Abstract: The present disclosure provides a gene combination for expressing and producing terrequinone A in Escherichia coli and use thereof. The gene combination includes a tdiAS gene, a tdiBS gene, a tdiCS gene, a tdiDS gene, a tdiES gene, an sfpS gene, an ScCKS gene, and an AtIPKS gene with nucleotide sequences set forth in SEQ ID NOS:1 to 8. In the present disclosure, a recombinant engineered strain capable of producing terrequinone A having anti-cancer activity is obtained by separately constructing recombinant plasmids pC02 and pU03 through the eight genes and transforming the two recombinant plasmids into E. coli. The content of terrequinone A in a fermentation broth thereof is 106.3 mg/L, which has potential application value in the biopharmaceutical field.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using multi-variate mixture datastructures and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using neural networks and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using multi-variate mixture datastructures is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. An asset return metrics calculation request datastructure is obtained. The number of sessions to utilize for calculating asset return metrics data is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. Neural networks are used as encoder to generate a set of latent variables. Latent variables are simulated with neural networks as decoder such that the decoded simulated market scenarios follow dynamic dependencies and volatilities of historical market risk factors.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using neural networks. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: Provided herein are particles comprising a polymer substrate comprising one or more hyaluronic acid chains; and two or more peptide moieties bound directly to each hyaluronic acid chain. In some embodiments, the two or more peptide moieties comprising collagen-binding peptide (CBP) and von Willebrand binding peptide (VBP). The particles can be utilized in, e.g., methods of hemostatic treatment.

Abstract: Provided herein are various functionalized particles comprising a shell, dendritic linkers, and functional moieties. The dendrimer linkers allow very large numbers of functional moieties to be bound to the shell. The functional moieties may comprise peptides which synergistically promote platelet aggregation and hemostasis in wounded tissues. The functionalized particles may further be effectors of wound healing, thrombolysis and other functions, depending on the selection of functional moiety. Functionalized polymers having these functions are provided as well.

Abstract: A brushless motor includes a stator core having 3M teeth, with M being an integer not less than 2, a rotor received in the stator core, 3M coils respectively wound around the 3M teeth, and a printed circuit board (PCB) fixed to the stator core and electrically connected to the 3M coils such that the 3M coils are connected in delta and correspond to three phase windings. Each phase includes at least two coils connected in parallel.

Abstract: A cooling system and method for a machining process. The system includes a coolant supply and a coolant activation assembly, where the coolant supply is configured to provide coolant to the coolant activation assembly. The coolant activation assembly includes a plurality of piezoelectric actuators. Each of the plurality of piezoelectric actuators is configured to emit a coolant stream and to impart an ultrasonic or megasonic vibration component to each coolant stream. The coolant activation assembly is configured to provide cooling to a focal point in a work zone by directing multiple coolant streams emitted by the plurality of piezoelectric actuators to the focal point such that multiple coolant streams converge at the focal point.

Abstract: Provided is a protective concrete for weaken the intensity of proton radiation, and it is prepared by mixing 525# cement 500-700 Kg; barite sand 1000-1400 kg, barite stone 1500-1800 Kg, lead powder 180-200 Kg and water 170-180 Kg. The barite that can absorb the proton radiation is used, so the present concrete is much better than conventional concretes in weakening the proton radiation, and 1.5 m-thick wall without lead plates which is prepared with the present concrete can achieve the same effect on weakening the proton radiation with a conventional 2 m-thick wall covering with lead plates.

Abstract: Described herein is a system for generating a plurality of coolant beams that converge at a focal point for advanced heat transfer. The system utilizes a variable strength activation of coolant and superposition of coolant beams generated by multiple actuators for increased cooling strength increase, thereby avoiding activation saturation in conventional systems. Each coolant beams is activated to carry an ultrasonic or megasonic vibration component. In addition, the system includes a coolant activation assembly having a plurality of actuators for generating the coolant beams. The coolant activation assembly further includes supporting components for positioning the actuators so that all of the coolant beams generated by these actuators converge at the focal point. Experimental results show that the system provides significantly improved workpiece quality in a machining process. Compared with the most advanced existing system, this system offers a further improvement of up to 12.