Abstract: A zero turning radius mower park brake system includes a park brake pawl on a transmission which engages a park brake to a pair of independently driven traction wheels. A park brake link may be pivotably mounted to the park brake pawl and connected to a left steering lever and a right steering lever. The park brake link may pivot while moving the park brake pawl forward to a park brake engaged position if only one of the steering levers is moved outward from a neutral traction drive position.

Abstract: The present disclosure relates to computer generated topographies from computer correlations of neurobehavioral phenotype mapping data and gene expression mapping data. Neurobehavioral phenotype mapping data is obtained for a selected phenotype and correlated with gene expression mapping data for one or more genes to define a phenotype-gene pair topography for each phenotype-gene pair. A score for each phenotype-gene pair is determined based on the correlation. The scores are used to identify genes, or drug targets, associated with the respective gene of the respective phenotype-gene pair. Conversely, gene expression mapping data is obtained for a selected gene and correlated with neurobehavioral phenotype mapping data for one or more phenotypes to define a gene-phenotype topography for each gene-phenotype pair. A score for each gene-phenotype pair is determined based on the correlation. The scores are used to identify a phenotype associated with the respective phenotype-gene pair.

Abstract: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

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: 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: The present disclosure relates to computer generated topographies from computer correlations of neurobehavioral phenotype mapping data and gene expression mapping data. Neurobehavioral phenotype mapping data is obtained for a selected phenotype and correlated with gene expression mapping data for one or more genes to define a phenotype-gene pair topography for each phenotype-gene pair. A score for each phenotype-gene pair is determined based on the correlation. The scores are used to identify genes, or drug targets, associated with the respective gene of the respective phenotype-gene pair. Conversely, gene expression mapping data is obtained for a selected gene and correlated with neurobehavioral phenotype mapping data for one or more phenotypes to define a gene-phenotype topography for each gene-phenotype pair. A score for each gene-phenotype pair is determined based on the correlation. The scores are used to identify a phenotype associated with the respective phenotype-gene pair.

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: 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: 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: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: The present disclosure relates to computer generated topographies from computer correlations of neurobehavioral phenotype mapping data and gene expression mapping data. Neurobehavioral phenotype mapping data is obtained for a selected phenotype and correlated with gene expression mapping data for one or more genes to define a phenotype-gene pair topography for each phenotype-gene pair. A score for each phenotype-gene pair is determined based on the correlation. The scores are used to identify genes, or drug targets, associated with the respective gene of the respective phenotype-gene pair. Conversely, gene expression mapping data is obtained for a selected gene and correlated with neurobehavioral phenotype mapping data for one or more phenotypes to define a gene-phenotype topography for each gene-phenotype pair. A score for each gene-phenotype pair is determined based on the correlation. The scores are used to identify a phenotype associated with the respective phenotype-gene pair.

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: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: An exemplary welding consumable according to the invention is provided and includes up to about 0.13 wt % carbon, about 0.3 wt % to about 1.4 wt % manganese, about 7.25 wt % to about 11.5 wt % nickel, about 0.6 wt % to about 1.2 wt % molybdenum, about 0.2 wt % to about 0.7 wt % silicon, up to about 0.3 wt % vanadium, up to about 0.05 wt % titanium, up to about 0.08 wt % zirconium, up to about 2.0 wt % chromium, and a balance of iron and incidental impurities.

Abstract: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: The present disclosure includes improved apparatuses and methods related to meat processing. One example method includes performing a first cut in association with a rinsing process performed on an animal, wherein performing the first cut includes severing at least one jugular vein of the animal; and subsequent to performing the first cut, performing a second cut that includes one of: making an incision in a vena cava of the animal that provides an exit location for treatment solution introduced into a circulatory system of the animal; and severing the vena cava of the animal such that the severed vena cava provides an exit location for treatment solution introduced into the circulatory system of the animal.

Abstract: The present disclosure includes improved apparatuses and methods related to meat processing. One example method includes performing a first cut in association with a rinsing process performed on an animal, wherein performing the first cut includes severing at least one jugular vein of the animal; and subsequent to performing the first cut, performing a second cut that includes one of: making an incision in a vena cava of the animal that provides an exit location for treatment solution introduced into a circulatory system of the animal; and severing the vena cava of the animal such that the severed vena cava provides an exit location for treatment solution introduced into the circulatory system of the animal.

Abstract: An exemplary welding consumable according to the invention is provided and includes up to about 0.13 wt % carbon, about 0.3 wt % to about 1.4 wt % manganese, about 7.25 wt % to about 11.5 wt % nickel, about 0.6 wt % to about 1.2 wt % molybdenum, about 0.2 wt % to about 0.7 wt % silicon, up to about 0.3 wt % vanadium, up to about 0.05 wt % titanium, up to about 0.08 wt % zirconium, up to about 2.0 wt % chromium, and a balance of iron and incidental impurities.