Abstract: The present disclosure provides a method for separating a neural crest derived cell from peripheral blood. In the present disclosure, a mononuclear cell is separated from the peripheral blood and then directly cultured, thereby maximizing use of a neural crest stem cell with a differentiation potential to avoid loss of the neural crest stem cell. In the method of the present disclosure, a sample to be separated is derived from the peripheral blood. The method shows less trauma and low cost. Most importantly, compared to extracting the neural crest derived cell from tissues, the neural crest derived cell extracted from the peripheral blood can be used clinically as a type of biomarker.

Abstract: Provided is a dual-channel fluorescence sensor based on in-situ synthesis of carbon dots on halloysite nanotubes (HNT) and loaded with a lanthanide metal-organic framework, which can implement rapid and simultaneous visual detection of DPA and TC. By using methods for preparing and using a dual-channel visual multicolor fluorescent probe above, the sensor has high stability and sensitivity, and is conducive to quick, accurate and intuitive detection of a biomarker.

Abstract: The present disclosure provides a method for predicting the amount of recoverable oil and gas resources from in-situ conversion of shale. The method can quantitatively evaluate the amount of the recoverable oil resource and the amount of the recoverable gas resource from in-situ conversion of shale with different TOC and Ro and improve the prediction accuracy and efficiency for the amount of the recoverable oil and gas resources from in-situ conversion of shale.

Abstract: An apparatus includes a first digital-to-time converter (DTC) and a second DTC. The first DTC includes a sequence of delay stages. Each of the delay stages adds a delay to an input signal based on a control signal. Each delay stage includes a comparator and a capacitor coupled to an input of the comparator and to ground. The second DTC is coupled in parallel to the first DTC. The second DTC adds a delay to the input signal based on a complement of the control signal.

Abstract: An apparatus includes a first digital-to-time converter (DTC) and a second DTC. The first DTC includes a sequence of delay stages. Each of the delay stages adds a delay to an input signal based on a control signal. Each delay stage includes a comparator and a capacitor coupled to an input of the comparator and to ground. The second DTC is coupled in parallel to the first DTC. The second DTC adds a delay to the input signal based on a complement of the control signal.

Abstract: The subject invention pertains to compositions and methods of eradicating biofilms and/or inhibiting the formation of a biofilms or fouling by non-biofilm forming organisms using elasnin-based compositions. In certain embodiments, the elasnin can be combined with antimicrobial compounds and/or traditional coating ingredients.

Abstract: Pharmacological compositions comprising a cationic nonribosomal peptide (CNRP) or a salt thereof are described. Further, methods of treating a bacterial infection in a subject by administering to the subject a CNRP or a salt thereof are provided.

Abstract: Certain embodiments of the invention pertain to pharmaceutical compositions comprising a cationic nonribosomal peptide (CNRP) or a salt thereof. Certain other embodiments of the invention pertain to methods of treating a bacterial infection in a subject by administering to the subject a CNRP or a salt thereof.

Abstract: A method and apparatus are disclosed for training a pattern recognition system, such as a speech recognition system, using an improved objective function. The concept of rank likelihood, previously applied only to the decding process, is applied in a novel manner to the parameter estimation of the training phase of a pattern recognition system. The disclosed objective function is based on a pseudo-rank likelihood that not only maximizes the likelihood of an observation for the correct class, but also minimizes the likelihoods of the observation for all other classes, such that the discrimination between classes is maximized. A training process is disclosed that utilizes the pseudo-rank likelihood objective function to identify model parameters that will result in a pattern recognizer with the lowest possible recognition error rate. The discrete nature of the rank-based rank likelihood objective function is transformed to allow the parameter estimations to be optimized during the training phase.

Abstract: In general, the present invention determines and applies weights for class pairs. The weights are selected to better separate, in reduced-dimensional class space, the classes that are confusable in normal-dimensional class space. During the dimension-reducing process, higher weights are preferably assigned to more confusable class pairs while lower weights are assigned to less confusable class pairs. As compared to unweighted Linear Discriminant Analysis (LDA), the present invention will result in decreased confusability of class pairs in reduced-dimensional class space. The weights can be assigned through a monotonically decreasing function of distance, which assigns lower weights to class pairs that are separated by larger distances. Additionally, weights may also be assigned through a monotonically increasing function of confusability, in which higher weights would be assigned to class pairs that are more confusable.

Abstract: In general, the present invention determines and applies weights for class pairs. The weights are selected to better separate, in reduced-dimensional class space, the classes that are confusable in normal-dimensional class space. During the dimension-reducing process, higher weights are preferably assigned to more confusable class pairs while lower weights are assigned to less confusable class pairs. As compared to unweighted Linear Discriminant Analysis (LDA), the present invention will result in decreased confusability of class pairs in reduced-dimensional class space. The weights can be assigned through a monotonically decreasing function of distance, which assigns lower weights to class pairs that are separated by larger distances. Additionally, weights may also be assigned through a monotonically increasing function of confusability, in which higher weights would be assigned to class pairs that are more confusable.