The temperature dependence of amorphous magnesium carbonate (AMC), a precursor of crystalline magnesium carbonate hydrates, was investigated using synchrotron X-ray scattering experiments with atomic pair distribution function (PDF) and X-ray absorption fine structure analysis. PDF analysis revealed that there were no substantial structural differences among the AMC samples synthesized at 20, 60, and 80 °C. In addition, the medium-range order of all three AMC samples was very similar to that of hydromagnesite. Stirring in aqueous solution at room temperature caused the AMC sample to hydrate immediately and form a three-dimensional hydrogen-bonding network. Consequently, it crystallized with the long-range structural order of nesquehonite. The Mg K-edge X-ray absorption near-edge structure spectrum of AMC prepared at 20 °C was very similar to that of nesquehonite, implying that the electronic structure and coordination geometry of Mg atoms in AMC synthesized at 20 °C are highly similar to those in nesquehonite. Therefore, the short-range order (coordination environment) around the Mg atoms was slightly modified with temperature, but the medium-range order of AMC remained unchanged between 20 and 80 °C.Mineral trapping through the precipitation of carbonate minerals is a potential approach to reduce CO There are four main trapping mechanisms that can securely store CO2, namely, structural/stratigraphic trapping, residual trapping, solubility trapping, and mineral trapping6. Mineral trapping involves the injection of CO2 into basalt or ultramafic rock, whereby a geochemical reaction occurs between the injected CO2 and the alkaline minerals in the basalt or ultramafic rock, leading to the precipitation of carbonate phases. This effectively locks the CO2 in immobile secondary phases for geological timescales6 and minimizes the risk of leakage to the atmosphere. The inherent stability of mineral carbonation is evidenced by the fact that more than 70% of the total carbon in the Earth’s crust is present in the form of carbonates7.Mineral carbonation is a promising approach for reducing the concentration of CO2 pressure, and pH all affect the type of crystalline magnesium carbonate hydrate that forms during carbonation. To date, 10 forms of crystalline magnesium carbonate hydrates have been recognized in the MgO–CO2–H2O system8. Nesquehonite (MgCO3·3H2O or Mg(HCO3)(OH)·2H2O), hydromagnesite [Mg5(CO3)4(OH)2·4H2O], and dypingite [Mg5(CO3)4(OH)2·5H2O] are the most dominant phases among the crystalline magnesium carbonate hydrates. The crystal structures of nesquehonite and hydromagnesite are shown in Figs. 6 octahedra running along the b-axis9. Each MgO6 octahedra is linked to three CO3 groups lying parallel to the c-axis. The ribbons are highly interconnected by a hydrogen-bonding network of water molecules. In contrast, hydromagnesite , AMC crystallizes into nesquehonite by vigorous stirring in an aqueous solution16, while at higher temperatures (above approximately 55 °C), hydromagnesite forms18. Although both nesquehonite and hydromagnesite grow directly from AMC at different temperatures, the structure of AMC has not been extensively investigated. In particular, it is unclear whether AMC itself has a temperature-dependent structure .Amorphous magnesium carbonate (AMC), which can be produced by mixing aqueous solutions containing MgIn this study, we investigated the temperature dependence of the AMC structure using synchrotron X-ray scattering experiments with pair distribution function (PDF) analysis and X-ray absorption fine structure (XAFS) measurements. Herein, we report the temperature dependence of the short- and medium-range structural features of AMC.n value in the AMC chemical formula MgCO3·xH2O. From the weight loss results, it was calculated that AMC20, AMC60, and AMC80 had x values of 2.2, 1.9, and 1.6, respectively, suggesting that the average chemical formula of the AMC samples is approximately MgCO3·2H2O, which is in good agreement with that of AMC obtained at 15°C14. As mentioned below, since there are no substantial structural differences in AMC samples, the variation of water content does not affect the medium-range structural order of AMC.AMC samples were synthesized at temperatures of 20, 60, and 80 °C . The water content in the AMC samples was determined using thermogravimetry/differential thermal analysis (TG/DTA). The weight losses associated with dehydration were approximately 32, 29, and 26 wt% for AMC20, AMC60, and AMC80, respectively. Therefore, the water content of the AMC samples tended to decrease as the synthesis temperature increased. The water content corresponds to the 21. Notably, there are no substantial structural differences in the nearest-neighbor correlations among the three AMC samples, that is, the medium-range structural order of AMC exhibits no temperature dependence in this temperature range. In other words, although the crystal structure of the crystalline magnesium carbonate hydrate produced from AMC changes with temperature (from nesquehonite to hydromagnesite via dypingite), the medium-range structural order of AMC remains unchanged. AMC acts as a pluripotent source for different magnesium carbonate hydrates.The X-ray PDF patterns of the AMC20, AMC60, and AMC80 samples are shown in Fig. 10, demonstrating the close match between AMC20 and hydromagnesite. This indicates that the AMC samples have a similar medium-range order to that of hydromagnesite. The PDF pattern of hydromagnesite has been well characterized based on the crystal structure8. The interatomic correlation peaks at 1.3, 2.1, and 3.1 Å are assigned to C–O bonds, Mg–O bonds, and Mg–Mg interactions, respectively. The peak at 4.2 Å corresponds to the second nearest-neighbor interaction between Mg and O atoms.The PDF patterns of nesquehonite, dypingite, and hydromagnesite formed by stirring the AMC samples for 2 h at 20, 60, and 80 °C, respectively, are shown in Fig. 22. This transformation is considered to occur via a solvent-mediated transformation mechanism14, that is, Mg2+ and CO32− ions rapidly aggregate to form AMC with the medium-range order of hydromagnesite. By stirring the AMC in aqueous solution, it immediately hydrates and transforms to the long-range order of nesquehonite. The agitated solution environment has the advantage of forming a three-dimensional hydrogen-bonding network9. Thus, AMC with a hydromagnesite-like structure may transform into nesquehonite by hydration and crystallization processes. Considering that all the AMC samples have a hydromagnesite-like structure, the hypothesis that nesquehonite subsequently dissolves to precipitate dypingite via a solvent-mediated transformation is quite reasonable. That is, when nesquehonite dissolves in aqueous solution and returns to AMC, its structure reverts to hydromagnesite-type structure. The transformation to dypingite is therefore very advantageous because dypingite possesses a hydromagnesite-type structure as well13.The fact that the medium-range order of AMC resembles that of hydromagnesite, even when formed at 20 °C, raises the question of why the structure of AMC20 is similar to that of hydromagnesite (grown at 80 °C) instead of that of nesquehonite (grown at 20 °C). The answer may lie in the transformation behavior of nesquehonite. AMC crystallizes into nesquehonite when stirred at room temperature (below approximately 55 °C), but after a few hours to a few days, it transforms to dypingite23, which means that it can differentiate between structures with subtle modifications in local coordination and electronegativity24. This sensitivity makes it extremely useful for fingerprinting Mg-bearing phases24. The Mg K-edge XANES spectra of carbonates show prominent post-edge features between 1321 and 1350 eV24. Dolomite [MgCa(CO3)2] and calcite (CaCO3) share a post-edge feature at 1324 eV, but magnesite (MgCO3) has no such features, despite being an isostructure of dolomite and calcite, because of subtle differences in the local coordination and electronegativity.Mg K-edge X-ray absorption near-edge structure (XANES) spectroscopy is strongly dependent on the local electronegativity and multiple scattering27. The near-edge region contributed by the three components showed a main peak centered at 1315 eV (marked B) with shoulders at 1312 and 1320 eV   around the Mg atoms is slightly modified depending on the synthesis temperature, but the medium-range order of AMC remains unchanged in the temperature range from 20 to 80 °C. AMC20 therefore has a hybrid structure, featuring structural characteristics of both nesquehonite and hydromagnesite. Under an agitated solution environment at room temperature, AMC crystallizes with a nesquehonite structure by forming a hydrogen-bonding network, but at elevated temperatures, it crystallizes into dypingite or hydromagnesite without any change in the short- or medium-range order.2 sequestration is the leakage of CO2 from the storage reservoir. In this study, it was revealed that the medium-range structural order of AMC is similar to that of hydromagnesite. AMC crystallizes into nesquehonite with stirring, and then gradually transforms to dypingite, which has a hydromagnesite-like structure. Since AMC and dypingite both have hydromagnesite-like structures13, this suggests that the hydromagnesite-like structure is the most stable of the magnesium carbonate hydrate structures. The present and previous studies allow the prediction of the long-term stability of AMC and dypingite (hydromagnesite) in aqueous conditions22. Carbonation using AMC is likely to be the most effective sequestration technique for reducing atmospheric CO2 concentrations.One of the major risks related to geological CO2  and Na2CO3  were used as the starting materials. Solutions of 0.5 M MgCl2 and 0.5 M Na2CO3 were held at 20, 60, or 80 °C, and then mixed at a 1:1 volume ratio. The pH values of the solutions were about 10.5 without adjustment. A white suspension of AMC formed on mixing, which was immediately filtered out and washed with distilled water heated to the same temperature, and then collected and dried overnight in air. The AMC samples obtained at 20, 60, and 80 °C were denoted as AMC20, AMC60, and AMC80, respectively. X-ray diffraction patterns for AMC20, AMC60, and AMC80 are shown in Supplementary Fig. Commercially available MgCl−1 under argon flow (200 mL min−1).The water content in the AMC samples was determined using TG/DTA . Approximately 10 mg of AMC and an α-alumina reference were placed in Al pans, and subsequently heated from 50 to 550 °C at a heating rate of 10 °C minQ range of 0.3 to 25 Å−1. The obtained X-ray total scattering data were transformed to total scattering structure functions [S(Q)] and atomic PDFs [G(r)] using PDFgetX2 software29. The obtained PDF profiles were analyzed using PDFgui software30.Synchrotron X-ray total scattering measurements were performed at beamline BL22XU of SPring-8, Japan. Approximately 50 mg of the sample was loaded into a Kapton capillary tube with an inner diameter of 2 mm and length of 15 mm. The incident beam was monochromatized to a wavelength of 0.206225 Å using a Si(111) double-crystal monochromator. Data were collected in the 31 of the Photon Factory (PF), High Energy Accelerator Research Organization (KEK), Japan. Powder samples were mounted on carbon adhesive tape in a sample holder and placed in the experimental chamber under ultra-high vacuum. A grazing incidence monochromator served as the Mg K absorption edge in the energy region from 1290 to 1350 eV. The samples were placed with their surface perpendicular to the incident X-ray beam. The spot size of the incident X-ray beam was approximately 2.0 × 0.5 mm. Energy calibration was performed using the MgCO3 spectrum.Mg K-edge XAFS measurements were performed at beamline BL11A32. Nesquehonite and hydromagnesite possess MgO6 octahedra as interior structural units. Thus, the total energies of the MgO6 octahedra were calculated using the second-order Møller–Plesset (MP2) perturbation theory with the 6–311+G basis set. The simulated structural model was built based on previous reports10. One terminal hydrogen atom was added to each oxygen atom of the MgO6 octahedra.Ab initio calculations were performed using the quantum chemical calculation software package Gaussian-09Supplementary Figures.


Infecting large portions of the global poultry populations, the avian infectious bronchitis virus (IBV) remains a major economic burden in North America. With more than 30 serotypes globally distributed, Arkansas, Connecticut, Delaware, Georgia, and Massachusetts are among the most predominant serotypes in the United States. Even though vaccination is widely used, the high mutation rate exhibited by IBV is continuously triggering the emergence of new viral strains and hindering control and prevention measures. For that reason, targeted strategies based on constantly updated information on the IBV circulation are necessary. Here, we sampled IBV-infected farms from one US state and collected and analyzed 65 genetic sequences coming from three different lineages along with the immunization information of each sampled farm. Phylodynamic analyses showed that IBV dispersal velocity was 12.3 km/year. The majority of IBV infections appeared to have derived from the introduction of the Arkansas DPI serotype, and the Arkansas DPI and Georgia 13 were the predominant serotypes. When analyzed against IBV sequences collected across the United States and deposited in the GenBank database, the most likely viral origin of our sequences was from the states of Alabama, Georgia, and Delaware. Information about vaccination showed that the MILDVAC-MASS+ARK vaccine was applied on 26% of the farms. Using a publicly accessible open-source tool for real-time interactive tracking of pathogen spread and evolution, we analyzed the spatiotemporal spread of IBV and developed an online reporting dashboard. Overall, our work demonstrates how the combination of genetic and spatial information could be used to track the spread and evolution of poultry diseases, providing timely information to the industry. Our results could allow producers and veterinarians to monitor in near-real time the current IBV strain circulating, making it more informative, for example, in vaccination-related decisions. Infectious bronchitis is caused by the worldwide distributed avian gammacoronavirus infectious bronchitis virus (IBV) , a highlThe development of modern data visualization platforms such as Nextstrain  allows tIn this study, we explore the local dispersal of IBV within the poultry farms. We described the spatiotemporal spread and evolution of 132 IBV samples across one state of the United States and additionally built an interactive web visualization dashboard based on the Nextstrain platform. Our derivative of the Nextstrain platform provides a portal for complete phylodynamic analysis by research staff while affording producers' redacted views into the origins, spread, and phylodynamic analysis that preserve anonymity across producers who contribute samples for analysis but enable producers to learn valuable information about their operations.We gathered IBV isolates from 2016 to 2019 from 52 poultry farms in North Carolina. Oropharyngeal swab samples of sick birds were tested at the Mississippi Poultry Research and Diagnostic Laboratory (PRDL) (79 samples) or the North Carolina Veterinary Diagnostic Laboratory System (NCVDLS) (53 samples). In all cases, IBV presence was confirmed by real-time reverse transcriptase PCR (RTPCR) . From thwww.megasoftware.net .Sequences were aligned using Mega X, available at ware.net . The recware.net . The aliware.net . To calcy et al.  based ony et al. . The relhttps://www.ncbi.nlm.nih.gov/nucleotide/ , implemented in BEAST v2.5.0 . By usinFollowing the analytical framework described by Dellicour et al.  and usinTo determine the effect of different predictors in the spread of IBV, we collected the outputs generated by the previous continuous phylogeographic analysis, then used the analytical framework developed by Dellicour et al. , considehttps://nextstrain.org) (https://github.com/nextstrain/auspice)  . IBV seqain.org)  and visuauspice) . Auspiceauspice) .In the modified dashboard , each daThe user is then able to request the visualization for any data set they have access to by clicking the link in their browser. The resulting REST API request is made via the Auspice web server, which is then routed to the Auspice CLI backend where the JWT is used as a unique identifier in the database to determine the level of access. For datasets that users have level 2 access to, the backend server redacts all location and sensitive information prior to returning the response to the frontend web server—no sensitive information is allowed to leave the backend server if a user is unauthorized to view it. The visualization information is then returned from the frontend web server to the client where their browser renders it for viewing. This architecture is designed specifically to enhance data security and preserve anonymity. First, by leveraging industry-standard JWT technology to cryptographically sign identity verification, we can ensure that individuals requesting data are rightfully accessing it. Second, by separating the frontend and backend servers, we are able to leverage strict isolation between the data (which resides on the backend server) and any publically available services (which reside on the frontend server). Strict firewall and access policies ensure the backend server has a far lower attack surface. Furthermore, this separation ensures that when data leaves the backend server, they are already anonymized before going out over the Internet, providing an additional layer of security beyond that provided by SSL encryption for all web traffic.The results of the phylogeographic analyses estimated the time of the most recent common ancestor for the studied IBV sequences as between 2005 and 2006. The ancestral reconstruction showed that the most likely strain of origin in North Carolina poultry farms was Arkansas DPI, with a root state posterior probability (RSPP) = 0.68, followed by Georgia 13 (RSPP = 0.24), and Georgia 98 (RSPP = 0.09) . PhylodyThe vaccination program carried out on the farms was not specified for most of the collected sequences (74.5%). However, from the ones with available information (25.5%), MILDVAC-MASS + ARK was the vaccine that was applied on all farms .In addition, we confronted our samples with publicly available S1 gene IBV information nationwide . We founQ statistic method. Our results showed that two of the variables analyzed, precipitation and runoff, had positive Q values when they were treated as resistance factors. However, they did not show enough predictive Bayes factor support; therefore, these variables should not be considered as good predictors for IBV dissemination  can be found in the article/RC and GM conceived the paper ideas. MJ, RC, and GM participated in the design of the study. RC and AB coordinated the IBV sequence data collection. MJ conducted data processing and cleaning. MJ performed the phylogeographic analysis. DR and AC designed and implemented the different security levels that accompanied Nextstrain visualization. MJ, RC, DR, AC, AB, and GM wrote and edited the manuscript. All authors discussed the results and critically reviewed 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.


A‐ (SICI) or GABABergic (CSP) signalling. For several conditions, these findings are plausible based on established evidence of involvement of the GABAergic system; for some others, they may inform future research around such mechanisms. Challenges of TMS include lack of complete understanding of the neural underpinnings of the measures used: hypotheses and analyses should be based on existing clinical and preclinical data. Further pitfalls include gathering sufficient numbers of participants, and the effect of confounding factors, especially medications. TMS‐EEG is a unique perturbational technique to study the intrinsic properties of the cortex with excellent temporal resolution; while it has the potential to provide further information of use in interpreting effects of genetic variants, currently the links between measures and neurophysiology are less established. Despite these challenges, TMS is a tool with potential for elucidating the system‐level in vivo functional consequences of genetic variants in people carrying genetic changes of interest, providing unique insights.Advances in genetics may enable a deeper understanding of disease mechanisms and promote a shift to more personalised medicine in the epilepsies. At present, understanding of consequences of genetic variants mainly relies on preclinical functional work; tools for acquiring similar data from the living human brain are needed. Transcranial magnetic stimulation (TMS), in particular paired‐pulse TMS protocols which depend on the function of cortical GABAergic interneuron networks, has the potential to become such a tool. For this report, we identified and reviewed 23 publications on TMS studies of cortical excitability and inhibition in 15 different genes or conditions relevant to epilepsy. Reduced short‐interval intracortical inhibition (SICI) and reduced cortical silent period (CSP) duration were the most commonly reported findings, suggesting abnormal GABA To translate epilepsy genetics to precision medicine, tools for interpreting disease mechanisms at the level of the individual patient are needed.TMS‐EMG paired‐pulse measures, which reflect the function of GABAergic cortical interneuron circuits, may be particularly relevant for some genetic epilepsies.For several reviewed conditions, TMS‐EMG provides evidence of altered inhibition, in keeping with hypotheses of the effects of the conditions on the GABA system.More research is required to expand and corroborate these findings, including the role of TMS‐EEG.1The epilepsies are a heterogeneous group of conditions characterized by predisposition to recurrent seizuresThe rapidly increasing knowledge of the genetics of epilepsies has contributed to the understanding of their pathophysiological mechanisms and improved diagnostic ability on the individual level.Transcranial magnetic stimulation (TMS) is a non‐invasive means of studying cortical excitability employing electromagnetic induction Figure .7 PairedStudying TMS in genetically‐defined patient groups may decrease inter‐individual variability and increase power for detecting clinically useful signatures of pathophysiological processes yielding biomarkers for diagnosis and treatment of these conditions. This approach has already been employed for several genetic conditions of relevance to epilepsy, which we now review.2Transcranial magnetic stimulation involves using a time‐varying magnetic field generated over the head to induce an electric field reaching the cerebral cortex Figure . When suA number of factors affect cortical excitability as measured by TMS. In the motor system, which has been most extensively studied so far, GABAergic inhibitory interneurons are thought to be involved in modulation of I‐waves, which propagate down the corticospinal tract following stimulation of the motor cortex.When coupled with electromyography (EMG), the TMS pulse is applied over the primary motor cortex, typically the hand area Figure . As a baAergic inhibition.A‐mediated inhibition.Bergic inhibition.Bergic inhibition.In paired‐pulse TMS Figure , a condiEEG may be used to measure oscillatory activity of the cortex, which reflects excitatory and inhibitory postsynaptic potentials generated by populations of cortical neurons.2.1A summary of the effects of major classes of antiepileptic drugs in healthy controls is presented in Table 3A literature search was conducted using PubMed for “transcranial magnetic stimulation” AND (“genetics” OR “genes” OR “syndrome” OR “gene”) NOT rTMS. See Material 3.1A receptors would be expected to alter SICI. Conditions affecting release of glutamate may be hypothesized to alter ICF or SICF.Conditions directly affecting release of GABA may be hypothesized to affect SICI, LICI or CSP; conditions affecting function of postsynaptic GABA3.1.1GABRG2 gene encodes the gamma‐2 subunit of the GABAA receptor. GABRG2 mutations have been identified in epilepsy syndromes of varying severity.Aergic inhibition,Aergic neurotransmission may be detected using TMS.The 3.1.2GRIN1, and two glutamate‐binding regulatory subunits. The four types of glutamine‐binding subunits, GluN2A‐2D, are encoded by the genes GRIN2A‐D.GRIN1, GRIN2A and GRIN2B are implicated in epilepsy and other neurological and neurodevelopmental conditions.NMDARs are ionotropic glutamate receptors. They consist of two glycine‐N‐serine binding GluN1 subunits, encoded by GRIN1 and GRIN2B genes.GRIN1 and rs7301328, rs3764028, and rs1805247 for GRIN2B. No association with epilepsy has been reported for any of these SNPs; rs1805247 was reported to be enriched in individuals with bipolar disorder.Mori and others studied cortical excitability in 77 healthy participants with specific single nucleotide polymorphisms (SNPs) in the Aergic inhibition and glutamatergic facilitation.GRIN2B rs1805247, compared to participants homozygous for the A variant, heterozygotes  had significantly greater ICF at 15 ms, in keeping with enhanced NMDAR function.For rs4880213, compared to participants homozygous for the C variant or heterozygotes, participants homozygous for the T variant  had less SICI.3.1.3STX1B encodes syntaxin‐1B, a part of the SNARE complex, which mediates synaptic vesicle release from the presynaptic membrane.Stx1b null mutant mouse suggested a crucial role for syntaxin‐1B in spontaneous and evoked fast synaptic vesicle exocytosis in glutamatergic and GABAergic synapses.STX1B have been identified in patients with fever‐associated epilepsies of variable severity.STX1B mutations showed no differences to controls in any paired‐pulse measures.Aergic and glutamatergic excitability in asymptomatic carriers of STX1B mutations, perhaps influenced by compensatory mechanisms during maturation.Nine carriers of pathogenic 3.1.4TRPV1 has been demonstrated in glutamatergic and GABAergic neurons in hippocampal and temporal cortex specimens from patients with medial temporal lobe epilepsy and it has been proposed as a potential therapeutic target in epilepsy.Transient receptor potential vanilloid 1 (TRPV1) channels are non‐selective cation channels which regulate the release of glutamate, and are implicated in hippocampal LTP and long‐term depression (LTD).TRPV1 gene: rs222749, which is not linked to substantial changes in the expression or properties of the TRPV1 channel, and rs222747, which is associated with enhanced functionality of the channel.TRPV1 regulates glutamatergic synaptic transmission in humans.Mori and others studied cortical excitability in participants with one of two particular SNPs in the 3.2Conditions affecting membrane excitability of cortical excitatory interneurons and corticospinal neurones might be expected to influence cortical excitability as measured by rMT, for example. However, conditions affecting other neuronal populations may have different effects.3.2.1ATP1A3 gene,+/K+‐ATPase ‐ the main subunit expressed in neurons.+/K+‐ATPase may lead to changes in membrane excitability and affect other integral processes dependent on ionic gradients.Alternating hemiplegia of childhood (AHC) is a rare neurodevelopmental condition characterized by paroxysmal episodes of hemiplegia and other neurological features, as well as fixed clinical features. Over half of patients have epilepsy; other common features include intellectual disability, ataxia and movement disorders.In seven patients with AHC tested between attacks, rMT was significantly lower compared to both healthy controls and people with epilepsy.3.2.2SCN1A encodes the alpha subunit of the type 1 voltage‐gated sodium channel (NaV 1.1).SCN1A are associated with a wide range of epilepsy phenotypes.In a TMS study of five adults with DS, SICI was absent; the difference compared to controls was significant.Lack of SICI in DS patients was thought to reflect low sensitivity of inhibitory networks to subthreshold stimuli,SCN1A splice site polymorphism  is associated with febrile seizures.An Aergic inhibition was demonstrated in Dravet syndrome, in keeping with reduced NaV1.1 function in GABAergic interneurons in this condition.SCN1A variant linked with risk of febrile seizures.Taken together, reduction of GABA3.3Repeat expansions are an increasingly recognised cause of disease in genetic neurological disorders. The effects on cortical excitability are unlikely to be uniform, but rather may reflect different pathological mechanisms in different conditions.3.3.1CSTB, which encodes cystatin B, a cysteine protease inhibitor.Unverrich‐Lundborg disease (EPM1) is an autosomal recessive progressive myoclonic epilepsy caused by mutations in Aergic and GABABergic inhibition.There is evidence for changes in GABAergic signalling in EPM1. Using vesicular GABA transporter (VGAT) immunohistochemistry in knock‐out mice, reduction in the density of cortical GABA terminals and in cortical thickness was demonstrated.CSTB expansion mutations, compared to controls, rMT was significantly higher in patients, thought to be influenced by patients’ AED polytherapy and/or increased scalp‐to‐cortex distance.Bergic inhibition; this was interpreted to possibly reflect a compensatory increase in inhibition to counteract hyperexcitability.CSTB expansion correlated with both rMT and CSP.In a series of TMS studies of patients with biallelic In a separate study of paired‐pulse TMS in ten patients with EPM1, compared to controls, SICI was reduced.CSTB expansion mutations.TMS‐EEG over the motor area was performed in seven individuals with biallelic CSTB, with the other allele affected by a common point mutation (c.202C>T).TMS‐EMG results were also reported for five compound heterozygous patients, all of whom had a monoallelic repeat expansion in Aergic inhibition.B inhibition and the degree of prolongation may correlate with genotypic and phenotypic severity. However, lower amplitudes of N100 and P180 in EPM1B inhibition , also known as familial adult myoclonic epilepsy (FAME), autosomal dominant cortical myoclonus and epilepsy (ADCME), and benign adult familial myoclonic epilepsy (BAFME), is characterized by myoclonus affecting the limbs distally and generalized tonic‐clonic seizures, with onset in the second or third decade.Compared to controls, six members of a Dutch FCMTE pedigree had reduced SICI at ISIs 2 and 3 ms.Aergic inhibition, also seen in other myoclonic epilepsies. It would be interesting to explore possible changes in GABABergic inhibition, implied by reduced duration of CSP, using LICI.Findings of reduced SICI in both studies suggest impaired GABA3.3.3FMR1 gene. The product of this gene is the fragile X mental retardation protein 1 (FMRP), an RNA‐binding protein with high brain expression levels. A CGG repeat number of over 200 is associated with epigenetic silencing of the FMR1 gene.Fragile X‐related disorders are associated with excess repeats of the CGG trinucleotide in the promoter region of the Fragile X premutation involves 50‐200 excess CGG repeats and is associated with RNA gain‐of‐function toxicity.A receptor subunits have been demonstrated in animal models of the full mutationA inhibition was shown in the cerebellumA agonist allopregnanolone, implying an underlying impairment in GABAAergic inhibition.Excessive glutamate‐mediated signalling via metabotropic group I receptors (mGluRI) is among presumed disease mechanisms.Aergic dysfunction with relatively preserved GABAB function.Thirteen asymptomatic women harboring the Fragile X premutation had significantly less SICI at 2 ms compared to controls.A inhibition, along with excessive glutamatergic signalling, as a disease mechanism.B inhibition.In a study of 18 individuals with Fragile X, compared to controls, patients had reduced SICI and increased ICF and LICI.3.43.4.1EPM2A or EPM2B genes, which lead to abnormalities in the regulation of glycogen metabolism and polyglucosan accumulations (Lafora bodies).Lafora body disease (EPM2) is another progressive myoclonic epilepsy with autosomal recessive inheritance.A‐ and GABAB‐mediated inhibition in EPM2B. The finding of reduced ICF was suggested to reflect a compensatory phenomenon to counteract epileptic activity.Canafoglia and coworkers studied paired‐pulse TMS in ten patients with EPM1 and five patients with EPM2.3.4.2NEU1 gene, which encodes a lysosomal neuraminidase.Sialidosis is an autosomal recessive lysosomal storage disorder caused by mutations in the Aergic, and possibly also GABABergic, inhibition.Compared to controls, 12 individuals with sialidosis I had greater MEP amplitudes, while SICI and CSP duration were significantly reduced.3.4.3NF1 gene encoding neurofibromin 1, an inhibitor of the RAS pathway.Neurofibromatosis I is an autosomal dominant disorder caused by loss‐of‐function mutations in the Aergic) cortical inhibition.A knock‐out mouse model of NF1 showed increased RAS‐dependent GABA release and impairment of LTP and learning.3.4.4Prader‐Willi syndrome (PWS) is characterized by infantile hypotonia, developmental delay, excessive eating, behavioural problems, hypogonadism, short stature, and characteristic facial features; seizures are present in 10%–20%.Civardi and others studied 21 patients with PWS, of whom 13 had a deletion and 8 had UPD.A receptor; reduced expression of GABRA5 and GABRB3 was demonstrated in lymphoblastoid cells from individuals with PWS.GABRA4 and GABRG2, which encodes the gamma‐2 subunit common in postsynaptic GABAA receptors, was also shown.Aergic transmission are unclear.The 15q11‐q13 region contains 50‐100 genes, including genes encoding subunits of the GABAAergic inhibition in the deletion subgroup. It was noted that the incidence of seizures in PWS patients with deletions is higher compared to those with UDP.Decreased SICI in patients with deletions compared to patients with UDP implies lower GABAARs not affected by the mutation;Reduced ICF was contrary to the authors' hypothesis.3.4.5MECP2 (methyl‐CpG‐binding protein‐2) gene. The protein product, MeCP2, regulates gene expression and is especially abundant in neurons; its function appears integral for normal brain function throughout life.GABRB3 and UBE3A, which encodes a ubiquitin protein ligase involved in proteostasis in the brain, have been demonstrated.Mecp2 in GABAergic neurons was sufficient for reproducing many characteristics of Rett syndrome, including electrographic seizures.Rett syndrome is a disorder characterized by developmental regression, including early loss of language and hand skills, gait abnormalities, and stereotypical hand movements; epilepsy is also common.Bergic inhibitionIn a single‐pulse TMS study of seventeen patients with Rett syndrome, motor thresholds were significantly elevated compared to controls, interpreted to reflect reduced brain volume and abnormal cortico‐cortical connections.3.4.6ALDH5A1 gene leading to impaired gamma‐aminobutyric acid (GABA) degradation and, in turn, to accumulation of gamma‐hydroxybutyric acid (GHB) and GABA.Succinic semialdehydase (SSADH) deficiency is an autosomal recessive disorder caused by mutations in the B receptors, which has been demonstrated in an animal model of SSADH deficiency, could lead to increased binding of GABA to presynaptic GABAB receptors and reduced activity‐dependent secretion of GABA.A‐receptor activation.Eight patients with SSADH deficiency were studied and results compared to those of parents (obligate heterozygotes), and healthy controls.4Aergic transmission (Figure GABRG2,SCN1A‐related Dravet syndrome.We have presented several examples of epilepsy‐related genetic conditions with evidence of altered inhibition or facilitation as measured by TMS. For many, findings are congruent with hypotheses of the effects of the conditions/variants on the GABA system, particularly synaptic GABAn Figure . This is4.1Aergic SICI was demonstrated in both EPM1 and EPM2,B inhibition.B inhibition but lower amplitudes of TEP components N100 and P180 might suggest the opposite.Bergic inhibition in EPM1 should be studied further.Decreased GABAAergic drugs on TEP components were sometimes seen only on the contralateral hemisphere, suggesting that changes in GABAAergic inhibition may be associated with altered thalamocortical connectivity.In EPM1, the TMS‐EEG power spectra changes and altered inter‐trial coherence were thought to reflect impaired function of cortico‐cortical/cortico‐subcortical motor circuits.EPM1 has emerged as a particularly informative condition also due to the genotype‐TMS correlation that emerged: the size of the expansion mutation correlated with rMT and CSP duration.GABAergic inhibition was also impaired in familial cortical myoclonic tremor with epilepsy,Aergic mechanisms in myoclonus of other etiologies than EPM1: in rats, intraventricular injection of a GABAA antagonist precipitated myoclonus.Aergic drugs such as benzodiazepines in the treatment of myoclonus of various etiologiesA impairment in the pathogenesis. In a single report of a patient with focal epilepsy of unknown etiology, whose seizures included negative myoclonus involving the right upper limb, [123I]iomazenil single photon emission computed tomography suggested reduced GABAA function in the left medial frontal area.Aergic transmission in epilepsies with myoclonus are plausible.There is evidence for the role of GABA4.2reduced GABAB activity.A activity, it is useful to consider whether reported data on subunit expression are compatible with synaptic or extrasynaptic changes. The GABAergic system is complex and remains a focus of research; used optimally, TMS could contribute to elucidating GABAergic changes in disease. Overall, with expanding use of these measures, and previously voiced concerns,For some conditions and findings, synthesising TMS results with pathophysiology was not straightforward. In SSADH deficiency, contrary to the authors’ hypothesis in a condition with elevated levels of GABA, TMS findings suggested Aergic inhibition is well‐established, SICI may, particularly with higher CS intensities, be contaminated by SICF. Therefore, findings of reduced SICI could be influenced by increased glutamatergic facilitation captured by SICF.GRIN1 SNP rs4880213, reduced SICI did not appear to be explained by increased SICF.Although the dependence of SICI on GABATRPV1, homozygotes for a SNP linked with enhanced channel functionality showed increased SICF, whereas for another SNP not known to influence channel function, no differences according to genotype were seen.GRIN1 and GRIN2B SNPsIn contrast to studies in patients or mutation carriers, in two studies TMS parameters were correlated with genotype at polymorphic sites. For 4.3A recognised problem in studies of brain stimulation is inter‐trial variability.GRIN1 and GRIN2B SNPs, the sample included no minor allele homozygotes, likely to reduce power.Especially for studies of SNPs with relatively high allelic frequencies, care in experimental design should be reflected in choice of participants. In the study of STX1B is an emerging epilepsy gene; mutations are expected to affect synaptic function. However, carriers of mutations in this gene, most of whom had a history of seizures providing some evidence of pathogenicity, failed to show evidence of altered GABA or glutamatergic neurotransmission as measured by TMS‐EMG.Even with sound hypotheses, not all reviewed studies identified TMS changes. An obvious limitation of TMS‐EMG is that it only samples the motor cortex. Although the germline variants are present in all DNA of an individual, there are regional differences in gene expression in the brain. Normal patterns, for example of GABA inhibition, demonstrated by TMS‐EMG do not rule out abnormalities in GABAergic activity within other networks or regions. In these respects, TMS‐EEG may offer advantages. Further, this technique may allow use of intensities lower than those required for TMS‐EMG,Some genetic epilepsies, such as EPM1,Aergic effects of benzodiazepines to lead to erroneous findings of impaired SICI, but the mechanisms of action for many AEDs are incompletely characterised. Although studying unaffected carriers or patients in remission could be advantageous in this regard, such cases may not be fully representative of the condition of interest, as exemplified by the normal findings in asymptomatic carriers of STX1B.The possible confounding effect of medications, particularly AEDs, is a further limitation and must be considered in the interpretation of findings Table . One wou4.4GABRG2 variant R43Q. Indeed, any use of TMS for diagnostic purposes would require integration with other investigations. As such, it could be valuable in certain scenarios. When patients are found to have a previously unreported variant in a disease‐implicated gene, it may not be clear whether the variant is pathogenic or not. For some genes, both loss of function and gain of function variants may be pathogenic, and the directionality of the change in function may not be apparent based on phenotype alone. Such a situation could arise, for example, in the context of a patient with severe epilepsy found to harbor an SCN1A mutation not previously reported, with a phenotype not fully concordant with Dravet syndrome. The ability to use an in vivo functional assessment of an entire system, using TMS, could be valuable in this situation, and have implications for therapeutic choices, such as avoiding treatment with sodium channel blockers.It is important that studies are hypothesis‐driven, and avoid circularity of arguments. Among the conditions discussed in this review, similar changes in TMS parameters are described for very different conditions. The notion that pathophysiological specificity of TMS may be overestimated is exemplified by findings of not only decreased SICI, but also increased ICF in people with the 5Genes associated with epilepsy are being uncovered at a rapid pace. As a result, more tools are required to study the in vivo effects of variants. Despite limitations, TMS could be useful for exploring the consequences of variants on the function of interneuron circuits. TMS‐EMG has already been successfully applied to this end in several conditions; more research is required to expand and corroborate these findings. TMS‐EEG may offer a further tool to do so, but the neurophysiological underpinnings do need to be better characterised.None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.Supplementary MaterialClick here for additional data file.


Aeq) measurement on a construction site of a reinforced concrete structure high-rise residential building in northern China. The noise conditions within the site in different spatial areas and temporal stages was analyzed. Binaural recording of 10 typical construction noises, including earthwork machinery, concrete machinery, and hand-held machinery, were performed. The physical acoustics and psychoacoustic characteristics were analyzed with the aid of a sound quality analysis software. A total of 133 construction workers performing 12 types of tasks were asked about their subjective evaluation of the typical noises and given a survey on their noise experience on the construction site. This was done to explore the acoustic environment on the construction site, the environmental experience of construction workers, the impact of noise on hearing and on-site communications, and the corresponding influencing factors. This research showed that the noise situation on construction sites is not optimistic, and the construction workers have been affected to varying degrees in terms of psychological experience, hearing ability, and on-site communications. Partial correlation analysis showed that the construction workers’ perception of noise, their hearing, and their on-site communications were affected by the noise environment, which were correlated to varying degrees with the individual’s post-specific noise, demand for on-site communications, and age, respectively. Correlation analysis and cluster analysis both showed that the annoyance caused by typical construction noise was correlated to its physical and psychoacoustic characteristics. To maintain the physical and mental health of construction workers, there is a need to improve on the fronts of site management, noise reduction, equipment and facility optimization, and occupational protection.Construction noise is an integral part of urban social noise. Construction workers are more directly and significantly affected by construction noise. Therefore, the construction noise situation within construction sites, the acoustic environment experience of construction workers, and the impact of noise on them are highly worthy of attention. This research conducted a 7-month noise level (L In the process of urbanization, cities are often occupied by a large number of construction sites, which has caused the problem of construction noise. Although most construction noise is not continuous, its high sound pressure level and overall long exposure time have caused some problems to surrounding residents. In China, the number of complaints about construction noise ranked first among all types of noise in 2019, accounting for 45.4% of all complaints . The harAeq) as an evaluation index of noise exposure  and ER is set to 3 dB , Sharpness (S), Fluctuation strength (F), and Roughness (R). This model was proved to be suitable for different types of noise-induced annoyance estimation .2 and includes two construction areas  and a living area for workers (area 3)  . There aAeq measurement for 30 s at the center point of each grid from 10:00 to 11:00 for one working day per week. The measuring tool was a B&K 2250 sound level meter with a measuring height of 1.5 m. The overall measurement lasted for 30 weeks (2019.04.05–2019.10.17). There were no measurements in the week of 6, 22, 24, and 28, due to site and weather conditions, and thus a total of 26 measurements were taken. Except for the grid points that were restricted by on-site operating conditions, a total of 4539 valid data were obtained for the 26 weeks. The site was divided into 270 square grids of side length 10 m. The position occupied by the main building was removed, and so 184 grids were actually measured. Two surveyors performed LAeq, L5, L95, and psychoacoustic indicators include Loudness (N)  , Fluctuaness (N) . The pers (N)  (5 . In addis (N) (5  and resis (N)  and evaluation of the interference on on-site communications (EIOSC), evaluation of the annoyance by typical noises, and individual characteristics . Among tIn the form of a focus-group, four project managers who were familiar with the conditions of the construction site were invited to conduct a professional evaluation of the post-specific noise level and the demand for on-site communications level of the construction site for each type of workers. The evaluation index was a nine-level numerical level.To eliminate the effect of other variables, partial correlation analysis was employed to investigate the relationship between the results of the self-evaluation of the workers and the post-specific noise, the demand for on-site communications, age and working years. In addition, principal components analysis was used to cluster the ten typical noises according to the 133 respondents’ evaluation of the annoyance by typical noises. The Jonckheere–Terpstra test was subsequently used to investigate common features within each cluster of noises.Aeq) were in the range of 51.0–112.0 dB(A). These data were compared with the occupational noise exposure standards. The results showed that among the 4539 data points, the limit values set by various countries and institutions were exceeded to varying degrees (EX = 80 dB(A), and 4.3% exceeded the limit of Lex = 85 dB(A).The 4539 measured SPL data  and 34.6%  of the respondents believed that the impact of noise on their hearing ability and on-site communications has been “Moderately high impact” or higher, respectively. The two self-evaluation results were highly discrete, which indicated that the subjective evaluation was also affected by other factors.In order to explore the factors for noisiness level and self-evaluation results, the three variables were put to perform partial correlation analysis with post-specific noise, demand for on-site communications, age, and working years.r(133) = 0.497, p = 0.000] (There was a significant positive correlation between the noisiness level of the construction and the post-specific noise [= 0.000] . It can r(133) = 0.538 p = 0.000] had different effects on the SEHA. In other words, workers believed that post-specific noise affected their hearing ability. It is worth mentioning that demand for on-site communications [r(133) = −0.357 p = 0.000] was negatively correlated with impact on the hearing ability, and could also be related to the adaptability of workers on the construction site.The post-specific noise level  and age [r(133) = 0.192 p = 0.028]. It meant the post-specific noise and the hearing loss due to age had a significant impact on on-site communications. At the same time, it was found that EIOSC was negatively correlated with the demand for on-site communication [r(133) = −0.312 p = 0.000], and could also be related to the adaptability of workers on the construction site.The EIOSC was significantly negatively correlated with the post-specific noise   model , the PA A) model . The PA 017] See . In addiW test  was used to evaluate the typical noise annoyance of 133 respondents, and principal components analysis was carried out on this basis. To identify the optimized components, varimax rotation was applied. Based on eigenvalues and scree plot analysis , three components (KMO = 0.735 > 0.7) were extracted, and they covered 57.9% of the total variance. Component 1 (C1) explained 22.1% of the variance in the data set, including the dump truck, the loader, the excavator and the concrete vibrator. Component 2 (C2) explained 20.2% of the variance in the data set, including the jackhammer, the angle grinder, the electric drill and the electric screwdriver. Component 3 (C3) explained 15.6% of the variance in the data set, including the breakers and the concrete pump  filters (one-third octave band) . RelativC3 > FC2 > FC1 , which indicated that the mechanical noise in C3 had the characteristics of high Fluctuation strength. Significant differences were also found in the Sharpness values of the three components, and SC2 > SC3 > SC1 , which indicated that the mechanical noise in C2 had high Sharpness.To further explore the characteristics of components, Jonckheere–Terpstra test was used to explore the difference between the three components in typical mechanical noise psychoacoustic indicators. The results showed that a significant difference was found in the Fluctuation strength among the three components, and F2 accounted for a relatively high proportion, resulting in the sharp feeling of sound.Sharpness is a measure of spectral shape and refers to the proportion of high-frequency energy relative to the total energy . It can 3 group could reflect this feature  = 0.728 p = 0.017], which showed that Zwicker’s PA model was useful for evaluating the annoyance degree of construction noise and had a certain applicability. However, the correlation between the two and various psychoacoustic indicators was not consistent   See , which wAeq in-creases over 65 dB(A)  . In thisThis research conducted a 30-week construction site noise monitoring to summarize the noise level, collected 10 typical construction noises, analyzed their physical and psychoacoustic characteristics, and obtained objective data on the occupational environmental noise of construction workers. A questionnaire survey of 133 construction workers of different types of work was conducted to determine the impact of construction noise. The results showed that the noise situation on construction sites is not optimistic, and the construction workers have been affected to varying degrees in terms of psychological experience, hearing ability, and on-site communications. The respondent’s post-specific noise level was significantly positively correlated with the evaluation of the noisiness. The self-evaluated impact on hearing ability was significantly positively correlated with the post-specific noise level and was significantly negatively correlated with the demand for communications. The impact on on-site communications was significantly positively correlated with the respondent’s post-specific noise level and their age, and significantly negatively correlated with the demand for communications. In addition, correlation analysis and cluster analysis both showed that the annoyance caused by typical construction noise was correlated to its physical and psychoacoustic characteristics. These subjective and objective data and their correlation provided a basis for strengthening on-site management, implementing site noise reduction, optimizing construction machinery, and providing hearing protection for construction workers.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.Ethical review and approval was not required for this study in accordance with the local legislation and institutional requirements. The participants provided written informed consent to participate in the study.XY: noise collection and acoustic analysis, questionnaire design, and manuscript writing. YW: site noise measurement and questionnaire survey. RZ: statistical analysis of the data. YZ: overall supervision, research design, and the structuring of the manuscript. All authors contributed to the article and approved the submitted version.YW is employed by the Railway No.9 Bureau Group 4th Engineering Co., Ltd., China. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The ongoing civil war in Yemen has severely restricted imports of food and fuel, disrupted livelihoods and displaced millions, worsening already high pre-war levels of food insecurity. Paired with frequent outbreaks of disease and a collapsed health system, this has brought rates of wasting in children under five to the country’s highest recorded levels, which continue to increase as the crisis worsens and aid becomes increasingly limited. In their planning of services to treat and prevent wasting in children, humanitarian agencies rely on a standard calculation to estimate the expected number of cases for the coming year, where incidence is estimated from prevalence and the average duration of an episode of wasting. The average duration of an episode of moderate and severe wasting is currently estimated at 7.5 months—a globally-used value derived from historical cohort studies. Given that incidence varies considerably by context—where food production and availability, treatment coverage and disease rates all vary—a single estimate cannot be applied to all contexts, and especially not a highly unstable crisis setting such as Yemen. While recent studies have aimed to derive context-specific incidence estimates in several countries, little has been done to estimate the incidence of both moderate and severe wasting in Yemen.In order to provide context-specific estimates of the average duration of an episode, and resultingly, incidence correction factors for moderate and severe wasting, we have developed a Markov model. Model inputs were estimated using a combination of treatment admission and outcome records compiled by the Yemen Nutrition Cluster, 2018 and 2019 SMART surveys, and other estimates from the literature. The model derived estimates for the governorate of Lahj, Yemen; it was initialized using August 2018 SMART survey prevalence data and run until October 2019—the date of the subsequent SMART survey. Using a process of repeated model calibration, the incidence correction factors for severe wasting and moderate wasting were found, validating the resulting prevalence against the recorded value from the 2019 SMART survey.k of 2.59, and 3.86 months, for an incidence correction factor k of 3.11, respectively. It was found that the annual caseload of moderate wasting was 36% higher and the annual caseload of severe wasting 58% higher than the originally-assumed values, estimated with k = 1.6.The average durations of an episode of moderate and severe wasting were estimated at 4.86 months, for an incidence correction factor The model-derived incidence rates, consistent with findings from other contexts that a global incidence correction factor cannot be sufficient, allow for improved, context-specific estimates of the burden of wasting in Yemen. In crisis settings such as Yemen where funding and resources are extremely limited, the model’s outputs holistically capture the burden of wasting in a way that may guide effective decision-making and may help ensure that limited resources are allocated most effectively.The online version contains supplementary material available at 10.1186/s13031-021-00400-6. Food security crises are closely linked to armed conflict . As a reIn Yemen, where the country’s current hunger crisis is largely the result of warring parties’ deliberate efforts to restrict food, and the war has crippled the economy and disrupted livelihoods, the role of conflict in creating the conditions that drive wasting among children is strikingly clear. The ongoing civil war in Yemen has resulted in what has been called the worst humanitarian crisis in recent history. The latest IPC analysis for Yemen revealed that 13.5 million people  were facing high levels of acute food insecurity—in IPC Phase 3 or above . NutritiSince the conflict in Yemen began in 2015, humanitarian agencies have scaled up efforts to both prevent, identify and treat cases of Global Acute Malnutrition—Moderate Acute Malnutrition (MAM), also known as moderate wasting, and Severe Acute Malnutrition (SAM), also known as severe wasting– in children under five. . This approach for incidence estimation has been proposed for use in the CMAM model to estimate under-five wasting caseloads across all contexts [Estimates of the prevalence of wasting are available through cross-sectional SMART surveys conducted annually in Yemen, and the average duration of an untreated episode of moderate and severe wasting has been estimated at 7.5 months  of moderate and severe wasting. Our model examines the governorate of Lahj, Yemen. Lahj was selected in accordance with guidance from UNICEF team members given the severity of the situation and the relative strength of data reporting from the region given that the area is not one of active conflict.The model-derived incidence correction factors provide adjusted, context-specific estimates of incidence which may allow for more effective service planning and policy decisions by providing more accurate estimates of the number of new cases expected to develop. Additionally, the model framework provides a tool for burden estimation and planning which simulates ground realities using routinely-collected data and therefore does not require direct observation, as has been the case for several similar studies in other regions which estimated the average duration of an episode through longitudinal cohort studies. The model also seeks to capture the complete system of interactions between rates of incidence, the various stages in the progression of wasting, as well as rates of treatment and their respective outcomes, which collectively determine the burden of wasting. Because the model captures each of these paths, decision-makers can modify the model’s parameters to holistically simulate the long- and short-term consequences of a potential decision, such as scaling up a given intervention, and use the model’s outputs to guide decisions about future interventions.The burden of wasting is determined by a set of paths between moderate wasting, severe wasting and severe wasting with complications as well as treatment admissions and outcomes, creating a network of states for children to move between. This system easily maps onto the general framework of a Markov model, which was used to represent this system. These models are commonly used for probabilistic modeling, especially in epidemiological studies where there are a defined number of outcomes or health states being modeled . To defiHealthy, Moderately Wasted, Severely Wasted, Severely Wasted with Complications, TreatmentM, TreatmentS, TreatmentSC and Deceased. If a child resides in either the Moderately Wasted, Severely Wasted, or Severely Wasted with complications state, they are wasted but not currently in treatment. Because the model accounts for rates of defaulters (children who stop attending treatment appointments before reaching discharge criteria) and non-responders (children for whom treatment fails), being wasted but not currently in treatment does not imply that the child has never been admitted to treatment; the term untreated is therefore not used in the model. During the model’s simulation, children can move between states at a set of monthly transition rates, representing the probability that a child in one state will move to another (or remain in the same one) from one month to the next. All possible transitions are depicted by the arrows in Fig. A child can be classified as being either non-wasted, wasted but not in treatment, in-treatment for wasting or deceased. The Markov model presented distinguishes between each level of wasting  and its respective treatment program. It is composed of eight nutritional states: Moderately Wasted, Severely Wasted or Severely Wasted with complications) before entering a treatment state.A child must reside in a wasting state  in which they previously resided.A child in treatment for any form of wasting may default from or fail to respond to treatment, in which case they would transition from the treatment state back to the wasting state . It was therefore assumed that these children, in addition to those who were wasted but not enrolled in treatment, would have been included in the SMART survey’s reported prevalence. Thus, in the model’s initial distribution, children enrolled in treatment for at least one month would be included in the ed state . The samed state .Table 1IAll transition probabilities representing the probability of a child being admitted to a treatment program  were estimated from monthly Yemen nutrition cluster data provided by the UNICEF Yemen country office. This data included complete records of rates of admission and respective outcomes of children enrolled in TSFPs for moderating wasting treatment, OTPs for severe wasting treatment and TFCs for complicated severe wasting treatment for all CMAM interventions implemented across the governorate.The general mortality rate  surveys . Given b of CMAM . Both trcontexts , 18. Thecontexts . All traentclass1pt{minimaseverely wasted state—incidence or default from treatment—and those determining that a probability that a child leaves the severely wasted state—through recovery, treatment or mortality. Thus, in terms of the model’s states, children can leave the Severely Wasted state either by entering the Moderately Wasted state (recovery) or entering the Deceased state. Children enter the Severely Wasted state when they develop severe wasting from moderate wasting, representing the rate of incidence, or transition from Severely Wasted (default.) Because the prevalence of moderate wasting is directly influenced by the incidence of severe wasting, the incidence of severe wasting needed to first be estimated, before being used to inform the estimation of moderate wasting prevalence.With all other transition rates calculated, the incidence of severe and moderate wasting was found using a process of repeated model calibration. As shown in Fig. Severely Wasted state would represent the prevalence of untreated severe wasting. To remain consistent with the original assumptions about the combined prevalence of untreated and currently-in-treatment cases represented by the SMART prevalence, the estimated prevalence used for validation against SMART results was calculated as the sum of the number of children in the Severely Wasted state at month 14  and the number of children admitted to treatment within the past month.Given that the incidence of severe wasting was the single unknown in Fig. Healthy state. It was also assumed that cases of severe wasting could improve to moderate wasting, contributing an additional path of entry into the Moderately Wasted state. Using each of the transition probabilities (either entering or leaving the Moderately Wasted state) shown in Fig. Model calibration was also used to estimate the average duration of a moderate wasting episode. Because the model framework assumes all severe wasting cases develop from existing cases of moderate wasting, the prevalence of moderate wasting is also directly affected by the incidence of severe wasting. The model-derived incidence rate of severe wasting was therefore used as an input in the process of determining the average duration of an episode of moderate wasting. Spontaneous recovery was also assumed to occur among cases of moderate wasting, where a child with moderate wasting but not currently in treatment can return directly to the In order to quantify uncertainty within the model—either resulting from the general uncertainty within program records or the use of several data sources from contexts outside of Yemen—a one-way, deterministic sensitivity analysis was conducted by putting each of the transition probabilities through a range of possible values while holding all others constant and examining the resulting effect on the estimated number of incident cases .This wask of 3.11. This k-value was then used to estimate the incidence of severe wasting (the Moderately Wasted to Severely Wasted transition probability), and through subsequent model calibration, the average duration of an episode of moderate wasting was estimated at 4.64 months, for an incidence correction factor k of 2.59. A comparison of the original episode durations, incidence correction factors and annual caseloads and the respective context-specific values is presented in Table Upon repeated model calibration, it was found that the average duration of an episode of severe wasting was 3.86 months, with a corresponding incidence correction factor Table A comparison of the monthly number of incident cases of moderate and severe wasting is shown in Fig. While the model-derived incidence rates could not be directly validated, the uncertainty within the data used to estimate these values could be quantified by examining the effect of varying model input values (transition probabilities) on model-derived outcome estimates of wasting incidence. The detailed results of these sensitivity analyses are presented in Additional file In this manuscript, we provide context-specific estimates of incidence for moderate and severe wasting among under-five children in Lahj, Yemen. We also provide a framework for holistically assessing the burden of wasting which considers the complete system of bidirectional paths which determine the prevalence and incidence of wasting. Accurate estimates of the incidence of wasting are critical for projecting the needs of a program and planning accordingly. Although data from cross-sectional prevalence surveys such as SMART are available, these estimates provide only a snapshot of the situation at a single point in time. By basing assessments on prevalence alone, decision-makers must rely on a metric which varies seasonally and is also dependent on rates of mortality, recovery and treatment coverage. Estimates of monthly incidence, presenting the number of children developing new cases of moderate and severe wasting who will therefore require treatment, provide insights that may be of practical use to decision-makers in their planning.k = 1.6, the number of children admitted to treatment sometimes surpasses the expected monthly caseload, leading to coverage estimates over 100%. Additionally, when the incidence correction factor is assumed to remain unchanged from year to year, the only source of variance in the annual caseload calculation comes from cross-sectional prevalence data. Because of this, estimated caseloads for wasting in Yemen over the last 3 years have remained relatively stable; this does not align with the general instability caused by the conflict or extensive reports showing that the nutrition situation has continued to deteriorate over the past years.The model-derived incidence rates align with the consensus within the literature that a single incidence correction factor of 1.6 cannot be sufficient and underestimates caseload, leaving populations of children in need of treatment unaccounted for. Relying on incidence estimates that are not context-specific to guide service planning may lead to potential shortages of program inputs. To the authors’ knowledge, other studies have not explored the incidence of moderate and severe wasting in Yemen, so our results cannot be assessed against other comparable findings. However, qualitative evidence from Yemen further confirms that previous estimates of caseload were considerable underestimates. First, historical program data from Yemen have shown that when caseload is calculated using Several studies, using data from cohort studies in Mali, Burkina Faso, Niger and Nigeria, have found context-specific incidence correction factors –12. All While several other studies have aimed to estimate context-specific incidence correction factors in other contexts, the majority have examined severe wasting, and little has been done to explore context-specific incidence rates in Yemen. To the authors’ knowledge, little work has been done to examine the incidence of moderate and severe wasting together, under the same framework. Rather than considering the development of moderate wasting, severe wasting and severe wasting with complications independently of each other, the model framework allows for a consideration of the complete system of interactions that they form. With more accurate estimates of the incidence of severe wasting, the model captures the rate at which moderate wasting is expected to develop into severe wasting. Deriving this information would generally require direct observation of a cohort of moderately wasting children. Additionally, by representing the burden of wasting in a model of this form, the model can not only estimate expected caseload, but also be used as a tool to simulate various scenarios in order to guide planning decisions. For example, by adjusting the rate of treatment for moderate wasting and running the model for several months, decision-makers can understand both the long- and short-term effects of a scale-up of a given treatment program. This would allow them to understand the effects of such a decision on not just the burden of moderate wasting, but also the burden of severe wasting and severe wasting with complications in the long-term. These insights may form the basis of further cost-effective analyses of various programs. Thus, accurate estimates of incidence are not only critical in determining the number of children in need, but also in more holistically assessing the burden of malnutrition.The model’s outputs may provide a number of insights about program coverage. An alternative, simple approach to adjusting incidence estimates could entail using expected program coverage and treatment admissions to find the total number of treated and untreated cases of wasting, but doing so would assume confidence in current coverage estimates. While coverage may be estimated directly by representative sampling, such estimates remain scarce. Given that a key unknown of the coverage calculation is the number of untreated cases, our model provides a means of estimating this—representing the total number of developing cases which will require treatment—which does not rely on an existing coverage estimate, but which may instead be used to inform more accurate estimates of program coverage.As is the case with many mathematical models seeking to capture complex processes, our model had several limitations. First, a fundamental property of Markov models is that of “memorylessness”—the assumption that the future states depend only on the current state and not any past states . This asSeveral limitations were presented by the limited data available for validation. SMART survey results were a primary source of validation of the model’s results. Because SMART relies on representative sampling in order to estimate prevalence for the entire governorate, a level of uncertainty is expected within its results. The 2018 SMART survey also notes that settlements for internally displaced people (IDPs) were excluded from the sampling frame . It is kDespite its limitations, our model can provide decision-makers with important insights about the expected burden of wasting. Additionally, the model’s ability to holistically capture all determinants of the monthly prevalence of wasting may provide a potential alternative to conducting in-person cross-sectional surveys such as SMART, allowing humanitarian agencies to direct efforts and funds elsewhere. Future work may entail extending the model framework to other conflict-effected settings in order to produce more accurate caseload estimates and consider the expected instability of conflict settings. This may serve to validate the utility and generalizability of the model in other contexts. Future work may also entail building upon the model to explore seasonal changes in the incidence of wasting by considering the direct and indirect drivers of wasting. Incorporating these factors would allow the model to operate with a predictive capacity; by capturing relationships between the incidence of wasting and its underlying causes, the model may anticipate how a change in ground realities may result in a change in the incidence of wasting. The adjusted incidence rates provide a more context-specific improvement from the standard, global estimates, but this approach also assumes the previous year’s caseload can be used to anticipate caseload for the following year. Capturing the determinants of wasting within the model framework would further improve upon this approach by allowing incidence estimates to reflect changes on the ground.In this manuscript, we present context-specific estimates for the incidence of moderate and severe wasting in Lahj, Yemen. Accurate estimates of incidence are critical in anticipating program needs and holistically assessing the burden of wasting among children. Confirming the assertion that a single incidence correction factor cannot be sufficient, our results show that previous estimates led to considerable underestimates of caseload and left entire populations of wasted children unaccounted for. In addition to providing improved estimates of caseload, the model may also be used as a decision-making tool, allowing users to modify its parameters to understand the long- and short-term implications of a given interventional decision, which may be used to guide future cost-effectiveness analysis. Additionally, by seeking to estimate the total number of cases of wasting—both treated and untreated—the model provides a basis for providing improved estimates of program coverage. In crisis settings such as Yemen where funding and resources are extremely limited, the model’s outputs may help ensure that limited resources are allocated most effectively and holistically capture the burden of wasting in a way that can facilitate effective decision-making and intervention strategies.Additional file 1. Results of Model Sensitivity Analysis.


LMNA gene, which encodes for Lamin A/C, are associated with cardiac conduction disease (CCD). We previously reported that Lamin A/C variants p.R545H and p.A287Lfs*193, which were identified in CCD patients, decreased peak INa in HEK‐293 cells expressing Nav1.5. Decreased peak INa in the cardiac conduction system could account for patients’ atrioventricular block. We found that serine 22 (Ser 22) phosphorylation of Lamin A/C was decreased in the p.R545H variant and hypothesized that lamin phosphorylation modulated Nav1.5 activity. To test this hypothesis, we assessed Nav1.5 function in HEK‐293 cells co‐transfected with LMNA variants or treated with the small molecule LBL1 (lamin‐binding ligand 1). LBL1 decreased Ser 22 phosphorylation by 65% but did not affect Nav1.5 function. To test the complete loss of phosphorylation, we generated a version of LMNA with serine 22 converted to alanine 22 (S22A‐LMNA); and a version of mutant R545H‐LMNA that mimics phosphorylation via serine 22 to aspartic acid 22 substitution (S22D‐R545H‐LMNA). We found that S22A‐LMNA inhibited Lamin‐mediated activation of peak INa by 63% and shifted voltage‐dependency of steady‐state inactivation of Nav1.5. Conversely, S22D‐R545H‐LMNA abolished the effects of mutant R545H‐LMNA on voltage‐dependency but not peak INa. We conclude that Lamin A/C Ser 22 phosphorylation can modulate Nav1.5 function and contributes to the mechanism by which R545H‐LMNA alters Nav1.5 function. The differential impact of complete versus partial loss of Ser 22 phosphorylation suggests a threshold of phosphorylation that is required for full Nav1.5 modulation. This is the first study to link Lamin A/C phosphorylation to Nav1.5 function.Variants in the  INa.Lamin A/C Ser 22 phosphorylation can modulate Nav1.5 function and contributes to the mechanism by which R545H‐LMNA alters Nav1.5 function in cardiac conduction disease. Abolishing phosphorylation with S22A‐LMNA reduces channel function, but decreasing phosphorylation with the small molecule LBL1 does not, suggesting a threshold of Ser 22 phosphorylation required for Nav1.5 modulation. Mimicking phosphorylation with S22D‐R545H‐LMNA abolished the effects of mutant R545H‐LMNA on voltage‐dependency but not peak  These disorders comprise over 12 clinically heterogeneous syndromes , often associated with eventual dilated cardiomyopathy .Laminopathies are a broad spectrum of diseases involving cardiac and skeletal anomalies attributed to mutations in the v1.5 is primarily localized within the sarcolemma, in contrast to the nuclear localization of Lamin A/C. Mutations in SCN5A are a major cause of AV block, sick sinus syndrome, progressive CCD, and eventual DCM leading to sudden death  and shift the voltage‐dependency of steady‐state inactivation of Nav1.5  that binds Lamin A/C in the N‐terminal region encompassing Ser 22  was purchased (OriGene) in a pCMV6‐AC‐GFP (C‐terminus fused) vector backbone. Mutant R545H‐LMNA and phosphorylation LMNA constructs (S22A‐LMNA and S22D‐R545H‐LMNA) were generated using site‐directed mutagenesis kit (Qiagen). We substituted alanine (GCG) for serine (TCG) at position 22 (p.S22A) in wild‐type LMNA to prevent phosphorylation; to mimic phosphorylation in the R545H‐LMNA mutant we substituted aspartic acid (GAC) for serine (TCG) at position 22 (p.S22D) in the R545H‐LMNA plasmid. DNA sequencing and western blot analyses confirmed successful mutagenesis , supplemented with 10% fetal bovine serum (ATCC). Cells were serum‐starved overnight and transiently transfected with identical amounts of plasmid DNA using Effectene (Qiagen). Cells were treated 24 hrs post‐transfection with vehicle dimethyl sulfoxide (1% DMSO), 5 or 10 µM LBL1. Cells were harvested 24 hrs post‐treatment or 48 hrs post‐transfection.2.3Protein was isolated in lysis buffer (in mM): Tris 20, etheylenediaminetetraacetic acid (EDTA) 10, NaCl 100, 1% SDS, 1× complete protease inhibitor (Roche), and 1× phosphatase cocktail inhibitor (Sigma Aldrich). SDS‐PAGE used 4%–15% gradient Tris–HCl polyacrylamide precast gels in 1× Tris/Glycine/SDS running buffer (Bio‐Rad). For high molecular weight proteins, overnight wet transfer was performed ; for medium and low molecular weight proteins, fast semi‐dry transfer was performed  using a Novex Semi‐Dry Blotter apparatus (Invitrogen). Transfers were onto nitrocellulose membranes (GE Life Sciences). Primary antibodies : mouse monoclonal anti‐pan‐sodium channel (1:500) and anti‐beta‐tubulin ; rabbit anti‐total‐lamin (1:2000), anti‐phospho‐lamin , and anti‐alpha‐actinin 2 (1:2000) . Secondary antibodies (Thermo Fisher): goat anti‐mouse‐HRP (1:5000) and goat anti‐rabbit‐HRP (1:5000). Primary antibodies were incubated overnight at 4°C. Secondary antibodies were incubated at room temperature for 1 h, developed using SuperSignal West Pico or Femto (Thermo Fisher), and imaged using ChemiDoc XRS+ (Bio‐Rad). Membranes were stripped with Restore Stripping buffer (Thermo Fisher) at 55°C for 15 min followed by shaking at room temperature for 30 min. Antibody and blocking solutions were prepared in 0.1% Tween‐20 in Tris‐buffered saline solution (in mM): NaCl 150, Tris 10 (pH 7.4). Bands were quantified with ImageJ (NIH).2.42 1.8, MgCl2 1, 4‐(2‐hydroxyethyl)‐1‐piperazineethanesulfonic acid (HEPES) 5, and Glucose 10 (pH 7.4 adjusted with NaOH). Pipette resistances were 1.5–3 MΩ; solution (in mM): NaF 10, CsF 110, CsCl 20, ethylene glycol tetraacetic acid (EGTA) 10, and HEPES 10 (pH 7.35 adjusted with CsOH). After achieving a giga‐seal, the test pulse current was nulled by adjusting the pipette capacitance compensator with both fast and slow components. After break‐in, the whole‐cell charging transient was nulled by adjusting whole‐cell capacitance and series resistance. Voltage control protocols were generated with Axopatch 200B amplifier/Digidata 1440A or MultiClamp 700A acquisition system using pCLAMP‐10 software (Molecular Devices). Whole‐cell recording was analyzed using Clampfit 10.2 (Molecular Devices). All experiments were carried out at room temperature. Conductance G (V) was calculated by the equation: I is the peak current, Erev is the measured reversal potential, and Vm is the membrane potential. The normalized peak conductance was plotted as a function of membrane potentials. Steady‐state inactivation was estimated by pre‐pulse protocols (500 ms) from a holding potential of −140 mV. Steady‐state activation and inactivation were fitted with the Boltzmann equation: y = [1 + exp ((Vm − Vh)/k)] − 1, where y represents variables; Vh, midpoint; k, slope factor; and Vm, membrane potential.Cells were harvested using trypsin‐EDTA (Thermo Fisher) for 2 min and transferred to the patch chamber for whole‐cell recording of GFP‐labeled cells, which was performed as previously described  or constitutive phosphorylation in the R545H‐LMNA mutant (S22D‐R545H‐LMNA). We selected the R545H‐LMNA mutant for our study due to the previously described decrease in Ser 22 phosphorylation. To mimic loss of phosphorylation, we genetically substituted alanine (GCG codon) for serine (TCG codon) residue at position 22 (p.S22A); conversely, to mimic phosphorylation, we genetically substituted aspartic acid (GAC codon) for serine (TCG codon) residue at position 22 (p.S22D). We confirmed successful mutagenesis by sequencing and western blot  while leaving steady‐state activation unaltered  to mimic phosphorylation at that site. We then measured peak INa in cells transfected with S22D‐R545H‐LMNA. We anticipated that S22D‐R545H‐LMNA would restore peak INa to the levels seen with wild‐type LMNA. However, a similar loss of peak INa occurred in cells expressing S22D‐R545H‐LMNA, indicating that it was unable to rescue the R545H‐LMNA effect on peak INa . Thus, mimicking Ser 22 phosphorylation partially rescued Nav1.5 function disrupted by the R545H‐LMNA mutation, as normal voltage‐dependency was restored but peak INa levels were not  domain at the C‐terminal end encompassing the 545 residue . In the case of the A287Lfs mutant, which does not result in loss of Ser 22 phosphorylation levels, this head‐to‐tail interaction is likely abolished due to the frame shift and subsequent truncation of this region of the protein. This truncation could explain why modulation of Ser 22 phosphorylation level does not occur in the A287Lfs mutant. The complex structure of lamin polymers may also explain why a twofold increase in LBL1 concentration induced a significant loss of Ser 22 phosphorylation. Experimental approaches aimed at exploring these possibilities, while important, are outside the scope and focus of this study.Dimerization of Lamin A/C is driven by coiled‐coil formation of its central rod domains (Ho & Lammerding, LMNA mutations and CCD. Small molecules that enhance, rather than block, Ser 22 phosphorylation might partially restore Nav1.5 function in disease caused by R545H and similar LMNA mutations. This would mimic the partial rescue of Nav1.5 function we observed in cells expressing the S22D‐R545H‐LMNA variant. It could also help to improve diagnosis and prognosis in patients with similar LMNA mutations by developing tools that could assess their Lamin A/C phosphorylation state.From a clinical perspective, our results suggest that Lamin A/C phosphorylation may be a potential therapeutic target for patients with specific v1.5 function. This phosphorylation appears to be part of the mechanism by which the R545H‐LMNA mutation affects Nav1.5 function. To our knowledge, this is the first study to link Lamin A/C phosphorylation and Nav1.5 function.In summary, our data indicate that Lamin A/C Ser 22 phosphorylation modulates NaAll authors declare no conflict of interest.Michael A. Olaopa performed the research. Michael A. Olaopa and Tomohiko Ai performed the analyses. Michael A. Olaopa, Tomohiko Ai, Matteo Vatta, and Beth A. Habecker contributed to conceptualization and design of the study. Bo Chao, Xiangshu Xiao, and Beth A. Habecker contributed analytic tools and resources. Michael A. Olaopa wrote the manuscript with significant contributions from all co‐authors. All authors reviewed and approved the final manuscript.