QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

Second T = 3/2 state in 9^9B and the isobaric multiplet mass equation

Recent high-precision mass measurements and shell model calculations~[Phys. Rev. Lett. {\bf 108}, 212501 (2012)] have challenged a longstanding explanation for the requirement of a cubic isobaric multiplet mass equation for the lowest $A = 9$ isospin quartet. The conclusions relied upon the choice of the excitation energy for the second $T = 3/2$ state in $^9$B, which had two conflicting measurements prior to this work. We remeasured the energy of the state using the $^9{\rm Be}(^3{\rm He},t)$ reaction and significantly disagree with the most recent measurement. Our result supports the contention that continuum coupling in the most proton-rich member of the quartet is not the predominant reason for the large cubic term required for $A = 9$ nuclei

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc

Gamma ray production cross sections in proton induced reactions on natural Mg, Si and Fe targets over the proton energy range 30 up to 66 MeV

Gamma-ray excitation functions have been measured for 30, 42, 54 and 66 MeV proton beams accelerated onto C + O (Mylar), Mg, Si, and Fe targets of astrophysical interest at the separate-sector cyclotron of iThemba LABS in Somerset West (Cape Town, South Africa). A large solid angle, high energy resolution detection system of the Eurogam type was used to record Gamma-ray energy spectra. Derived preliminary results of Gamma-ray line production cross sections for the Mg, Si and Fe target nuclei are reported and discussed. The current cross section data for known, intense Gamma-ray lines from these nuclei consistently extend to higher proton energies previous experimental data measured up to Ep ~ 25 MeV at the Orsay and Washington tandem accelerators. Data for new Gamma-ray lines observed for the first time in this work are also reported.Comment: 11 pages, 6 figures. IOP Institute of Physics Conference Nuclear Physics in Astrophysics VII, 28th EPF Nuclear Physics Divisional Conference, May 18-22 2015, York, U

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Posttraumatic stress disorder predicts poor health-related quality of life in cardiac patients in Palestine

BACKGROUND: The longitudinal association of posttraumatic stress disorder (PTSD) with health-related quality of life (HRQL) in cardiac patients' remains poorly studied, particularly in conflict-affected settings. MATERIALS AND METHODS: For this cohort study, we used baseline and one-year follow-up data collected from patients 30 to 80 years old consecutively admitted with a cardiac diagnosis to four major hospitals in Nablus, Palestine. All subjects were screened for PTSD and HRQL using the PTSD Checklist Specific and the HeartQoL questionnaire. We used a generalized structural equation model (GSEM) to examine the independent predictive association of PTSD at baseline with HRQL at follow-up. We also examined the mediating roles of depression, anxiety, and stress at baseline. RESULTS: The prevalence of moderate-to-high PTSD symptoms among 1022 patients at baseline was 27∙0%. Patients with PTSD symptoms reported an approximate 20∙0% lower HRQL at follow-up. The PTSD and HRQL relationship was largely mediated by depressive and anxiety symptoms. It was not materially altered by adjustment for socio-demographic, clinical, and lifestyle factors. DISCUSSION: Our findings suggest that individuals with a combination of PTSD and depression, or anxiety are potentially faced with poor HRQL as a longer-term outcome of their cardiac disease. In Palestine, psychological disorders are often stigmatized; however, integration of mental health care with cardiac care may offer an entry door for addressing psychological problems in the population. Further studies need to assess the effective mental health interventions for improving quality of life in cardiac patients

Development of transportation models based on students’ interest in a parking charging system at Universiti Malaysia Sabah (UMS)

Transportation management and sustainable transportation planning were critical. A well-planned transportation system is extremely beneficial in terms of efficiency and environmental friendliness. To that end, parking charging was one of the transportation management topics covered in this study. A parking charging system is one in which a user can leave their vehicle at a particular place and pay a price based on the amount of time it was left unattended. Given the rising use of private vehicles, which has resulted in an increase in congestion and air pollution, it is believed that a parking fee system can be implemented to alleviate the situation. The primary purpose of this research is to develop a transportation model based on the parking price factor in Ringgit Malaysia (RM). At the completion of the study, a transportation model based on parking rates will be developed, and it is projected that once implemented, the percentage of private vehicles that use public transportation will increase. This model is deemed necessary in order to mitigate the harmful effect of an excessive number of private vehicles at UMS. The State Preference Survey (SPS) method was used. A questionnaire form was developed and distributed online to 300 respondents among the students of the Faculty of Engineering at UMS, in order to collect the required data. The data collected was then analyzed using linear regression to develop several transportation logistic models. The transportation models that have been developed in the form of a logistic model that can reflect the willingness of UMS students to shift from private vehicles to public transport. These models predict that when the parking price increases, the percentage shift of private vehicles to public transport will increase linearly. It is also found that 100% of drivers are willing to shift from private vehicles to public transport if the parking price per hour is RM 4.00. Shifting private vehicle users to public transportation may assist lower the number of private vehicles on the road and thus indirectly help mitigate the negative consequences of an excess of private automobiles