ChPD-1 and chPD-L1 sequence identities and similarities were lower compared with those of humans and other mammalian species. Furthermore, in phylogenetic analysis, chPD-1 and chPD-L1 were grouped separately from the mammalian PD-1 and PD-L1 sequences. As in other species, chPD-1 and chPD-L1 sequences showed signal peptide, extracellular domain, a transmembrane domain and intracellular domain. Based on the three dimensional (3D) structural homology, chPD-1, and chPD-L1 were similar to 3D structures of mammalian PD-1 and PD-L1. Further, Ig V domain of chPD-1 and the Ig V and Ig C domains of chPD-L1 were highly conserved with the mammalian counterparts. In vitro binding interaction studies using Superparamagnetic Dynabeads® confirmed that recombinant soluble chPD-1/PD-L1 fusion proteins and surface chPD-1/PD-L1 proteins interacted with each other on COS cells. Two monoclonal antibodies specific against chPD-1 and five antibodies against chPD-L1 were developed and their specific binding characteristics confirmed by immunofluorescence staining and Western blotting.In the present study, we determined the  Programmed death 1  is a co inhibitory immunoreceptor molecule belonging to the CD28/CTLA-4/ICOS/B7 immunoglobulin (Ig) superfamily  viral infections and cancer  with Glutamax, supplemented with 10% fetal calf serum, 1,000 U/ml penicillin and 1 mg/ml streptomycin. DF-1 cells are continuous cell lines of EV-0 chicken embryo fibroblasts. COS and DF-1 cells were transfected by using Lipofectamine 2000 reagent . COS cells were used for immunofluorescence and Western blot assays. DF-1 cells were used for immunofluorescence staining.Monkey kidney cell-derived COS cells and chicken fibroblast cell-derived DF-1 cell were used for the https://zhanglab.ccmb.med.umich.edu/I-TASSER/) to identify the predictive 3D structural models  model quality assessment server. The multiple aligned sequences were submitted to ESPript 3.0 (http://espript.ibcp.fr/ESPript/ESPript/index.php) analysis, using I-TASSER database server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) generated chPD-1 and chPD-L1 protein structures (PDB) as the templates to predict secondary structures of chPD-1 and chPD-L1  database online server (The full-length cDNA of chPD-1 and chPD-L1 were amplified by PCR using primers designed from the predicted sequences of the genes in the NCBI databases . Plasmidhttp://smart.embl-heidelberg.de/). Primers containing restriction enzyme sites were designed to flank the extracellular domains of chPD-1 and chPD-L1 . Hybridomas that cross react with chPD-1 and chPD-L1 were initially selected based on dot blot ELISA (data not shown).Monoclonal antibodies were produced against the recombinant chPD-1 and chPD-L1. Immunizations and generation of hybridomas were carried out by the DC biosciences, Dundee, Scotland (6) were plated in a six-well culture plates. Recombinant expression constructs pKW06-chPD-1, pKW06-chPD-L1, pKW06-chPD-1-human-IgG1Fc, and pKW06-chPD-L1-human-IgG1Fc were transfected into COS cells (90–95% confluence) by Lipofectamine 2000 . COS cells were fixed in 4% paraformaldehyde  and permeabilized with 0.1% Triton X-100 . After blocking in 5% bovine serum albumin (BSA) in phosphate-buffered saline (PBS) for 30 min, cells were incubated  with mouse monoclonal anti-chPD-1 and chPD-L1 antibodies (1:500 in 5% BSA). After washing the cells three times to remove any unbound antibodies, cells were incubated  with Alexa 568 (COS cells)/488 (DF-1 cells)-conjugated goat anti-mouse antibodies (1:200 in 5% BSA). Finally, after washing, cells were stained  with DAPI (1:10000) and viewed by using a Leica  TCS SP2 confocal laser-scanning microscope. The experiments were performed in triplicate.COS and DF-1 cells (1 × 10The supernatants from COS cells transfected with pKW06-chPD-1-human-IgG1Fc and pKW06-chPD-L1-human-IgG1Fc were treated with TruPAGE LDS sample buffer and boiled for 10 min at 95°C. The samples were then loaded on a 4–12% TruPAGE Precast Gel (Bio-Rad) and transferred onto PVDF membranes. Immunoblots were blocked with 5% skim milk (2.5 g skim milk powder was dissolved in 50 ml of TBST) for 1 h at room temperature. Soluble proteins of chPD-1 and chPD-L1 were detected using monoclonal anti-chPD-1 and chPD-L1 antibodies (1:50 in 5% skim milk), after which blots were washed three times in TBST. Then, blots were incubated  with secondary antibody IRDye 680RD goat anti-mouse IgG (1:200 in 5% skim milk). Finally, after washing, blots were visualized using Odyssey Clx (LI-COR).Dynabeads® Biotin Binder are 2.8 μm superparamagnetic beads that binds biotinylated ligands (proteins or antibodies) were used to examine the interactions between chPD-1 and chPD-L1. Dynabeads® Biotin Binder (45 μl) were mixed with 7.5 μg/ml of biotinylated rabbit anti-human IgG, in 1,500 μl of 0.05% PBS-Tween, and incubated on a rotary mixer . After 1 h incubation, the tube was held against a magnet to attract the conjugated beads and the fluid was removed using a pipette. The conjugated beads were then washed twice with PBS-Tween by magnet as described above. Soluble chPD-1-human-IgG1Fc and chPD-L1-human-IgG1Fc fusion proteins of an estimated concentration of 5 μg/ml were incubated with the Dynabeads on rotary mixer  and washed two times with PBS-T and one time with PBS. Dynabead-bound fusion proteins were suspended in 500 μl of culture medium containing FCS, and incubated with COS cells transfected with pKW06-chPD-1 and pKW06-chPD-L1 plasmids . Cells were washed and fixed with 1:1 acetone: methanol and stained with Giemsa . Before imaging on the EVOS digital microscope . The percentage of binding of surface expressed chPD-1 to soluble chPD-L1-human-IgG1Fc and surface expressed chPD-L1 to soluble chPD-1-human-IgG1Fc was determined in 10 randomly selected fields by scoring COS cells with 5 or more beads as positive under Leica  DM IRB light microscope. IF staining and confocal microscopy analysis were also performed by using anti-chPD-1 and chPD-L1 monoclonal antibodies as described above.To clone chPD-1 and chPD-L1 gene, total RNA was extracted from Con A-stimulated chicken splenocytes, essentially as described previously : −3.09, TM-score: 0.37 ± 0.12 and root mean square deviation (RMSD): 15.3 ± 3.4 Å . The besCartoon 3D structural diagram of chPD-1 Ig V domain shows a two layer β sandwich, a topology characteristic of Ig V type domains . A superInitial dot blot ELISA screening showed that the supernatants from two hybridomas reacted with chPD-1 and five hybridomas reacted with chPD-L1 (data not shown). Western blot analysis confirmed that two chPD-1 antibodies and five chPD-L1 antibodies identified in the dot blot ELISA were specific and identified the respective proteins . Figure For the PD-1 and PD-L1 binding assay, the soluble chPD-1-human-IgG1Fc and chPD-L1-human-IgG1Fc fusion proteins were immobilized onto Dynabeads® by using human IgG and incubated on COS cells transfected with the pKW06-chPD-1 and pKW06-chPD-L1 plasmids. After fixation and staining, specific binding was quantified by counting the beads under light microscope. The soluble chPD-1-human-IgG1Fc fusion protein was bound to surface of the pKW06-chPD-L1 transfected COS cells, as observed by rosette formation . SimilarThe immune system plays an important role in the control of viral pathogenesis and tumorigenesis Cully, . During The PD-1 and PD-L1 sequence homologies were lower in chPD-1 and chPD-L1 sequences compared with those of the mammalian species (Jeon et al., In the present study, we showed that recombinant chPD-1 and chPD-L1 interacted with each other. The strong binding interactions between chPD-1 and chPD-L1 demonstrated that these molecules participate in the suppression of antigen specific immune responses, which is similar to other species (Okazaki et al., In conclusion, we describe the characterization of the chPD-1 and chPD-L1 molecules and their interactions. We have also developed a panel of mAbs that specifically identified the chicken PD-1 and PD-L1 homologs. Despite their specific binding to the chPD-1 and chPD-L1, none of the mAbs were able to prevent the interactions of the two proteins. Nevertheless, the results of this work will form the technical basis for future research to explore the role of PD-1/PD-L1 pathway in the latency mechanisms and immunosuppression of Marek's disease and other chronic viral infections of chickens.All datasets generated for this study are included in the article/VR, WM, and VN conceived and designed the study, analyzed the data, and drafted the manuscript. VR and WM performed the experiments. YS participated in design and analysis of the results. All authors read and approved the final 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.


Graphene and graphene-related materials have received great attention because of their outstanding properties like Young's modulus, chemical inertness, high electrical and thermal conductivity, or large mobility. To utilize two-dimensional (2D) materials in any practical application, an excellent characterization of the nanomaterials is needed as such dimensions, even small variations in size, or composition, are accompanied by drastic changes in the material properties. Simultaneously, it is sophisticated to perform characterizations at such small dimensions. This review highlights the wide range of different characterization methods for the 2D materials, mainly attributing carbon-based materials as they are by far the ones most often used today. The strengths as well as the limitations of the individual methods, ranging from light microscopy, scanning electron microscopy, transmission electron microscopy, scanning transmission electron microscopy, scanning tunneling microscopy (conductive), atomic force microscopy, scanning electrochemical microscopy, Raman spectroscopy, UV–vis, X-ray photoelectron spectroscopy, X-ray fluorescence spectroscopy, energy-dispersive X-ray spectroscopy, Auger electron spectroscopy, electron energy loss spectroscopy, X-ray diffraction, inductively coupled plasma atomic emission spectroscopy to dynamic light scattering, are discussed. By using these methods, the flake size and shape, the number of layers, the conductivity, the morphology, the number and type of defects, the chemical composition, and the colloidal properties of the 2D materials can be investigated. Two-dimensional (2D) materials, especially graphene or graphene-related materials, have been studied extensively in the past decade regarding their outstanding properties such as mechanical strength, chemical inertness, high electrical and thermal conductivity, high mobility, or optical transmittance molecules or by doping with other elements , like monodispersed nanoparticles, can be fully described by the radius and is therefore easy to be characterized. In contrast, non-spherical nanomaterials, such as all kinds of 2D materials, demand a more complex characterization. Exfoliation-based preparation methods lead to irregular shapes and a large distribution in sizes. Therefore, at least the average length and width have to be known to get a first impression of the flake size and of the surface area. Top-down syntheses produce graphene flakes ranging from several micrometers down to a few nanometers in size. A detailed statistical analysis of the flake size distribution is recommended  or to the average (Feretmean)  or high-resolution transmission electron microscopy (HRTEM) are suitable, but for large flake sizes excessing the nanometer range, they might run into limitations caused by the slow throughput or by their resolution  can be discriminated by their flake size. This was proven by the characterization of the flake size distribution by transmission electron microscopy (TEM) and atomic force microscopy (AFM). From these analyses, metrics have been developed to characterize the size from the extinction spectra of the dispersion containing the respective nanomaterials :In addition to the imaging methods, the layer thickness and the number of layers, respectively, can also be determined by Raman spectroscopy. With an increased number of graphene layers, the G-peak is subject to a slight shift to lower wavenumbers, whereas the D-peak undergoes changes in shape, width, and position  Ferrari. The intIt is still questionable if such an empirical formula is valid for all combinations of materials, e.g., it needs to be validated if the coefficients in this model need to be adapted by experimental settings, e.g., by the laser wavelength of the Raman spectrometer. Nevertheless, this method has the advantage to estimate the number of graphene layers in a fast and cheap way and over a large sample area. The disadvantages of this method come with the fact that the error of this metric is around 25%  was determined by BET adsorption  studies are helpful Osaki, . Here tyFabrication methods for the 2D materials following a top-down approach are additionally needed to identify and to characterize contaminations, impurities, and functionalities introduced during the synthesis. The importance arises from the fact that even low contaminants bear the risk of changing the chemical and physical properties of the nanomaterial. The comparison of differently prepared materials is not an easy task up to now as there are no widely accepted standards in material characterization that have been established, e.g., in organic chemistry, by giving data from nuclear magnetic resonance and mass spectrometry (MS) when a new compound is reported. During fabrication, the carbon nanomaterials get contaminated by the species used for the preparation, the purification, or the transfer process . A huge advantage of this method comprises high surface sensitivity. The nature of functionalities can be retrieved from the characteristic binding energy of every element, e.g., the C1s peak of carbon (at about 286 eV) and the O1s peak (at around 532 eV) are present in the XPS spectra of graphene prepared by exfoliation techniques. A shift in the binding energies is attributed to the binding partners of every atom, and therefore it is possible to determine the exact moiety of an oxygen functionality . SECM is based on the spatially resolved imaging of electrochemical processes that is detected by micro- or nanoelectrodes. If the applied voltage is sufficiently high, Feobtained  , wherein the (0 0 2) diffraction peak at around 25°-30° indicates the distance between the graphene layers and the (1 0) diffraction peak at around 40°-45° indicates the short-range order in the stacked graphene layers  or X-ray fluorescence analysis (XRF) measures the characteristic X-ray radiation of every element in the sample. Whereas, EDX is always applied in combination with any kind of electron microscopy and therefore irradiates the sample with electrons, XRF uses X-rays. By introducing heteroatoms into the graphene lattice, the electronic, mechanical, or chemical properties can be tailored. EDX measurements confirmed the successful introduction of germanium into the graphene lattice for a later application in catalysis  is blue-shifted compared to the PL of GO (around 600 nm)  and flake size, but both methods can be performed very easily and are cheap and fast; therefore, they must not be neglected for fast screening. Due to this, both methods can be used to check the graphene concentrations in inks or to check their stability in terms of agglomeration. In the case of the graphene quantum dots (GQDs), these are characterized as nanometer-sized fragments of graphene that show unique properties especially in their luminescence, making these materials attractive for bio-applications (Bacon et al., Despite the outstanding properties ascribed to the 2D nanomaterials which have been demonstrated and extensively reviewed (Cheng et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TAR-DNA binding protein 43 (TDP-43) proteinopathy is a common brain pathology in elderly persons, but much remains to be learned about this high-morbidity condition. Published stage-based systems for operationalizing disease severity rely on the involvement (presence/absence) of pathology in specific anatomic regions. To examine the comorbidities associated with TDP-43 pathology in aged individuals, we studied data from the National Alzheimer’s Coordinating Center (NACC) Neuropathology Data Set. Data were analyzed from 929 included subjects with available TDP-43 pathology information, sourced from 27 different American Alzheimer’s Disease Centers (ADCs). Cases with relatively unusual diseases including autopsy-proven frontotemporal lobar degeneration (FTLD-TDP or FTLD-tau) were excluded from the study. Our data provide new information about pathologic features that are and are not associated with TDP-43 pathologies in different brain areas—spinal cord, amygdala, hippocampus, entorhinal cortex/inferior temporal cortex, and frontal neocortex. Different research centers used cite-specific methods including different TDP-43 antibodies. TDP-43 pathology in at least one brain region was common (31.4%) but the pathology was rarely observed in spinal cord (1.8%) and also unusual in frontal cortex (5.3%). As expected, TDP-43 pathology was positively associated with comorbid hippocampal sclerosis pathology and with severe AD pathology. TDP-43 pathology was also associated with comorbid moderate-to-severe brain arteriolosclerosis. The association between TDP-43 pathology and brain arteriolosclerosis appears relatively specific since there was no detected association between TDP-43 pathology and microinfarcts, lacunar infarcts, large infarcts, cerebral amyloid angiopathy (CAA), or circle of Willis atherosclerosis. Together, these observations provide support for the hypothesis that many aged brains are affected by a TDP-43 proteinopathy that is more likely to be seen in brains with AD pathology, arteriolosclerosis pathology, or both.The online version of this article (10.1186/s40478-018-0641-y) contains supplementary material, which is available to authorized users. There is an evolving appreciation of a common brain disease with TAR-DNA binding protein 43 (TDP-43) proteinopathy that mimics Alzheimer’s disease (AD) clinically , 39, 50 “TDP-43 pathology” lacks a universally applied specific connotation, but refers to phosphorylated TDP-43 deposits in cytoplasmic , intranuclear, perivascular, and/or neurite-like structures. TDP-43 pathology may also manifest as a decrease in the normal (non-phosphorylated) TDP-43 in the nucleus , 18, 30.Prior published findings suggest that vascular factors may cause or exacerbate the disease process that manifests neuropathologically as TDP-43 (with or without HS pathology) in the aged brain , 47, 49.The AD Centers (ADCs) program has constituted a critical resource for research on AD and related dementias in the U.S. This network derived from a National Institutes of Health (NIH)-funded initiative that started in 1984 and has included more than 30 different ADCs geographically dispersed across the U.S. Each ADC follows a longitudinal cohort of generally elderly individuals reflecting a broad spectrum of clinical diseases and pathologic manifestations. The National Alzheimer’s Coordinating Center (NACC) oversees data collection by the ADCs. For research subjects that died and came to autopsy, a standardized form was created by NACC to describe the neuropathology in a systematic manner, and for correlation with clinical, radiographic, genetic, and biochemical parameters in the same persons. The latest Neuropathology (NP) Form was updated in 2014, and is referred to as version 10 (v10). The NACC NP Form v10 incorporated detailed neuropathological data including Thal phase for Aβ plaques , relativhttps://www.alz.washington.edu/NONMEMBER/NP/rdd_np.pdf). Inclusion criteria for this study were neuropathology data available through the NACC NP Form v10, age at death ≥65 years, and non-missing data on TDP-43 referent to at least one of the five brain regions of interest (see below). Exclusion criteria were the presence of at least one of 19 rare neurological diseases  derived from 30 different ADCs with autopsies reported using the NACC NP v10 form, which started in 2014, through the data freeze of July 11th 2018. Autopsies were performed within each of the contributory ADCs. The database comprises a standardized set of data collected based on the NACC NP v10 data collection form , and frontal neocortex, with the response categories “no”, “yes”, “not assessed”, and “missing/unknown”. Because data on TDP-43 pathology in spinal cord contained more “not assessed’” or “missing/unknown” values  genotype , and other health conditions at the last clinical visit (via self-report) including diabetes, hypertension, hypercholesterolemia, and thyroid disease ).Descriptive statistical analyses were performed for sex, age at death (both available in the NACC NP Form v10), and years of education, apolipoprotein E  were extracted from the NACC NP dataset. We excluded 476 subjects who had at least one rare neurological disease listed in Additional file Subjects who were assessed by ADC neuropathologists using the NACC NP Form v10 and died at age 65 years or older . There were no statistically significant differences in sex, APOE genotype, educational attainment, and other health conditions between subjects with and without TDP-43 pathology.Table We compared TDP-43 pathology frequencies between HS present and absent in each brain region , TDP-43 pathologies in amygdala, hippocampus, and frontal neocortex were not associated with arteriolosclerosis pathology.By contrast, arteriolosclerosis pathology was associated with TDP-43 pathology in amygdala and EC/inferior TCTX when adjusted for sex, age at death, Here we present data focusing on TDP-43 pathology in the aged human brain, using a large sample with autopsy confirmation, sourced from multiple high-quality research centers. Our data provide new information about comorbidities that are and are not apparently associated with TDP-43 pathologies in different brain regions. TDP-43 pathology is strongly associated with advanced AD and brain arteriolosclerosis pathologies.There are some potential pitfalls in our study sample, as we have discussed previously . ContribAPOE genotype and TDP-43 pathology status in multiple brain regions is unusual, and the statistical power it provides is important. Further, the derivation of data from 27 different research centers with expertise in research in AD and related dementias is a strength because the autopsy data may be considered more generalizable than the results from a single neuropathologist or small group of pathologists. For the foreseeable future, it seems unlikely that all research centers will agree on a single protocol for TDP-43 IHC, and we consider it a strength that the current study incorporates results from multiple research centers using site-specific protocols.Despite the challenges and potential pitfalls, there also are considerable strengths related to this use of the NACC NP data set. We note that despite the abovementioned sources of data variability, our study found evidence of strong associations between TDP-43 proteinopathy and other factors . These data were collected from individuals who died and came to autopsy over the past 4 years (NACC NP Form v10), providing both fresh data and relatively up-to-date clinical and pathological testing modalities. The study of nearly 1000 brains with APOE ɛ4/ɛ4 carriers); and age-related TDP-43 pathology is predominantly seen in the medial temporal cortex, uncommon in frontal neocortex, and very rare in spinal cord.There are three main findings that we describe: TDP-43 pathology is strongly associated with advanced AD pathology; TDP-43 pathology is associated with increasingly severe arteriolosclerosis pathology  tends to occur [There is a relatively large extant literature on the relatively common comorbidity of TDP-43 pathology with AD, providing a compelling evidence that the pathologies co-occur whether or not they directly interact mechanistically , 30, 48.athology , 28, 33.to occur , 16.Compared with AD, the literature on the association between TDP-43 pathology and cerebrovascular is smaller, and overlaps with the paradigm of hypoxia/ischemia. As stated by Zarow et al. , “HS hasAdditional file 1:Table S1. Exclusion criteria in the National Alzheimer’s Coordination Center Neuropathology Form. Table S2. Missing frequency of TDP-43 pathology in each brain region collected on the National Alzheimer’s Coordination Center Neuropathology Form version 10 (n = 929). Table S3. Variables for Alzheimer’s disease and cerebrovascular disease pathologies in the National Alzheimer’s Coordination Center Neuropathology Form version 10. Table S4. Frequency of TDP-43 antibody used in each Alzheimer’s Disease Center. (PDF 181 kb)


Periodic review of resident performance is an important aspect of residency training. Amongst allopathic residency programs, it is expected that the performance of resident physicians which can be grouped based on the ACGME core competencies, be assessed so as to allow for effective feedback and continuous improvement. Review of monthly evaluation forms for residents in the core ACGME programs at Marshall University and the University of Toledo demonstrated a wide spread in the number of Likert questions that faculty were asked to complete. This number ranged from a low of 7 in Surgery to a high of 65 in Psychiatry . Correlation and network analysis were performed on these data. High degrees of correlations were noted between answers to questions (controlled for each resident) on these forms at both institutions. In other words, although evaluation scores varied tremendously amongst the different residents in all the programs studied, scores addressing different competencies tended to be very similar for the same resident, especially in some of the programs which were studied. Network analysis suggested that there were clusters of questions that produced essentially the same answer for a given resident, and these clusters were bigger in some of the different residency program assessment forms. This seemed to be more the rule in the residency programs with large numbers of Likert questions. The authors suggest that reducing the number of monthly questions used to address the core competencies in some programs may be possible without substantial loss of information. Evaluation of post graduate or resident performance has been occurring since the development of such programs in the early 20th century following the famous ‘Flexner’ report calling out for improvements in the education of physicians . HoweverIn the current study, we attempted to evaluate the information present in the monthly residency evaluation forms used by core ACGME programs at the University of Toledo and Marshall University. Hypothesizing that there might be redundancy in some of these forms, especially those that asked what superficially seemed to be a large number of duplicative questions, we decided to perform analysis examining how much correlation the answer to the different queries (controlled for the specific resident) had with each other.TM software. These monthly evaluations of all residents were obtained in a de-identified manner  and shared as Excel worksheets. These worksheets were converted into csv files for importation into the open source analysis package R. Both the University of Toledo and Marshall University IRB categorized the study as IRB exempt.Records of the monthly evaluations of all residents from the academic year 2015–16 from the core residency programs of pediatrics (PED), internal medicine (IM), Obstetrics and Gynecology (OB), family and community medicine (FM), psychiatry (Psych) and Surgery (Surg) were queried from a data base created and maintained with New InnovationsCorrelation and network analysis were performed on only the Likert scale questions from each institution. As obstetrics and gynecology at UT used many different forms for different rotations, we did not attempt correlation or network analysis with this residency. In addition to the Likert scale questions, all of the residency evaluation forms had summative questions and allowed for the entry of free text comments. These fields were not analyzed in this paper.Data analysis of these Likert questions was performed with R,  primarilSome of the residency programs provided data that was suitable for principal component analysis. Rather than try to substitute average values into records to replace missing values, we restricted this analysis to those programs where a large percentage of resident evaluations were complete for every question. These were restricted to Marshall where there was a paucity of resident-level specific Likert questions. As the Marshall Psychiatry program had very few complete records, we restricted the principal component analysis to Marshall IM, OB, PEDS, Surg and FM.Details for each residency program are provided in Much of the Likert data correlated well within the resident evaluations, especially at the Marshall programs . This isFrom these correlation values, network analysis was also performed. For Marshall PED, there were essentially three groups of highly correlated answers. For Marshall IM, the degree of correlation was so high as to make specific networks somewhat speculative. For Marshall OB, there were also three large grouping. In contrast, the answers to questions were only modestly correlated. Marshall Psych, like IM, had such highly correlated answers that groupings were difficult to visualize. Although Marshall Surgery had only 7 questions, the answers to all correlated quite well with each other. For the UT programs, the correlations were less high as previously noted. UT_PED showed a very highly correlated group of 4 questions with much less correlation noted between the remaining questions. UT_IM had a number of pockets of highly correlated answers as did UT_FM and UT_Psychiatry. UT Surgery had a large group of highly correlated questions with a subset (approximately 15%) of questions that correlated poorly with all others.When we performed principal component analysis, we found that > 95% of the variance could be explained by the first 5 principal components in all programs. In all but FM, almost all of the variance was contained within the first 2 principal components .10.1080/Although it was tempting to speculate that the longer questionnaires had greater correlation values, this was not necessarily the case. In fact, the Marshall Surgery program with only 7 Likert questions had the highest average correlations between the answers. Next highest at Marshall was the Psych questionnaire with 65 Likert questions. Conversely at UT, Psych had the lowest average correlation between answers to Likert questions while having the highest number of Likert questions within that institution.The evaluation of post-graduate physician trainees in ACGME programs has undergone an infusion of rigor and structure during the past decades. Some of this can be attributed to the introduction of the six core competencies approximately 20 years ago .While increased granularity of evaluation is clearly desirable, our data would suggest that we see diminishing returns. As demonstrated in When we examined our data between the different institutions, it was apparent that the correlation coefficients varied more within the UT than Marshall programs, suggesting that not only were the correlations between answers to questions lower but the variability of said correlations was greater. This suggests that more information might be present in the UT evaluations; unfortunately, without a true gold standard it is difficult to confirm this. As we examine the specific Likert scale responses between departments, it was also clear that the scores given to the Marshall Psych residents were on average lower than that for the other core residency programs. This is evident despite the Likert scale being extended to 6 for some questions on the Psych form, whereas 5 was the upper limit for the other program questions. This discrepancy is almost certainly due to the Psych residency being new and our cadre of Psych residents limited to the PGY1 and 2 years for the academic year under consideration. It seems that the Likert questions alone for the Psychiatry form at Marshall would require in excess of one hour to complete. That said, it would be unlikely that faculty would uniformly fill such a form out with diligence and thoughtfulness. The IM questionnaires from both institutions as well as Psychiatry, FM and Surgery from UT each had nearly ½ that number of Likert questions and would also appear to be perceived as onerous. In contrast, Marshall Surgery and Marshall Family Medicine managed to evaluate their residents with much briefer forms.Following the analysis presented in this manuscript, the authors have concluded that residency evaluation forms should be simplified. It would seem that an evaluation form focused on the six basic competencies along with an additional question or two regarding other relevant activities  would r


The active AlN films, purposely grown with the c-axis tilted, effectively excite shear modes displaying excellent in-liquid performance, with electromechanical coupling and quality factors of around 3% and 150, respectively, which barely vary after graphene integration. Raman spectra reveal that the as-grown graphene is composed of less than five weakly coupled layers with a low density of defects. Two functionalization protocols of the graphene are proposed. The first one, based on a covalent binding approach, starts with a low-damage O2 plasma treatment that introduces a controlled density of defects in graphene, including carboxylic groups. After that, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry is used to covalently bind streptavidin molecules to the surface of the sensors. The second functionalization protocol is based on the non-covalent bonding of streptavidin on hydrophobic graphene surfaces. The two protocols end with the effective bonding of biotinylated anti-IgG antibodies to the streptavidin, which leaves the surface of the devices ready for possible IgG detection.We present the successful growth of few-layer graphene on top of AlN-based solidly mounted resonators (SMR) using a low-temperature chemical vapour deposition (CVD) process assisted by Ni catalysts, and its effective bio-functionalization with antibodies. The SMRs are manufactured on top of fully insulating AlN/SiO Gravimetric biosensors based on microscale mechanical or electromechanical resonators have attracted significant interest in recent years mainly due to the high sensitivity and selectivity they can attain if properly functionalized –2, the sA critical step in the manufacturing of gravimetric biosensors is the functionalization of their active surface, which provides the sensor with the desired selectivity and sensitivity to the targeted species. Selectivity mainly depends on the specificity of the receptor  to the targeted species and the non-specific binding degree of other species that can be achieved; effective functionalization platforms should accommodate a large density of adequately oriented receptors and be easily passivated against non-specific binding. Sensitivity in acoustic resonators depends on their ability to detect small changes of the resonant frequency, which is essentially a matter of design. The functionalization schemes also play an essential role. In fact, for a given design, the sensitivity not only depends on the density of the attached active receptors, but also on their distance to the device surface. For example, the interaction length of shear-mode resonators operating in liquid appears to be limited to the near-surface region . A decreFunctionalization platforms are typically based on solid or porous gold films , owing tSo far, only few works dealing with the integration of graphene-based functionalization platforms on piezoelectric resonators have been reported. Besides, high-quality graphene-based layers are typically transferred to the active area of the devices after being grown at high temperatures on copper catalysts , or fromIn this work, we demonstrate that few-layer graphene can be grown directly on top of SMR-type acoustic resonators without a noticeable degradation of their characteristics and then functionalized to manufacture gravimetric biosensors, which eliminates the need to use complex transfer methods. Defect-free few-layer graphene was selectively grown through a low-temperature (650 °C) CVD process on Ni  thin-filThe thickness of the catalyst Ni layers was reduced to 100 nm, which was thin enough as not to compromise the resonator performance but sufficient to avoid the formation of Ni droplets on the surface of the SMRs during the heating processes due to dewetting . However−1, which suggests the presence of less than five weakly coupled layers [−1 suggests that slowing down the cooling rate to reach 600 °C delivers graphene with better quality. The hydrophobic character of the surface was confirmed by dropping water on it with a pipette, which revealed the lack of liquid adhesion to the surface.Graphene layers were characterized by Raman spectroscopy with a B&BTek S415-532S spectrometer using a 532 nm laser operated at powers up to 50 mW. The Raman probe is mounted on a microscope that can focus the light to a spot of 2 µm in diameter on the sample surface. The uniformity and quality of the coverage was assessed by performing multiple measurements throughout the samples. d layers . Further2H2 flow ratio and cooling rate) on the defect density and number of graphene layers. We noticed that both lowering the C2H2 flow rates and increasing the overall pressure (up to 12 mTorr) leads to a decrease of the D peak. We also observed that no graphene grows when the overall pressure raises above a given threshold (around 20 mTorr). A more detailed study of the influence of growth conditions on the characteristics of the graphene layers grown in our system can be found in [We also analysed the influence of the process parameters  in 10 mM phosphate-buffered saline (PBS). This step requires the generation of carboxyl groups (–COOH) on the surface of the sample capable of binding the NH groups of the linker, in our case EDC.The first step of this functionalization protocol is the incubation of the sample in a solution of 0.2 nM 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) with 0.8 mM 2 RF-plasma in a reactive ion etcher. To prevent the negatively biased substrates from being bombarded by the energetic ions present in the plasma, a glass shield was placed 1 mm apart from the surface of the substrates. This way, only thermalized reactive oxygen radicals and neutral reactive species were able to reach the graphene surface by efficient diffusion through the gas discharge [2 flow for our system). The samples were characterized by Raman spectroscopy before and after each plasma treatment (of different durations) to monitor the amount of defects and the integrity of the graphene layer. 2 plasma treatment of adequately shielded graphene layers successfully promotes the creation of defects without significant damage of the graphene structure. The evidence that these defects are mainly carboxyl groups is the efficient binding of amine groups of the EDC linker, evinced by the detection measurements that will be show in this section.Since the as-grown graphene contained very low density of defects, being significantly hydrophobic, a specific treatment was required in order to promote the creation of controlled defects and the necessary carboxyl groups. This was achieved by subjecting the samples to a low-damage Oischarge . The pro2 plasma treatment is that the initially hydrophobic graphene layer becomes hydrophilic, as observed by analysing the adhesion of a droplet of DI water to the surface; the adhesion improved after the plasma treatment, indicating the increase of hydrophilicity. This is important as this treatment strongly determines the type of binding of the reagents to the surface in covalent and non-covalent bio-functionalization. An additional effect of the OAfter the plasma treatment, the samples were exposed to the different solutions by circulating the liquids containing the reagents with the aid of the peristaltic pump while the frequency response of the resonator was continuously recorded. The response of the SMR-based gravimetric sensor is comparable to that obtained in similar biosensors subjected to a silane-based functionalization .According to previous studies , non-covIn the present experiment , after tThe two functionalization methods appear to be remarkably similar. However, although non-covalent bonding seems much simpler and apparently yields similar results than the covalent functionalization protocol, some considerations should be taken into account when it comes to choose the most appropriate. Firstly, one has to consider the lack of specificity of the non-covalent bond. Given that streptavidin directly binds to graphene, other species with similar functional groups (primary amine) may also do so. Therefore, high-purity reagents are needed for a proper functionalization. Additionally, one has to ensure the effective blocking of the non-covered surface to avoid false positives. Plasma-treated surfaces for covalent binding seem to be less reactive and then more selective for binding the desired species. A deep analysis of the advantages and drawbacks of the non-covalent functionalization method, which is beyond the purpose of this work, needs to be done before concluding that this functionalization method is as reliable as the covalent one. However, regardless of the functionalization method, graphene directly grown on gravimetric sensors emerges as a promising platform for the bio-functionalization of industrially scalable biosensors.2 radical plasma treatment inducing a controlled density of defects on the graphene layer. In the second approach, non-covalent functionalization of pristine as-grown graphene was also demonstrated by firmly binding streptavidin to untreated graphene surfaces. Although both functionalization approaches appear to provide similar results, their suitability should be analysed in terms of their specificity to particular targets.AlN-based solidly mounted resonators (SMRs) operating in the shear mode were covered with graphene layers intended as functionalization platforms for gravimetric biological sensors. The graphene layers were directly deposited on the resonators through a relatively low-temperature LPCVD process that allowed for the preservation of the integrity of the whole multi-layered structure as well as its electrical response. The graphene-covered resonators were tested as biosensors for the detection of anti-IgG antibodies. Two functionalization protocols were explored. In the first one, EDC/NHS chemistry was used to covalently bind streptavidin molecules to the surface of the sensors after a low-damage O2) acoustic impedance of the fully insulating reflector, the Ir/AlN/Mo piezoelectric stack and the Ni catalyst that covers the active area of the device  and low , the source of the carbon atoms. The samples were then introduced in the CVD chamber, which was initially evacuated to a base pressure in the range of 10−7 Torr before being filled with Ar at a pressure of 11 mTorr to start the thermal sequence for graphene growth shown in Graphene was grown on top of the SMRs in a custom-built, cold-wall low-pressure CVD reactor. Although this kind of CVD process is frequently used for graphene growth , as far 2H2/Ar mixture to keep the pressure constant at 12 mTorr. The samples were exposed to this ambient for 5 min, during which C2H2 was catalytically decomposed on the Ni surface letting the resulting carbon atoms to diffuse into the Ni film. In the next step the C2H2 flow was cut off to allow the samples to cool down to 600 °C at a slower controlled rate of 15 °C/min under an Ar pressure of 15 mTorr. During this stage, carbon atoms segregated from the Ni film to form the graphene layer on its surface. The cooling rate was critical because the graphene film maintains a quasi-equilibrium with the atmosphere and the carbon inside the Ni film. The number of layers of the resulting graphene depended on the cooling rate and time of this step. Finally, the heater was switched off to let the sample freely cool down to room temperature keeping the Ar pressure at 15 mTorr. Since the graphene layer stabilizes when the temperature is low enough, the cooling should be as quick as possible. Under our experimental conditions the temperature reaches 400 °C in less than 2 min, with an initial cooling rate close to 200 °C/min.In a first step, the samples were heated to 650 °C at a rate of 50 °C/min, during which the Ni film was cleaned from oxides and impurities. Afterwards, acetylene was fed into the chamber while adjusting the CThe sensors were characterized by measuring the electrical impedance of the resonators from 100 MHz to 6 GHz with an Agilent N5230A network analyser using calibrated RF probes for on-wafer contacting. In order to verify whether the high-temperature process affected the performance of the devices, the SMRs were carefully assessed before and after the growth of the graphene layer.® application that allowed assessing the frequency with less than 1 kHz accuracy each 7 s. For in-liquid measurements, we used a fluidic system made of PMMA that includes a PDMS chamber with an O-ring to seal a 30 µL cavity around the active area of the device see . A peris


Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply biology instructors with questions for college students in introductory biology classes, two algorithms were developed. One generates questions from a formal representation of photosynthesis knowledge. The other collects biology questions from the web. The questions generated by these two methods were compared to questions from biology textbooks. Human students rated questions for their relevance, fluency, ambiguity, pedagogy, and depth. Questions were also rated by the authors according to the topic of the questions. Although the exact pattern of results depends on analytic assumptions, it appears that there is little difference in the pedagogical benefits of each class, but the questions generated from the knowledge base may be shallower than questions written by professionals. This suggests that all three types of questions may work equally well for helping students to learn.The online version of this article (doi:10.1186/s41039-016-0031-7) contains supplementary material, which is available to authorized users. Such question generators are used for generating homework exercises, quizzes, exams, questions to be asked in class, and many other purposes.In mathematics and similar topics, question generation is widely used perhaps because it is so simple to implement. For instance, an infinite number of questions can be generated from the template “Solve for In subjects where questions must be answered in natural language, instructors still need a large supply of questions for their homework assignments, quizzes, exams, etc. This paper focuses on technology for generating questions whose answers are in natural language.A recent review of educational systems with automatic question generation (NP (DT some) (NNS fruits)))))(NP (NNP John)). University biology students were recruited to act as judges. They rated the three sets of questions on four measures. Our primary question was whether the machine-generated questions were worse than the human-generated ones. We were also interested in whether the two types of human-authored questions were of different quality.To do the comparison, we first implemented a method for machine-generating questions from a biology knowledge base. A second set of questions was formed by selecting questions retrieved from the web. The authors of most web questions were probably students and not biology experts. Our third set of questions was comprised of professional human-generated questions from textbooks and a biology study website hashasThe knowledge base distinguishes classes and instances. Classes define an ontology, that is, a hierarchical classification of all that exists. For example, result is a predicate and photosynthesis001 and oxygen001 are instances. The second and third lines indicate that they are instances of the classes photosynthesis and oxygen, respectively.In the first line, There are features common to every instance of a class. For example, any instance of photosynthesis can produce oxygen. These are not properties of the class per se, so a special instance named a prototype instance is used to represent relations common to all members of a class. Any other instance of a class is assumed to have the relations involving the prototype instance of the class as well as the relations in the prototype instances of the super-classes. A class can only have one prototype instance.The relations directly stored in the knowledge base are called atomic relations. Some relations are inferred during the question generation process, and those are called secondary relations. Similarly, the pre-existed predicates in the knowledge base are called as atomic predicates, and those that are introduced by reasoning only are called secondary predicates. The meanings of atomic predicates are defined in KM  was larger than the similarity score of the human-provided seed question concepts . Note that there has been many methods developed for calculating semantic similarity, often based on the ontology in WordNet | is not trivial. A brute force way is for each shared predicate of the two concepts, for each pair of different objects connected to the concepts by that predicate, to enumerate all the ancestors of the two objects and see whether they have an ancestor in common. If they do, a counter is increased by 1; otherwise, the counter stays the same as before. When all pairs for the predicate are explored, the value of the counter is ||.However, calculating | is zero but similarity is non-zero, the calculation does not need to be run because it will be false for this A,B pair.In principle, this calculation is run for every possible pair of concepts, We used eight seed questions to test the algorithm, one for each of these eight pairs: anabolism and catabolism, genotype and phenotype, gill and lung, mitosis and meiosis, nematode and annelid, plasma membrane and cell wall, spore and gamete, and transcription and translationThe algorithm generated an average of 842 questions per seed questions, and the standard deviation was very high: 1721. Several seed questions will be discussed in turn.The first seed pair was cell wall and plasma membrane. This pair caused generation of eight new questions plus the original seed question. Of the eight new questions, six were reasonable biology questions based on our (limited) experience. Moreover, two of the six questions were asked by someone else before, as confirmed using Google search. The other four did not appear in a Google search, which is interesting. Thus, cell wall and plasma membrane is an example of a good seed question.The second seed pair was anabolism and catabolism. The generator found no new questions; it generated only the seed question. This occurred because the restrictions generated from the seed question contained some uncommon predicates.The third seed pair was gill and lung. The generator’s output was 4750 questions. Most of the questions would not make good comparisons. This seed question led to few restrictions, which caused the output of too many pairs of concepts.The second and third seed pairs are representative of seven out of the eight questions. Except for the cell wall and cell membrane seed pair, all the seed pairs led to either too many or too few generated questions.These results indicate that the number and the quality of the questions generated from the constraints varied substantially. The variance could be due to the algorithm, the seed questions, and/or the knowledge base. Every seed question is a good comparison question itself. But the knowledge base sometimes did not have adequate descriptions for the concepts appeared in the seed questions, and this led to over-generation. Some concepts in the seed question were attached to uncommon predicates in the knowledge base, and it led to under-generation. It suggested that our method of inducing constraints from seed questions was unstable in part because it depended too strongly on the detailed content of the knowledge base.These results suggested that any technique that relied on traversing large parts of the knowledge base in an attempt to generate deep, thought-provoking questions was likely to be as fragile as our method. Knowledge bases are just very large and very complex. Nonetheless, instructors still need questions to help their students exercise their knowledge. We decided that some help was probably better than no help, so we decided to focus on generating questions that tapped only small amounts of knowledge. In surveying textbooks, we had noticed that about half their questions were shallow, factual ones . This suggested that although instructors probably put a high value on deep questions, they probably also want students to have enough factual knowledge to be able to tackle deep questions, so they probably ask shallow questions as preparation for the deeper ones. If high-quality shallow questions could be generated, then this would remove a load from the instructors and authors, thus allowing them to focus on generating deeper questions. As the remainder of this document demonstrates, even the comparatively modest goal of generating moderately shallow questions proved challenging.chloroplast is included in our reduced knowledge base. A chloroplast is a part of a plant cell, but the concept of plant cell is not in the knowledge base because it is not directly related to photosynthesis or one of its subevents.We choose to study questions about a single topic so that the human judges could more easily refresh their biology knowledge before judging the questions. The chosen topic was photosynthesis, which is a type of event. Thus, we created a subset of the knowledge base that had only knowledge about photosynthesis, its subevents, and concepts that were directly related to such events. For instance, photosynthesis occurs in chloroplasts, so the concept photosynthesis class. Some facts about photosynthesis are general to all three types, so the knowledge base uses photosynthesis for them. Other facts are specific to plants, so the knowledge base uses the subclass, photosynthesis_by_plants. The facts about algae and photosynthetic prokaryotes were removed from the knowledge base so that the knowledge base remains inside the focus of introductory biology textbooks.Also, the knowledge base was biologically accurate, so it represented details that are irrelevant to students in our target population. For instance, the process of photosynthesis is somewhat different in plants, algae, and photosynthetic prokaryotes. The original knowledge base contained information about all three types of photosynthesis and used three different class names to distinguish them; all were subclasses of the sub-classsub-classIn order to simplify the generation of questions, information about instances that would normally be inferred via inheritance from distal prototypes and classes was copied onto the instances themselves. First, features of the prototype instance of a class were copied to all the instances of the class. Second, the transitive closure of the subclass relationship was represented. For instance, givenancestorthe secondary relationswere added. Other secondary relations were also added and will be described later.Questions were generated from question schemas, where a schema is a template with extra information. A question schema consisted of variables, constraints, and question templates. The constraints were matched against the knowledge base in order to bind template variables to concepts in the knowledge base and thus generate a question.The question schemas depended on the structure of the semantic network we used. As mentioned earlier, the semantic network was extracted from a knowledge base that was used for question-answering purpose, where the entire knowledge base was described in terms of class, entity, event, and relations among these concepts. Since generating questions about classes  makes no sense in our teaching domain, we focused on generating questions about entities, events, and their relations. Briefly put, we generated “what is” questions to have students learn compositional relations between an event and its subevents. We generated “input and output” questions to have students learn relationships between events belonging to the same parent event. We generated “where” questions to have students learn locational relations between events and entities. We generated “function” questions to have students learn how entities participate in events. This subsection discusses general design issues for the question schemas. Subsequent subsections discuss issues that are specific to the question types just mentioned.One general issue is that generating all questions of a certain type from the same question schema can be boring to the student. Thus, each question schema had several versions that generated different but synonymous questions, such as “What are the 2 stages of photosynthesis?” and “Could you describe the two sub-processes of photosynthesis?” Also, as mentioned earlier, only facts about plant photosynthesis remained after removing knowledge about two other kinds of photosynthesis. Thus, “photosynthesis” and “photosynthesis by plants” both appear in questions and they have the same meaning. Thus, the knowledge base itself introduces some more-or-less random variation in the generated output that helps prevent boredom.A difficult problem in natural language generation is generating a referring expression that is both succinct and unambiguous . This solution is clearly not perfect. For instance, it sometimes generates spurious contextualization, such as “the Calvin cycle of photosynthesis.” It also is strongly affected by the reduction of the knowledge base. For instance, it generates “chloroplast” rather than “the chloroplast of the plant cell” because the reduction eliminated the concept plant cell from the knowledge base. Despite the simplicity of this policy, it does an adequate job on a natural language generation problem that is known to be very difficult to solve perfectly . When such a relationship is absent, then the noun phrase is generated without a determiner . However, the policy is deliberately implemented slightly different in different question templates in order to increase the variability of the language.Similarly, generating the determiner for a noun phrase is also a difficult problem  is not handled at all. Our assumption is that capitalization would not affect learning. Indeed, many of the minor disfluencies in the generated questions probably would not affect learning, which is confirmed in the later analysis.The rest of the section explains how the questions were generated type by type.“What is” questions ask students to explain a concept directly. A simple example is “What is photosynthesis?” Such questions can be answered in many ways, such as explaining the chemical equation of photosynthesis in the student’s own words and pointing out the different stages of the photosynthesis process. The chemical equation actually describes the input and output of the process, which belong to the behavior part of a system. Pointing out the stages refers to the structure part. We decided to have our “what is” questions focus on the structure instead of behavior, because behavior could be handled by other types of questions. Because photosynthesis is an event, all its parts and subparts are also events. Because the evaluation focuses on photosynthesis, rather than cell anatomy, the part-of relations between entities  were removed. Thus, our “what is” questions all ask students to describe the different stages of a process.The key to generating this type of questions is identifying the appropriate concept, which is “photosynthesis” in the example. In principle, any class in the knowledge base can become the questioning concept, but many of them are too general or too specific. For example, it does not make sense to ask “What is chemical process?” As it turns out, neither the too-general nor too-specific concepts have subevents, so generating “what is” questions based only on the part-of relationship rules out those inappropriate concepts.In order to make it clear that the subevents are to be described, the question schema used the template, such as “What is [the number of sub-events] stages of [the concept].” In order to generate such questions, it is necessary to know the number of subevents of each event. This is pre-computed during the preparation phase and stored as a secondary relationship.Some events have too many (five or more) subevents defined in the knowledge base. Pointing out the number of events in this case may confuse the students, because different people may have different criteria for distinguishing a subevent. In this case, the question schema does not mention the number and instead uses the following template: “Please explain the process of [concept].”site is a predefined atomic predicate, the question can be composed by generating a question about its object. As mentioned earlier, simple policies were used for generating referring expressions, so spurious context phrases and articles were sometimes generated. In fact, instead of “where does the Calvin cycle occur?”, the system actually generated “Where does the calvin cycle of photosynthesis occur?”“Where” questions are classified into two types. The first one is to question the site of an event. For example, “Where does the Calvin cycle occur?” Generating this type of questions is fairly straightforward. Since The second type of “where” question is questioning the origin or the destination of a movement. The knowledge base describes a movement in terms of three atomic predicates. They separately specify the origin, the destination, and the object that is moving. A movement must happen under some circumstance, which means that it must belong to a parent event. So the two corresponding question templates are “In [parent event], where does the [object of the movement] in [destination] come from?” and “In [parent event], where does the [object of the movement] go after it leaves [origin]?”raw_material and result. Just as their names indicate, the predicate raw_material describes the inputs of a chemical process and the predicate result describes its outputs. Again, if the attached process to the predicate is a subevent of another one, the parent event needs to be included to provide the context. Examples of the templates for the input and output questions are, respectively, “What does the [process] require?” and “What does the [process] produce?”An “input and output” question focuses on the behavior of a process. The most straight forward “input and output” questions ask what a single process needs for starting and what products it produces. These questions can be easily generated from the knowledge base predicates Because the products of one process often serve as the raw materials of another one, we devised questions to help students review these connections. In generating such a question, the generator finds processes that have the connecting relations and then puts them into the templates. If the two processes are the subevents of the same process, the parent process will also be included in the question to give the context. So one question template is “How does [process1] support [process2] in [parent process]?” This question template involves somewhat more knowledge than the others.enables, inhibits, causes, agent, site, and raw_material. Because all these predicates describe how the subject of the predicate (an object) can affect an event, they can all be used to generate a function question. For instance, one question template is “What is the role of [subject] in [event]?”“Function” questions ask about the role of an entity or event in another event. The predicates in the knowledge base corresponding to the role concept are as follows: What_is. A “what is” question, with two subeventsSite_of. A “where” questionRaw_material(photosynthesis). An “input” questionRole_of. A “function” questionWhen the constraints in a question schema are matched against the knowledge base, they bind the variables of the schema. This produces an instance of the schema, such as these:Now that the “question” is written as a relation, we can have several different natural language templates for each type of relation so that students would not get bored when they were answering the same type of questions multiple times. In the process of transforming the relations into the natural language questions, the program randomly selects one natural language template from several predefined ones.Because the knowledge base was limited to photosynthesis, the generator produced 56 questions: 6 were “what is” questions, 17 were “where” questions, 17 were “input and output” questions, and 16 were “function” questions.For a topic like biology, which is widely taught, standard question-answering websites, such as ask.com and yahoo answer, can be used to find student-generated questions. We used a Google custom search engine to search the entire Internet with these question-answering websites prioritized. Figure Since Google’s search is driven by a list of keywords, it is crucial to choose the keywords properly. To make the collected questions comparable with the machine-generated questions, the keywords need to specify both the concept to be questioned and the type of question. Let us first discuss how keywords for concepts were generated.An initial list of concepts was extracted from the chapter on photosynthesis in a biology textbook and an article on photosynthesis in Wikipedia. However, the same concept may have several different names that are used by different people and websites. For example, a cell membrane is also called a plasma membrane. Since a keyword search does not know about synonymous terms, the problem needs to be resolved when keywords are generated. To cover all the variations, we used WordNet  were covered by machine-generated questions and 35 were not. Because one human question sometimes covered more than one machine-generated question, 28 machine-generated questions were covered by the 24 human-made questions. Therefore, of the 56 machine-generated questions, 28 (50 %) matched human questions and 28 did not. The latter figure is important, because it suggests that humans may be failing to generate enough questions to completely cover the knowledge in the knowledge base.In order to understand the differences between the two sets of questions, we first annotated all the questions according to their topic. We classified topics as structure, behavior, or function, as this three-way distinction is common . The remaining “what is” question asked about an entity. The “behavior” annotation was not suitable for that question, so that question was split into only two ones.Table For the structure and behavior-1 categories, there was considerable overlap of the machine- and human-generated questions. These are fairly simple questions, so it is not surprising that the two sources tended to generate the same questions. For the behavior-2 and function categories, which tend to include more complex questions, the overlap was much smaller. In terms of a distribution of question types, the machine generated many fewer behavior-2 questions and many more behavior-1 questions than the humans.This analysis seems to be the first one to study the coverage issue of question generation, so there is no previous work or baseline for comparison. Rus et al.  examinedN = 20. Forty random machine-generated questions were selected because a bigger number of random questions makes a better representation of the corresponding type of questions. These selections formed a photosynthesis question base with 80 questions in total.The research question we wanted to answer was “What are the relative qualities of questions generated from a knowledge base, from searching the web and from a professional source of questions?” For the evaluation, we randomly selected 40 questions generated from the knowledge base, 20 questions from the web search, and 20 from the list of professional questions. There are only 20 questions selected from the web search and professional questions, because the pools of these two types of questions were not big enough to provide 40 random questions. This imbalance would not contribute to any observed results in the later experiment except that the power of the analysis is limited to the smaller groups, Fluency had the judges rate the grammatical correctness. Ambiguity asked them whether the question was semantic ambiguous or not. After the judge answered the question, he/she was asked whether answering the question helped him/her understand or review any concepts, which was our measure of pedagogy. For depth, we asked the judges to rate how much thinking was involved in their answering of the question. The first two measures, fluency and ambiguity, were borrowed from a question generation challenge  = 960 data points per measure . When the judgments of duplicates were removed, there were 867 data points for each measure.A typical method for evaluating machine-generated questions is to have two or more people rate every question independently, and the mean of their ratings is treated as the final score of the question. On questions where the scores from the raters were substantially different, the raters discuss and resolve their conflicts. In our experiment, the 12 participants were the raters, but it was impossible for them to discuss and resolve discrepant judgments, so the scores were combined without discussion and adjustment.We used two different methods for combining scores across judges. The first one assumes judges may be biased, so it normalizes their scores and makes every judge’s mean score for a particular measure be zero with a standard deviation of one. This method assumes that the scores given by the judges form a ratio scale, and thus, it is sensible to take means and standard deviations. The second method assumes that the scales are ordinal instead of ratio, so it is not sensible to take the mean and standard deviation. This method uses medians to compare scores.z-transformation:z-score of every judge/measure combination has a mean of 3, which is the center of the 1–5 scale.One way to aggregate across the 12 judges is simply to take the means of their scores, as shown in Table z-scores, as shown in Table t tests. The results are shown in Table To aggregate across the 12 judges, we simply took the means of their t tests did find some significant differences. The web-generated questions were more relevant to students’ knowledge than the other two types of questions. This difference was not surprising because the web-mining method tended to collect questions from students.The Professionally written questions beat the questions generated from the knowledge base in both pedagogy and depth. The questions mined from the web received marginally higher pedagogy ratings than the questions generated from the knowledge base. This is consistent with the analysis of coverage presented earlier which showed that more questions from humans than machines were classified as behavior-2 or function, which seem intuitively to be deeper and potentially more pedagogically useful types of questions.However, even after normalization, the scores from different raters on the same question and measure were often far apart. This suggests that human judges may be unreliable for those particular measures on those particular questions. Because it was impossible to call back the participants so that they could discuss and agree upon a rating, a second analysis was done discarding the questions/measure combinations with high standard deviation.For the measure pedagogy, the results are shown in Table For the measure depth, the results are shown in Table Although removing question/measure pairs from the sample is one way to cope with unreliability, it reduces power, which makes it harder to tell when two methods of generating questions are truly equal on a measure. Thus, for our second analysis, we used medians instead of means, on the grounds that they are less sensitive to outliers.Among the 867 responses, there were 435 for machine-generated questions, 217 for Google-collected questions, and 215 for textbook questions. Table For most measures, the medians were the same. The exception was that the medians of fluency and ambiguity suggest that web-generated questions were better than the other two. However, the underlying distributions for all three question sources on the measure’s relevance, fluency, and ambiguity show that all were similar and all were probably not a major source of pedagogical difficulty.Pedagogy and depth are the two measures we most care about. The histograms look similar across question sources.I was introduced to represent the identity of the question source, and the significance level of the coefficient of the variable I represents the reliability of the difference. For example, in comparing the pedagogy of knowledge base questions to that of web questions, I equals 1 when the question is from the web and I equals 0 when the question is from the knowledge base. The significance of the coefficient of I is the reliability of its difference between the two question sources.To test whether there any significant differences existed, we ran quantile regression Koenker, , which iThe quantile regression comparisons suggest that the differences in the medians that are apparent in Table z-scores. The latter showed that the knowledge base questions were shallower and less pedagogically beneficial than the professional questions and that the web-based questions were in between the other two types on all measures except relevance, where they were ranked higher than both the professional and knowledge base questions.The pattern of median results is not consistent with the pattern of results from the means of the These conflicting results leave us with an interpretation challenge. The main issue is how to interpret the conflicting results for pedagogy and depth. The histograms of Table If the goal of generating a question is to enhance students’ learning, is relevance, fluency, ambiguity, or depth of the question going to affect the pedagogy? To answer this and similar questions, we calculated the correlations of the measures. The original 867 responses per measure were used without aggregating or normalizing or dividing by question source.The result was showed on Table These results are consistent with those of Table p < 0.001, F = 6.414, df = 74). A Tukey HSD test was performed to test the group-by-group differences. If behavior-2 and function questions are considered as deep questions, then both should get higher scores than behavior-1 and structure questions. Both behavior-2 and function questions do have significantly higher scores than behavior-1 questions. But only behavior-2 questions have a significant higher depth score than structure questions. It is probably because some function questions have very obvious answers. For example, “What do chloroplasts enable plant cells to do?” can be simply answered as “photosynthesis,” whereas behavior-2 questions were defined such that their answers always required inference.When we measured the overlap of the human-generated questions and the machine-generated questions, we used four annotations: structure, behavior-1, behavior-2, and function. It seems intuitively clear that behavior-2 and function questions are deeper than the other two types. To explore whether students agreed with this intuition, each of the 80 questions used in the experiment were annotated with “behavior-1,” “behavior-2,” “function,” or “structure” as described in Section n = 35), and behavior-1 and structure questions were grouped together as the shallow group, whose mean of the depth score is 2.51 . The difference is significant according to t test , with a large effect size (Cohen’s d = 0.92). This suggests again that our coding of questions corresponded to the depth judgments of the 12 raters.The analysis was repeated with just two categories, shallow and deep. That is, behavior-2 and function questions were aggregated together as the deep group, whose mean depth score was 2.85  by the questions generated from the knowledge base. As expected, the machine-generated questions had a much higher coverage of shallow human questions than deep human questions. This is not a surprise because each question generation schema used with the knowledge base only accessed a small number of knowledge base relationship. We tried generating complex question schema using seed questions to infer the pattern of knowledge base relationships for the schema. This turned out to be too sensitive to the details of the knowledge base. Some seed questions generated far too many question schemas for a human author to check, and other seed questions generated too few candidates. Therefore, when questions are generated from a knowledge base, it seems that a practical method is to have a human write the constraints for generation.z-scores: for pedagogy and depth, knowledge base < professionalz-scores with removal of questions with high inter-rater variability: no reliable difference on pedagogy, reliable difference on depthMedians: no reliable differencesKnowledge base vs. professional:z-scores: for relevance, knowledge base < webz-scores with removal of questions with high inter-rater variability: no reliable differences on pedagogy and depthdepth, knowledge base > webMedians: for fluency and ambiguity, knowledge base < web; for Knowledge base vs. web mining:z-scores: no reliable differences z-scores with removal of questions with high inter-rater variability: no reliable differences on pedagogy and depthMedians: for fluency and ambiguity, web > professionalWeb mining vs. professional:In terms of the quality of the machine-generated question, our experiment shows that the questions from the three different sources have only minor differences in their quality ratings. Although some significant differences in their fluency and ambiguity ratings were observed, the differences in pedagogical and depth ratings were unreliable, appearing only with some statistical tests and not others. In particular, we found:If these ratings from 12 biology students can be trusted as proxies for the pedagogical quality of the questions, it appears that all three sources are equally beneficial on the whole.However, a different analysis did show a difference in the depth. Unlike the main evaluation, which was conducted on a sample of each type of questions, this analysis compared all the machine-generated questions to all the human-generated questions, both professional and web-mined, merged together. When we classified questions as shallow  vs. deep (both behavior-2 and function), we found strong correlations with the judgements of the 12 biology students but the ratings systems were not identical. Using our classifications, we found that about 50 % of the human questions were deep whereas only about 30 % of the machine questions were deep. Moreover, only 15 % of the human deep questions were also generated by the machine, while 70 % of the human shallow questions were generated by the machine. These figures are not terribly surprising, as they are consistent with the design goals for the machine-generated questions. However, we were pleasantly surprised to see that the machine generated a very high percentage of the shallow questions that humans generated.Given a knowledge base, we developed a rule-based algorithm to generate biology questions and developed a method to collect biology questions by searching the web with keywords. When 12 biology students judged the pedagogical merit, depth, relevance, fluency, and ambiguity of the two different types of questions to professional human-made questions, the pattern of results depended on whether we assumed the judgements were a ratio scale or an ordinal scale. If we assumed a ratio scale, then professionally generated questions were rated as deeper and more pedagogically beneficial than questions generated from the knowledge base but there were no differences in fluency and ambiguity. On the other hand, if we assumed an ordinal scale, then there were differences in fluency and ambiguity but no differences in depth or pedagogy except that depth of the questions from the web was smaller than that of the machine-generated questions. Correlations of the measures suggest that depth and pedagogical merit were highly correlated but no other pair of measures were.However, when we classified the questions by their topic, there were differences due to the design of our question schemas, in that about 50 % of the human-generated questions addressed deep topics (function and behavior-2) whereas only about 30 % of the machine-generated questions did. However, the machine-generated questions did cover a large fraction of the shallow human-generated questions. Conversely, only 50 % of the machine-generated questions were covered by human-generated questions.This suggests that machine-generated questions can play an important role in instruction in that they can generate the shallow questions that human authors would otherwise need to generate. This would leave human authors to focus on generating deep questions. Moreover, if our method for collecting human-generated questions is a fair representation of questions that humans generate, then it appears that humans are failing to generate about half the shallow questions that students need. Thus, this suggests that machines may be better than humans in that they can generate a more complete set of shallow questions.However, the results also sound a cautionary note, in that the biology students did not perceive much difference in the depth or in the pedagogical benefits of the different types of questions. It is not clear whether our classifications or their judgements are better predictors of the actual pedagogical value of the questions. One possibility is that the biology students tend to interpret a question as a beneficial one when they are unsure about the answer no matter whether the answer can be easily learned  or not (deep questions). If the biology students are correct that even shallow questions are beneficial if one does not know the answer, then the machine-generated questions should be as good as the human-generated ones at least for the beginners to get familiar with a subject like biology. We actually have already built a simple tutoring system that help non-biology majors students learn photosynthesis with the machine-generated questions and found a significant difference between pre-test and post-test scores.Another caution concerns the generality of the question generation schemas, which must match the structure of the knowledge base. If a new knowledge base were used with a different structure, new questions schemas would be needed.removing knowledge to change a college knowledge base into a high school one would not require changing the question schemas, adding more knowledge may bring in new predicates and thus necessitate new question schemas.Moreover, the knowledge base can be considered to be a model of an ideal student’s knowledge


TARDBP gene are also found in inherited and sporadic ALS, indicating that dysfunction in TDP-43 is causative for ALS. To model TDP-43-linked ALS in rodents, we generated TDP-43 knock-in mice with inherited ALS patient-derived TDP-43M337V mutation. Homozygous TDP-43M337V mice developed normally without exhibiting detectable motor dysfunction and neurodegeneration. However, splicing of mRNAs regulated by TDP-43 was deregulated in the spinal cords of TDP-43M337V mice. Together with the recently reported TDP-43 knock-in mice with ALS-linked mutations, our finding indicates that ALS patient-derived mutations in the TARDBP gene at a carboxyl-terminal domain of TDP-43 may cause a gain of splicing function by TDP-43, however, were insufficient to induce robust neurodegeneration in mice.Abnormal accumulation of TAR DNA-binding protein 43 (TDP-43), a DNA/RNA binding protein, is a pathological signature of amyotrophic lateral sclerosis (ALS). Missense mutations in the  TARDBP gene, encoding TDP-43, have been identified in inherited and sporadic ALS, implicating TDP-43 dysfunction as a central component for ALS pathogenesis [Abnormal accumulation of TDP-43 has been identified as a pathological signature of amyotrophic lateral sclerosis (ALS), an adult neurodegenerative disease characterized by a selective loss of motor neurons, and a part of frontotemporal dementia (FTD) . Cytoplaogenesis . TDP-43 ogenesis . Howeverogenesis .Tardbp gene. Of more than 50 known mutations, we chose TDP-43M337V mutation for the following reasons: TDP-43M337V protein has a long half-life in cells, the ALS patients with this mutation show earlier disease onset [Tardbp gene by utilizing CRISPR/Cas9 genome-editing technology  revealed that there was no difference in expression level between wild-type and TDP-43M337V/M337V mice  random mutagenesis, showed modest neurodegenerative phenotype [The present study demonstrates that homozygous TDP-43henotype . These mM337V/M337V mice showed splicing deregulation of TDP-43 target mRNAs, Kcinp2, Sort1, and Sema3f, suggesting that M337 V mutation causes a gain of function in TDP-43. Gain of TDP-43 function is also suggested in TDP-43Q331K and TDP-43M323K mice [M337V mice, while they were moderately upregulated in TDP-43Q331K [M337V mice.Our TDP-4323K mice , 10. All23K mice . In our -43Q331K . This diM337V knock-in mice further strengthen the findings that mutations at the C-terminal region of TDP-43 likely cause a gain of TDP-43 splicing function at an initial stage of the disease, which may be followed by the loss of TDP-43 function due to a loss of TDP-43 proteins from nuclei.All knock-in mice carrying ALS-linked missense mutations in TDP-43 do not show robust TDP-43 pathology even in homozygous mutant mice. Perhaps, additional conformational change of TDP-43 protein may be needed to develop TDP-43 pathology. Finally, our results from TDP-43Additional file 1. Material and methods. Figure S1. Direct sequencing of Tardbp gene exon 6 in heterozygous TDP-43M337V knock-in mice. Figure S2. Body weights were not affected in TDP-43M337V knock-in mice. Figure S3. Relative expression levels of Notch1 and Nek1 mRNAs were not altered in the brain of aged homozygous TDP-43M337V mice. Figure S4. Splicing was not altered in Eif4h or Poldip3, which are also regulated by TDP-43, in the brain of aged homozygous TDP-43M337V mouse brains. Figure S5. Gliosis was not observed in ventral horn of aged (700 days-old) homozygous TDP-43M337V mice. Figure S6. The number of Gems was not affected in ventral horn neurons of aged (700 days-old) homozygous TDP-43M337V mice.


While dilute arrays of ionized F4TCNQ molecules are seen to behave like isolated subcritical charges, dense arrays show emergent supercriticality. In contrast to compact supercritical clusters, these extended arrays display both supercritical and subcritical characteristics and belong to a new physical regime termed “frustrated supercritical collapse”. Here carriers in the far-field are attracted by a supercritical charge distribution, but their fall to the center is frustrated by subcritical potentials in the near-field, similar to trapping of light by a dense cluster of stars in general relativity.The photon-like behavior of electrons in graphene causes unusual confinement properties that depend strongly on the geometry and strength of the surrounding potential. We report bottom-up synthesis of atomically-precise one-dimensional (1D) arrays of point charges on graphene that allow exploration of a new type of supercritical confinement of graphene carriers. The arrays were synthesized by arranging F Placement of charge centres with atomic precision on graphene allows exploration of new types of confinement of charge carriers. Here, the authors fabricate atomically precise arrays of point charges on graphene and observe the onset of a frustrated supercritical regime. Due to this relativistic behavior, electrostatic confinement of charge carriers in graphene is very different than that seen in more conventional materials3. Indeed, trapping electrons by placing point charges on graphene is formally analogous to trapping light by a gravitational field: something only possible near extremely dense matter4. Such localization, however, is possible for graphene around very strong Coulomb centers in the so-called supercritical regime10, which allows a degree of localization otherwise impossible to achieve in pristine graphene. This behavior is formally equivalent to the supercritical collapse of atoms having ultra-heavy nuclei in quantum electrodynamics (QED)15. This atomic analogy, however, is only useful for charge distributions that can be approximated as a single-point charge. Here, we demonstrate a new supercritical regime, “frustrated supercriticality”, that is accessible through careful arrangement of point charge distributions on a graphene surface. Frustrated supercriticality reflects an interplay between near-field and far-field electronic behavior for charge distributions that are globally supercritical but locally subcritical. Electronic behavior here is analogous to photons gravitationally trapped within a star cluster that has no black holes. The ability to charge and discharge such states via local electrodes raises the prospect of designing localized electronic states without compromising graphene crystallinity, and hence integrating them into extremely high-mobility nanoscale devices.Graphene’s photon-like carrier dispersion provides fertile ground for testing exotic predictions of quantum electrodynamics, as well as for developing novel quantum electron optics4TCNQ molecules on clean, back-gated graphene FET devices. STM spectroscopy (STS) measurements reveal that dilute charged arrays with large inter-molecule spacings d ≥ 10 nm scatter surrounding Dirac fermions and induce no bound states in the nearby pristine graphene. For denser charged arrays with d ≤ 10 nm, however, STS shows the emergence of a new quasi-bound state with an energy near the Dirac point. This state extends into the pristine graphene and is able to trap charge, as observed through spatially resolved charging maps. We are able to explain this behavior by modeling the combined array/graphene system via tight-binding calculations that take screening into account. Our simulations reveal that when intermolecular distance in a 1D array is greater than the graphene screening length then each molecule behaves like an isolated subcritical Coulomb center. For intermolecular separations less than the screening length, however, our simulations reveal the emergence of a new type of collective supercritical state with energy near the Dirac point. This frustrated supercritical state is seen theoretically even for systems composed of only two-point charges and the wavefunction spread scales with inter-charge separation. In the semiclassical limit, this behavior is shown to be nearly equivalent to a general relativistic treatment of trapped light.Demonstrating frustrated supercriticality in graphene requires the ability to position static charges with a level of precision currently unobtainable by conventional top-down lithography. We achieved the necessary precision via a bottom-up synthesis technique that yields charge-tunable, periodic, self-assembled one-dimensional (1D) arrays of F2 layer covering a doped Si wafer, the latter providing an electrostatic back-gate. F4TCNQ molecules , a linear chain molecule that self-assembles into monolayer-high islands on graphene with perfectly straight edges17  with unit cells having multiples of the moiré period a. F4TCNQ arrays with d = 2a, 3a, 4a, and 5a can be seen in Fig. 15. The molecular charge state, for example, is negative when the gate voltage is 30 V for all molecular arrays down to (and including) a periodicity of 2a.Our FET devices were fabricated by placing a CVD-grown graphene monolayer on top of a hexagonal boron nitride (h-BN) flake resting on an SiOles Fig.  were uses17 Fig. . As seenern Fig. . The preI/dV point spectroscopy on pristine graphene at different distances from the center of an F4TCNQ molecule along a line perpendicular to the charged array  pinned at EF (arising from phonon-assisted inelastic tunneling18) and another local minimum at Vs ≈ −0.18 V for Vg = 30 V that indicates the Dirac point energy (ED). ED is seen to lie 115 meV below the Fermi energy after accounting for the inelastic gap, corresponding to a carrier density of ne ≈ 9.5 × 1011 cm−2 for Vg = 30 V. In arrays with a large intermolecular spacing of 5a, the spectra at points adjacent to F4TCNQ molecules  develops a systematically higher spectral weight and a clear resonant structure near ED as the array period is reduced to 2a  in the vicinity of arrays having different periodicity. This was done by performing dray Fig. . All dI/les Fig.  exhibit  2a Fig. . The reseature ~10 meV pin4TCNQ molecules generate only a subcritical Coulomb potential16, the development of a resonance near ED in more closely packed arrays suggests a collective effect whereby the array somehow surpasses the supercritical threshold and induces new quasi-bound states7. This hypothesis is supported by charging behavior observed near dense d = 2a arrays, as seen in Fig. a array) and the right side is imaged via a dI/dV map that shows electronic structure in the pristine graphene to the right of the array for VS = −0.12 V and Vg = 20 V. Sharp rings are seen on the right that are indicative of charging behavior (similar rings have been seen previously by STM due to the charging of adsorbed molecules and defects on various surfaces27). The rings of Fig. Since isolated charged FI/dV point spectra of Fig. A” shows the new graphene resonance induced by the charged molecular array as seen in Fig. Vg is lowered from Vg = 30 V to 24 V, this feature moves up in energy, as expected for a density-of-states feature when EF is lowered by reduction of Vg. An additional peak marked “B” can also be seen that moves opposite to A as Vg is lowered, indicating that it is a charging peak rather than a density-of-states feature27. For Vg > 20 V, peak B is caused by the discharging of state A  as it is pulled above EF by STM tip-induced local gating. For Vg < 20 V, peak B jumps across EF and continues to move down in energy, as expected for a charging peak since state A has now crossed to the other side of EF (the empty state side) and must be pulled below EF to become charged . The charging behavior observed for this new graphene state confirms its localized nature . Figure d, at the energy of the quasi-bound resonance  becomes constricted when the reduced screening length cuts into the potential that supports it. This explains why no signs of supercriticality are seen experimentally for our d = 5a arrays, since the inter-charge separation in this case is on the order of λS. Supercriticality develops for denser arrays as the inter-charge spacing falls below the screening length (d < λS).The effect of screening on this process can be seen in Fig. c/2 < Z < Zc can be summed up in a semi-classical description of graphene carriers under the effective potential, Vtot(r), of a point charge distribution that is supercritical in the far-field but subcritical in the near-field. The supercritical regime is generally characterized semiclassically by the existence of a finite potential barrier that traps carriers on the charge distribution side of the barrier . On the other hand, if a mass distribution consists of isolated masses that each have no event horizon  but that extend close to RSC of the aggregate, then photons incident from outside of RSC can be trapped gravitationally in an extreme case of gravitational lensing. Unlike near a black hole, the photon’s orbit will not end with a fall onto one of the stars, but will rather meander endlessly within the cluster, permanently bound by its gravitational field. This is completely analogous to the frustrated supercritical orbits of graphene charge carriers that remain trapped in the near-field of a cluster of subcritical charges whose total charge > ZC  flakes exfoliated onto a SiO2/Si substrate. h-BN flakes were exfoliated onto heavily doped silicon wafers and annealed at 500 °C for several hours in air prior to graphene transfer. The graphene was grown on copper foil by the CVD method and transferred to the h-BN/SiO2 substrate via a poly methyl methacrylate stamp38. Electrical contact was made to the graphene by depositing Ti (10-nm thick)/Au (30-nm thick) electrodes using the stencil mask technique.A back-gated graphene/h-BN/SiO39.STM/STS measurements were performed under UHV conditions at T = 5 K using a commercial Omicron LT STM with tungsten tips. STM topography was obtained in constant-current mode. STM tips were calibrated on a Au(111) surface by measuring the Au(111) Shockley surface state before all STS measurements. STS was performed under open feedback conditions by lock-in detection of an alternating tunnel current with a bias modulation of 6–16 mV (r.m.s.) at 400 Hz added to the tunneling bias. WSxM software was used to process all STM imagesI/dV curves from the bare LDOS calculations , Simulated dSupplementary InformationPeer Review File