When to Leave the Stones Unturned: Using Proportionality to Navigate Discovery Efficiently, Effectively, and Ethically

Discovery is intended to be an efficient, truth-seeking process with the ultimate goal of achieving just, speedy, and inexpensive dispute resolution. However, the consistent and extensive abuse of discovery has cast a shadow on the intended purpose of the process. For various ill- and well-intentioned reasons, attorneys abuse the process by conducting unnecessarily excessive and expensive discovery. One such reason for excessive and expensive discovery—and the focus of this Article—is the over-zealous advocacy of attorneys who leave no stone unturned out of fear of legal malpractice claims. To combat such excessive and expensive discovery, the Federal Rules of Civil Procedure emphasize a proportionality principle to limit the scope of discovery. But, despite the many revisions and amendments, the practicalities of the proportionality principle still remain ambiguous. In an attempt to resolve ambiguity, this Article offers realistic methods attorneys can implement to achieve proportionality in discovery, such as early case assessments, fact-finder assessments, written agreements with clients, and early judicial involvement. Furthermore, this Article proposes an ethical safeharbor to be added to the ABA Model Rules of Professional Conduct to protect well-intentioned attorneys who utilize the suggested proportionality methods. With these suggested proportionality methods and the proposed safe-harbor, this Article endeavors to curtail discovery abuse, protect attorneys, and allow for greater access to affordable and attainable justice

Application of machine-learning algorithms to predict the transport properties of Mie fluids

The ability to predict transport properties of fluids, such as the self-diffusion coefficient and viscosity, has been an ongoing effort in the field of molecular modelling. While there are theoretical approaches to predict the transport properties of simple systems, they are typically applied in the dilute gas regime and are not directly applicable to more complex systems. Other attempts to predict transport properties are done by fitting available experimental or molecular simulation data to empirical or semi-empirical correlations. Recently, there have been attempts to improve the accuracy of these fittings through the use of Machine Learning (ML) methods. In this work, the application of ML algorithms to represent the transport properties of systems comprising spherical particles interacting via the Mie potential is investigated. To this end, the self-diffusion coefficient and shear viscosity of 54 potentials are obtained at different regions of the fluid-phase diagram. This data set is used together with three ML algorithms, namely k-Nearest Neighbours, Artificial Neural Network and Symbolic Regression, to find correlations between the parameters of each potential and the transport properties at different densities and temperatures. It is shown that ANN and KNN perform to a similar extent, followed by SR, which exhibits larger deviations. Finally, the application of the three ML models to predict the self-diffusion coefficient of small molecular systems, such as krypton, methane and carbon dioxide is demonstrated using molecular parameters derived from the so-called SAFT-VR Mie equation of state [J. Chem. Phys. 139, 154504 (2013)] and available experimental vapour-liquid coexistence data.UK Engineering and Physical Sciences Research Council (EP-441SRC) via an Industrial Cooperative Award in Science & Technology (ICASE) co-funded by IBM, project ID 2327699 - EP/T517689/1A.P. is supported by a “Maria Zambrano Senior” fellowship, financed by the European Union within the NextGenerationEU program and the Spanish Ministry of UniversitiesHartree National Centre for Digital Innovatio

First-principles calculation of the intrinsic aqueous solubility of crystalline druglike molecules

We demonstrate that the intrinsic aqueous solubility of crystalline druglike molecules can be estimated with reasonable accuracy from sublimation free energies calculated using crystal lattice simulations and hydration free energies calculated using the 3D Reference Interaction Site Model (3D-RISM) of the Integral Equation Theory of Molecular Liquids (IET). The solubilities of 25 crystalline druglike molecules taken from different chemical classes are predicted by the model with a correlation coefficient of R = 0.85 and a root mean square error (RMSE) equal to 1.45 log(10) S units, which is significantly more accurate than results obtained using implicit continuum solvent models. The method is not directly parametrized against experimental solubility data, and it offers a full computational characterization of the thermodynamics of transfer of the drug molecule from crystal phase to gas phase to dilute aqueous solution.PostprintPeer reviewe

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

Editorial Message

We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and QSPR models generated by both machine learning (Random Forest and Support Vector Machines) and regression (Partial Least Squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A dataset of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references

Parents' perception of self-advocacy of children with myositis: an anonymous online survey

<p>Abstract</p> <p>Background</p> <p>Children with complex medical issues experience barriers to the transition of care from pediatric to adult providers. We sought to identify these barriers by elucidating the experiences of patients with idiopathic inflammatory muscle disorders.</p> <p>Methods</p> <p>We collected anonymous survey data using an online website. Patients and their families were solicited from the US and Canada through established clinics for children with idiopathic inflammatory muscle diseases as well as with the aid of a nonprofit organization for the benefit of such individuals. The parents of 45 older children/young adults suffering from idiopathic inflammatory muscle diseases were surveyed. As a basis of comparison, we similarly collected data from the parents of 207 younger children with inflammatory muscle diseases. The survey assessed transition of care issues confronting families of children and young adults with chronic juvenile myositis.</p> <p>Results</p> <p>Regardless of age of the patient, respondents were unlikely to have a designated health care provider assigned to aid in transition of care and were unlikely to be aware of a posted policy concerning transition of care at their pediatrician's office. Additionally, regardless of age, patients and their families were unlikely to have a written plan for moving to adult care.</p> <p>Conclusions</p> <p>We identified deficiencies in the health care experiences of families as pertain to knowledge, self-advocacy, policy, and vocational readiness. Moreover, as children with complex medical issues grow up, parents attribute less self-advocacy to their children's level of independence.</p

Diquat Derivatives: Highly Active, Two-Dimensional Nonlinear Optical Chromophores with Potential Redox Switchability

In this article, we present a detailed study of structure−activity relationships in diquaternized 2,2′-bipyridyl (diquat) derivatives. Sixteen new chromophores have been synthesized, with variations in the amino electron donor substituents, π-conjugated bridge, and alkyl diquaternizing unit. Our aim is to combine very large, two-dimensional (2D) quadratic nonlinear optical (NLO) responses with reversible redox chemistry. The chromophores have been characterized as their PF_6^− salts by using various techniques including electronic absorption spectroscopy and cyclic voltammetry. Their visible absorption spectra are dominated by intense π → π^* intramolecular charge-transfer (ICT) bands, and all show two reversible diquat-based reductions. First hyperpolarizabilities β have been measured by using hyper-Rayleigh scattering with an 800 nm laser, and Stark spectroscopy of the ICT bands affords estimated static first hyperpolarizabilities β_0. The directly and indirectly derived β values are large and increase with the extent of π-conjugation and electron donor strength. Extending the quaternizing alkyl linkage always increases the ICT energy and decreases the E_(1/2) values for diquat reduction, but a compensating increase in the ICT intensity prevents significant decreases in Stark-based β_0 responses. Nine single-crystal X-ray structures have also been obtained. Time-dependent density functional theory clarifies the molecular electronic/optical properties, and finite field calculations agree with polarized HRS data in that the NLO responses of the disubstituted species are dominated by ‘off-diagonal’ β_(zyy) components. The most significant findings of these studies are: (i) β_0 values as much as 6 times that of the chromophore in the technologically important material (E)-4′-(dimethylamino)-N-methyl-4-stilbazolium tosylate; (ii) reversible electrochemistry that offers potential for redox-switching of optical properties over multiple states; (iii) strongly 2D NLO responses that may be exploited for novel practical applications; (iv) a new polar material, suitable for bulk NLO behavior

Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic

AimsCardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both 'direct', through infection, and 'indirect', through changes in healthcare.Methods and resultsWe used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0).Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths.ConclusionSupply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic