Wu H, Manu, Jiao R, Ma J. Temporal and spatial dynamics of scaling-specific features of a gene regulatory network in Drosophila. Nat Commun. 2015 Dec 8;6:10031.
A fruit fly’s head is the right size for its body, and an elephant’s head is the right size for its body. But how does nature ensure that living organisms develop proportionally–sized body parts? Jun Ma, PhD, and his colleagues report in Nature Communications find one can view an entire developing embryo as a single, unified dynamic system. The maternally derived, size-dependent information interpreted locally can spread in space and time throughout the embryo. A gene regulatory network governs this process when it decodes the size cues derived from a maternal protein gradient called Bicoid. The paper is among the latest work from Ma’s team, which is working to experimentally reconstruct and computationally simulate how size-dependent patterns emerge in the fruit fly embryo. They hope that better understanding of how organisms develop at the earliest stages of life may provide important clues into typical human development and certain types of birth defects.
Lu Y, Lu Y, Deng J, Peng H, Lu H, Lu LJ. A novel essential domain perspective for exploring gene essentiality. Bioinformatics. 2015 Sep 15;31(18):2921-9.
Genes identified with indispensable functions are essential; however, the traditional gene-level studies of essentiality have several limitations. This study introduces a new perspective to characterize gene essentiality by analyzing protein domains, the independent structural or functional units of a polypeptide chain. The researchers developed an algorithm-based model to identify essential domains and used simulated datasets to test the model and applied the model to six microbial species. When utilizing these essential domains to reproduce the annotation of essential genes, they received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, they presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. This paper describes the first systematic analysis on gene essentiality on the level of domains.
Tan L, Holland S, Deshpande A, Chen Y, Choo D, Lu LJ. A semi-supervised SVM model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging. Brain Behav. 2015 Oct 12;5(12):e00391.
A new computer program that analyzes functional brain MRI scans of hearing-impaired children may help predict whether the children will develop effective language skills within two years of cochlear implant surgery. This study describes a computer program that evaluates how specific regions of the brain respond to auditory stimulus tests that hearing-impaired infants and toddlers receive before surgical implantation. The study included 44 infants and toddlers (aged 8 to 67 months), 23 of whom received cochlear implant surgery. Two years following surgery, the team measured language performance. The study identified two features from the computer analysis that are potential biomarkers for predicting cochlear implant outcomes. With additional research and development, the computer model could become a practical tool that allows clinicians to reduce the number of children who undergo an invasive and costly procedure and are then disappointed when the implants do not deliver hoped-for results.
Connolly N, Anixt J, Manning P, Ping-I Lin D, Marsolo KA, Bowers K. Maternal metabolic risk factors for autism spectrum disorder-An analysis of electronic medical records and linked birth data. Autism Res. 2016 Aug;9(8):829-37.
An estimated one out of 45 children are affected by autism spectrum disorder (ASD). Suspected causal factors are genetics, environment and the interaction of both. This study strengthens evidence linking autism to maternal obesity and diabetes and demonstrates that electronic medical data can verify and establish the extent of this link across large populations. The researchers analyzed a variety medical record and birth data from patients and mothers to help identify risk factors. Using birth records from Southwest Ohio (part of the Cincinnati Children’s primary service area), the researchers compared mothers who had a child diagnosed with ASD to mothers of children with a non-autism developmental disorder. They also included in their comparison mothers with children having no developmental disorders. According to study data, pregnant mothers with obesity or gestational diabetes were 1.5 times more likely to have a child with ASD compared to mothers of children without developmental disorders. The increased risk of ASD for pregnant mothers with both obesity and gestational diabetes was two-fold. The findings fit well into an increasing body of evidence associating obesity and gestational diabetes with the development of autism.
Venek V, Scherer S, Morency L-P, Rizzo A, Pestian J. Adolescent Suicidal Risk Assessment in Clinician-Patient Interaction. IEEE Transactions on Affective Computing.  2016 Jan:18;Volume: PP Issue: 99.
Suicide among adolescents is a major public health problem and the third leading cause of death in the United States for children from ages 13-18. This paper presents work to investigate if suicidal risk is determinable by observing a patient-clinician conversation including the communicated verbal information, conversational dynamics, and vocal characteristics. The researchers studied the verbal and nonverbal acoustic information related to 60 audio-recorded interviews of 30 suicidal and 30 non-suicidal adolescents interviewed by a case worker. The researchers analyzed the recordings to reveal statistical differences between suicidal v. non-suicidal adolescents, and to investigate the behaviors of those who attempted suicide repeatedly v. those who attempted it once. Variables analyzed included conversation dynamics (such as speak and pause times), verbal information (such as use of personal pronouns), and acoustic information (such as pitch and volume). The study identified significant statistical differences between the three groups of patients: non-suicidal patients, suicidal repeaters and suicidal non-repeaters. Using hierarchical classification methods, the researchers were able to successfully discriminate between the three groups of patients.