Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering

In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actions involved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alter the function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering

Marketplace Lending as a New Means of Raising Capital in the Internal Market: True Disintermediation or Reintermediation?

Marketplace lending,enabled by technological innovation, represents a new opportunity for raising capital.It is regarded by the EU as having the potential to expand the financing options of SMEs and improve the integration of the Internal Capital Market. However,applying traditional legal categories and existing laws to marketplace lending and to other examples of the new \u201cplatform economy\u201d is not simple. Member States have adopted very different regulatory responses towards marketplace lending, with negative effects on the internal market.The essence of the regulatory dilemma consists in determining whether marketplace lending represents \u2013as it has been depicted by platforms themselves, particularly in contractual agreements through disclaimers \u2013 a true disintermediated method of raising capital, an innovative form of intermediation, or a traditional kind of intermediation disguised in new and fashionable clothing.The answer to this question has relevant consequences for the regulatory treatment of marketplace lending and it requires a uniform response in the EU, at least with respect to the largest cross-border platforms. After briefly describing marketplace lending in Europe and the various current trends in regulating it, the paper discusses the main regulatory issues from the perspective of the above-mentioned issues.It analyzes the recently adopted Regulation on European Crowdfunding Services Providers in order to verify whether the regulatory choices that it has made are effective,both for the further development of marketplace lending and for addressing the associated risks

Estimation et évaluation d'incertitude d'indicateurs agrométéorologiques par télédétection en vue de supporter la lutte phytosanitaire

La caractérisation de la variabilité spatiale des conditions agrométéorologiques est essentielle à la prévision des insectes ravageurs et des maladies des cultures (IRMC) et à leur gestion spécifique par site. L’objectif de notre étude a été de modéliser, estimer et spatialiser à l'échelle locale et régionale des indicateurs agrométéorologiques (IAM) ainsi que leurs incertitudes. L'imagerie multispectrale et la thermographie infrarouge aéroportée ont été utilisées pour estimer à l'échelle locale des IAM dont le NDVI (Normalized Difference Vegetation Index), la proportion de couverture végétale (PCV), la température de surface (TS) et l'indice TVDI (Temperature/Vegetation Dryness Index) de l’humidité de surface. Deux nouveaux indicateurs ont été proposés: l’indicateur MTVX (Modified Temperature/Vegetation Index) de la température de l’air près de la surface (TAPS), et l’indice des conditions de stress thermique des cultures (ISTC). Les IAM ont été estimés à l'échelle régionale à l’aide des images satellite AVHRR (Advanced Very High Resolution Radiometer). Les incertitudes résultantes (ICR) des IAM ont été formulées sur la base de la loi de propagation des incertitudes. La spatialisation des IAM a été réalisée selon une approche dynamique basée sur un krigeage multivariable intégrant les facteurs dominants de leur variabilité spatiale. Les IAM ont démontré de fortes variabilités intraparcellaires, locales et régionales. Ils permettent de répondre aux besoins de caractérisation des conditions agrométéorologiques qui régissent les occurrences et le développement des IRMC. Des corrélations élevées ont été observées entre les mesures d'occurrence de plusieurs IRMC des cultures maraîchères et les indicateurs thermiques TS, TVDI, MTVX et ISTC. Celles-ci démontrent que les conditions de température qui prédominent à la surface des champs influencent davantage les IRMC. Ces indicateurs devraient être privilégiés dans la prévision des IRMC et dans la mise en place d’approche de gestion intégrée des ravageurs. Les aspects novateurs de la modélisation des indicateurs MTVX et ISTC, la formulation des ICR et leur estimation en tout point du territoire, la mise en place d'un cadre formel basé sur les ICR et un coefficient de performance globale pour évaluer et comparer différents modèles d'estimation des IAM, ainsi que l’approche de spatialisation dynamique, constituent des apports majeurs de notre étude.The characterization of the spatial variability of agrometeorological conditions is essential to the prediction and site-specific management of crop pests and diseases (CPD). The aim of our study was to model, estimate and spatialize local and regional agrometeorological indicators (AMI) and their uncertainties. Airborne multispectral imaging and infrared thermography were used to estimate AMIs at local scale such as the Normalized Difference Vegetation Index (NDVI), Percent Canopy Cover (PCC), Surface Temperature (ST) and the Temperature/Vegetation dryness index (TVDI), an indicator of surface moisture. Two new indicators were also proposed: the Crop Heat Stress Index (CHSI) and the Modified Temperature/Vegetation Index (MTVX), an indicator of the near-surface air temperature. AMIs were estimated at the regional scale using satellite images from the Advanced Very High Resolution Radiometer (AVHRR). The formulation of resultant uncertainties (RUC) of AMIs was based on the law of propagation of uncertainty. The spatialization of observed AMIs in-field was performed using a dynamic approach based on a multivariate kriging that integrated the dominant factors of their spatial variability. AMIs showed a high spatial variability at intra-site, local, and regional scales. They meet the need of the characterization of agrometeorological conditions under which the CPDs appear and develop. High correlations were observed between measures of the occurrence of several vegetable CPDs and thermal indicators like ST, TVDI, MTVX, and CHSI. These correlations show that surface temperature and near-surface air temperature have the most influence on the occurrence and the development of CPDs. Therefore, these indicators should be used in forecasting and in the implementation of an Integrated Pest Management (IPM) approach. Major contributions of our study are the innovative aspects of the modeling of indicators MTVX and ISTC, the formulation of the RUs of AMIs and their estimation anywhere in the area of interest, the establishment of a formal framework based on RUs and a global performance index to evaluate and compare different models used to estimate AMIs, and the dynamic spatialization approach

Characterizing non-exponential growth and bimodal cell size distributions in fission yeast:An analytical approach

Unlike many single-celled organisms, the growth of fission yeast cells within a cell cycle is not exponential. It is rather characterized by three distinct phases (elongation, septation, and reshaping), each with a different growth rate. Experiments also showed that the distribution of cell size in a lineage can be bimodal, unlike the unimodal distributions measured for the bacterium Escherichia coli. Here we construct a detailed stochastic model of cell size dynamics in fission yeast. The theory leads to analytic expressions for the cell size and the birth size distributions, and explains the origin of bimodality seen in experiments. In particular, our theory shows that the left peak in the bimodal distribution is associated with cells in the elongation phase, while the right peak is due to cells in the septation and reshaping phases. We show that the size control strategy, the variability in the added size during a cell cycle, and the fraction of time spent in each of the three cell growth phases have a strong bearing on the shape of the cell size distribution. Furthermore, we infer all the parameters of our model by matching the theoretical cell size and birth size distributions to those from experimental single-cell time-course data for seven different growth conditions. Our method provides a much more accurate means of determining the size control strategy (timer, adder or sizer) than the standard method based on the slope of the best linear fit between the birth and division sizes. We also show that the variability in added size and the strength of size control in fission yeast depend weakly on the temperature but strongly on the culture medium. More importantly, we find that stronger size homeostasis and larger added size variability are required for fission yeast to adapt to unfavorable environmental conditions

The state of the art in integrating machine learning into visual analytics

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions

Financial model for private finance initiative projects applied to school buildings

Private Finance Initiative (PFI) has become a major procurement method in the UK and worldwide. The number of signed PFI deals is growing, but competition is restricted to those companies that are able to afford the initial investment. The bidding cost of PFI projects are high, and bidding companies are not compensated if the client does not award them the project. This is the reason behind several recent high-profile tender xvithdra« als. and is considered a major barrier for private companies wanting to take part in the bidding process. There is an obvious need for a tool to enable construction organizations to participate in PFI projects; one that can support these organizations in a decision-making process that is compatible with their project selection strategies, and will allow them to bid for PFI projects with clearer goals and reduced costs. A computer-based financial model was developed to predict the cost and cash flow of PFI projects, enabling project teams to assess investment decisions at the tendering stage. The proposed model consists of four modules to identify the required building area, predict the construction cost, distribute the occupancy cost, and predict the cash flow of the project. The output of the model provides the project investment results, such as the Net Present Value (NPV), Internal Rate of Return (IRR), Debt Service Coverage Ratio (DSCR), payback period and investment growth ratio. The model can predict the unitary payment but also allows the user to define the unitary payment. The reports of the model contain the cash flow and investment ratio for both types of unitary payment. The model attempts to provide the information required to assess the feasibility and affordability of the project. It gives the private sector the chance to assess the project before they spend unrecoupable funds on the project. It allows the public sector to determine the project cost, cash flow, unitary charge, and provide the information to be used for the Public Sector Comparator. The data required for the development of the model was collected from different sources. The model was initially developed on spreadsheet software: the final version was transformed into a web-based model using the Hypertext Preprocessor (PHP) and Javascript programming languages. The completed model was then sent to many practitioners for validation and assessment of both the concept and numerical application. The responses received show the valuable role the model could play in PFI projects.Ministry of Higher Education, Saudi Arabi