Screening of metabolic modulators identifies new strategies to target metabolic reprogramming in melanoma

The prognosis of metastatic melanoma remains poor due to de novo or acquired resistance to immune and targeted therapies. Previous studies have shown that melanoma cells have perturbed metabolism and that cellular metabolic pathways represent potential therapeutic targets. To support the discovery of new drug candidates for melanoma, we examined 180 metabolic modulators, including phytochemicals and anti-diabetic compounds, for their growth-inhibitory activities against melanoma cells, alone and in combination with the BRAF inhibitor vemurafenib. Two positive hits from this screen, 4-methylumbelliferone (4-MU) and ursolic acid (UA), were subjected to validation and further characterization. Metabolic analysis showed that 4-MU affected cellular metabolism through inhibition of glycolysis and enhanced the effect of vemurafenib to reduce the growth of melanoma cells. In contrast, UA reduced mitochondrial respiration, accompanied by an increase in the glycolytic rate. This metabolic switch potentiated the growth-inhibitory effect of the pyruvate dehydrogenase kinase inhibitor dichloroacetate. Both drug combinations led to increased production of reactive oxygen species, suggesting the involvement of oxidative stress in the cellular response. These results support the potential use of metabolic modulators for combination therapies in cancer and may encourage preclinical validation and clinical testing of such treatment strategies in patients with metastatic melanoma

Inner ear tissue preservation by rapid freezing: improving fixation by high-pressure freezing and hybrid methods

In the preservation of tissues in as ‘close to life’ state as possible, rapid freeze fixation has many benefits over conventional chemical fixation. One technique by which rapid freeze-fixation can be achieved, high pressure freezing (HPF), has been shown to enable ice crystal artefact-free freezing and tissue preservation to greater depths (more than 40μm) than other quick-freezing methods. Despite increasingly becoming routine in electron microscopy, the use of HPF for the fixation of inner ear tissue has been limited. Assessment of the quality of preservation showed routine HPF techniques were suitable for preparation of inner ear tissues in a variety of species. Good preservation throughout the depth of sensory epithelia was achievable. Comparison to chemically fixed tissue indicated that fresh frozen preparations exhibited overall superior structural preservation of cells. However, HPF fixation caused characteristic artefacts in stereocilia that suggested poor quality freezing of the actin bundles. The hybrid technique of pre-fixation and high pressure freezing was shown to produce cellular preservation throughout the tissue, similar to that seen in HPF alone. Pre-fixation HPF produced consistent high quality preservation of stereociliary actin bundles. Optimising the preparation of samples with minimal artefact formation allows analysis of the links between ultrastructure and function in inner ear tissues

A short food frequency questionnaire to assess intake of seafood and n-3 supplements: validation with biomarkers

<p>Abstract</p> <p>Background</p> <p>Seafood intake is associated with beneficial effects for human health. Seafood provides a number of nutrients beyond the traditionally known long chain marine n-3 fatty acids EPA, DPA and DHA, such as protein, vitamin D, iodine, selenium and vitamin B₁₂. Valid assessment of dietary seafood and n-3 supplement intakes are becoming increasingly crucial when giving recommendations to populations as seafood consumption is regarded as an important part of a healthy and balanced diet.</p> <p>Methods</p> <p>The aim was to validate a short FFQ developed for assessment of dietary intake of seafood and n-3 supplements using the biomarkers marine n-3 fatty acids in erythrocytes and 25(OH)D in serum.</p> <p>Results</p> <p>Fifty-three healthy Norwegians aged 30-64 years with a mean BMI of 25 kg/m²were compliant with the study protocol. 70% reported eating seafood for dinner one to two times per week, and 45% reported to eat seafood as spread, in salads or as snack meal three to five times or more per week. The FFQ correlated significantly with both the levels of marine n-3 fatty acids (r = 0.73, p < 0.0001) and with 25(OH)D (r = 0.37, p < 0.01). Mean level of marine n-3 and of 25(OH)D were 232 ± 65 μg/g erythrocytes and 73 ± 33 nmol/L serum, respectively.</p> <p>Conclusion</p> <p>The present short FFQ predicted strongly the levels of marine n-3 fatty acids in erythrocytes, and predicted fairly good the level of serum 25(OH)D and may therefore be a valid method for assessment of seafood and n-3 supplements intake among adults.</p

Crustal structure of the Arabian Plate: New constraints from the analysis of teleseismic receiver functions

An edited version of this paper was published by Elsevier Science. Copyright 2005, Elsevier Science. See also: http://dx.doi.org/10.1016/j.epsl.2004.12.020; http://atlas.geo.cornell.edu/SaudiArabia/publications/Al-Damegh%202005.htmReceiver functions for numerous teleseismic earthquakes recorded at 23 broadband and mid-band stations in Saudi Arabia and Jordan were analyzed to map crustal thickness within and around the Arabian plate. We used spectral division as well as time domain deconvolution to compute the individual receiver functions and receiver function stacks. The receiver functions were then stacked using the slant stacking approach to estimate Moho depths and Vp/Vs for each station. The errors in the slant stacking were estimated using a bootstrap re-sampling technique. We also employed a grid search waveform modeling technique to estimate the crustal velocity structure for seven stations. A jackknife re-sampling approach was used to estimate errors in the grid search results for three stations. In addition to our results, we have also included published receiver function results from two temporary networks in the Arabian shield and Oman as well as three permanent GSN stations in the region. The average crustal thickness of the late Proterozoic Arabian shield is 39 km. The crust thins to about 23 km along the Red Sea coast and to about 25 km along the margin of the Gulf of Aqaba. In the northern part of the Arabian platform, the crust varies from 33 to 37 km thick. However, the crust is thicker (41?53 km) in the southeastern part of the platform. There is a dramatic change in crustal thickness between the topographic escarpment of the Arabian shield and the shorelines of the Red Sea. We compared our results in the Arabian shield to nine other Proterozoic and Archean shields that include reasonably well determined Moho depths, mostly based on receiver functions. The average crustal thickness for all shields is 39 km, while the average for Proterozoic shields is 40 km, and the average for Archean shields is 38 km. We found the crustal thickness of Proterozoic shields to vary between 33 and 44 km, while Archean shields vary between 32 and 47 km. Overall, we do not observe a significant difference between Proterozoic and Archean crustal thickness. We observed a dramatic change in crustal thickness along the Red Sea margin that occurs over a very short distance. We projected our results over a cross-section extending from the Red Sea ridge to the shield escarpment and contrasted it with a typical Atlantic margin. The transition from oceanic to continental crust of the Red Sea margin occurs over a distance of about 250 km, while the transition along a typical portion of the western Atlantic margin occurs at a distance of about 450 km. This important new observation highlights the abruptness of the breakup of Arabia. We argue that a preexisting zone of weakness coupled with anomalously hot upper mantle could have initiated and expedited the breakup

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

The detection and monitoring of emotions are important in various applications, e.g. to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g.EEG and GSR), environmental data (e.g. audio and weather), videos (e.g. for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%