Abstract: A sealed electrical connector includes a seal retainer defining a plurality of wire openings, a compliant mat seal having a plurality of apertures, and a plug. The plug has a base inserted within one of the plurality of wire openings and a cylindrical post extending outwardly from the base that is inserted within one of the plurality of apertures. A method of manufacturing such a sealed electrical connector is also presented.

Abstract: A cascade type thrust reverser device for a turbomachine of an aircraft, comprising a cascade including first partitions, second partitions intersecting the first partitions, and cavities, and a casing including a housing into which the cascade can be inserted in a first direction, the casing and the cascade being in relative translation with respect to one another in the first direction. The casing comprises a perforated wall intended to be in contact with an air flow and including orifices and wall strips with no orifices and intended to face the first walls of the cascade when the device is in a first position in which the cascade is entirely positioned in the housing.

Abstract: A system for evaluating mental health of patients includes a memory and a control system. The memory contains executable code storing instructions for performing a method. The control system is coupled to the memory and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to perform the method: A selection of answers associated with a patient is received. The selection of answers corresponds to each question in a series of questions from mental health questionnaires. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with the patient. The unprocessed MRI data is processed to output a set of MRI features. Using a machine learning model, the selection of answers and the set of MRI features are processed to output a mental health indication of the patient.

Abstract: Data employed in computations is processed so that during computations more of the data can be fit into or maintained in a smaller but higher speed memory than an original source of the data. More specifically, a sensitivity value is determined for various items of the data which reflect the number of bits in the data items that are not garbage bits, and only information in the data items that are indicated by the sensitivity value to not be garbage bits are necessarily effectively retained. At least the information that is not garbage bits and the corresponding associated sensitivity are packed together. The results of computations that are performed using the data items as at least one of the operands for the computation are associated with a sensitivity that is derived from the individual sensitivities of the operands used in the computation.

Abstract: A computer-implemented method for authenticating an online user with an access control server (ACS) is provided.

Abstract: A system includes a display device, a user interface, a memory, and a control system. The memory contains machine readable medium including machine executable code storing instructions for performing a method. The control system is coupled to the memory, and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to display, on the display device, a series of questions from mental health questionnaires. The series of questions includes text and answers for each question. From the user interface, a selection of answers of each of the displayed series of questions is received from a patient. Using a Bayesian Decision List, the received selection of answers is processed to output an indication of mental health of the patient. The indication of mental health identifies a kappa opioid receptor antagonist to which the patient would likely be a higher responder.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions are selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions is selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: A method of manufacturing a composite material that is used to make blocks, bricks, tiles, or payers. The method comprises of making a liquid conglomerate material, of making a solid conglomerate material, of mixing the liquid conglomerate material and the solid conglomerate material together to make the composite material, and lastly of pressing the composite material into either a block, a brick, a tile, or a paver.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions are selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions is selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: The present patent application relates to a backpack for domiciliary medical services, for optimising the transport, protection, cleaning and organisation of basic medical items (devices and consumables) when used in the care of a patient during his or her respective domiciliary medical visit and consultation by a doctor or health professional. The backpack for domiciliary medical services also allows easy access to the medical and personal items being carried, and provides versatile adjustment and adaptation of each of its interior spaces according to the need of the health professional. Furthermore, using oxygen, UV light, water, soap or ammonia, among others, the backpack and all the items that it contains can be cleaned and disinfected against microorganisms and bacteria that may become impregnated in or adhere to the backpack.

Abstract: The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.

Abstract: A system for evaluating mental health of patients includes a memory and a control system. The memory contains executable code storing instructions for performing a method. The control system is coupled to the memory and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to perform the method: A selection of answers associated with a patient is received. The selection of answers corresponds to each question in a series of questions from mental health questionnaires. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with the patient. The unprocessed MRI data is processed to output a set of MRI features. Using a machine learning model, the selection of answers and the set of MRI features are processed to output a mental health indication of the patient.

Abstract: The present disclosure provides systems and methods for automating the QC of MRI scans. Particularly, the inventors trained machine learning classifiers using features derived from brain MR images and associated processing to predict the quality of those images, which is based on the ground truth of an expert's opinion. In one example, classifiers that utilized features derived from preprocessing log files (textual files output during MRI preprocessing) were particularly accurate and demonstrated an ability to be generalized to new datasets, which allows the disclosed technology to be scalable to new datasets and MRI preprocessing pipelines.

Abstract: The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.

Abstract: A system for evaluating mental health of patients includes a memory and a control system. The memory contains executable code storing instructions for performing a method. The control system is coupled to the memory and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to perform the method: A selection of answers associated with a patient is received. The selection of answers corresponds to each question in a series of questions from mental health questionnaires. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with the patient. The unprocessed MRI data is processed to output a set of MRI features. Using a machine learning model, the selection of answers and the set of MRI features are processed to output a mental health indication of the patient.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions is selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: A system includes a display device, a user interface, a memory, and a control system. The memory contains machine readable medium including machine executable code storing instructions for performing a method. The control system is coupled to the memory, and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to display, on the display device, a series of questions from mental health questionnaires. The series of questions includes text and answers for each question. From the user interface, a selection of answers of each of the displayed series of questions is received from a patient. Using a Bayesian Decision List, the received selection of answers is processed to output an indication of mental health of the patient. The indication of mental health identifies a kappa opioid receptor antagonist to which the patient would likely be a higher responder.

Abstract: The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.