The folding of naturally occurring, single domain proteins is usually well-described as a simple, single exponential process lacking significant trapped states. Here we further explore the hypothesis that the smooth energy landscape this implies, and the rapid kinetics it engenders, arises due to the extraordinary thermodynamic cooperativity of protein folding. Studying Miyazawa-Jernigan lattice polymers we find that, even under conditions where the folding energy landscape is relatively optimized (designed sequences folding at their temperature of maximum folding rate), the folding of protein-like heteropolymers is accelerated when their thermodynamic cooperativity enhanced by enhancing the non-additivity of their energy potentials. At lower temperatures, where kinetic traps presumably play a more significant role in defining folding rates, we observe still greater cooperativity-induced acceleration. Consistent with these observations, we find that the folding kinetics of our computational models more closely approximate single-exponential behavior as their cooperativity approaches optimal levels. These observations suggest that the rapid folding of naturally occurring proteins is, at least in part, consequences of their remarkably cooperative folding.

This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms.

Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures which limit their applicability to protein complexes with known structures. In this work, we explore sequence based protein binding affinity prediction using machine learning. Our paper highlights the fact that the generalization performance of even the state of the art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. We also propose a novel sequence-only predictor of binding affinity called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its Python code are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#island.

An exactly solvable model based on the topology of a protein native state is applied to identify bottlenecks and key-sites for the folding of HIV-1 Protease. The predicted sites are found to correlate well with clinical data on resistance to FDA-approved drugs. It has been observed that the effects of drug therapy are to induce multiple mutations on the protease. The sites where such mutations occur correlate well with those involved in folding bottlenecks identified through the deterministic procedure proposed in this study. The high statistical significance of the observed correlations suggests that the approach may be promisingly used in conjunction with traditional techniques to identify candidate locations for drug attacks.

This research examines the empowerment of women entrepreneurs in the context of entrepreneurial financing in France. It explores the factors that allow some women entrepreneurs to access certain categories of external finance more easily. The theoretical framework used is based on the concept of empowerment, explored through its personal and relational dimensions. The study relies on a quantitative approach, using data from a representative of women entrepreneurs. The results show that the status of a founder affects access to external finance in different ways: it increases the chances of successful fundraising, but reduces the chances of obtaining bank finance. This finding highlights the importance of empowerment dynamics, which vary according to the type of financing. In addition, characteristics such as the presence of a spouse in the business, high income, membership of a professional network and the diversity of this network complete the analysis of inequalities in access. This study, the first of its kind in France, suggests ways of enriching our understanding of the diversity of situations experienced by female founders, thus helping to deconstruct the homogeneous image of women's entrepreneurship.

This paper reviews the status of women in science, physics in particular, in Cyprus. We describe the development of physics in the country, focusing on the contributions and participation of women. We present statistical data for the last several years, reviewing the percentage of women who are pursuing physics as a subject of study or as a profession. We report the gender ratios at different career stages and find that while women are well represented in undergraduate studies, female physicists are underrepresented in senior positions. We discuss factors that might affect the career evolution of women in physics in Cyprus.

In this paper we present the Women in Computer Vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in the computer vision field. Computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in academia and in industry. WiCV is organized especially for the following reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

The Commission Femmes et Astronomie of the French Astronomical Society, has conducted a statistical study aimed at mapping the current presence of women in French professional astronomy and establishing a baseline for tracking its evolution over time. This study follows an initial survey carried out in 2021, which covered eight astronomy and astrophysics institutes (1,060 employees). This year, the scope was expanded to 11 institutes, bringing together a total of 1,525 employees, including PhD students, postdoctoral researchers, academics, as well as technical and administrative staff, representing about 57% of the whole French community. We examined how the proportion of women varies according to career stage, level of responsibility, job security, and income. The results are compared to the 2021-2022 survey and appear to illustrate the well-known "leaky pipeline", with one of the main bottlenecks being access to permanent positions. The study shows that the proportion of women consistently declines with increasing job security, career seniority, qualification level, and salary.

Tourism's increasing share of Gross Domestic Product throughout the world, its impact on employment and its continuous growth justifies the interest it raises amongst entrepreneurs and public authorities. However, this growth coexists with intense competition; as a result of which, tourism companies must continuously innovate in order to survive and grow. This is evident in the diversification of tourism products and destinations, the improvement of business processes and the incorporation of new technologies for intermediation, amongst other examples. This paper expounds on the factors that explain the propensity for innovation amongst tourism entrepreneurs and it may help governments to promote innovation that is based on those determining factors. The hypotheses are tested using a logistic regression on 699 international tourism entrepreneurs, taken from the 2014 Global Adult Population Survey of the Global Entrepreneurship Monitor project. The propensity for innovation amongst tourism entrepreneurs has a statistically significant relationship to gender, age, level of education and informal investments in previous businesses.

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.