Isabelle/PIDE as Platform for Educational Tools

The Isabelle/PIDE platform addresses the question whether proof assistants of the LCF family are suitable as technological basis for educational tools. The traditionally strong logical foundations of systems like HOL, Coq, or Isabelle have so far been counter-balanced by somewhat inaccessible interaction via the TTY (or minor variations like the well-known Proof General / Emacs interface). Thus the fundamental question of math education tools with fully-formal background theories has often been answered negatively due to accidental weaknesses of existing proof engines. The idea of "PIDE" (which means "Prover IDE") is to integrate existing provers like Isabelle into a larger environment, that facilitates access by end-users and other tools. We use Scala to expose the proof engine in ML to the JVM world, where many user-interfaces, editor frameworks, and educational tools already exist. This shall ultimately lead to combined mathematical assistants, where the logical engine is in the background, without obstructing the view on applications of formal methods, formalized mathematics, and math education in particular.Comment: In Proceedings THedu'11, arXiv:1202.453

Automated Generation of User Guidance by Combining Computation and Deduction

Herewith, a fairly old concept is published for the first time and named "Lucas Interpretation". This has been implemented in a prototype, which has been proved useful in educational practice and has gained academic relevance with an emerging generation of educational mathematics assistants (EMA) based on Computer Theorem Proving (CTP). Automated Theorem Proving (ATP), i.e. deduction, is the most reliable technology used to check user input. However ATP is inherently weak in automatically generating solutions for arbitrary problems in applied mathematics. This weakness is crucial for EMAs: when ATP checks user input as incorrect and the learner gets stuck then the system should be able to suggest possible next steps. The key idea of Lucas Interpretation is to compute the steps of a calculation following a program written in a novel CTP-based programming language, i.e. computation provides the next steps. User guidance is generated by combining deduction and computation: the latter is performed by a specific language interpreter, which works like a debugger and hands over control to the learner at breakpoints, i.e. tactics generating the steps of calculation. The interpreter also builds up logical contexts providing ATP with the data required for checking user input, thus combining computation and deduction. The paper describes the concepts underlying Lucas Interpretation so that open questions can adequately be addressed, and prerequisites for further work are provided.Comment: In Proceedings THedu'11, arXiv:1202.453

Graphitizability of Polymer Thin Films: An In Situ TEM Study of Thickness Effects on Nanocrystalline Graphene/Glassy Carbon Formation

Polymer pyrolysis has emerged as a versatile method to synthesize graphenoid (graphene like) materials with varying thickness and properties. The morphology of the thin film, especially the thickness, greatly affects the graphitizability and the properties of the graphenoid material. Using in situ current annealing inside a transmission electron microscope (TEM), the thickness-dependent structural evolution of the polymer film with a special focus on thickness effects is followed. At high temperatures, thin samples form large graphene layers oriented parallel to the substrate, whereas in thick samples multi-walled cage-like structures are formed. Moleclar Dynamics (MD) simulations reveal a film thickness of 40 Å below which, the carbonized layers align parallel to the surface. For thicker samples, the orientation of the layers becomes increasingly misoriented starting from the surface to the center. This structural change can be attributed to the formation of bonded multi-layers from the initially unsaturated activated edges. The resulting cage-like structures are stable even during simulated annealing at temperatures as high as 3500 K. An atomistic understanding of the formation of these structures is presented. The results clearly indicate the critical effect of thickness on the graphitizability of polymers and provide a new understanding of the structural evolution during pyrolysis

Hole dynamics in a photovoltaic donor-acceptor couple revealed by simulated time-resolved X-ray absorption spectroscopy

Theoretical and experimental methodologies that can characterize electronic and nuclear dynamics, and the coupling between the two, are needed to understand photoinduced charge transfer in molecular building blocks used in organic photovoltaics. Ongoing developments in ultrafast pump-probe techniques such as time-resolved X-ray absorption spectroscopy, using an X-ray free electron laser in combination with an ultraviolet femtosecond laser, present desirable probes of coupled electronic and nuclear dynamics. In this work, we investigate the charge transfer dynamics of a donor-acceptor pair, which is widely used as a building block in low bandgap block copolymers for organic photovoltaics. We simulate the dynamics of the benzothiadiazole-thiophene molecule upon photoionization with a vacuum ultraviolet (VUV) pulse and study the potential of probing the subsequent charge dynamics using time-resolved X-ray absorption spectroscopy. The photoinduced dynamics are calculated using on-the-fly nonadiabatic molecular dynamics simulations based on Tully's Fewest Switches Surface Hopping approach. We calculate the X-ray absorption spectrum as a function of time after ionization at the Hartree-Fock level. The changes in the time-resolved X-ray absorption spectrum at the sulfur K-edge reveal the ultrafast charge carrier dynamics in the molecule occurring on a femtosecond time scale. These theoretical findings anticipate that ultrafast time-resolved X-ray absorption spectroscopy using an X-ray probe in combination with a VUV pump offers a new approach to investigate the detailed dynamics of organic photovoltaic materials

Developing Recombinant Antibodies by Phage Display Against Infectious Diseases and Toxins for Diagnostics and Therapy

Antibodies are essential molecules for diagnosis and treatment of diseases caused by pathogens and their toxins. Antibodies were integrated in our medical repertoire against infectious diseases more than hundred years ago by using animal sera to treat tetanus and diphtheria. In these days, most developed therapeutic antibodies target cancer or autoimmune diseases. The COVID-19 pandemic was a reminder about the importance of antibodies for therapy against infectious diseases. While monoclonal antibodies could be generated by hybridoma technology since the 70ies of the former century, nowadays antibody phage display, among other display technologies, is robustly established to discover new human monoclonal antibodies. Phage display is an in vitro technology which confers the potential for generating antibodies from universal libraries against any conceivable molecule of sufficient size and omits the limitations of the immune systems. If convalescent patients or immunized/infected animals are available, it is possible to construct immune phage display libraries to select in vivo affinity-matured antibodies. A further advantage is the availability of the DNA sequence encoding the phage displayed antibody fragment, which is packaged in the phage particles. Therefore, the selected antibody fragments can be rapidly further engineered in any needed antibody format according to the requirements of the final application. In this review, we present an overview of phage display derived recombinant antibodies against bacterial, viral and eukaryotic pathogens, as well as microbial toxins, intended for diagnostic and therapeutic applications

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Evaluation of a Broad Panel of SARS-CoV-2 Serological Tests for Diagnostic Use

Serological testing is crucial in detection of previous infection and in monitoring convalescent and vaccine-induced immunity. During the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) pandemic, numerous assay platforms have been developed and marketed for clinical use. Several studies recently compared clinical performance of a limited number of serological tests, but broad comparative evaluation is currently missing. Within this study, a panel of 161 sera from SARS-CoV-2 infected, seasonal CoV-infected and SARS-CoV-2 naïve subjects was enrolled to evaluate 16 ELISA/ECLIA-based and 16 LFA-based tests. Specificities of all ELISA/ECLIA-based assays were acceptable and generally in agreement with the providers’ specifications, but sensitivities were lower as specified. Results of the LFAs were less accurate as compared to the ELISAs, albeit with some exceptions. We found a sporadic unequal immune response for different antigens and thus recommend the use of a nucleocapsid protein (N)- and spike protein (S)-based test combination when maximal sensitivity is necessary. Finally, the quality of the immune response in terms of neutralization should be tested using S-based IgG tests

Sensing Molecules with Metal–Organic Framework Functionalized Graphene Transistors

Graphene is inherently sensitive to vicinal dielectrics and local charge distributions, a property that can be probed by the position of the Dirac point in graphene field‐effect transistors. Exploiting this as a useful sensing principle requires selectivity; however, graphene itself exhibits no molecule‐specific interaction. Complementarily, metal–organic frameworks can be tailored to selective adsorption of specific molecular species. Here, a selective ethanol sensor is demonstrated by growing a surface‐mounted metal–organic framework (SURMOF) directly onto graphene field‐effect transistors (GFETs). Unprecedented shifts of the Dirac point, as large as 15 V, are observed when the SURMOF/GFET is exposed to ethanol, while a vanishingly small response is observed for isopropanol, methanol, and other constituents of the air, including water. The synthesis and conditioning of the hybrid materials sensor with its functional characteristics are described and a model is proposed to explain the origin, magnitude, and direction of the Dirac point voltage shift. Tailoring multiple SURMOFs to adsorb specific gases on an array of such devices thus generates a versatile, selective, and highly sensitive platform for sensing applications

Quantitative Assessment of Whole-Body Tumor Burden in Adult Patients with Neurofibromatosis

Purpose Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis are at risk for multiple nerve sheath tumors and premature mortality. Traditional magnetic resonance imaging (MRI) has limited ability to assess disease burden accurately. The aim of this study was to establish an international cohort of patients with quantified whole-body internal tumor burden and to correlate tumor burden with clinical features of disease. Methods We determined the number, volume, and distribution of internal nerve sheath tumors in patients using whole-body MRI (WBMRI) and three-dimensional computerized volumetry. We quantified the distribution of tumor volume across body regions and used unsupervised cluster analysis to group patients based on tumor distribution. We correlated the presence and volume of internal tumors with disease-related and demographic factors. Results WBMRI identified 1286 tumors in 145/247 patients (59%). Schwannomatosis patients had the highest prevalence of tumors (P = 0.03), but NF1 patients had the highest median tumor volume (P = 0.02). Tumor volume was unevenly distributed across body regions with overrepresentation of the head/neck and pelvis. Risk factors for internal nerve sheath tumors included decreasing numbers of café-au-lait macules in NF1 patients (P = 0.003) and history of skeletal abnormalities in NF2 patients (P = 0.09). Risk factors for higher tumor volume included female gender (P = 0.05) and increasing subcutaneous neurofibromas (P = 0.03) in NF1 patients, absence of cutaneous schwannomas in NF2 patients (P = 0.06), and increasing age in schwannomatosis patients (p = 0.10). Conclusion WBMRI provides a comprehensive phenotype of neurofibromatosis patients, identifies distinct anatomic subgroups, and provides the basis for investigating molecular biomarkers that correlate with unique disease manifestations

Genomic characterization of malignant progression in neoplastic pancreatic cysts

Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) are non-invasive neoplasms that are often observed in association with invasive pancreatic cancers, but their origins and evolutionary relationships are poorly understood. In this study, we analyze 148 samples from IPMNs, MCNs, and small associated invasive carcinomas from 18 patients using whole exome or targeted sequencing. Using evolutionary analyses, we establish that both IPMNs and MCNs are direct precursors to pancreatic cancer. Mutations in SMAD4 and TGFBR2 are frequently restricted to invasive carcinoma, while RNF43 alterations are largely in non-invasive lesions. Genomic analyses suggest an average window of over three years between the development of high-grade dysplasia and pancreatic cancer. Taken together, these data establish non-invasive IPMNs and MCNs as origins of invasive pancreatic cancer, identifying potential drivers of invasion, highlighting the complex clonal dynamics prior to malignant transformation, and providing opportunities for early detection and intervention