The U.S. legal system gives contracting parties significant freedom to customize the procedures that will govern their future disputes.' With forum selection clauses, parties can decide where they will litigate future disputes.2 With fee-shifting provisions, they can choose who will pay for these suits. 3 And with arbitration clauses, they can make upfront decisions to opt out of the traditional legal system altogether.4 Parties can also waive their right to appeal,5 their right to a jury trial,6 and their right to file a class action.7 Bespoke procedure, in other words, is commonplace in the United States. Far less common, however, are bespoke discovery provisions. Potential litigants rarely agree to alter the scope of discovery prior to a dispute.8 Once a lawsuit is filed, the Federal Rules of Civil Procedure encourage parties to work together to develop a joint discovery plan but parties rarely negotiate such agreements ex ante. Nor are bespoke discover agreements common in arbitration. Even when parties agree to arbitrate their claim, they seldom negotiate the scope of their discovery rights once they get into arbitration. Scholars examing the empirical record have deemed discovery provisions so rare as to be mearly mythical.
Slides from CESSDA Webinar on Data Discovery Data discovery is a crucial stage in the research process, especially in the social sciences and humanities as many valuable studies have originated from secondary data. Speakers from the Czech Social Science Data Archive (CSDA), Johana Chylíková and Martin Vávra, took participants on a tour through five core elements of the discovery process. These elements are 1) identification of the purpose of the specific data use intended, 2) finding an appropriate data resource, 3) setting up a search query, 4) selecting the data and finally 5) evaluation of the data quality. The guest speaker, sociologist Kristýna Bašná from the Institute of Sociology, Czech Academy of Sciences, presented her personal experience with discovering data in the context of her research on civic culture and democracy.
The burdens and challenges of discovery—especially electronic discovery—are usually associated with civil, not criminal cases. This is beginning to change. Already common in white-collar crime cases, voluminous digital discovery is increasingly a feature of ordinary criminal prosecutions. This Article examines the explosive growth of digital evidence in criminal cases and the efforts to manage its challenges. It then advances three claims about criminal case discovery in the digital age. First, the volume, complexity, and cost of digital discovery will incentivize the prosecution and the defense to cooperate more closely in cases with significant amounts of electronically stored information (ESI). Second, cooperation between the parties will not be sufficient to address the serious challenges that digital discovery presents to the fair and accurate resolution of criminal cases. And third, for that reason, digital discovery in criminal cases needs to be regulated more closely. In crafting such regulation, courts and legislators can build on the civil procedure model, which has grappled with the challenges of electronic discovery for over two decades. The civil procedure experience suggests that cooperation between the parties, active judicial involvement, and more detailed rules are essential to the effective management of digital discovery. The civil litigation model has its limitations, however, and policymakers must chart new ground to address some of the unique demands of criminal cases. Recognizing the significant resource and bargaining disparities in criminal cases, judges need to limit certain negotiated waivers of discovery so as to prevent abuse. Where the interests of justice demand it, courts may also need to help defendants obtain access to digital discovery in detention or gather digital evidence from third parties. These and other measures can help ensure that the cost and complexity of digital discovery do not undermine the fairness and accuracy of criminal proceedings.
and prosecutors. Part I of this Article argues that the conventional theory of hearsaydiscovery balance does not reflect the reality of modem federal practice. An imbalance has arisen because, in the last quarter century, developments in the law of evidence and confrontation are at odds with developments-or one might say nondevelopments-in the law of criminal discovery. Since enactment of the Federal Rules of Evidence in 1975, both the law of evidence and modem Confrontation Clause doctrine have evolved toward broader admission of hearsay in criminal cases. Contrary to conventional theory, that evolution has at least matched-and probably has outpaced-the trend toward more liberal admission of hearsay in civil cases. But while federal courts criminal cases, the rules of criminal discovery show no sign of adapting to that reality. As a result, in comparison to other litigants, federal criminal defendants now face a litigation environment that features both minimum discovery and maximum admissible hearsay. Part II offers some proposals to address that imbalance by expanding a defendant's right to learn in advance what hearsay he must face, and his right to gather "ammunition" to contest that hearsay. Where appropriate, I have included proposals that would require the amendment of existing rules. But recognizing the practical difficulties facing any rule-making initiative, my principal focus is to suggest more effective means of applying Rule 16, the Jencks Act, and the Brady doctrine-the major discovery tools presently available to criminal defendants-to the task of contesting prosecution hearsay. This Article is not a critique of developments in the law of evidence, nor of the Court's application of the Confrontation Clause to hearsay. It is not an argument that more, or less, hearsay should be admitted in criminal cases. Instead, it takes as a starting point the undeniable reality that, for good or ill, today's federal criminal trials include a wider variety of admissible hearsay than ever before. My aim is to show how the process of criminal discovery can and should adapt to that reality to correct the hearsay-discovery balance when the government relies on hearsay.
Yeasts, usually defined as unicellular fungi, occur in various fungal lineages. Hence, they are not a taxonomic unit, but rather represent a fungal lifestyle shared by several unrelated lineages. Although the discovery of new yeast species occurs at an increasing speed, at the current rate it will likely take hundreds of years, if ever, before they will all be documented. Many parts of the earth, including many threatened habitats, remain unsampled for yeasts and many others are only superficially studied. Cold habitats, such as glaciers, are home to a specific community of cold-adapted yeasts, and, hence, there is some urgency to study such environments at locations where they might disappear soon due to anthropogenic climate change. The same is true for yeast communities in various natural forests that are impacted by deforestation and forest conversion. Many countries of the so-called Global South have not been sampled for yeasts, despite their economic promise. However, extensive research activity in Asia, especially China, has yielded many taxonomic novelties. Comparative genomics studies have demonstrated the presence of yeast species with a hybrid origin, many of them isolated from clinical or industrial environments. DNA-metabarcoding studies have demonstrated the prevalence, and in some cases dominance, of yeast species in soils and marine waters worldwide, including some surprising distributions, such as the unexpected and likely common presence of Malassezia yeasts in marine habitats. ; TG acknowledges support from the Spanish Ministry of Science and Innovation for grant PGC2018-099921-B-I00, cofounded by European Regional Development Fund (ERDF); from the Catalan Research Agency (AGAUR) SGR423; from the European Union's Horizon 2020 research and innovation program (ERC-2016–724173); from the Gordon and Betty Moore Foundation (Grant # GBMF9742). JG acknowledges support from the Lendület Program (award no. 96049) of the Hungarian Academy of Sciences and the Eötvös Lóránd Research Network. Q-MW was supported by grants No. 31961133020 and No. 31770018 from the National Natural Science Foundation of China (NSFC). ASA and FEB were supported by grant 9343 from the Gordon and Betty Moore Foundation: https://doi.org/10.37807/GBMF9343. ; "Article signat per 12 autors/es: Teun Boekhout, Anthony S. Amend, Fouad El Baidouri, Toni Gabaldón, József Geml, Moritz Mittelbach, Vincent Robert, Chen Shuhui Tan, Benedetta Turchetti, Duong Vu, Qi-Ming Wang & Andrey Yurkov " ; Postprint (published version)
As the prevalence of Alzheimer's disease (AD) grows, so do the costs it imposes on society. Scientific, clinical, and financial interests have focused current drug discovery efforts largely on the single biological pathway that leads to amyloid deposition. This effort has resulted in slow progress and disappointing outcomes. Here, we describe a "portfolio approach" in which multiple distinct drug development projects are undertaken simultaneously. Although a greater upfront investment is required, the probability of at least one success should be higher with "multiple shots on goal," increasing the efficiency of this undertaking. However, our portfolio simulations show that the risk-adjusted return on investment of parallel discovery is insufficient to attract private-sector funding. Nevertheless, the future cost savings of an effective AD therapy to Medicare and Medicaid far exceed this investment, suggesting that government funding is both essential and financially beneficial.
We study the problem of discovering joinable datasets at scale. We approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportion between join candidate attributes. We implement our approach in a system called NextiaJD, and present experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains similar predictive performance to that of hash-based methods, yet we are able to scale-up to larger volumes of data. Also, NextiaJD generates a considerably less amount of false positives, which is a desirable feature at scale. ; This work is partly supported by Barcelona's City Council under grant agreement 20S08704. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). ; Peer Reviewed ; Postprint (published version)
A common view within the pharmaceutical industry is that there is a problem with drug discovery and we should do something about it. There is much sympathy for this from academics, regulators and politicians. In this article I propose that lessons learnt from evolution help identify those factors that favour successful drug discovery. This personal view is influenced by a decade spent reviewing drug development programmes submitted for European regulatory approval. During the prolonged gestation of a new medicine few candidate molecules survive. This process of elimination of many variants and the survival of so few has much in common with evolution, an analogy that encourages discussion of the forces that favour, and those that hinder, successful drug discovery. Imagining a world without vaccines, anaesthetics, contraception and anti-infectives reveals how medicines revolutionized humanity. How to manipulate conditions that favour such discoveries is worth consideration.
Ninety-seven percent of federal convictions come from guilty pleas. Defendants rely on prosecutors for much of the information about the government's case on which the decision to plead is based. Although federal prosecutors routinely turn over most necessary discovery to the defense, the law does not generally require them to turn over any discovery before the guilty plea. This can lead to innocent defendants pleading guilty and to guilty defendants pleading guilty without information that could have affected the agreed-upon sentence. This Article argues that the lack of a judicially enforceable pre-plea discovery regime flouts structural protections that due process is supposed to provide. Defendants who plead not guilty and go to trial get a jury to adjudicate guilt and a judge to preside over the proceedings and pronounce sentence. The judge and jury hear an adversarial presentation of the evidence, and the judge at sentencing can consider an even broader spectrum of information about the defendant and the crime. But defendants who plead guilty effectively act as their own judge and jury. Unfortunately, because prosecutors are not required to provide any pre-plea discovery, the defendant who pleads guilty may not have nearly as much information as the judge and jury would have had at trial and sentencing. The Supreme Court has employed a balancing test to determine whether a particular procedure comports with due process. This Article proposes tailoring that test to the pre-plea discovery context. The proposed test would ask (1) whether the defense is getting sufficient information before the guilty plea to promote accurate sorting of the innocent from the guilty and reasonably informed and consistent sentencing; (2) whether there are clear rules that allow judges, before a guilty plea, to regulate prosecutors' decisions not to disclose; and (3) whether the production of pre-plea discovery in a given case imposes undue costs on society. One hopeful development is that several district courts, pursuant to ...
Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age‐associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare ; DB is supported by the German Research Foundation (Forschungsstipendium; BA 6276/1-1). CYE is supported by Swiss National Science Foundation [163898]. VNG is supported by grants from National Institutes of Health, and by the Russian Federation grant 14.W03.31.0012. DWL presented the results of research supported in part by research grants and funds from the National Institutes of Health, the Wisconsin Partnership Program, the Progeria Research Foundation, the American Federation for Aging Research, and the University of Wisconsin-Madison School of Medicine and Public Health and Department of Medicine, as well as the facilities and resources of the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government. MSL is supported by an LUMC research fellowship and a VIDI grant from the Netherlands scientific organization (NWO- ALW-016.161.320). A.M.-M. is supported by grants from the Instituto de Salud Carlos III co-funded by Instituto de Salud Carlos III and FEdeR (CP14/00105 and PI15/00134). SM was supported by the FWO-OP/Odysseus program (42/FA010100/32/6484). SJO's current work is funded by The Glenn Award from the Glenn Foundation for Medical Research. MR is supported by the Swiss National Science Foundation and the European Union Horizon 2020 program. MSK is supported by grants from the Danish Cancer Society (#R167-A11015_001), the Independent Research Fund Denmark (#7016- 00230B) and the Novo Nordisk Foundation (NNF17OC0027812). ; Peer reviewed
Following the success of our first therapeutic discovery conference in 2017 and the selection of King Abdullah International Medical Research Centre (KAIMRC) as the first Phase 1 clinical site in the Kingdom of Saudi Arabia, we organized our second conference in partnership with leading institutions in academic drug discovery, which included the Structural Genomic Constorium (Oxford, UK), Fraunhofer (Germany) and Institute Material Medica (China) ; the participation of members of the American Drug Discovery Consterium ; European Biotech companies ; and local pharma companies, SIPMACO and SudairPharma. In addition, we had European and Northern American venture capital experts attending and presenting at the conference. The purpose of the conference was to bridge the gap between biotech, pharma and academia regarding drug discovery and development. Its aim primarily was to: (a) bring together world experts on academic drug discovery to discuss and propose new approaches to discover and develop new therapies ; (b) establish a permanent platform for scientific exchange between academia and the biotech and pharmaceutical industries ; (c) entice national and international investors to consider funding drugs discovered in academia ; (d) educate the population about the causes of diseases, approaches to prevent them from happening and their cure ; (e) attract talent to consider the drug discovery track for their studies and career. During the conference, we discussed the unique academic drug discovery disrupting business models, which can make their discoveries easily accessible in an open source mode. This unique model accelerates the dissemination of knowledge to all world scientists to guide them in their research. This model is aimed at bringing effective and affordable medicine to all mankind in a very short time. Moreover, the program discussed rare disease targets, orphan drug discovery, immunotherapy discovery and process, the role of bioinformatics in drug discovery, anti-infective drug discovery in the era of bad bugs, natural products as a source of novel drugs and innovative drug formulation and delivery. Additionally, as the conference was organized during the surge of the epidemic, we dedicated the first day (25 February) to coronavirus science, detection and therapy. The day was co-organized with the King Saud bin Abdulaziz University for Health Sciences, Kingdom of Saudi Arabia(KSA) Ministry of Education to announce the grant winner for infectious diseases. Simultaneously, intensive courses were delivered to junior scientists on the principle of drug discovery, immunology and clinical trials, as well as rare diseases. The second therapeutics discovery forum provided a platform for interactive knowledge sharing and the convergence of researchers, governments, pharmaceuticals, biopharmaceuticals, hospitals and non-profit organizations on the topic of academic drug discovery. The event presented showcases on global drug discovery initiatives and demonstrated how collaborations are leading to successful new therapies. In line with the KSA 2030 vision on becoming world leaders with an innovative economy and healthy population, therapeutic discovery is becoming an area of interest to science leaders in the kingdom, and our conference gave us the opportunity to identity key areas of interest as well as potential future collaborations.
When commentators, lawyers, judges, politicians, business people-anyone really-are looking to heap abuse on part of the civil process, they complain about discovery. But in truth, civil discovery is treated cruelly and often misunderstood. This is the case for two reasons. First, we do not know much about what actually happens in civil discovery in different types of cases. As a result, people seem to fill in the gaps of knowledge with their priors, which are, in turn, dependent on a few examples that loom large in their imaginations. Whatever limited reliable evidence about discovery we do have-and it is indeed very limited-is too often ignored in favor of reflexive vilification. Second, critics rarely consider the public benefits of discovery or its positive externalities, instead focusing mostly on its private, largely monetary costs and benefits.' The only way to prevent discovery from being abused is to know more about it and to evaluate its full costs and benefits. This Article proposes a modest change to litigation practice to help scholars, judges, and policymakers learn the truth about discovery. I propose that every discovery request be entered in the court docket. Given electronic filing, courts and litigants will incur few costs from this change, and researchers can analyze the information collected to determine the extent of discovery use and abuse.
ENGAGE projectnbsp;aims to build an information infrastructure for Public Sector Information (PSI) which is typically an aggregated data published by national and local governments, or other public bodies. An automated or semi-automated discovery of PSI datasets would cater for the needs of researchers in social science, behavioral science, and economics who want to consider freely available PSI sources for their study, in addition to other data perhaps collected or compiled via a dedicated research project. The researchers could then provide either an explicit or an implicit feedback for the relevance of the discovered PSI to their research needs. This would add up to the quality of data exposed via ENGAGE infrastructure, and empower other ENGAGE users: researchers, as well as citizens who are the second major ENGAGE user category, with the links from PSI aggregated data to the concepts and other datasets including well curated micro-data. We see our poster presentation as a means to suggest an approach to data linking and to gather requirements from data practitioners for the rest of the project.
As the prevalence of Alzheimer's disease (AD) grows, so do the costs it imposes on society. Scientific, clinical, and financial interests have focused current drug discovery efforts largely on the single biological pathway that leads to amyloid deposition. This effort has resulted in slow progress and disappointing outcomes. Here, we describe a "portfolio approach" in which multiple distinct drug development projects are undertaken simultaneously. Although a greater upfront investment is required, the probability of at least one success should be higher with "multiple shots on goal," increasing the efficiency of this undertaking. However, our portfolio simulations show that the risk-adjusted return on investment of parallel discovery is insufficient to attract private-sector funding. Nevertheless, the future cost savings of an effective AD therapy to Medicare and Medicaid far exceed this investment, suggesting that government funding is both essential and financially beneficial.
ETHNOPHARMACOLOGICAL RELEVANCE: Ethnopharmacological investigations of traditional medicines have made significant contributions to plant-derived drugs, as well as the advancement of pharmacology. Drug discovery from medicinal flora is more complex than generally acknowledged because plants are applied for different therapeutic indications within and across cultures. Therefore we propose the concept of "reverse ethnopharmacology" and compare biomedical uses of plant taxa with their ethnomedicinal and popular uses and test the effect of these on the probability of finding biomedical and specifically anticancer drugs. MATERIALS AND METHODS: For this analysis we use data on taxonomy and medical indications of plant derived biomedical drugs, clinical trial, and preclinical trial drug candidates published by Zhu et al. (2011) and compare their therapeutic indications with their ethnomedicinal and popular uses as reported in the NAPRALERT® database. Specifically, we test for increase or decrease of the probability of finding anticancer drugs based on ethnomedicinal and popular reports with Bayesian logistic regression analyses. RESULTS: Anticancer therapy resulted as the most frequent biomedicinal indication of the therapeutics derived from the 225 drug producing higher plant taxa and showed an association with ethnomedicinal and popular uses in women's medicine, which was also the most important popular use-category. Popular remedies for dysmenorrhoea, and uses as emmenagogues, abortifacients and contraceptives showed a positive effect on the probability of finding anticancer drugs. Another positive effect on the probability of discovering anticancer therapeutics was estimated for popular herbal drugs associated with the therapy of viral and bacterial infections, while the highest effect was found for popular remedies used to treat cancer symptoms. However, this latter effect seems to be influenced by the feedback loop and divulgence of biomedical knowledge on the popular level. CONCLUSION: We introduce the concept of reverse ethnopharmacology and show that it is possible to estimate the probability of finding biomedical drugs based on ethnomedicinal uses. The detected associations confirm the classical ethnopharmacological approach where a popular remedy for disease category X results in a biomedical drug for disease category X but does also point out the existence of cross-over relationships where popular remedies for disease category X result in biomedical therapeutics for disease category Y (Zhu et al., 2011). ; This paper is based on an oral presentation held at the 16th ISE Congress in Yulin, China (May 16-18th, 2016) and we would like to thank the organizers for the excellent congress and their hospitality. We are thankful also to Rich Spjut for the valuable comments on a draft version of this paper. GIS was a recipient of The Swiss Government Excellence Postdoctoral Scholarship awarded by the Federal Commission for Scholarships for Foreign Students (FCS). ML and CW acknowledge funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 606895. SC gratefully acknowledges funding from the RYC-2012-11455 and ECO2015-66593-P. MEC and SC acknowledge funding of the MINECO-Spain project (MTM2013-42323-P and MTM2016-77501-P).
