Dark Matter

I introduce here the problem of dark energy, a substance that appears to be pushing the Universe to expand ever faster and discuss the large effort currently in place to understand its origin. I describe the surprising recent discovery of a widening crack in the cathedral of modern cosmology arising from the measurement of the expansion rate of the Universe. And I argue that gravitational waves observations can help us to either repair that crack, or to bring down that magnificent building, in case it turns out to be fatally flawed. Before all women and all men. Before animals, plants, archaeans, bacteria. Before the Earth was formed and the stars were lit. Before everything we know, the Universe was immersed in an amorphous and oblivious darkness.

Predicting the Conformational Variability of Oncogenic GTP-bound G12D Mutated KRas-4B Proteins at Cell Membranes

KRas proteins are the largest family of mutated Ras isoforms, participating in a wide variety of cancers. Due to their importance, large effort is being carried out on drug development by small-molecule inhibitors. However, understanding protein conformational variability remains a challenge in drug discovery. In the case of the Ras family, their multiple conformational states can affect the binding of potential drug inhibitors. To overcome this challenge, we propose a computational framework based on combined all-atom Molecular Dynamics and Metadynamics simulations able to accurately access conformational variants of the target protein. We tested the methodology using a G12D mutated GTP bound oncogenic KRas-4B protein located at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. Two main orientations of KRas-4B at the anionic membrane have been obtained and explored. The corresponding angles have been taken as reliable reaction coordinates so that free-energy landscapes have been obtained by well-tempered metadynamics simulations, revealing the local and global minima of KRas-4B binding to the cell membrane, unvealing reactive paths of the system between the two preferential orientations and highlighting opportunities for targeting the unique metastable states through the identification of druggable pockets.

Soccer Team Performance Forecasting using artificial neural network

Recently a large effort was spent on forecasting the outcome of sporting events. Due to forecasting perspective, the presence of competition introduces particular modeling challenges, which in turn limit the applicability of standard techniques. The objective of this study is to create a soccer team performance-forecasting model based on Artificial Neural networks that is capable of forecasting soccer players’ performance depending on teams’ history and behavior in previous matches as an input. The proposed model was trained and tested using a dataset including the features of Egypt Telecommunications club 15 years soccer team participating in the Egyptian Football Association Youth Dorian. Simulation results indicated that the proposed model could be classified as a stable predication model especially for soccer team’s status and performance, achieving high accuracy rate up to 95%.

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world’s population of children, and about 30–50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2–5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700–900 cm−1 range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.

Fast 3D Rotation Estimation of Fruits Using Spheroid Models

Automated fruit inspection using cameras involves the analysis of a collection of views of the same fruit obtained by rotating a fruit while it is transported. Conventionally, each view is analyzed independently. However, in order to get a global score of the fruit quality, it is necessary to match the defects between adjacent views to prevent counting them more than once and assert that the whole surface has been examined. To accomplish this goal, this paper estimates the 3D rotation undergone by the fruit using a single camera. A 3D model of the fruit geometry is needed to estimate the rotation. This paper proposes to model the fruit shape as a 3D spheroid. The spheroid size and pose in each view is estimated from the silhouettes of all views. Once the geometric model has been fitted, a single 3D rotation for each view transition is estimated. Once all rotations have been estimated, it is possible to use them to propagate defects to neighbor views or to even build a topographic map of the whole fruit surface, thus opening the possibility to analyze a single image (the map) instead of a collection of individual views. A large effort was made to make this method as fast as possible. Execution times are under 0.5 ms to estimate each 3D rotation on a standard I7 CPU using a single core.

On the effective resolution of WRF simulations at microscale grid resolution.

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

What is the optimal luteal support in assisted reproductive technology?

The need for luteal phase support in IVF/ICSI is well established. A large effort has been made in the attempt to identify the optimal type, start, route, dosage and duration of luteal phase support for IVF/ICSI and frozen embryo transfer. These questions are further complicated by the different types of stimulation protocols and ovulation triggers used in ART. The aim of this review is to supply a comprehensive overview of the available types of luteal phase support, and the indications for their use. A review of the literature was carried out in the effort to find the optimal luteal phase support regimen with regards to pregnancy related outcomes and short and long term safety. The results demonstrate that vaginal, intramuscular, subcutaneous and rectal progesterone are equally effective as luteal phase support in IVF/ICSI. GnRH agonists and oral dydrogesterone are new and promising treatment modalities but more research is needed. hCG and estradiol are not recommended for luteal phase support. More research is needed to establish the most optimal luteal phase support in frozen embryo transfer cycles, but progesterone has been shown to improve live birth rate in some studies. Luteal phase support should be commenced between the evening of the day of oocyte retrieval, and day three after oocyte retrieval and it should be continued at least until the day of positive pregnancy test. So, in conclusion still more large and well-designed RCT’s are needed to establish the most optimal luteal phase support in each stimulation protocol, and especially in frozen embryo transfer.

Raising College Access and Completion: How Much Can Free College Help?

Free college proposals have become increasingly popular in many countries of the world. To evaluate their potential effects, we develop and estimate a dynamic model of college enrollment, performance, and graduation. A central piece of the model, student effort, has a direct effect on class completion, and an indirect effect in mitigating the risk of not completing a class or not remaining in college. We estimate the model using rich, student-level administrative data from Colombia, and use the estimates to simulate free college programs that differ in eligibility requirements. Among these, universal free college expands enrollment the most, but it does not affect graduation rates and has the highest per-graduate cost. Performance-based free college, in contrast, delivers a slightly lower enrollment expansion yet a greater graduation rate at a lower per-graduate cost. Relative to universal free college, performance-based free college places a greater risk on students but is precisely this feature that delivers better outcomes. Nonetheless, the modest increase in graduation rates suggests that additional, complementary policies might be required to elicit the large effort increase needed to raise graduation rates.

Cometary Comae-Surface Links

A comet is a highly dynamic object, undergoing a permanent state of change. These changes have to be carefully classified and considered according to their intrinsic temporal and spatial scales. The Rosetta mission has, through its contiguous in-situ and remote sensing coverage of comet 67P/Churyumov-Gerasimenko (hereafter 67P) over the time span of August 2014 to September 2016, monitored the emergence, culmination, and winding down of the gas and dust comae. This provided an unprecedented data set and has spurred a large effort to connect in-situ and remote sensing measurements to the surface. In this review, we address our current understanding of cometary activity and the challenges involved when linking comae data to the surface. We give the current state of research by describing what we know about the physical processes involved from the surface to a few tens of kilometres above it with respect to the gas and dust emission from cometary nuclei. Further, we describe how complex multidimensional cometary gas and dust models have developed from the Halley encounter of 1986 to today. This includes the study of inhomogeneous outgassing and determination of the gas and dust production rates. Additionally, the different approaches used and results obtained to link coma data to the surface will be discussed. We discuss forward and inversion models and we describe the limitations of the respective approaches. The current literature suggests that there does not seem to be a single uniform process behind cometary activity. Rather, activity seems to be the consequence of a variety of erosion processes, including the sublimation of both water ice and more volatile material, but possibly also more exotic processes such as fracture and cliff erosion under thermal and mechanical stress, sub-surface heat storage, and a complex interplay of these processes. Seasons and the nucleus shape are key factors for the distribution and temporal evolution of activity and imply that the heliocentric evolution of activity can be highly individual for every comet, and generalisations can be misleading.

Learning a Hierarchical Global Attention for Image Classification

To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature representation by exploiting local information with a fixed receptive field, demonstrating distinguished capacities on image classification. Recent works concentrate on efficient feature exploration, which neglect the global information for holistic consideration. There is large effort to reduce the computational costs of deep neural networks. Here, we provide a hierarchical global attention mechanism that improve the network representation with restricted increase of computation complexity. Different from nonlocal-based methods, the hierarchical global attention mechanism requires no matrix multiplication and can be flexibly applied in various modern network designs. Experimental results demonstrate that proposed hierarchical global attention mechanism can conspicuously improve the image classification precision—a reduction of 7.94% and 16.63% percent in Top 1 and Top 5 errors separately—with little increase of computation complexity (6.23%) in comparison to competing approaches.