Treatment of Ligament Constructs with Exercise-conditioned Serum: A Translational Tissue Engineering Model.

In vitro experiments are essential to understand biological mechanisms; however, the gap between monolayer tissue culture and human physiology is large, and translation of findings is often poor. Thus, there is ample opportunity for alternative experimental approaches. Here we present an approach in which human cells are isolated from human anterior cruciate ligament tissue remnants, expanded in culture, and used to form engineered ligaments. Exercise alters the biochemical milieu in the blood such that the function of many tissues, organs and bodily processes are improved. In this experiment, ligament construct culture media was supplemented with experimental human serum that has been 'conditioned' by exercise. Thus the intervention is more biologically relevant since an experimental tissue is exposed to the full endogenous biochemical milieu, including binding proteins and adjunct compounds that may be altered in tandem with the activity of an unknown agent of interest. After treatment, engineered ligaments can be analyzed for mechanical function, collagen content, morphology, and cellular biochemistry. Overall, there are four major advantages versus traditional monolayer culture and animal models, of the physiological model of ligament tissue that is presented here. First, ligament constructs are three-dimensional, allowing for mechanical properties (i.e., function) such as ultimate tensile stress, maximal tensile load, and modulus, to be quantified. Second, the enthesis, the interface between boney and sinew elements, can be examined in detail and within functional context. Third, preparing media with post-exercise serum allows for the effects of the exercise-induced biochemical milieu, which is responsible for the wide range of health benefits of exercise, to be investigated in an unbiased manner. Finally, this experimental model advances scientific research in a humane and ethical manner by replacing the use of animals, a core mandate of the National Institutes of Health, the Center for Disease Control, and the Food and Drug Administration

Multiband effective bond-orbital model for nitride semiconductors with wurtzite structure

A multiband empirical tight-binding model for group-III-nitride semiconductors with a wurtzite structure has been developed and applied to both bulk systems and embedded quantum dots. As a minimal basis set we assume one s-orbital and three p-orbitals, localized in the unit cell of the hexagonal Bravais lattice, from which one conduction band and three valence bands are formed. Non-vanishing matrix elements up to second nearest neighbors are taken into account. These matrix elements are determined so that the resulting tight-binding band structure reproduces the known Gamma-point parameters, which are also used in recent kp-treatments. Furthermore, the tight-binding band structure can also be fitted to the band energies at other special symmetry points of the Brillouin zone boundary, known from experiment or from first-principle calculations. In this paper, we describe details of the parametrization and present the resulting tight-binding band structures of bulk GaN, AlN, and InN with a wurtzite structure. As a first application to nanostructures, we present results for the single-particle electronic properties of lens-shaped InN quantum dots embedded in a GaN matrix.Comment: 10 pages, 5 figures, two supplementary file

Enhancing Terminal Deoxynucleotidyl Transferase Activity on Substrates with 3' Terminal Structures for Enzymatic De Novo DNA Synthesis.

Enzymatic oligonucleotide synthesis methods based on the template-independent polymerase terminal deoxynucleotidyl transferase (TdT) promise to enable the de novo synthesis of long oligonucleotides under mild, aqueous conditions. Intermediates with a 3' terminal structure (hairpins) will inevitably arise during synthesis, but TdT has poor activity on these structured substrates, limiting its usefulness for oligonucleotide synthesis. Here, we described two parallel efforts to improve the activity of TdT on hairpins: (1) optimization of the concentrations of the divalent cation cofactors and (2) engineering TdT for enhanced thermostability, enabling reactions at elevated temperatures. By combining both of these improvements, we obtained a ~10-fold increase in the elongation rate of a guanine-cytosine hairpin

Power operations in the Stolz--Teichner program

The Stolz--Teichner program proposes a deep connection between geometric field theories and certain cohomology theories. In this paper, we extend this connection by developing a theory of geometric power operations for geometric field theories restricted to closed bordisms. These operations satisfy relations analogous to the ones exhibited by their homotopical counterparts. We also provide computational tools to identify the geometrically defined operations with the usual power operations on complexified equivariant $K$-theory. Further, we use the geometric approach to construct power operations for complexified equivariant elliptic cohomology.Comment: 49 pages. Small fixe

Haystack: a panoptic scene graph dataset to evaluate rare predicate classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work

Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work

ProCKSI: a decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information

Background: We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures. Results: We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure. Conclusion: Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface

Preclinical Efficacy of a Carboxylesterase 2-Activated Prodrug of Doxazolidine

Doxazolidine (Doxaz) is a functionally distinct formaldehyde conjugate of doxorubicin (Dox) that induces cancer cell death in Dox-sensitive and resistant cells. Pentyl PABC-Doxaz (PPD) is a prodrug of Doxaz that is activated by carboxylesterase 2 (CES2), which is expressed by liver, non-small cell lung, colon, pancreatic, renal, and thyroid cancer cells. Here, we demonstrate that in two murine models, PPD was effective at slowing tumor growth and demonstrated markedly reduced cardiotoxic and nephrotoxic effects, as well as better tolerance, relative to Dox. Hepatotoxicity, consistent with liver expression of the murine CES2 homolog, was induced by PPD. Unlike irinotecan, a clinical CES2-activated prodrug, PPD produced no visible gastrointestinal damage. Finally, we demonstrate that cellular response to PPD may be predicted with good accuracy using CES2 expression and Doxaz sensitivity, suggesting that these metrics may be useful as clinical biomarkers for sensitivity of a specific tumor to PPD treatment

Fast, robust and laser-free universal entangling gates for trapped-ion quantum computing

A novel two-qubit entangling gate for RF-controlled trapped-ion quantum processors is proposed theoretically and demonstrated experimentally. The speed of this gate is an order of magnitude higher than that of previously demonstrated two-qubit entangling gates in static magnetic field gradients. At the same time, the phase-modulated field driving the gate, dynamically decouples the qubits from amplitude and frequency noise, increasing the qubits' coherence time by two orders of magnitude. The gate requires only a single continuous RF field per qubit, making it well suited for scaling a quantum processor to large numbers of qubits. Implementing this entangling gate, we generate the Bell states $|\Phi^+\rangle$ and $|\Psi^+\rangle$ in $\leq 313$ $\mathrm{\mathrm{\mu}}$s with fidelities up to $98^{+2}_{-3}$ % in a static magnetic gradient of only 19.09 T/m. At higher magnetic field gradients, the entangling gate speed can be further improved to match that of laser-based counterparts