Influence of Direct-fed Microbial Blend and on Rumen Fermentation Pattern and Degradability During Simulated Ruminal Acidosis.

Introduction: The use of probiotics and phytobiotics has attracted interest because of their protective effect against acidosis. (FE) is considered a good source of bioactive compounds, mainly monoterpene α-pinene. This study aimed to investigate the effect of a direct-fed microbial blend (Pro) and FE on rumen fermentation parameters under normal and acidosis conditions.

Predicting and Grouping Digitized Paintings by Style using Unsupervised Feature Learning.

Objective: To create an system to aid in the analysis of art history by classifying and grouping digitized paintings based on stylistic features automatically learned without prior knowledge.

Material And Methods: 6,776 digitized paintings from 8 different artistic styles (Art Nouveau, Baroque, Expressionism, Impressionism, Realism, Romanticism, Renaissance, and Post-Impressionism) were utilized to classify (predict) and cluster (group) paintings according to style. The method of unsupervised feature learning with K-means (UFLK), inspired by deep learning, was utilized to extract features from the paintings.

From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system.

Objective: To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies.

Materials And Methods: Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk.

A linear/nonlinear characterization of resting state brain networks in FMRI time series.

Resting state functional connectivity studies in fMRI have been used to demonstrate that the human brain is organized into inherent functional networks in the absence of stimuli. The basis for this activity is based on the spontaneous fluctuations observed during rest. In the present study, the time series generated from these fluctuations were characterized as either being linear or nonlinear based on the Delay Vector Variance method, applied through an examination of the local predictability of the signal.

MRI diffusion tensor tracking of a new amygdalo-fusiform and hippocampo-fusiform pathway system in humans.

Purpose: To use MRI diffusion-tensor tracking (DTT) to test for the presence of unknown neuronal fiber pathways interconnecting the mid-fusiform cortex and anteromedial temporal lobe in humans. Such pathways are hypothesized to exist because these regions coactivate in functional MRI (fMRI) studies of emotion-valued faces and words, suggesting a functional link that could be mediated by neuronal connections.

Materials And Methods: A total of 15 normal human subjects were studied using unbiased DTT approaches designed for probing unknown pathways, including whole-brain seeding and large pathway-selection volumes.

Neuronal fiber pathway abnormalities in autism: an initial MRI diffusion tensor tracking study of hippocampo-fusiform and amygdalo-fusiform pathways.

MRI diffusion-tensor tracking (DTT) was performed in 17 high-functioning adolescents/adults with autism and 17 pairwise-matched controls. White matter pathways involved in face processing were examined due to the relevance of face perception to the social symptoms of autism, and due to known behavioral and functional imaging findings in autism. The hippocampo-fusiform (HF) and amygdalo-fusiform (AF) pathways had normal size and shape but abnormal microstructure in the autism group.