gebhart/patrepeval huggingface dataset:
The alpha subunit of RNA polymerase and transcription antitermination (1996)

The N gene product of coliphage gamma, with a number of host proteins (Nus factors), regulates phage gene expression by modifying RNA polymerase to a form that overrides transcription-termination signals. Mutations in host nus genes diminish this N-mediated antitermination. Here, we report the isolation and characterization of the rpoAD305E mutation, a single amino acid change in the carboxy terminal domain (CTD) of the alpha subunit of RNA polymerase, that enhances N-mediated antitermination. A deletion of the 3' terminus of rpoA, resulting in the expression of an alpha subunit missing the CTD, also enhances N-mediated antitermination and, similar to rpoAD305E, suppresses the effect of nus mutations. Thus, the N-Nus complex may be affected through contacts with the CTD of the alpha subunit of RNA polymerase, as is a group of regulatory proteins that influences initiation of transcription. What distinguishes our findings on the N-Nus complex from those of previous studies with transcription proteins is that all of the regulators characterized in those studies bind DNA and influence transcription initiation; whereas the N-Nus complex binds RNA and affects transcription elongation. A screen of some previously identified rpoA mutations that influence transcription activators revealed only one other amino acid change, L290H, in the CTD of the alpha subunit, that influences antitermination. Although our results provide evidence that interactions of the alpha subunit of RNA polymerase must be considered in forming models of transcription antitermination, they do not provide information as to whether the interactions of alpha that ultimately influence antitermination occur during initiation or during elongation of transcription.
Anode surface bioaugmentation enhances deterministic biofilm assembly in microbial fuel cells (2020)

Abstract Microbial fuel cells (MFCs) are devices that can generate energy while aiding biodegradation of waste through the activity of an electroactive mixed biofilm. Metabolic cooperation is considered essential for MFCs’ efficiency, especially during early-anode colonization. Yet, the specific ecological processes that drive the assembly of an optimized anode-attached community remain unknown. Here, we show, using 16S rRNA gene amplicon and shotgun metagenomic sequencing that bioaugmentation of the anode surface with an electroactive consortium originating from a well-established anodic biofilm, dominated by different Desulfuromonas strains, resulted in an extremely rapid voltage generation (reaching maximal voltage within several hours). This was in sharp contrast to the highly stochastic and slower biofilm assembly that occurred when the anode-surface was not augmented. By comparing two inoculation media, wastewater and filtered wastewater, we were able to illustrate two different "source-communities" for newly arriving species that with time colonized the anode surface in a different manner and resulted in dramatically different community assembly processes. Remarkably, an efficient anode colonization process was obtained only if unfiltered wastewater was added, leading to a near-complete replacement of the bioaugmented community by Geobacter lovleyi . We propose that anode bioaugmentation reduced stochasticity by creating available niches that were quickly occupied by specific newly-arriving species that positively supported the fast establishment of a highly-functional anode biofilm.
The reachability-bound problem (2010)

We define the reachability-bound problem to be the problem of finding a symbolic worst-case bound on the number of times a given control location inside a procedure is visited in terms of the inputs to that procedure. This has applications in bounding resources consumed by a program such as time, memory, network-traffic, power, as well as estimating quantitative properties (as opposed to boolean properties) of data in programs, such as information leakage or uncertainty propagation. Our approach to solving the reachability-bound problem brings together two different techniques for reasoning about loops in an effective manner. One of these techniques is an abstract-interpretation based iterative technique for computing precise disjunctive invariants (to summarize nested loops). The other technique is a non-iterative proof-rules based technique (for loop bound computation) that takes over the role of doing inductive reasoning, while deriving its power from the use of SMT solvers to reason about abstract loop-free fragments.
The mTORC1/4E-BP1 axis represents a critical signaling node during fibrogenesis (2018)

Myofibroblasts are the key effector cells responsible for excessive extracellular matrix deposition in multiple fibrotic conditions, including idiopathic pulmonary fibrosis (IPF). The PI3K/Akt/mTOR axis has been implicated in fibrosis, with pan-PI3K/mTOR inhibition currently under clinical evaluation in IPF. Here we demonstrate that rapamycin-insensitive mTORC1 signaling via 4E-BP1 is a critical pathway for TGF-β<sub>1</sub> stimulated collagen synthesis in human lung fibroblasts, whereas canonical PI3K/Akt signaling is not required. The importance of mTORC1 signaling was confirmed by CRISPR-Cas9 gene editing in normal and IPF fibroblasts, as well as in lung cancer-associated fibroblasts, dermal fibroblasts and hepatic stellate cells. The inhibitory effect of ATP-competitive mTOR inhibition extended to other matrisome proteins implicated in the development of fibrosis and human disease relevance was demonstrated in live precision-cut IPF lung slices. Our data demonstrate that the mTORC1/4E-BP1 axis represents a critical signaling node during fibrogenesis with potential implications for the development of novel anti-fibrotic strategies.
Motion Planning of Autonomous Road Vehicles by Particle Filtering (2019)

This paper describes a probabilistic method for realtime decision making and motion planning for autonomous vehicles. Our approach relies on the fact that driving on road networks implies apriori defined requirements that the motion planner should satisfy. Starting from an initial state of the vehicle, a map, the obstacles in the region of interest, and a goal region, we formulate the motion-planning problem as a nonlinear non-Gaussian estimation problem, which we solve using particle filtering. We assign probabilities to the generated trajectories according to their likelihood of obeying the driving requirements. Decision making and collision avoidance is naturally integrated in the approach. We develop a receding-horizon implementation and verify the method in simulated real road scenarios and in an experimental validation using a scaled mobile robot setup with car-like dynamics. The results show that the method generates dynamically feasible trajectories for a number of scenarios, such as collision avoidance, overtaking, and traffic-jam scenarios. In addition, the computation times and memory requirements indicate that the method is suitable for real-time implementation.
Synthesis of (+)-Lycopladine A (2007)

(+)-Lycopladine A is a Pawettimine alkaloid, isolated from Lycopodium complantum, which shows modest cytotoxic activity. The novel pyridyl-fused hydrindanone core is constructed via a diastereoselective gold(I)-catalyzed 5-endo-dig cyclization.
Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs (2021)

We propose a centralized multi-robot motion planning approach that leverages machine learning and mixed-integer programming (MIP). We train a neural network to imitate optimal MIP solutions and, during execution, the trajectories predicted by the network are used to fix most of the integer variables, resulting in a significantly reduced MIP or even a convex program. If the obtained trajectories are feasible, i.e., collision-free and reaching the goal, they can be used as they are or further refined towards optimality. Since maximizing the likelihood of feasibility is not the standard goal of imitation learning, we propose several techniques aimed at increasing such likelihood. Simulation results show the reduced computational burden associated with the proposed framework and the similarity with the optimal MIP solutions.
Thermal Characterization of Combustion Chamber Deposits on the HCCI Engine Piston and Cylinder Head Using Instantaneous Temperature Measurements (2009)

&lt;div class="htmlview paragraph"&gt;Extending the operating range of the gasoline HCCI engine is essential for achieving desired fuel economy improvements at the vehicle level, and it requires deep understanding of the thermal conditions in the cylinder. Combustion chamber deposits (CCD) have been previously shown to have direct impact on near-wall phenomena and burn rates in the HCCI engine. Hence, the objectives of this work are to characterize thermal properties of deposits in a gasoline HCCI engine and provide foundation for understanding the nature of their impact on autoignition and combustion. The investigation was performed using a single-cylinder engine with re-induction of exhaust instrumented with fast-response thermocouples on the piston top and the cylinder head surface. The measured instantaneous temperature profiles changed as the deposits grew on top of the hot-junctions. The change in peak temperature phasing was found to be closely correlated with the deposit thickness, thus paving the way for future in-situ tracking of deposit growth. Analysis of data provided insight about the impact of deposit thickness on local heat fluxes at the CCD/metal interface, and enabled calculation of the CCD thermal diffusivity. The results produced distinctly different correlations for the cylinder head surface versus the piston top.&lt;/div&gt;
Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties (2023)

Efficiently and reliably estimating uncertainty is an important objective in deep learning. It is especially pertinent to autoregressive sequence tasks, where training and inference costs are typically very high. However, existing research has predominantly focused on tasks with static data such as image classification. In this work, we investigate Ensemble Distribution Distillation (EDD) applied to large-scale natural language sequence-to-sequence data. EDD aims to compress the superior uncertainty performance of an expensive (teacher) ensemble into a cheaper (student) single model. Importantly, the ability to separate knowledge (epistemic) and data (aleatoric) uncertainty is retained. Existing probability-space approaches to EDD, however, are difficult to scale to large vocabularies. We show, for modern transformer architectures on large-scale translation tasks, that modelling the ensemble logits, instead of softmax probabilities, leads to significantly better students. Moreover, the students surprisingly even outperform Deep Ensembles by up to ~10% AUROC on out-of-distribution detection, whilst matching them at in-distribution translation.
Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat (2021)

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.
Modeling of plasma-induced damage and its impacts on parameter variations in advanced electronic devices (2011)

A comprehensive model predicting the effects of plasma-induced damage (PID) on parameter variations in advanced metal–oxide–semiconductor field-effect transistors (MOSFETs) is proposed. The model focuses on the silicon recess structure (Si loss) in the source/drain extension region formed by high-energy ion bombardment during plasma etching. The model includes the following mechanisms: (1) damaged layer formation by ion impact and penetration, (2) Si recess structure formation by a subsequent wet etch, (3) MOSFET performance degradation, and (4) MOSFET parameter variation. Based on a range theory for plasma-etch damage, the thickness of the damaged layer exhibits a power-law dependence on the energy of the ion incident on the surface of Si substrate. Assuming that the damaged layer was formed during a gate or an offset spacer etch process, the depth of Si recess (dR) is a function of the depth profile of the created defect site (ndam), the wet-etch stripping time (tw), and the energy of the incident ion. It was found that dR also showed a power-law dependence on the average ion energy E−ion estimated from applied self-dc-bias voltage for various tw. As for MOSFET performance degradation, the threshold voltage (Vth) shifted and the shift (ΔVth) increased with an increase in E−ion and a decrease in gate length. This induces an increase in subthreshold leakage current (Ioff) for MOSFET. Technology computer-aided-design simulations were performed to confirm these results. By integrating the presented PID models, parameter variations could be predicted: Using a Monte Carlo method, it was demonstrated that PID increases parameter variations such as Vth and Ioff. It also was found that the variation in E−ion induces Vth and Ioff variations, comparable to that induced by other process parameter fluctuations such as dopant fluctuation and gate length. In summary, considering the effects of PID on parameter variations is vital for designing future ultralarge-scale-integrated circuits with billions of built-in MOSFETs.
Stabilization of G-Quadruplex DNA and Inhibition of Telomerase Activity by Square-Planar Nickel(II) Complexes (2006)

Two new alkylamine-substituted nickel(II)-salphen complexes have been prepared and their interactions with DNA investigated. FRET studies have shown that these complexes have a remarkable ability to stabilize G-quadruplex DNA. Furthermore, TRAP/Taq assays have shown that these complexes inhibit telomerase at low micromolar concentrations.
Studies of jet quenching using isolated-photon + jet correlations in PbPb and pp collisions at sNN=2.76 TeV (2012)

Results from the first study of isolated-photon + jet correlations in relativistic heavy ion collisions are reported. The analysis uses data from PbPb collisions at a centre-of-mass energy of 2.76 TeV per nucleon pair corresponding to an integrated luminosity of 150 b -1 recorded by the CMS experiment at the LHC. For events containing an isolated photon with transverse momentum p T > 60 GeV/c and an associated jet with p Jet T > 30 GeV/c, the photon + jet p T imbalance is studied as a function of collision centrality and compared to pp data and pythia calculations at the same collision energy. Using the p T of the isolated photon as an estimate of the momentum of the associated parton at production, this measurement allows an unbiased characterisation of the in-medium parton energy loss. For more central PbPb collisions, a significant decrease in the ratio p Jet T /p T relative to that in the pythia reference is observed. Furthermore, significantly more p T > 60 GeV/c photons in PbPb are observed not to have an associated p Jet T > 30 GeV/c jet, compared to the reference. However, no significant broadening of the photon + jet azimuthal correlation is observed.
Table Recomputation-Based Higher-Order Masking Against Horizontal Attacks (2018)

Masking is a class of well-known countermeasure against side-channel analysis by employing the idea of secret sharing. The theoretical security proof model of higher-order masking was initiated by Ishai, Sahai, and Wagner, and Barthe et al. pushed forward it by proposing a more refine security definition named as t-SNI security. In CHES 2016, a new attack called horizontal side-channel attacks (HSCAs) came forward and successfully broke the Rivain-Prouff countermeasure, which has been proved to satisfy the t-SNI security. It presents a dilemma: instead of more secure, masking with higher-order may be more vulnerable due to the HSCA. Although there already exists an effective countermeasure for the Rivain-Prouff scheme, it is quite difficult to apply this method in the table recomputation-based higher-order masking schemes, such as the scheme introduced by Coron in EUROCRYPT 2014. To fill this gap, we propose a new table recomputation-based higher-order masking scheme, named as table compression masking (TCM) scheme. While meeting the t-SNI security, our new countermeasure is also secure against the HSCA. We give the formal security proof under the t-SNI security definition, as well as a heuristic security analysis considering the HSCA. Our analysis shows that, by dividing the full lookup table into many distinct parts and shifting them by refreshed shares, the same share will never be manipulated for more than twice in TCM scheme. This feature gives a heuristic security against HSCA. To our best knowledge, our countermeasure is the first solution for table recomputation-based higher-order masking to resist HSCA.
Purification and characterization of retrovirally transduced hematopoietic stem cells. (1992)

We have developed a method for the purification of retrovirally transduced hematopoietic stem cells, based on a modification of the stem cell purification protocols developed by Spangrude et al. [Spangrude, G., Heimfeld, S. & Weissman, I. (1988) Science 241, 58-64] and Spangrude and Scollay [Spangrude, G. & Scollay, R. (1990) Exp. Hematol. 18, 920-926], that depends upon the use of bone marrow cells isolated from 5-fluorouracil-treated mice that have been subsequently cocultivated with recombinant retrovirus-producing cell lines. We found that purified cell populations bearing a Sca 1+ Lin- Thy 1- surface phenotype represent a 50-100% pure population of spleen colony-forming cells (CFU-S day 12). Animals injected with 300 or more purified cells were consistently radioprotected and reconstituted in multiple lineages with donor cells. Sca 1+ Lin- Thy 1- CFU-S day 12 stem cells were shown to be efficiently (100%) transduced by the recombinant retroviruses used in the study. Gene transfer into long-term reconstituting stem cells, as evidenced by Southern blot analysis of mature hematopoietic cell types 3 months after transplantation, was observed only in recipients injected with large numbers (approximately 4000-5000) of the purified cells. The development of methods for purifying retrovirally transduced stem cells should prove extremely useful for various studies in which it is of interest to characterize the activity of a specific gene product (e.g., growth factor, receptor, oncogene) specifically in primitive hematopoietic cell types.
Rapid Identification of a Novel Small Molecule Phosphodiesterase 10A (PDE10A) Tracer (2012)

A radiolabeled tracer for imaging therapeutic targets in the brain is a valuable tool for lead optimization in CNS drug discovery and for dose selection in clinical development. We report the rapid identification of a novel phosphodiesterase 10A (PDE10A) tracer candidate using a LC-MS/MS technology. This structurally distinct PDE10A tracer, AMG-7980 (5), has been shown to have good uptake in the striatum (1.2% ID/g tissue), high specificity (striatum/thalamus ratio of 10), and saturable binding in vivo. The PDE10A affinity (K(D)) and PDE10A target density (B(max)) were determined to be 0.94 nM and 2.3 pmol/mg protein, respectively, using [(3)H]5 on rat striatum homogenate. Autoradiography on rat brain sections indicated that the tracer signal was consistent with known PDE10A expression pattern. The specific binding of [(3)H]5 to rat brain was blocked by another structurally distinct, published PDE10A inhibitor, MP-10. Lastly, our tracer was used to measure in vivo PDE10A target occupancy of a PDE10A inhibitor in rats using LC-MS/MS technology.
Variable-Frequency Microwave Curing of Photosensitive Polynorbornene Dielectric (2012)

Polymer dielectrics are widely used in micro-electro-mechanical systems (MEMS) and electronic packaging. Fully curing polymer dielectrics can require lengthy cure cycles. Microwave-induced reactions and curing can occur at temperatures lower than convective heating systems due to localized energy absorption, resulting in faster thermal curing of polymer dielectrics. In this study, the epoxy-based cross-linking and curing of a negative-tone photosensitive polynorbornene (PNB) dielectric by variable-frequency microwave (VFM) processing was investigated as a function of cure time and cure temperature. In additional, the feasibility of curing the polymer in air compared to traditional nitrogen ambient curing was studied. The chemical changes occurring during the cross-linking of the polymer and the effect of processing conditions on the degree of cross-linking in cured films were monitored by Fourier transform infrared spectroscopy (FTIR). FTIR studies show that the chemical structure of VFM-cured PNB films is the same as thermally-cured films. The results also show that rapid curing of PNB by VFM in as little as five minutes is feasible. Further, a significantly higher rate of chemical conversion can be achieved by VFM processing at temperatures lower than conventional thermal curing. The properties of the cured films were studied by comparing the chemical, mechanical, and electrical properties of VFM-cured films to those of the thermally-cured films. The VFM-cured films showed comparable or improved properties compared to the thermally-cured films. The VFM-cured films exhibited higher hardness, higher reduced modulus, and a lower degree of swelling compared to the thermally-cured samples. The residual stress and dielectric constant of VFM-cured films were slightly higher than those of the conventionally-cured films.
MEMS Sensors for Robots (2019)

Robots don't work well if quality of the input signal to robots is poor. Current low-cost gyroscopes and accelerometers for smartphones do not necessarily provide robots with rich information. Instead of them, high-performance gyroscopes and accelerometers with reasonable price are anticipated for robot control. In addition, new types of sensors including tactile sensors, hyperspectral imagers, taste sensors, and so on are anxiously longed for. In my talk, current issues on robot sensors are introduced. Then, some MEMS robot-sensors we have been working on for a long time are shown in order.