A model to predict the oscillation frequency for drops pinned on a vertical planar surface

Accurate prediction of the natural frequency for the lateral oscillation of a liquid drop pinned on a vertical planar surface is important to many drop applications. The natural oscillation frequency, normalized by the capillary frequency, is {mainly} a function of the equilibrium contact angle and the Bond number (Bo), when the contact lines remain pinned. Parametric numerical and experimental studies have been performed to establish a comprehensive understanding of oscillation dynamics. {An} inviscid model has been developed to predict the oscillation frequency for wide ranges of Bo and contact angles. The model reveals the scaling relation between the normalized frequency and Bo, which is validated by the numerical simulation results. For a given equilibrium contact angle, the lateral oscillation frequency decreases with Bo, implying that resonance frequencies will be magnified if the drop oscillations occur in a reduced gravity environment

Pseudo-Goldstone Boson Effects in Top-Antitop Productions at High Energy Hadron Colliders and Testing Technicolor Models

We study the top quark pair production process p+p(anti-p)-->top+antitop in various kinds of technicolor (TC) models at the Fermilab Tevatron Run II and the CERN LHC. The s-channel neutral pseudo-Goldstone bosons (PGB's) contribute dominately to the production amplitudes from its coupling to the gluons through the triangle loops of techniquarks and the top quark. Cross sections in different TC models with s-channel PGB contributions are calculated. It is shown that the PGB effects can be experimentally tested and different TC models under consideration can be distinguished at the LHC. Therefore, the p+p-->top+antitop process at the LHC provides feasible tests of the TC models.Comment: 10 pages in RevTex and 4 PS-files for the figures. Paramemter range is changed, and some references are added. Version for publication in Phys. Rev.

The effects of low frequency electrical stimulation on satellite cell activity in rat skeletal muscle during hindlimb suspension

<p>Abstract</p> <p>Background</p> <p>The ability of skeletal muscle to grow and regenerate is dependent on resident stem cells called satellite cells. It has been shown that chronic hindlimb unloading downregulates the satellite cell activity. This study investigated the role of low-frequency electrical stimulation on satellite cell activity during a 28 d hindlimb suspension in rats.</p> <p>Results</p> <p>Mechanical unloading resulted in a 44% reduction in the myofiber cross-sectional area as well as a 29% and 34% reduction in the number of myonuclei and myonuclear domains, respectively, in the soleus muscles (<it>P </it>< 0.001 <it>vs </it>the weight-bearing control). The number of quiescent (M-cadherin⁺), proliferating (BrdU⁺and myoD⁺), and differentiated (myogenin⁺) satellite cells was also reduced by 48-57% compared to the weight-bearing animals (<it>P </it>< 0.01 for all). Daily application of electrical stimulation (2 × 3 h at a 20 Hz frequency) partially attenuated the reduction of the fiber cross-sectional area, satellite cell activity, and myonuclear domain (<it>P </it>< 0.05 for all). Extensor digitorum longus muscles were not significantly altered by hindlimb unloading.</p> <p>Conclusion</p> <p>This study shows that electrical stimulation partially attenuated the decrease in muscle size and satellite cells during hindlimb unloading. The causal relationship between satellite cell activation and electrical stimulation remain to be established.</p

Preferential Paths of Air-water Two-phase Flow in Porous Structures with Special Consideration of Channel Thickness Effects.

Accurate understanding and predicting the flow paths of immiscible two-phase flow in rocky porous structures are of critical importance for the evaluation of oil or gas recovery and prediction of rock slides caused by gas-liquid flow. A 2D phase field model was established for compressible air-water two-phase flow in heterogenous porous structures. The dynamic characteristics of air-water two-phase interface and preferential paths in porous structures were simulated. The factors affecting the path selection of two-phase flow in porous structures were analyzed. Transparent physical models of complex porous structures were prepared using 3D printing technology. Tracer dye was used to visually observe the flow characteristics and path selection in air-water two-phase displacement experiments. The experimental observations agree with the numerical results used to validate the accuracy of phase field model. The effects of channel thickness on the air-water two-phase flow behavior and paths in porous structures were also analyzed. The results indicate that thick channels can induce secondary air flow paths due to the increase in flow resistance; consequently, the flow distribution is different from that in narrow channels. This study provides a new reference for quantitatively analyzing multi-phase flow and predicting the preferential paths of immiscible fluids in porous structures

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization

In Vivo Measurement of Brain GABA Concentrations by Magnetic Resonance Spectroscopy in Smelters Occupationally Exposed to Manganese

Background: Exposure to excessive manganese (Mn) levels is known to induce psychiatric and motor disorders including parkinsonian symptoms. Therefore finding a reliable means for early detection of Mn neurotoxicity is desirable. Objectives: Our goal was to study whether in-vivo brain levels of gamma-aminobutyric acid (GABA), N-acetylaspartate (NAA) and other brain metabolites in smelters were altered as a consequence of Mn exposure. Methods: T1-weighted MRI was used to visualize Mn deposition in the brain. Magnetic resonance spectroscopy (MRS) was used to quantify concentrations of NAA, glutamate and other brain metabolites in globus pallidus, putamen, thalamus, and frontal cortex from a well-established cohort of 10 male Mn-exposed smelters and 10 male age-matched control subjects. The MEGA-PRESS MRS sequence was used to determine GABA levels in a region encompassing the thalamus and adjacent parts of the basal ganglia ("GABA-VOI"). Results: Seven out of ten exposed subjects showed clear T1-hyperintense signals in the globus pallidus indicating Mn accumulation. We found a significant increase (82%; p=0.014) of GABA/tCr in the GABA-VOI of Mn-exposed subjects, as well as a distinct decrease (9%, p=0.04) of NAA/tCr in frontal cortex that strongly correlated (R= - 0.93, p<0.001) with cumulative Mn exposure. Conclusions: We demonstrated elevated GABA levels in the thalamus and adjacent basal ganglia and decreased frontal cortex NAA levels, indicating neuronal dysfunction in a brain area not primarily targeted by Mn. Therefore, the non-invasive in vivo MRS measurement of GABA and NAA may prove to be a powerful tool for detecting presymptomatic effects of Mn neurotoxicity

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Epidemiology, prehospital care and outcomes of patients arriving by ambulance with dyspnoea: An observational study

Background: This study aimed to determine epidemiology and outcome for patients presenting to emergency departments (ED) with shortness of breath who were transported by ambulance. Methods: This was a planned sub-study of a prospective, interrupted time series cohort study conducted at three time points in 2014 and which included consecutive adult patients presenting to the ED with dyspnoea as a main symptom. For this sub-study, additional inclusion criteria were presentation to an ED in Australia or New Zealand and transport by ambulance. The primary outcomes of interest are the epidemiology and outcome of these patients. Analysis was by descriptive statistics and comparisons of proportions. Results: One thousand seven patients met inclusion criteria. Median age was 74 years (IQR 61-68) and 46.1 % were male. There was a high rate of co-morbidity and chronic medication use. The most common ED diagnoses were lower respiratory tract infection (including pneumonia, 22.7 %), cardiac failure (20.5%) and exacerbation of chronic obstructive pulmonary disease (19.7 %). ED disposition was hospital admission (including ICU) for 76.4 %, ICU admission for 5.6 % and death in ED in 0.9 %. Overall in-hospital mortality among admitted patients was 6.5 %. Discussion: Patients transported by ambulance with shortness of breath make up a significant proportion of ambulance caseload and have high comorbidity and high hospital admission rate. In this study, >60 % were accounted for by patients with heart failure, lower respiratory tract infection or COPD, but there were a wide range of diagnoses. This has implications for service planning, models of care and paramedic training. Conclusion: This study shows that patients transported to hospital by ambulance with shortness of breath are a complex and seriously ill group with a broad range of diagnoses. Understanding the characteristics of these patients, the range of diagnoses and their outcome can help inform training and planning of services