Medicolegal entomology—a subfield of forensic entomology—is mainly used in medicolegal investigations to estimate the postmortem interval (PMI). The minimum PMI of a corpse invaded by necrophagous immature insects can be estimated because the PMI is near to or earlier than the oviposition time of the larvae that hatched and fed on the corpse. As the growth speeds of larvae differ depending on temperature and species, species-specific growth data are used to estimate the minimum PMI. While morphological identification of adult necrophagous flies can be done by a well-trained entomologist, identification of larvae is relatively difficult. Larvae can only be identified up to the family level and developmental stage by observing the posterior spiracles. For these reasons, the molecular biology method of DNA barcoding has been developed. DNA barcoding that targets the mitochondrial cytochrome c oxidase subunit I (COI) gene is commonly used. COI sequences are currently acquired using polymerase chain reaction (PCR) and Sanger sequencing, which are too time-consuming and complex for practical use in medicolegal investigations. To compensate for these limitations and facilitate the use of entomology for medicolegal investigation, we designed a multiplex real-time PCR system to identify nineteen forensically important species of Calliphoridae and Sarcophagidae flies collected in South Korea. In contrast to the Sanger nucleotide sequencing process, this technology only requires a one-step real-time PCR with melt curve analysis of amplicons generated by primers targeting species-specific single nucleotide polymorphisms (SNPs). Multiplex real-time PCR was performed for twelve species of Calliphoridae (four reactions) and for seven species of Sarcophagidae (three reactions). This assay is expected to make it easier and faster for investigating authorities to identify major species of necrophagous flies at beginning of investigation and to increase the utilization of entomological evidence in forensic investigations. The minimum postmortem interval (PMI) of a dead body can be estimated using larvae hatched on the body because the minimum PMI is near to or earlier than the oviposition time . As the To compensate for these limitations, we designed a rapid and simple assay using multiplex real-time polymerase chain reaction (PCR) to identify 12 species of forensically important Calliphoridae and seven species of Sarcophagidae flies commonly found in South Korea –12. This Aldrichina grahami , Calliphora lata , Calliphora vicina , Calliphora vomitoria , Chrysomya megacephala , Chrysomya pinguis , Lucilia caesar , Lucilia illustris , Lucilia sericata , Phormia regina , and Triceratopyga calliphoroides . The Sarcophagidae species were Sarcophaga albiceps , Sarcophaga crassipalpis , Sarcophaga dux , Sarcophaga haemorrhoidalis , Sarcophaga melanura , Sarcophaga peregrina , and Sarcophaga similis . The samples were identified morphologically and preserved in 70% ethanol at −20°C.Adult flies and larvae belonging to twelve Calliphoridae and seven Sarcophagidae species were collected from Gyeonggi Province, Busan, and Seoul using traps or during autopsy. The Calliphoridae species we used wereµL for experiments and stored at -20°C. To identify the samples used in this study, universal primers were used for Sanger sequencing targeting COI regions [Genomic DNA was extracted using Exgene™ Tissue SV mini Kit  according to the manufacturer's protocol. Samples were preserved in 70% ethanol after extraction. Nucleic acids were eluted in sterilized distilled water. DNA concentrations were determined using a NanoDrop 2000c Spectrophotometer . Aliquoted DNA was diluted to 1 ng/ regions . UniversLeu regions. Each primer set included two to five SNPs on its 3′ end. Primer 3 Web 0.4.0 [Primers were designed to be species-specific. To obtain the consensus sequences of each species, reference sequences from the National Center for Biotechnology Information (NCBI) GenBank . were aleb 0.4.0  was usedµL: TOPreal™ qPCR 2X PreMIX , 0.2 μM of forward primer, 0.2 μM of reverse primer, sterile water, and 1 ng of DNA. The amplification protocol consisted of initial denaturation at 95°C for 10 min, then 23 cycles of denaturation at 95°C for 10 s, annealing at 62°C for 15 s, and elongation at 72°C for 20 s. After the cycling stage, the melt curve stage was performed from 65°C to 85°C, with gradual temperature increases in increments of 0.3°C to acquire the melting temperature of the amplicon. We performed singleplex real-time PCR to determine the melting temperature of PCR products amplified using species-specific primer sets. Each species sample was tested with species-specific primer sets with included nontemplate controls. Samples from Calliphoridae and Sarcophagidae species were processed separately.This study was performed using a StepOnePlus™ Real-Time PCR System instrument . PCR was performed in duplicate or triplicate in a total reaction volume of 20 μM of primer “mg,” 0.30 μM of “il/ca,” and 0.35 μM of “re.” CC2 consisted of 0.20 μM of primer “la,” 0.20 μM of “am,” and 0.15 μM of “ca.” CC3 consisted of 0.15 μM of primer “gr,” 0.30 μM of “vi,” and 1.50 μM of “vo.” CC4 consisted of 0.10 μM of primer “pi,” 1.50 μM of “se,” and 0.1 μM of “cl.” CS1 consisted of 0.10 μM each of primer “me” and “pg.” CS2 consisted of 0.15 μM of primer “du” and 0.10 μM of primer “si.” CS3 consisted of 1.20 μM of primer “al,” 0.15 μM of “cr,” and 0.10 μM of “hm.”Multiplex real-time PCR was performed with combinations of primers with sufficiently different temperatures between amplicons for successful species-specific amplification. Based on the melting temperature of each amplicon obtained in the preceding singleplex reaction, a combination for the multiplex reaction was made. For each combination, two or three primer sets with amplicon melting temperature differences of about 2°C or more were used to allow identification of species by melting temperature. The combinations used in the assay were CC  and CS (combination of primers for Sarcophagidae). There were four combinations for CC  and thre L. ampullacea was amplified or not according to whether the 312th base of the COI sequence was C or T, respectively. In the case of “cl,” gDNA of C. vicina was amplified or not according to whether the 402nd base was C or T, respectively. In these cases, the ΔCt value—the difference between the Ct values—was used for precise identification. When amplified with primer “gr,” the ΔCt values were 3.0 and 4.1 for A. grahami and L. ampullacea, respectively. When amplified using primer “cl,” the ΔCt values for T. calliphoroides and C. vicina were 3.9 and 5.0, respectively. Primer “il/ca” is a primer that targets both L. illustris and L. caesar because of their high similarity. We performed multiplex PCR—using a primer combination based on the melting temperature obtained from the singleplex reaction—to confirm that only the target species was amplified in each of the combinations  was used as it finely resolves RT-PCR products ranging from 10 to 1000 bp. Amplicon samples in 6X Loading STAR  were run alongside a 25-bp DNA ladder  on a 3% gel (NuSieve™ 3:1 Agarose) in TAE buffer.To validate the assay, blind and cross-reaction tests were performed. The blind test was performed with adult flies and larvae. Every sample used in the blind test was identified by molecular DNA barcoding using COI; the samples were tested randomly. Adult flies were identified morphologically, and larvae were identified to the family level using identification keys based on posterior spiracles. The blind test was performed with 81 Calliphoridae flies, 25 Sarcophagidae flies, and 10 Muscidae flies, including larvae and adults, to validate the applicability of the assay. The result showed a 100% concordance rate between Calliphoridae and Sarcophagidae . We coulMedicolegal entomology is one way to estimate postmortem interval (PMI), but utilization is very low in Korea. This is not only because there are not many entomologists who can identify necrophagous flies morphologically, but also because of the disadvantages of existing molecular biology methods, which are time-consuming to use. Therefore, in order to improve the utilization of medicolegal entomology in criminal investigations, we aimed to design a quick and easy assay targeting the most common necrophagous flies found in Korea—Calliphoridae and Sarcophagidae.Multiplex PCR was chosen to reduce consumption of reagents, gDNA, and time and to simplify the procedure to identify the species of flies. The singleplex method requires seven to twelve reactions to identify one sample, whereas the multiplex method can yield results with only three or four reactions. When a blind test was performed on the multiplex reaction, the melt curve was accurate and showed a specific melting temperature. This result proves that species identification of larvae collected from crime scenes can be done precisely in just four hours—even less time depending on the DNA extraction methods used. The multiplex real-time PCR itself takes only 70 min, while methods using Sanger sequencing take about 7 h. We also confirmed with a sensitivity test that the assay can be performed with 1 ng or more of gDNA. However, the authors recommend using 1 ng of gDNA in this assay to avoid unexpected amplification of untargeted species.Because the experimenter needs only fly specimens, a DNA extraction kit, SYBR Green qPCR Mix, and a real-time PCR instrument, this assay has advantages in terms of cost and time compared to existing methods. The limitation of this assay is that species not considered in the design may or may not be amplified according to their similarity to the primers. Therefore, this assay is not suitable to apply to unreferenced species, but it can be redesigned with COI sequences of new species. Furthermore, this assay can be modified to identify necrophagous beetles such as Dermestidae and Silphidae.In conclusion, this assay is expected to be useful for investigating authorities to identify species of necrophagous flies in initial investigation before obtaining full COI sequences of fly samples by Sanger's sequencing process and to increase the utilization of entomological evidence in forensic investigations.


Clusters in systems as diverse as metal atoms, virus proteins, noble gases, and nucleons have properties that depend sensitively on the number of constituent particles. Certain numbers are termed ‘magic’ because they grant the system with closed shells and exceptional stability. To this point, magic number clusters have been exclusively found with attractive interactions as present between atoms. Here we show that magic number clusters exist in a confined soft matter system with negligible interactions. Colloidal particles in an emulsion droplet spontaneously organize into a series of clusters with precisely defined shell structures. Crucially, free energy calculations demonstrate that colloidal clusters with magic numbers possess higher thermodynamic stability than those off magic numbers. A complex kinetic pathway is responsible for the efficiency of this system in finding its minimum free energy configuration. Targeting similar magic number states is a strategy towards unique configurations in finite self-organizing systems across the scales. Magic number cluster with closed shells and increased stability often result from potential energy minimization between attractive atoms or particles. Here, Wang et al. show that such magic number clusters can also result from entropy maximization in colloidal systems with negligible interactions. Deviations from a pure statistical distribution of cluster sizes are also observed in the mass spectrum of small noble gas and metal clusters4. Clusters with atom numbers that follow magic numbers show enhanced stability as a result of the maximized numbers of neighbors7. This tendency provides a driving force to form clusters with closed, concentric shells, including the well-known Mackay icosahedra8. Atomic clusters therefore often possess complex geometries that differ from the typical face-centered cubic (FCC) bulk crystal structure10.The structure of particle clusters is strongly affected by geometric constraints. This first became apparent in the study of atomic nuclei. Nucleons preferentially arrange into shells, which lead to the identification of the series of numbers that permit closed shells as magic numbers11, the formation of colloidal clusters is typically governed by several factors. Colloids are initially stabilized against aggregation, which prevents them from  aggregating in solution13. However, colloids can  self-assemble into clusters by increasing short-range attraction17 or by applying geometric confinements26. Phenomena that contribute to the development of colloidal clusters during a confined self-assembly process include the interaction of soft ligand shells27, the presence of depletants17, capillary forces acting on particles during the drying process19, and entropy maximization28. The latter case is comparably easy to control in the experiment because it only requires weakly interacting colloids, which are described well via the simple hard sphere model30. Temperature as the control variable for atomic cluster formation is then replaced by the packing fraction of the hard spheres35.Whereas the appearance and structure of atomic clusters are commonly explained by potential energy minimization, especially at low temperature38. With increasing particle number, entropy maximization has been identified as the driving force favoring the formation of icosahedral symmetry of spherical colloidal clusters39. In such confined systems, the interface boundary acts as a source for heterogeneous nucleation40 of crystalline patches41 that subsequently expands towards the cluster center28. If the number of particles within the confining element is further increased, the effect of confinement is continuously reduced until, from a system size of approximately 100,000 particles, bulk crystal structures with FCC symmetry are retained28. Despite these general insights, the detailed structure of a large colloidal cluster formed from a discrete number of colloidal particles remains unknown. While the dominant occurrence of atomic clusters with magic numbers in mass spectra4 suggests self-adjustment in their sizes during formation by addition or removal of atoms to reach magic numbers under appropriate circumstances, the number of particles in a colloidal cluster remains fixed in confinement, which allows experiments to explore clusters of arbitrary sizes. This raises the fundamental question of whether some colloidal cluster sizes are thermodynamically preferred over others.Small numbers of nanoparticles and colloids pack into defined clusters with a wide variety of symmetriesHere, we demonstrate that the magic number phenomenon known from atomic clusters extends to the colloidal realm. We report a discrete family of icosahedral clusters spanning a large range of particle numbers and propose a geometric model based on the Mackay and anti-Mackay clusters to accurately describe all observed cluster structures. Simulations and high-precision free energy calculations reproduce and explain the experimental observation.45. Partial icosahedral clusters with incompletely developed five-fold symmetry axes at the surface  colloidal particles with different concentrations in a continuous oil phase produced by microfluidics . To rationalize the appearance of MCCs, we recall that 12 identical spheres readily arrange into an icosahedron around a central sphere. Subsequent Mackay shells can be added concentrically8. Figure 50 that consist of 20 additional tetrahedra over the icosahedron faces, filling their gaps with 30 tetrahedra over the icosahedron edges, and finishing the construction with 60 tetrahedra over the icosahedron vertices. The resulting Pentakis dodecahedron model51 maintains icosahedral symmetry and consists of 130 twinned tetrahedra. Finally, we apply spherical truncation to remove spheres in the model whose distances to the center is larger than the truncation radius. This mimics the effect of confinement of the confined colloids, which are forced into a spherical shape by the geometry of the emulsion droplet.Well-formed icosahedral clusters are characterized by distinct surface features. The surface is tiled with rectangles and truncated triangles, which alternate around five-fold axes. Together, these structural elements form a closed shell spanning the cluster’s surface Fig. . In analm and the number of anti-Mackay shells a, which is controlled by the truncation radius a types, specifying the total number of shells, m + a, and the number of anti-Mackay shells a. The distinct surface patterns of the MCCs are directly correlated with their crystal structure and can be used to deduce the number of Mackay and anti-Mackay shells. From the model, we identify that the rectangular surface features are the characteristic structural element associated with the anti-Mackay shells  images and the semi-transparent model images in projections along the symmetry axes a, deduced from their surface features. In all cases, experimental observation and model coincide. The model predicts that the number of colloids per cluster follows the approximate relationship 8. Furthermore, the number of colloidal particles per cluster decreases from type (m + a)a to type (m + a)a+1, i.e., when a Mackay shell converts to an anti-Mackay shell , colloids in a shrinking droplet during evaporation are modeled as hard spheres in spherical confinement with decreasing radius using event-driven molecular dynamics (EDMD) simulation (see Methods). This computational approach follows recent workzes Fig.  and geneN = 1905 in Fig. N = 1774 and N = 2166), suggesting size-dependent thermodynamic stability. This will be further elaborated below.Combining the two steps, the simulations reproduce the transformation from spherical clusters to icosahedral clusters observed in the experiment Fig. . Quenchiϕ = 0.51 during the compression stage and the expansion stage, respectively. In both cases, particles form concentric shells with occasional migration between neighboring shells. To reveal how kinetics varies throughout the cluster, we measure mean square displacement separately for particles starting in different shells. In the fluid phase  to 0.55 (solid phase) and subsequently decreased  creates large mismatch and gaps between facets and the spherical confinement. The icosahedral arrangement reduces this mismatch via more but smaller facets, improving local dense packing near the curved interface63. Our simulations reveal that there is always a preference for a certain anti-Mackay and Mackay clusters type. The introduction of a few anti-Mackay shells makes the cluster more spherical and compact compared to the truncated Mackay icosahedron  was mixed with the curing agent (10:1 ratio) and later poured onto the silicon wafer. Vacuum was applied to degas. The PDMS was cured in the oven at 85 °C overnight. PDMS molds were carefully peeled off and punched with a 0.75 mm diameter biopsy punch to create inlets and outlets. The structured PDMS bonded to a clean glass slide after treatment with oxygen plasma for 18 s at 30 W power. The channels were flooded with Aquapel (PPG Industries) to avoid wetting of water droplets. The device was put in the oven at 85 °C for 1 h for final cleaning.Microfluidic devices were produced by soft lithography as described in literature45. Syringe pumps (Cronus) controlled the flow rate of water and the oil phase . Emulsion droplets were collected in 1.5 mL glass vials and sealed with stretched parafilm (Sigma Aldrich) at the opening. Small holes were punched with a 0.4 mm needle (Henke Sass Wolf) into the parafilm to control the speed of evaporation from the vials. Vials were kept in the oven (85 °C), room (25 °C), and fridge (5 °C) for water evaporation.Monodisperse 244 nm PS particles of 1 wt% were suspended in water and loaded into 1 mL syringes (BD disposable syringes). 0.1 wt% perfluoropolypropyleneglycol-block-polyethyleneglycol-block-per-fluoropolypropyleneglycol surfactant was dissolved in perfluorinated carbon oil (3M Novec Engineering Fluid HFE 7500) PE/2 tubings (0.38 mm/1.09 mm) connect syringes to the microfluidicsOil droplets containing colloidal clusters were drop-casted on a silicon wafer for surface pattern examination via scanning electron microscopy (Zeiss Gemini Ultra 50 SEM). As colloidal clusters are randomly deposited on the wafer and are viewed only at the surface from one direction in the SEM, we adopt the following classification scheme. Colloidal clusters without spherical shape were classified as buckled clusters. Clusters with spherical shape and hexagonally close packed surface patterning and with grain boundary scars were classified as spherical clusters. We consider complete local five-fold axes as alternating rings with five complete rectangles and five complete triangles (of possibly varying sizes) at the surface. Those missing any or having under-developed rectangles or triangles are termed incomplete. Only three five-fold axes are visible from one side at most. Clusters with at least one complete local five-fold axis or three incomplete five-fold axes were classified as complete icosahedral clusters. Clusters with one or two local five-fold axes were classified as partially icosahedral clusters. More than 120 colloidal clusters were examined and classified for each histogram  STEM imaging mode at a camera length of 91 mm. The semi-convergence angle of the STEM probe (microprobe STEM) was reduced to 0.44 mrad to increase the depth of field (DOF) to image all parts of the sample completely in focus throughout the entire tilt series acquisition procedure78. The diffraction-limited resolution for the adapted semi-convergence angle was 2.7 nm at a respective DOF of 7.6 µm. The large sample size led to a strong decrease of resolution due to broadening of the STEM probe (multiple elastic scattering)79, which resulted in an estimated HAADF STEM probe diameter of about 90–120 nm at the bottom surface of the sample in regions with highest projected mass-thickness. The tilt series was acquired using FEI Tomography 4.0 software in a tilt angle range from −76° to 62° with 1° tilt increment, continuous and linear tilting scheme, enabling autofocus and tracking before the acquisition. In order to prevent morphological changes, e.g., shrinkage, of the sample during the electron tomographic tilt series acquisition, a low beam current of 50 pA was applied and the sample was illuminated for 10 min before performing the measurement.Colloidal clusters dispersed in oil phase were drop-casted directly onto a standard (200 mesh) Lacey carbon copper grid and dried overnight. The grid was mounted in the SEM (FEI Helios NanoLab 660) to identify a suitable structure, orientation, and cluster size for the three-dimensional (3D) analysis. A MCC with a size of 2.8 µm containing about 1200 colloidal particles was selected. The grid with the selected cluster was mounted onto the ultrathin single-tilt tomography holder (Fischione model 2020) and transferred to the transmission electron microscope. STEM tomography was performed using a dual probe- and image-side aberration-corrected FEI Titan80 over 50 iterations using FEI Inspect 3D software. Reconstructed volumes were visualized with VSG Avizo 8.1 for FEI systems software. A median filter minimized background noise, and a global threshold value was applied to segment the particles from the pore space.Tilt series alignment was performed using FEI Inspect 3D software (cross-correlation technique). The tomogram was reconstructed with the simultaneous iterative reconstruction technique (SIRT)m + 1)(m + 2)(m + 3)/6 spheres (Mackay core with m shells). Multiple tetrahedra share spheres at vertices, edges, and faces. The first step follows Mackay8 by placing 20 tetrahedral grains around a common vertex with the constraint that spheres in each shell touch spheres in the previous shell. In step two, 20 identical tetrahedra were placed over the faces such that the top shells of the Mackay icosahedron acted as mirror planes. In the third step, the gaps were filled by connecting existing vertices. These tetrahedra were slightly more deformed than the previous ones. In the fourth step, vertex regions were filled with pentagonal bipyramids made from five tetrahedral grains such that symmetry was preserved. These tetrahedra had the largest deformation. In the fifth step, all spheres outside of a central sphere with truncation radius Rcut were removed. Clusters with FCC structure for the free energy calculations in Fig. Clusters were generated by choosing the positions of identical spheres according to geometric construction rules. All models correspond to one or multiple domains with perfect, sheared, or otherwise deformed FCC crystal structure. Deformations were chosen to maximize symmetry and packing fraction. MCCs were constructed in five steps by placing 130 tetrahedral grains, each with  and truncated triangle features (edge lengths (m −l − a)/2 + 1 and l + 1) with an integer l that describes the amount of vertex truncation. Vertex truncation varies from cluster to cluster  complexity81. Collisions were handled in a stable fashion82 such that overlaps that occurred temporarily after compression steps were removed immediately. Spherical confinement was implemented as a hard spherical wall of radius R at packing fraction t* = t/τ with τ = σ(m/kBT)1/2 and pressure83Npc is the total number of particle–particle collisions and NWC the total number of wall collisions over time ttot*. Evaporation of the solvent was mimicked by incrementing the packing fraction from ϕ = 0.48 to ϕ = 0.55 in steps of 0.001. At each step, a simulation over a duration of Δt* = 500 was performed, which proofed sufficient for robust and reliable icosahedral ordering. Expansion from ϕ = 0.55 to ϕ = 0.48 using the same parameters tested for the presence of hysteresis Spheres in MCCs generated by the geometric model were colored in shades of blue according to the construction step when the sphere was added. Spheres in colloidal clusters obtained from numerical quenching were colored in shades of green according to the number of nearest neighbors  to highlight surface features.ϕ was calculated with the Einstein crystal method86 applied to hard spheres in hard spherical confinement implemented in a Monte Carlo simulation using harmonic springs, V(r) = λ(r − r0)2. Spring anchor points {r0} were chosen after minimizing the appearance of near-contacts between spheres via rapid compression of the colloidal cluster to high density and re-expansion to ϕ. The spring constant λ was increased logarithmically in discrete steps over the range 10−5 to 105. Particle swap moves62 sped up diffusion processes. At each step for λ the averaging was performed long enough for relaxation. The calculation for Fig. s in a cluster of S shells by distance to the center via s = [(R − r)(S − 0.5)/R]. The average shell thickness in the packing fractions simulated with EDMD was 0.95σ. Absolute free energy values were reported after subtracting the bulk contribution F0. Starting points for the free energy calculation are equilibrated simulation snapshots and constructed ideal FCC, Mackay, and anti-Mackay cluster. We are aware that the constructed clusters are not equilibrated. For this reason, the free energies are only upper limits in the case of the constructed clusters.Absolute free energy of a colloidal cluster at packing fraction N hard spheres in the FCC crystal structure was expressed using free volume theory87,f = 3. The constant s0 was obtained from a fit to EDMD simulations of hard spheres with periodic boundaries as s0 = 12.57  = −TSFCC, where the packing fraction ϕc(σc) = N(σ/Dc)3 was calculated with a confinement sphere diameterD0 in order to take into account the effect of confinement. In Fig. D0/σ = 0.4. In Supplementary Figure D0/σ = 0.436, 0.486, 0.520 for ϕ = 0.52, 0.55, 0.57, respectively. The effect of confinement increased with packing fraction.The entropy of a finite system of Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Source Data


Marine phytoplankton growth at high latitudes is extensively limited by iron availability. Icebergs are a vector transporting the bioessential micronutrient iron into polar oceans. Therefore, increasing iceberg fluxes due to global warming have the potential to increase marine productivity and carbon export, creating a negative climate feedback. However, the magnitude of the iceberg iron flux, the subsequent fertilization effect and the resultant carbon export have not been quantified. Using a global analysis of iceberg samples, we reveal that iceberg iron concentrations vary over 6 orders of magnitude. Our results demonstrate that, whilst icebergs are the largest source of iron to the polar oceans, the heterogeneous iron distribution within ice moderates iron delivery to offshore waters and likely also affects the subsequent ocean iron enrichment. Future marine productivity may therefore be not only sensitive to increasing total iceberg fluxes, but also to changing iceberg properties, internal sediment distribution and melt dynamics. Iron is critical for fueling marine primary productivity, but its concentration is often vanishingly low in the ocean. Here, the authors show that though icebergs serve as vehicles delivering the largest supply of iron to polar oceans, the amount of iron they carry varies widely. Increased biological activity following iceberg passage in the Southern Ocean is indicated by both satellite-derived chlorophyll9 and limited in situ observations12, supporting the hypothesis that icebergs are ocean Fe fertilizers. Ice discharge in both the Arctic and Antarctic has increased in response to recent climate change14; thus potentially increasing Fe supply to polar oceans, enhancing productivity and increasing the resultant carbon (C) export15. However, the magnitude and spatial distribution of iceberg Fe fertilization remains uncertain and has yet to be explicitly simulated in global ocean biogeochemical models16, with regional models unable to achieve consensus on the significance of iceberg fertilization in the present-day ocean20.Icebergs have long been considered an important supply route of iron (Fe) to marine phytoplankton7, recently suggested, using satellite-derived chlorophyll data, that icebergs drive >20% of Southern Ocean particulate organic carbon export (POC). This is supported by one model estimate of 30% for Antarctic runoff and icebergs17, yet other estimates of iceberg fertilization vary widely and are in some cases significantly smaller19. Similarly, there is disagreement about whether iceberg fertilization is more, or less, efficient as iceberg size increases8. Part of this uncertainty may arise due to the variable and poorly constrained mechanisms that distribute iceberg melt in surrounding waters and thus affect resource  availability in the water column22. Sporadic, localized upwelling driven by buoyant plumes of iceberg melt may result in surface Fe and macronutrient enrichment, but also phytoplankton dilution within a few kilometers of icebergs23. Conversely, iceberg basal and sidewall melt may form horizontal meltwater intrusions at depth that potentially enrich a larger spatial area with Fe but have no surface biogeochemical signature close to icebergs24.Direct observations of iceberg Fe concentrations and the magnitude of the resultant ocean fertilization are sparse, and to some extent contradictory. Duprat et al.27. In order to reduce this uncertainty, we present a global dataset of Fe concentrations measured directly from 206 iceberg samples (Source Data File). Our data reveals that iceberg Fe content is highly variable with a total dissolvable Fe concentration range of 2 nM to 2 mM and regional median Fe concentrations normally in the range 44–790 nM. Combining this new dataset with numerical modeling, we constrain lower limits to the fraction of iceberg-derived Fe that can be transported to the open ocean demonstrating that Fe loss processes from icebergs are highly sensitive to the location of Fe-rich layers within ice. Using these results, we explore the implications for primary production and C export, highlighting the large uncertainties that remain concerning the fate of iceberg-derived Fe immediately after its discharge into the ocean and variability in marine C export efficiency.A critical challenge in reconciling high and low estimates of iceberg fertilization is quantifying how much Fe is present in calved ice, and what fraction of this Fe enters the offshore, near-surface ocean. Estimates of this flux range from negligible, based on measured surface Fe distribution near some melting icebergs, to one of the largest Fe fluxes into the global ocean based on extrapolations from glacial sediments28. Fe displays a 6-order of magnitude difference between the highest (1.9 mM) and lowest (2.1 nM) concentration . Analysis of variance confirms that Fe in ice from Jökulsárlón  is significantly elevated (p < 0.003) relative to all other catchments. Differences in Fe content between all other catchments and regions were insignificant 31. Furthermore, the global mean 9.3 µM Fe (excluding Jökulsárlón) is comparable to previous estimates of mean iceberg labile Fe content scaled-up from ascorbic acid leaches of glacial sediments and estimated iceberg sediment load 6. The exceptionally high Fe (mean and median of 310 µM and 93 µM respectively) for Jökulsárlón is likely due to the volcanic ash that is visibly present within this ice. Whilst volcanic enrichment of glacier-ice is not unique to Iceland32, it is uncommon and thus these data are excluded from the global mean for flux calculations.In order to constrain iceberg Fe content, iceberg samples were collected from the coastal periphery of Antarctica, Greenland, Svalbard, Iceland and Patagonia. Total dissolvable Fe , Fe which is soluble in weak HCl after 6 months at pH <2, was determined from melted ice samples and represents the upper limit of potentially bioaccessible Fe in the marine environmention Fig. , n = 2013 yr−134, respectively, the mean (9.3 µM) global iceberg Fe concentration corresponds to iceberg fluxes of 4.3 and 10 Gmol Fe yr−1. However, whilst our mean Fe concentration suggests fluxes at the upper end of previous estimates, which produce large global fluxes relative to other Fe supply mechanisms16, the high variability in Fe content may have consequences for both Fe transfer to the open ocean and fertilization because 4% of ice sampled contains 91% of the Fe . The general agreement between our estimate of iceberg TdFe concentration (mean of 9.3 µM) and a methodologically independent estimate of labile Fe within icebergs 4, suggests that the mean Fe content of icebergs is surprisingly well constrained on a global scale.Regional Fe budgets and models for the marine environment generally assume that all supplied Fe is equal, i.e. that one unit of Fe delivered from atmospheric deposition has the same fertilizing effect as one unit of Fe delivered from icebergs if both sources overlap in time and space. However, several aspects of glacier-derived Fe sources are poorly constrained and these knowledge gaps may explain why modeled iceberg fertilization scenarios diverge between different model simulations; even when similar, and apparently conservative, mean ice Fe concentrations are used  content of ice  suggests a mean particle size of 8.5 µm, which is comparable to the coarse size fraction  of atmospheric dust  reflects the compositional difference between Fe-rich basal ice and non-basal ice, which contains only low concentrations of Fe derived from atmospheric deposition23. Such a large range in Fe concentrations within individual icebergs due to this inherent contrast likely explains why regional, or catchment specific, differences in Fe concentrations are not evident in a global dataset of this size . As catchment-specific observations are required to compute iceberg melt rates, we use summertime data from Sermilik fjord (Southeast Greenland), the only catchment globally for which iceberg distribution, morphology, size and melt rates have been previously constrained46.In order to explore how the rate at which the Fe content of iceberg meltwater varies with time after calving, we incorporate Fe content into a model in which iceberg melt rates have been constrained using observational data23, which are visible as narrow bands bisecting icebergs or as sediment-coated iceberg faces47. In our iceberg melt model, we consider three idealized Fe distribution scenarios: a random Monte Carlo distribution, a basal-dominated distribution, and a peripheral shell-dominated distribution 36. Nevertheless, both of these model simulations represent Arctic marine-terminating glaciers in fjord systems where icebergs have a moderately long residence time (81 ± 67 days for Sermilik46) and are exposed to relatively warm ocean waters (1–4 °C in Sermilik summertime surface waters45) before entering the Atlantic Ocean. We therefore consider that, in a global context, our simulated basal and shell scenarios for these conditions  differ between these scenarios and are not well characterized54. Critical unresolved questions are: how thick are Fe-rich layers, where are they located within different icebergs, how fast are these layers eroded in the marine environment, and how does this vary regionally?Whilst we observe no significant differences in TdFe between catchments herein Fig. , the dat45, yet poorly constrained in the global ocean. In Sermilik, 68–78% of summertime iceberg melt is injected into water beneath the shallow (10–20 m) surface layer, with this fraction predicted to remain entirely below the surface throughout summer45. However, this finding provides limited insight into the behavior of meltwater around large icebergs in the open ocean, where the mixed layer is deeper and melt rates are generally lower , such as down-wind of Patagonia, South Africa, and Australia61, had an Fe utilization that matched the modeled atmospheric flux5. The apparent difference between Fe supply and utilization for icebergs was attributed to potential temporal mismatches between supply and demand. However the spatial overlap of multiple Fe sources downstream of the Antarctic Peninsula makes it challenging to determine from satellite-derived data alone how efficient icebergs are as a source of Fe to surrounding waters18. Furthermore, satellite-derived chlorophyll data is less reliable at high latitudes62. Nevertheless, updating the total Fe flux to values at the upper end of the previously used range would amplify the apparent mismatch. However, this also raises a critical question concerning the heterogeneous nature of Fe in icebergs across any region 38 and <3 to 300 nM (Greenland)63. A key factor controlling the extent to which any source of particulate Fe is maintained in more bioavailable dissolved phases, with longer residence times in the water column, is the availability of dissolved organic ligands in seawater64. The saturation of ligands in ice-melt affected waters would act to cap the transfer of Fe between the particulate and dissolved phases; this process would limit the fraction of iceberg-derived TdFe available to support primary production on timescales of days to weeks after meltwater enters the water column65. In one of the only studies to investigate ligand availability around icebergs, Fe binding ligands were found to be close to saturation in the Weddell Sea65 and ligands are similarly hypothesized to limit the transfer of Fe into the dissolved phase downstream of the intense inorganic Fe outflows of large glaciers67. Such a constraint challenges the assumption that mean, or total, iceberg Fe content can be used to predict broad-scale biological effects because the capping effect of ligand availability could strongly moderate the transfer of iceberg-derived Fe from Fe-rich ice.Here we have considered only TdFe, which largely consists of labile particulate Fe (>0.2 µm). Dissolved Fe (<0.2 µm) concentrations are far lower in icebergs, and generally not proportional to TdFe, with reported dissolved Fe concentrations ranging from 4 to 610 nM (Weddell Sea)22. Assuming, unrealistically, this was uniformly distributed through the surface mixed layer with total measured Fe ligand concentrations in this region ranging 1.2–2.4 nM65, the maximum quantity of Fe that could be transferred into the dissolved phase in the surface mixed layer would be a freshwater endmember of ~1.2–2.4 µM. This assumes the residence time of ice-derived particles in the mixed layer is short, exchange between particulate and dissolved Fe phases is rapid and that iceberg melt is occurring in Fe-deficient waters. The maximum enrichment possible would be lower in regions where primary production was not Fe-limited, or where multiple Fe-sources overlapped65, and slightly higher in surface waters due to photochemical processes creating soluble dissolved Fe(II)65. For icebergs with a TdFe concentration close to the median (170 nM), a large fraction of the labile Fe present could therefore potentially be transferred to the dissolved phase. However, this declines to only 13–26% for icebergs with TdFe close to the mean (9.3 µM). Such a calculation is over-simplistic in many respects as it neglects to consider the different depths of intrusions and mechanisms of melt occurring in the water column22. Furthermore, it is unclear on what spatiotemporal scales iceberg-derived particulate Fe is removed from the water column and how quickly the labile particulate and dissolved Fe phases achieve equilibration. Nevertheless, for ice with high TdFe content, a high fraction of iceberg TdFe could plausibly be maintained in the mixed layer only under very dilute meltwater addition scenarios. Therefore, the use of a mean iceberg Fe content to represent highly variable iceberg Fe input into the ocean from a point source delivered at the surface is questionable18.We illustrate this by considering a mixing scenario between ice melt and Fe-deficient waters with an excess ligand concentration. Taking the freshwater excess calculated in melt enriched layers in iceberg-affected regions of the Weddell Sea suggests a melt water enrichment of ~0.1%9 and C export vary significantly68. This is especially the case in the Fe-limited Southern Ocean, where iceberg Fe fertilization is expected to have the greatest stimulatory effect69, but the relationship between Fe input and C export is complex70. The simplest method for estimating how any Fe input into the ocean affects C export, the quantity of POC sinking beneath 100 m depth, is by using Fe-to-C sequestration efficiencies72. Fe-to-C sequestration efficiencies have been estimated for several naturally Fe-fertilized, high-latitude regions . Yet Fe-to-C sequestration efficiencies vary widely from 17 to 2900 Kmol C mol−1 Fe and are thus challenging to apply at broader scales73. Even if they are applied to a specific regional ice melt Fe-enrichment scenario, for example the simplistic ~0.1% melt water enrichment scenario outlined above, a very broad range of POC export is plausible considering the range of iceberg Fe content  or to the maximum stable concentration of Fe in the water column (e.g. 2 nM), approximating upper limits to observed values74, is the same  or accounted for in some iceberg POC export calculations (e.g. ref. 7), meaning that differences in estimates of iceberg-Fe-fertilization can arise from how Fe is scaled to C export independently of how the iceberg Fe flux is quantified.In scaling Fe input to primary production using Fe:C cellular ratios, the effect of applying a cap to primary production , Fe may more efficiently fuel C export when a dilute Fe addition induces low primary production with high C export efficiency, than when a concentrated Fe addition induces high primary production but with low C export efficiency  and then stored double bagged until required in the field. Iceberg samples were collected by hand using metal-free tools. Sample collection was randomized at each fieldsite location by collecting ice samples at regular intervals along pre-defined transects using small boats. Targeted icebergs had visible widths of 0.5–20 m above the waterline. In total 1–5 kg ice pieces were retained in LDPE bags and melted at room temperature. The first 4 aliquots of meltwater were discarded. 125 mL of meltwater was retained unfiltered in trace metal clean LDPE bottles. Trace metal samples were then acidified to pH 1.9 by addition of 180 µL HCl  and allowed to stand upright for >6 months prior to analysis via inductively-coupled, plasma mass spectrometry  after dilution with 1 M HNO3 . Calibration was via standard addition with a linear peak response from 1 to 1000 nM Fe (R2 > 0.99). Analysis of the reference material CASS-6 (n = 12) yielded a Fe concentration of 26.6 ± 1.2 nM (certified 27.9 ± 2.1 nM). 51 sediment samples collected from iceberg surfaces or from embedded within icebergs (as per Ref. 38) and stored frozen were defrosted at room temperature and analyzed for particle size distributions within the size range 0.1–1000 µm using a Laser Analysette.Low-density polyethylene (LDPE) sample bottles were pre-cleaned in a three stage procedure 63, Kongsfjorden (n = 28)38, South Bay (n = 7)78 and Maxwell Bay (n = 7)78 is included alongside new data in these catchments  was used to produce 3–D representations of iceberg volume and Fe concentration at daily time steps. The horizontal and vertical grid resolution was 1 m, with fractional grid cells used for ice volumes < 1 m3. Iceberg lengths from 50 to 1000 m  were simulated; we then scaled the ice volume and Fe concentration by the observed iceberg distribution in Sermilik fjord to compute net ice volume and Fe loss, respectively , there is minimal difference between this formulation and one where wave erosion is applied to the entire iceberg vertical face (the difference being included within the uncertainty in ref. 45). However, for longer time periods which are comparable to the residence time within Sermilik fjord (mean of 81 days), the difference between these two wave-melt formulations becomes larger. Both approaches are therefore considered 79. The Monte Carlo case assigned Fe concentrations, randomly sampled from the observational dataset, to random grid cells until the total Fe content was equal to the shell case; all cells not assigned by the Monte Carlo method were set to low Fe ice. Iceberg simulations started on May 1st and were time stepped for 180 days. Basal layer thickness is poorly defined in the literature with general estimates of <3–10 m36. Here we used a fractional composition of 9% high Fe for basal/shell scenarios based on the TdFe data distribution. For substantially thinner basal layers, the difference between Basal/Shell and Monte Carlo scenarios in Fig. We tested 3 idealized iceberg Fe distribution scenarios: a shell distribution, a basal-layer distribution, and a Monte Carlo distribution. For the shell and basal-layer cases, high and low Fe ice was set to 100 µM and 170 nM, respectively; grid cells with high Fe constituted 9% of the total iceberg volume representing the 9% of samples with TdFe > TdFe80. Except where stated otherwise, a constant cellular Fe:C ratio was fixed as 3 µmol Fe mol−1 C 81. The relationship between primary production and C export was determined using data from Ref. 74 and the fit C export efficiency = −0.3484 × Log(PP) + 1.2239 with a cap applied at zero export to prevent negative values in C export. Except where stated otherwise, C export is defined at 100 m, and the mixed layer is defined as 0–100 m.Fe supply to primary producers was calculated as 2× the extrapolated flux from iceberg TdFe concentrations i.e. using a 1/fe ratio of 2 for high Fe watersSUPPLEMENTARY INFORMATIONPeer Review FileSource Data File


The integration of membrane distillation with reverse electrodialysis has been investigated as a sustainable sanitation solution to provide clean water and electrical power from urine and waste heat. Reverse electrodialysis was integrated to provide the partial remixing of the concentrate (urine) and diluate (permeate) produced from the membrane distillation of urine. Broadly comparable power densities to those of a model salt solution (sodium chloride) were determined during evaluation of the individual and combined contribution of the various monovalent and multivalent inorganic and organic salt constituents in urine. Power densities were improved through raising feed-side temperature and increasing concentration in the concentrate, without observation of limiting behaviour imposed by non-ideal salt and water transport. A further unique contribution of this application is the limited volume of salt concentrate available, which demanded brine recycling to maximise energy recovery analogous to a battery, operating in a ‘state of charge’. During recycle, around 47% of the Gibbs free energy was recoverable with up to 80% of the energy extractable before the concentration difference between the two solutions was halfway towards equilibrium which implies that energy recovery can be optimised with limited effect on permeate quality. This study has provided the first successful demonstration of an integrated MD-RED system for energy recovery from a limited resource, and evidences that the recovered power is sufficient to operate a range of low current fluid pumping technologies that could help deliver off-grid sanitation and clean water recovery at single household scale.  •Membrane distillation with reverse electrodialysis allows off-grid sanitation.•Heat and concentrate development with MD enhance RED power density.•The complex salt matrix within the feed does not limit power density.•Good energy recovery from small salt concentrate volumes with recycle. A2)Cross sectional area of one membrane (mC−1)Concentration (mol LmixGΔGibbs free energy of mixing (J)ED−1)Energy density (J kgF−1)Faraday constant Id−2)Current Density (A mJs−2 s−1)Total salt flux (mol mJw−2 s−1)Total water flux (mol mSolution conductivityLp−2 s−1 kg−1)Average water permeability coefficient for both anion and cation exchange membranes (kg mM−1)Molar mass of water (kg molmMass (kg)NNumber of cell pairs in membrane stacknMoles (mol)PPower (W)Pd−2)Power density (W mPs2 s−1)Average salt permeability coefficient for the anion and cation exchange membrane pair (mR−1 mol−1)Universal gas constant (8.314 J KS−1 mol−1)Molar entropy (J KTTemperature (K)wtwater mol salt−1)Number of water molecules transported with salt ions across the membranes vNumber of moles in 1 mol of saltzValency of the ionαPermselectivity (%)ΔμsDifference in chemical potential of saltΔμwDifference in chemical potential of waterδmAverage membrane thickness of the anion and cation exchange membrane pair (m)γActivity coefficientOsmotic coefficient (unitless)ηB Mixed concentrate and diluate; C ConcentrateEnergy extraction efficiency (%); Subscripts; DDiluateGPG = Gibbs power)Gibbs wWater; Abbreviations; SDG Sustainable development goalsMDMembrane distillationRED4+-N Ammonical nitrogen; NaCl Sodium chloride; OCV Open circuit voltageReverse electrodialysis; NOM Natural organic matter; VMD Vacuum membrane distillation; COD Chemical oxygen demand; NHLCLow concentration (refers to channel)HCHigh concentration (refers to channel)SOCState of charge1Sustainable small scale sanitation systems treating blackwater on-site have been recently innovated to address the water sustainable development goals SDG 6) in low income countries ,2. Sourc in low i−1); and, a salt rich retentate exceeding 20 mS cm−1  that can be achieved with MD whilst only increasing permeate conductivity by a small amount. The Gibbs free energy available can be harnessed as electrical energy via reverse electrodialysis (RED) which uses an alternating series of cation and anion exchange membranes separating concentrated and dilute solutions to produce a salinity gradient. The selective flow of anions and cations through the respective membranes creates an electrochemical potential across the stack, where at the electrodes, a redox reaction converts the ionic flow to an electric current. Reverse electrodialysis has gained considerable interest since the first demonstration by Pattle in 1954 where a gross power density of 0.05 W m−2 was reported . Th. Th−1  and nylon spacers with an open area of 35%  both 0.3 mm in thickness. The concentrate and diluate were pumped through the stack in a co-current configuration with peristaltic pumps. Titanium mesh plate electrodes coated with a Ru/Ir mixed metal oxide (MMO)  were fixed within the endplates of the stacks and acted as anode and cathode. An electrode rinse solution of 0.25 M NaCl was continuously circulated within the electrode compartments at a flow rate of 100 mL min−1 using a peristaltic pump . Galvanostatic measurements were conducted using an Iviumstat. h . Current was applied across the mesh working electrodes and Ag/AgCl reference electrodes  were placed in the anolyte/catholyte to measure electrical potential across the RED stack.The custom RED stack used throughout this study is illustrated in −1 from 0 to the maximum value, when the voltage of the stack reversed , [3.3OCV, V) is the electrochemical potential difference across the stack. Assuming ideal solutions of differing concentrations of a single salt either side of a perfectly selective membrane the OCVi across that membrane can be calculated from the Nernst equation [z is the valency of the ion (e.g. Na+ = +1), F is the Faraday constant (96485C mol−1), γ is the mean ionic activity coefficient of the counter-ion  and C the concentration of the counter-ion in either the concentrate or diluate (mol L−1). To calculate the potential across a RED stack this calculated OCVi can be multiplied by the number of membranes in the stack, therefore [For RED the open circuit voltage (equation :(14)OCViherefore :(15)OCViOCVi; however, becomes increasingly onerous when considering complex waters consisting of many ions as each counter-ion has a unique concentration gradient across the membrane. Multivalent ions have been shown to decrease the stack voltage and therefore power density [OCVi when a greater number of counter-ions are present [OCVideal from the conductivity OCVcOCVexp is the experimentally determined open circuit voltage (V), OCVcond the open circuit voltage calculated from the conductivity of the concentrate and diluate, and the permselectivity  characterises the average over all the cation and anion exchange membranes within the stack.The permselectivity  of an ion exchange membrane represents its ability to reject co-ions compared to that of an ideal selective ion exchange membrane (which will completely reject co-ions). Defined as the ratio of the measured and calculated ideal OCV it has therefore been calculated using: :(17)α=(O44.1−1 respectively) was carried out, achieving a power density of 0.57 W m−2, which is comparable to the literature using the same membranes (−1), achieved a power density of 0.32 W m−2, which can be expected since the conductivity is less than half that of sea water. A multivalent ion control was subsequently evaluated, which comprised comparable conductivity (21.3 mS cm−1) but with a lower NaCl fraction, and the inclusion of divalent salts (−1), despite comprising a more complex salt matrix, which included organic salts and organic compounds (d: ± 0.01 W). This indicated an absence of significant fouling within the relatively short experimental timeframes; however, longer term studies will be required to reveal the extent to which this “absence of fouling” would last, particularly at higher solute concentrations. Electrodialysis studies have shown that organic fouling behaviour is determined by specific properties of organic matter and not necessarily concentration [254nm absorbing organics within the diluate but little relationship between their concentration in the concentrate and permselectivity [−1), the power density was 20% greater for the synthetic, which we propose is due to a higher concentrate conductivity (21 and 12 mS cm−1 respectively). However, the conductivity was 40% higher, which should have resulted in a proportionately lower power density, which we ascribe to the wider transient properties of urine [−1), which resulted in a 13% reduction in power density  demonstrated a detrimental effect on power density (0.025 W m−2) due to the large internal resistance (75 Ω), attributable to high resistance of the diluate. Power density peaked (0.3 W m−2) at a diluate conductivity of 0.5 mS cm−1 and declined to 0.165 W m−2 as the rapid decline of internal resistance to 1.5 Ω at 3.8 mS cm−1 is offset by the decrease in electromotive force [The impact of solution concentration was further investigated in order to understand the dynamic conditions within MD-RED associated with gradual ion transfer into the permeate in recycle mode a, and MDve force . Weiner ve force ,54, whilve force ,55. Whilve force , potentive force , which dTemperature increased power density and reduced overall cell resistance c. Increa4.2−1  provided an average power density of 0.043 W m−2 for 0.1 h, compared to 0.048 W m−2 for 17 h (140 mL min−1 both compartments). Therefore, higher flowrates were required to accommodate for the dynamic conditions experienced in recycle mode, particularly the increasing diluate concentration. According to the theoretical values calculated from the Gibbs power, higher current densities achieve greater power densities as the concentrate and diluate approach equilibrium  were 0.38, 0.26 and 0.14 W m−2 respectively. For comparison, respective experimentally obtained power densities were 0.18, 0.20 and 0.13 W m−2 . The energy extraction efficiency (η) illustrates how closely experimentally obtained power represents the theoretically available power at varying current draws (−2) provided the overall greatest η, particularly until reaching a molality difference of 0.1 mol kg−1 where η remained above 60%.Within ISO standard 30500 on ‘non-sewered sanitation’, a single use system is engineered to withstand an average of ten users per day, which based on the median daily production of human urine of 1.4 L capreatment . This isreatment , low floilibrium a. When o13 W m−2 b and reant draws c. In thiIn recycle mode, total power dissipation is affected by osmotic transport, non-ideal salt transport and internal resistance. Osmotic transport was insignificant, as the maximum concentration demonstrated was 0.2 M, providing an osmotic pressure of ∼9.9 bar. Previous literature has reported that higher concentrate concentrations facilitate water transport, particularly higher than 1.5 M  ,36, wher−2) against the theoretical Gibbs free energy, under the conditions trialled , which is consistent with other similar studies obtaining between 45 and 60% when equal volumes of concentrate and diluate are utilised [utilised . Vermaasutilised . For uri5−1, conductivity 0.21 mS cm−1) provided comparable power densities (0.2 W m−2) to a simple NaCl matrix (0.32 W m−2) despite the complexity of the salt matrix, organic salts and organic constituents. Around 47% of the Gibbs free energy available was recoverable, which can be used in low power fluidic devices to permit overall water recovery from MD. For example, the mixing energy from l L urine and 1 L permeate is sufficient to operate an axial fan for sweep gas, or a micro-pump for the provision of liquid transport at head pressures and flow rates up to 500 mbar and 350 mL min−1 respectively. An increase in system surface area and cell pair number will scale the device to increase both current densities and voltage achievable to power larger scale devices [This study has provided the first successful demonstration of hybrid MD-RED using urine, to create a synergistic relationship in which high quality water can be produced using waste heat, and the subsequent salinity gradient generated in membrane distillation utilised for the production of electrical energy. The urine salt matrix combined with the high quality MD permeate (COD 253 mg L devices . Concent devices  will def devices  which im


Glossina palpalis palpalis remains the major vector of sleeping sickness in Côte d'Ivoire. The disease is still active at low endemic levels in Bonon and Sinfra foci in the western-central part of the country. In this study, we investigated the impact of a control campaign on G. p. palpalis population structure in Bonon and Sinfra foci in order to adapt control strategies. Genetic variation at microsatellite loci was used to examine the population structure of different G. p. palpalis cohorts before and after control campaigns. Isolation by distance was observed in our sampling sites. Before control, effective population size was high  with dispersal at rather short distance (731 m per generation). We found some evidence that some of the flies captured after treatment come from surrounding sites, which increased the genetic variance. One Locus, GPCAG, displayed a 1000% increase of subdivision measure after control while other loci only exhibited a substantial increase in variance of subdivision. Our data suggested a possible trap avoidance behaviour in G. p. palpalis. It is important to take into account and better understand the possible reinvasion from neighboring sites and trap avoidance for the sake of sustainability of control campaigns effects.  •Glossina palpalis palpalis show relatively important effective population size and modest dispersal before control.In Côte d’Ivoire HAT foci, •Genetic data analyses show that flies captured after control partly (at least) come from surrounding sites.•One allele at Locus GPCAG displayed a 328% allele frequency increase after control.•This locus (GPCAG) seems associated to resistance to control, by a mechanism which remains to be identified. There are two forms of HAT: one, known as gambiense HAT, due to T. brucei gambiense, is endemic in West and Central Africa and causes over 95% of current cases; the other, known as rhodesiense HAT, due to T. brucei rhodesiense, is endemic in East and Southern Africa and accounts for the remainder of cases (Human African Trypanosomiasis (HAT) is a centuries-old disease that has affected the lifestyle of people in sub-Saharan Africa . It is aof cases .The disease reemerged at the end of the 1990s, but renewed efforts from endemic countries brought the disease under control again . In thisTsetse control has recently become a key component of the overall activities of HAT control . HoweverIn Côte d'Ivoire, the development of cash crops (coffee and cocoa) in western part of the country has led to profound changes in the biotopes that are favorable to tsetse flies  so that Glossina palpalis palpalis in the Bonon focus. Sinfra subsamples were added to allow widening the studied area and get more precision on the initial structure of tsetse populations before any control in the Marahoue region.In this study, we aimed to assess how vector control can affect the population genetic structure of 22.1The study was carried out in the Bonon and Sinfra HAT foci located in the western-central part of Côte d'Ivoire . These f2.2Dmax) taken as the radius of the disc that contained all traps of the site (SS = π × Dmax2). Census densities (Dc) were computed as the number of captured flies per site (Nc) divided by SS . Those the name of which begins with the letter X are X-linked. We thus analyzed two data sets: one without these loci but with all individuals and the other with females only and all loci. More characteristics for describing the loci used can be found in the Tsetse flies were genotyped at 10 microsatellite loci . Locus XIn the laboratory, legs were dried and then subjected to chelex treatment as previously described . The 10 In order to confirm subspecies determination, we amplified partial sequences of ITS1 following the protocol described by All data and genotypes are available in the 2.4All data were formatted for Create  and tranG-based test of Fstat 2.9.4  and is more powerful than other combining procedures (L(L-1)/2 (15 if the number of loci L = 6) These tests are not independent. We used the Benjamini and Yekutieli false discovery rate (FDR) procedure  between locus pairs was assessed with the at 2.9.4  an updatat 2.9.4  with 10,ocedures . There arocedure  that is d with R .F-statistics  and FIT reflects the combination of both (e.g. (FIS and FIT can vary from −1 (heterozygote excess) to +1 (homozygote excess) and FST from 0  to +1 . In any case, the three statistics are linked by the famous relationship: 1-FIT = (1-FIS)(1-FST) , three atistics  can be dth (e.g. ). Under T) (e.g. ).FIS (homozygote excess) can be produced by systematic mating between related individuals like sib-mating (e.g. (In dioecious species (like tsetse flies), heterozygote excess is expected over all loci in small random-mating subpopulations (e.g. ). Multilng (e.g. ). It canng (e.g. ).FIS with an important variation across loci  (FST = 1/[4Ne(m + u) + 1], where u is the mutation rate of the locus /(4FST).A positive value for ructure)  at mutatus (e.g. ). If u <F-statistics were estimated through Weir and Cockerham's unbiased estimators  or of individuals among subsamples within the total sample (population subdivision test) . The p-value then corresponded to the number of times a statistic measured in randomized samples was as big as or bigger than the observed one . For FIS, the statistic used was f (Weir and Cockerham's FIS estimator). To test for subdivision, we used the G-based test  and FST (StrdErrFST) computed by jackknife over populations, and 5000 bootstraps over loci as described elsewhere  of lsewhere . Since jFIS and FST are augmented, StrdErrFIS is at least twice StrdErrFST (jackknives over loci), a positive correlation is expected between FIS and FST as is expected a positive correlation between FIS and the number of missing data (putative null homozygotes). If such correlations do not exist and if StrdErrFIS > StrdErrFST, then a Wahlund effect better explains the data. The significance of correlations was tested with a unilateral (ρ > 0) Spearman's rank correlation test with R. The presence of null alleles was also looked for with MicroChecker v 2.2.3 (ρ < 0) Spearman's rank correlation test between allele size and FIT, which is more powerful than other alternatives . In case of null alleles, both  v 2.2.3  and null v 2.2.3 . The adjrnatives . In caseFor local population structure studies, we used all subsamples, while for population subdivision studies we kept only comparisons between subsamples from the same cohort.FST estimates and the INA correction for Cavalli-Sforza and Edwards' chord distance  follows FST_R = a + b × Ln(DG) where a is a constant (intercept), Ln(DG) is the natural logarithm of the geographic distance between two sites and b is the slope of the regression. Rousset also demonstrated that the product of the effective population density De by the average of the squared axial distance between reproducing adults and their parents De×πb); that the neighborhood size is Nb = 1/b; and that the number of immigrants from neighboring sites at each generation is Nem = 1/(2πb), where Ne is the effective subpopulation size and m is the immigration rate. The parameter σ represents half the average parent offspring axial distance. A proxy for dispersal distances per generation δ can be obtained if Ne and average surface of a subpopulation (S) are known: De = Ne/S and S, we took either the surface computed above (SS). Another possibility is to use the threshold distance (DT) between two sites for FST to become positive, hence DT = ea/b-. This new parameter was then considered as the distance between the center of two neighboring subpopulations and hence to their diameter. The corresponding surface was then ST = π × (DT/2)2.Isolation by distance was undertaken with Rousset's approach  where, iDCSE and Ln(DG) or FST_R, computed with the INA or ENA correction by FreeNA, as recommended for microsatellite markers  by halving the bilateral p-value in case of positive correlation.Significance of isolation by distance was assessed through the 5000 bootstraps confidence intervals from FreeNA and with a Mantel test between  markers . BecauseNe) were estimated through several methods and softwares. The heterozygote excess method (http://www.t-de-meeus.fr/ProgMeeusGB.html). We then computed the average, minimal and maximal (MiniMax range) effective population sizes across methods weighted by the number of usable values obtained in each case. Effective population size reflects the demography and other phenomena as reproductive strategy and/or historical perturbations and its estimation also varies across methods (Ne and the census size (Nc) of the corresponding subpopulations  undertaken with PCA-Gen 1.2.1  between T0 subsample pairs and pairs between T0 and TX  with a Mann-Whitney-Wilcoxon U test with Rcmdr.The effect of control campaigns on the population genetic structure of en 1.2.1  for whic2.1   as show in see also .Fig. 2Ev3.2p-value = .0406) that did not stay significant with Benjamini and Yekutieli correction . We thus safely concluded that all markers are statistically independent.There was only one pair of loci in significant LD ; and a positive though not significant correlation between FIS and FST . No stuttering could be evidenced and a significant short allele dominance was detected for locus pGp27  that did not stay significant after Benjamini and Yekutieli correction .There were four pairs of loci in significant LD  and between FIS and FST (ρ = 0.4667) though marginally not significant . Three loci displayed short allele dominance: Loci X55–3, pGp27 and XB110  was in agreement with null alleles as the explanation for the important FIS observed at this locus . Furthermore, it is worth noting that the SAD signature was mainly du to the fact that all longest alleles were rare (average frequency < 0.006) and displayed a very small FIT (average < 0.042). Considering that with a frequency of pn = 0.2, the probability for heterozygous individuals between such alleles and a null allele is 2 × 0.2 × 0.006 = 0.0024 and the average of homozygote frequencies for these rare alleles is ~0.00005, with a total sample size of 296, this means a total expected number of falsely interpreted or true homozygous individuals for these long and rare alleles is 0.725. Thus, because null alleles affect preferentially the FIS of the most frequent alleles, and because here these are also the shortest ones, this observation invalidates the SAD test for XB110. This locus can simply be considered as only affected by null alleles. Heterozygote deficits observed at other loci can be explained by null alleles. Subsequent subdivision measures and testing were thus done with corrections implemented by FreeNA.The variation of  deficit . There wnd XB110 . This coFST across loci can be seen in the FST appeared completely outside the range of the nine other loci. Here, these subdivision measures are presented only for the sake of examining loci behavior. Indeed, values presented here both include temporal and spatial subdivision. Interestingly, this locus corresponds to a trinucleotide motive. It probably responds to some kind of selection.Variations of In order to avoid possibly biased estimates, we removed loci X55–3, pGp27 and GPCAG from subsequent analyses.To keep a number of loci above 5, we thus only studied female subsamples.3.4FST_R based tests with 95% confidence interval of the slope or with the Mantel test are significant while the DCSE based Mantel test is not. The 95% confidence interval of the slope is a very good indicator of what occurs and FST_R based methods should be less powerful than with DCSE , we could estimate a neighborhood size Nb = 667 individuals in 95%CI = [476…909] and an immigration of Nem = 106 individuals from neighboring sites (in 95%CI = [76…145]).Using the slope and its 95% confidence interval  than between traps at T0 and traps at TX (FST = 0.0035) but the difference was not significant .Accordingly, genetic subdivision appeared smaller between traps from T0 (b = 0.0086 in 95%CI = [0.0024…0.0219]. From De_T). On the contrary, effective population density using sampling surface (De_S) significantly drops to values similar to the census density.Modelling isolation by distance between contemporaneous subsamples after control was uneasy because of the small number of available points. It nevertheless provided a slope the bootstrap confidence interval of which did not contain 0 . After pooling all T0 traps together and all T1–4 together, we obtained a highly significant  subdivision (FST_FreeNA = 0.1286) for GPCAG consistent with what can be seen in As can be seen from 4In the Marahoue region (Bonon and Sinfra), before control, we noted relatively high population densities and short dispersal distances as compared to other studies De . Traps sDe_T and δT after treatment can illustrate this. Alternatively, it may also reflect the inappropriateness of defining the surface occupied by a subpopulation with the threshold distance for genetic subdivision. The relevance of such method was indeed strongly questioned , beforehttps://blast.ncbi.nlm.nih.gov/Blast.cgi (with the accession number AY033512.1) outputted no result but the sequence deposited by Salvelinus alpinus , with a putative mediator of RNA polymerase II transcription subunit 26 of Ceratitis capitata (at 69%) and Rhagoletis zephyria (at 72%), two Dipteran insects of the sub-order Brachycera like tsetse flies. Deeper investigations would be required to decipher the mechanism involved in the possible resistance associated with GPCAG allele 219. Insecticide resistance has never been reported in tsetse flies to our knowledge and we could find nothing more than recommendations on that issue in an old FAO report  were able to escape from control measures: either because this trinucleotide locus may code for something, or because it is part of a selected gene as an intronic sequence, or very close to it. It would be interesting to identify what caused this result. We have been unable to find to what corresponded the sequence where this locus is exactly. Indeed, a “highly similar sequences (megablast)” in GenBank at O report  and assuO report . MoreoveO report , using VO report . AlternaO report . As for O report  but, to If resistance is behavioral, the fact that a substantial proportion of resistant tsetse flies harboring the GpCAG-219 allele were captured in the trapping device after treatment suggests: i) that resistance is not absolute but statistical; and ii) that the proportion of resistant tsetse flies must be much higher in the pool of uncaptured flies.Glossina genomes and check if it is in or near a coding gene, determine its nature, and clarify if this trinucleotide microsatellite itself is responsible for what we have evidenced in the present paper.More researches are needed to locate this locus in the 5The work presented here shows that control campaigns has modified tsetse flies population structure. Although it has allowed reducing considerably tsetse fly's densities, it may also have selected for the emergence of flies resisting the treatment by a mechanism that remains to be identified. This result should be taken into account and new strategies developed to prevent reinvasion.Glossina palpalis palpalis of Bonon and Sinfra HAT foci. The different columns correspond to (in order): year, month and day of sampling; sampling site, treatment (To-T4), cohort (generations 1-11), trap names, latitude and longitude , individual labels, sex, the combination of treatment-Site-Trap to define subsamples and the different genotypes at the 10 loci (two columns per locus). The first row contains column headers and the other rows correspond to the different tsetse fly individuals.Raw data of Supplementary Table S1Microsatellite markers used for genotype Glossina palpalis palpalis .Supplementary Table S2The following are the supplementary data related to this article.


It is often assumed that Hebbian synaptic plasticity forms a cell assembly, a mutually interacting group of neurons that encodes memory. However, in recurrently connected networks with pure Hebbian plasticity, cell assemblies typically diverge or fade under ongoing changes of synaptic strength. Previously assumed mechanisms that stabilize cell assemblies do not robustly reproduce the experimentally reported unimodal and long-tailed distribution of synaptic strengths. Here, we show that augmenting Hebbian plasticity with experimentally observed intrinsic spine dynamics can stabilize cell assemblies and reproduce the distribution of synaptic strengths. Moreover, we posit that strong intrinsic spine dynamics impair learning performance. Our theory explains how excessively strong spine dynamics, experimentally observed in several animal models of autism spectrum disorder, impair learning associations in the brain. The operation of a neural circuit is shaped by the strength of synapses that mediate signal transduction between neurons. Activity-dependent modification of synaptic strength, termed synaptic plasticity, is considered to be an underlying mechanism of learning and memory , which has a thick tail and a peak at a rather weak strength    conditions can explain the abnormally high spine turnover rate observed in animal models of autism spectrum disorder (ASD)  for neurons with similar selectivity and falls off with their tuning-distance  because the correlation between the synaptic strength and spine volume has been experimentally demonstrated −2. This initial distribution approximates an experimentally observed spine volume distribution (see Methods). As we will see below, changing synaptic strengths by multiplicative STDP alone fails to sustain cell assemblies in the presence of spontaneous activity.To model activity-dependent plasticity, we assume that spine volume is proportional to synaptic strength  exhibited a slow but steady increase  = (αv + β)2/2 with zero drift coefficient  is ∂P/∂t = (∂2/∂v2)[D(v)P]. It indicates that the equilibrium is reached when the diffusion intensity D(v)P becomes volume-independent. This gives the equilibrium spine volume distribution 3 are non-spines and do not exhibit multiplicative STDP, whereas intrinsic spine dynamics are still present even for these small protrusions  and a depression of the spines if they are large (roughly >0.57 μm3). This transition is observed for large spines because the LTD amplitude, which is proportional to spine volume, dominates over the amplitude of LTP, which is independent of spine volume. LTP dramatically increases for spines >0.30 μm3 because strong intra-group connections serve as positive feedback on coincident firing between the neurons within the group, increasing the frequency of both LTP and LTD events. Therefore, STDP on its own leads to one fixed point of mean spine volume at a non-physiologically high value at around 0.57 μm3. This fixed point is controlled by the parameter setting the relative amplitude of LTD ∝1/D(v) with a mean spine volume of roughly 0.15 μm3 , so that the spine volumes fluctuate around 0.15 μm3. Conversely, changes in the mean spine volume are significantly affected by STDP when large (roughly >0.30 μm3), creating a larger-volume fixed point at approximately <0.55 μm3. In between these stable fixed points, there is a separation point (unstable fixed point) at around 0.50 μm3, which prevents the mean spine volume from moving in between these two stable fixed points. Therefore, while single spines are largely fluctuating and sporadically moving around the small and large mean spine-volume fixed points, a large cell assembly is stably maintained by the ensemble property of intra-assembly spines: here quantified as the mean intra-assembly spine volume.When the contributions from STDP and intrinsic spine dynamics are added together with an appropriate balance  = (αv + β)2/2 to characterize intrinsic spine dynamics in fmr1KO mice based on reported observations. Spine turnover is about twice as high in fmr1KO mice as in WT mice  explodes (≥100 Hz) due to unstable learning, (2) fades (≤1 Hz), or (3) is stably maintained within a physiological range (>1 Hz and <100 Hz) during the maintenance period. The peak potential connectivity of 10.4% was already optimized for the WT model, such that nearly all cell assemblies are stable. The frequency of fading assemblies increased if potential connectivity was too low and that of exploding assemblies increased if potential connectivity was too high. However, the WT model stably maintained cell assemblies over a range of the peak potential connectivity from 9.4 to 10.8%. In contrast, the maintenance of cell assemblies in the fmr1KO model was much more sensitive to potential connectivity. As indicated in fmr1KO model first improved up to ~50% maintenancerate, but soon started to decline again due to exploding assemblies, as potential connectivity increases. Given this result, it is an interesting possibility that fmr1KO mice with excessively strong intrinsic spine dynamics may locally up-regulate potential connectivity, relative to WT mice, to avoid catastrophic forgetting   = . The dynamics of membrane potential Vi of neuron i is described byA local network of m = 20 ms, resting potential V0 = −70 mV, adaptation Ai, refractory coefficient Ri, kth , synaptic strength j to neuron i, spike train j composed of nth  spike time ij from neuron j to neuron i uniformly sampled from interval  ms, and external input i (see below). Note that δ is the Dirac delta function. The time course of postsynaptic input is modeled using the alpha function  that takes 1 for t ≥ 0 and 0 for t < 0. The peak value of f(t) is about 0.39 mV. A spike is emitted when Vi reaches a spiking threshold at −50 mV and then Vi is reset to V0. Excitatory neurons receive adaptation input Ai that reflects a slow Na+-activated K+ current  in inhibitory neurons. Refractoriness is imposed by Ri. Ri is fixed at 0 after each spike of neuron i for 1 ms (absolute refractoriness), and then recovers toward 1 followingwith time constant τR = 3.5 ms (relative refractoriness). The differential equations are numerically solved using the Euler method with bin size Δt = 0.1 ms.with time constant τE, and inhibitory neurons, I, are sparsely connected. The connections from excitatory to inhibitory neurons (E → I) and those from inhibitory to excitatory neurons (I → E) are randomly generated with 10% connection probability. Each of these directed connections is mediated by a single synapse, whose strength is randomly drawn from a uniform distribution in the range  mV for E → I and  mV for I → E, respectively. According to our neuron model described above, a typical E → I or I → E synapse produces a postsynaptic potential of 6 mV. For simplicity, we assume no direct connections between inhibitory neurons. Excitatory neurons are equidistantly placed on a one-dimensional ring of circumference 1 a.u. that represents the feature space. Note that the tuning distance in feature space dfs does not necessarily correspond to the physical location of a neuron, or the distance between any two neurons. The potential connections from excitatory to excitatory neurons (E → E) are randomly generated according to the Gaussian probability profile (dfs≈0), and decays toward 0 with a length-constant of 0.1 as dfs increases. Although this E → E peak connection probability is optimized for memory retention, simulation outcomes are robust with this parameter in our model  of spines from a Poisson distribution In the network model of er λ = 3 .i also receives spike train In addition to the recurrent input, neuron t = 0 and ends when at least one group's mean intra-group spine volume reaches ≥ 0.49 μm3.We divide the feature space of excitatory neurons into four equally-sized consecutive parts and randomly select 40% of the neurons from each part as a stimulated neuron group . During kth  spine volume i, receiving signal from neuron j, is modeled byUnlike E → I and I → E synapses, which have fixed weights, E → E synapses ξ(t′) >= δ(t − t′). The above Langevin equation is numerically solved by the Euler method with bin size Δt = 0.1 ms. In addition, we set reflecting boundaries for spine volume to enforce T = 3.3 · 104 times shorter simulations by speeding up both STDP and intrinsic spine dynamics by factors T and v(t + Δt)−v(t) by intrinsic spine dynamics are diffusive and scale with the square root of time duration w of a spine with volume v is then assumed to bewith an averaging time constant τv ≥ vθ  and 0 for v < vθ . The median spine volume of Peq(v) is 0.047 μm3 and such a spine produces 0.8 mV of excitatory postsynaptic potential. We set vθ to be a threshold volume typically used in experiments to detect spines /2 and the second term is produced by the volume-dependent fluctuation αv + β. Hence, while the above two equations are identical, there is a semantic difference regarding what fluctuation means. According to the Itô interpretation, only the fluctuation drives spine volume changes and this fluctuation preserves mean spine volume [i.e., martingale (Øksendal, fluctuation enlarges mean spine volume, respectively, and the two effects are canceled. These two interpretations become the same in the special case of α = 0, namely, when the fluctuation is volume independent.In this view, changes in spine volume distribution are described by two terms: the first term is the drift term produced by apparent force α(αSimulations were performed in custom written C code with the forward Euler-integration method and a step size of 0.1 ms. Code was written to efficiently run on multiple GPUs to produce the trillions of random numbers needed to model the intrinsic spine dynamics per simulation up to 3,000 s. Post-simulation analysis was undertaken with MATLAB. Source code is available upon request.The datasets generated for this study are available on request to the corresponding author.HK and TT conceived the project. JH and KH performed numerical simulations. JH, KH, HK, and TT analyzed the results and drafted and revised the manuscript.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.