modern navigation without having to compromise our own internal navigation system or our ability to create mental maps.In total, we recruited 53  individuals to participate in the study. Using a Microsoft email blast, we recruited both Microsoft Employees (experts) and Interns (naïve). All participants gave informed consent and were compensated with a $50 Amazon gift card upon completion of the study. The study was approved by the Microsoft Research IRB committee.https://www.microsoft.com/en-us/research/product/soundscape/).Participants were classified as either Experts or Naïve based on their experience with the Microsoft campus. The Naïve group  consisted on Summer Interns at Microsoft who had just begun their internship. All participants in the Naïve group had been on the Microsoft campus for less than 3 weeks at the time of the study. The Expert group  consisted of Microsoft employees who had been on the Microsoft campus for at least 6 months at the time of the study (up to + 20 years). Within each group, individuals were randomly divided into two conditions: Turn-by-turn and Beacon. The Turn-by-turn group performed the task using turn-by-turn directions and the Beacon group performed the task using the Microsoft Soundscape app (p = 0.04) but not significantly less confident in their own navigation  compared to the Expert group (p = 0.4).Prior to the study, participants were asked two questions about their familiarity with the Microsoft campus and their navigation abilities . These self-reports of familiarity with the Microsoft campus and confidence in their own navigation abilities confirmed that the Naïve group was less familiar with the campus  and to inform users about nearby places of interest or intersections . For the purposes of this experiment, we disabled the callouts feature of the app to ensure that participants were navigating using only the audio beacons , see Supplementary Fig. All participants participated in a scavenger hunt activity in which they had to locate five different POIs, of varying degrees of difficulty to find, on the Microsoft campus Fig. . The GPS2, however the selected area was relatively central, and we reduced the scavenger hunt to a 0.12 km2 region.The Microsoft campus is very large, spanning over 2 km44. The Beacon group was not allowed to make shortcuts through buildings. Once the POI was found (determined by Soundscape based on GPS location), a picture of the next POI was presented, the audio beacon was updated to point towards the new POI’s location, and participants were instructed to continue to the next POI. The presentation order of the POIs was the same for all participants and was specifically arranged so that every POI had multiple access routes. Soundscape recorded timestamps, GPS locations and heading directions at a sampling rate of approximately 1 Hz throughout the entire duration of the study.The order of the POIs was picked to purposely avoid a direct path from one to the next, this way participants had to make active decisions on where to go. Pictures of each POI were displayed in the Soundscape app, one at a time, and participants were instructed to find the pictured location in the real world. See 47. In the pointing task  was used to analyze the distance (in pixels and later converted into meters) between the estimated location and the real location of all POIs, to come up with an error metric.In the second task, participants were given a simple map of the Microsoft campus (with no distinguishing features or labels) and asked to mark the locations of all five POIs  to determine familiarity with each of the POIs. After completing the scavenger hunt, participants performed two post-test pointing tasks, one from the same location as the pre-test  and specific statistical tests used are displayed next to each result.All statistics were performed using Prism 8 (We prepared a scavenger hunt with five different Points of Interest (POIs) that participants (n = 53) had to visit using either auditory beacons or turn-by-turn directions Fig. . Particip = 0.46), condition  = 0.003, p = 0.96), or interaction  = 0.09, p = 0.76), showing that at pre-test, there were no differences between groups in their ability to point to each of the POI locations . The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . See Prior to starting the scavenger hunt, we determined a baseline familiarity with the POIs. All participants stood in the same spot and performed a pointing task in which they pointed to all five POI locations, even though they were not visible. Using the amount of error as a measure of accuracy, we performed a 2 × 2 ANOVA across group (Expert and Naïve) and condition (Turn-by-turn and Beacon). We did not find a significant main effect of group  and main effect of condition  = 4.41, p < 0.05), but no main effect of group  = 1.53, p = 0.22). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . A post-hoc analysis (Tukey’s correction for multiple comparisons) revealed that the Naïve Beacon group was significantly better than the Naïve Turn-by-turn group. These data indicate that the Beacon group, especially Naïve Beacon users, displayed a better knowledge of the POI locations. See To determine how well participants learned the locations of the visited POIs, we performed a pointing task, after the scavenger hunt, from the same starting position as the initial pre-test pointing task (Location A). A 2 × 2 ANOVA across group (Expert and Naïve) and condition (Turn-by-turn and Beacon), demonstrated a significant interaction  but no main effect of group  = 0.03, p = 0.85) or condition  = 1.96, p = 0.16). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . While there was a visual improvement in learning between Naïve Beacon and Naïve Turn-by-turn groups, this was not statistically significant. See To assess the amount of learning that occurred from pre-test to post-test, we compared performance on the pointing task from location A at post-test and pre-test, using the difference in accuracy (post-test pointing error minus pre-test pointing error) as a metric of learning. A 2 × 2 ANOVA across group (Expert and Naïve) and condition (Turn-by-turn and Beacon) revealed a nearly significant interaction  and condition  but not interaction  = 1.96, p = 0.17). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . See In addition to pointing from the pre-test location, participants also pointed to all POIs from an entirely novel location  but not group  = 2.7, p < 0.1) or interaction  = 3.0, p = 0.08). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . See After the scavenger hunt, participants were asked to identify the POI locations on a simple map outline of the area with no labels Fig. A. A 2 × p < 0.0001) but no main effect of group  = 1.27, p = 0.26) or interaction  = 3.70, p = 0.06). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon . As expected, there was more overlap between groups in the Turn-by-turn conditions as they all used the same directions. Despite the differences in route, there was no difference in the total distance that was traversed by all groups . A 2 × 2 ANOVA across group (Expert and Naïve) and condition (Turn-by-turn and Beacon) revealed no significant main effect of group , condition  = 0.52, p = 0.47), or interaction  = 0.05, p = 0.81). The mean and standard deviations for all groups were as follows: Experts Turn-by-turn , Experts Beacon , Naïve Turn-by-turn , and Naïve Beacon .While all participants in the Turn-by-turn groups used the same route to visit all five POIs, the participants in the Beacon group were free to use any route Fig. C and 4E.The goal of the study presented here was to present an alternative to turn-by-turn navigation that is effective and is still engaging users into their environment to promote spatial learning. We show that using auditory beacons to navigate can lead to greater explorative behavior and the formation of more accurate mental maps of the surrounding environment when compared to turn-by-turn navigation. Thus, demonstrating that it is possible to use GPS technology and promote learning through active navigation. In this study, groups of both familiar (Experts) and unfamiliar (Naïve) participants were asked to find the locations of different POIs on a scavenger hunt across the Microsoft campus. Half of the groups were guided to the POIs using traditional turn-by-turn directions (Turn-by-turn) and the other half used the auditory navigation app Soundscape (Beacon) to find the POIs. While the pre-post performance showed little differences between the conditions in the Expert group, the Naïve Beacon group demonstrated a more accurate spatial representation of the Microsoft campus as reflected by the significant improvement (pre-post) in their ability to point to each of the POI locations, both from a familiar and a novel location, as well as to identify the POI locations on a simple overhead map of the area. It was not surprising that experts, who were familiar with the campus, did not exhibit a significant difference in learning as we expected them to have a reasonable mental map already formed.48, as well as pedestrians37.There are several limitations of the current study that we feel are important to acknowledge. First, while we were able to test four different groups, we ideally would have had greater numbers in each of the individual groups. A power analysis, assuming a medium effect size (d = 0.5) determined we would need 80 participants in total. Time was a limiting factor, especially in our naïve summer interns as they were extremely busy throughout the day and the duration of their internship was short. Participants were often run during an extended lunch break or before/after work hours. Second, the Turn-by-turn groups were given verbal directions by an experimenter as opposed to a navigation app. One issue we encountered when testing turn-by-turn navigation apps was that these apps could not accurately inform a walking participant of when they needed to turn. The area we used for our study required participants to walk along paved and dirt paths within the Microsoft campus and the turn-by-turn navigation apps failed to reliably direct participants to each of the POIs. Lastly, this experiment was designed for walking navigation and the area chosen, containing complex intersections and buildings, was selected based on the absence of roads for safety concerns. Many turn-by-turn systems might be better suited for actual road or car navigation as the goal is less about learning the environment and more about getting to the desired location in a timely manner  By taking a more active role in their own navigation, participants were able to construct a better understanding of the spatial layout of a real-world environment. Using a spatial auditory navigation app to promote active navigation, we can theoretically engage the hippocampus52 resulting in a stronger and more accurate mental map of the explored space. It is worth noting that while the Soundscape auditory navigation app was originally designed for use by sight-impaired individuals, all the participants in this study had normal or corrected vision. Participants are likely still navigating primarily using vision. This auditory navigation app was an aid to support spatial navigation and the formation of a cognitive map53.Nevertheless, this does not detract from the main point and results of this paper: (i) Turn-by-turn directions are a passive form of navigation that do not support spatial learning of the surrounding environmentIt is clear that GPS technology is an integral part of our everyday lives and it is unlikely that society would give up such an important technological advance. While the benefits of using this technology are undeniable, it is important to consider how navigation should be implemented, as GPS is a global positioning system that can support different forms of navigation. Rather than replacing the decision-making process, our work proposes to augment navigation with a rich sensory experience that engages perception and cognitive processing. After all, just as technology cannot replace the benefits of physical exercise, it cannot replace the benefits of staying mentally active.55. Nor it is surprising that some of the first signs of cognitive decline associated with Alzheimer is the deterioration of spatial awareness23. Importantly, recent studies have found that exercising spatial cognition might protect against age-related memory decline19. Therefore, it is important that, while using technology, we still engage with the world around us as we know it is important for brain health.While our ability to navigate might seem like an accessory to our modern lives, at one point these navigational skills were critical for our own ancestral survival. It is not surprising that some of the most powerful mnemonic techniques, for example the memory palace, involve setting mental pictures of items or facts in locations in an imaginary place, such as a building or a town. Memories become easier to recall when they’re associated with physical locations, even if only in the imaginationIn this paper we are not suggesting that we need to outright reject technology, we simply need to rethink the way we interact with technology, to design and create in ways that engage the brain rather than ignore it. Indeed, there might be hybrid solutions or situations such as in-car navigation where traditional turn-by-turn GPS will still outperform auditory beacons. Nevertheless, auditory beacon navigation is a step into a new era in which automation does not simply replace our evolutionary functions and remove us from our environment, but rather feeds into our sensory inputs to promote active engagement with the world around us. In that regard, GPS navigation based on auditory beacons is a sensory augmentation that helps us create a stronger connection with our environment.Supplementary Information 1.Supplementary Video 1.Supplementary Audio 1.