Preventing Emissions from Slipping Through the Cracks: How Collaboration on New Technologies to Detect Violations and Minimize Emissions Can Efficiently Enforce Existing Clean Air Act Regulations

The link between air pollution and poor public health is well known and has been farther documented during the COVID-19 pandemic, 1 but EPA has outdated methods and rules to detect air emissions. Enforcing existing environmental regulations presents challenges because the detection and monitoring technologies identified in the regulations, or the regulation language itself, may not sufficiently identify environmental pollution, let alone complex environmental fraud. How can EPA best use new technologies and concepts to detect violations, with the intent of minimizing emissions, to improve human health and environmental outcomes during the lengthy process of drafting and publishing new regulations? As EPA \u27s expertise lies in the promulgation and enforcement of emission standards, not in developing software fixes or manufacturing technologies to detect or address violations, collaboration with other stakeholders is important to achieve overall emission reductions. This Article identifies the need for a collaborative approach with industry and public interest groups to explicitly adopt certain technologies and methods to detect violations, and it provides supporting case studies from recent mobile and stationary source air enforcement cases illustrating that improved detection leads to industry-developed technologies that minimize emissions. If regulated entities choose to use these technologies to monitor and maintain their own compliance with the Clean Air Act, overall emissions will decrease, with a likely increase in public health. This Article recommends that all stakeholders work together to propose new detection methods and remedial technologies that EPA may use to collect evidence for enforcement actions and to resolve noncompliance. These technologies may be incorporated into future regulations to improve transparency and fairness in the enforcement process, ultimately minimizing the likelihood of complex litigation that may delay remedial actions that address excessive emissions

The Law and Economics of Habitat Conservation: Lessons from an Analysis of Easement Acquisitions

There is a growing interest in incentive-based policies to motivate conservation by landowners. These policies include full- and partial-interest land purchases, tax-based incentives, and tradable or bankable development rights. Using legal and economic analysis, this paper explores potential pitfalls associated with the use of such policies. Incentive-based policies promise to improve the cost effectiveness of habitat preservation, but only if long-run implementation issues are meaningfully addressed. While the paper compares conservation policies, particular attention is devoted to the use of conservation easements and in particular a set of easement contracts and transactions in the state of Florida. The easement analysis highlights the importance of conservation policies' interactions with property markets, land management practices, and bureaucratic incentives. Specific challenges include difficulties associated with the long-term enforcement and monitoring of land use restrictions, the lack of market prices as indicators of value for appraisal, and the way in which incentives target specific properties for protection.

Preventing Emissions from Slipping Through the Cracks: How Collaboration on New Technologies to Detect Violations and Minimize Emissions Can Efficiently Enforce Existing Clean Air Act Regulations

22 pagesThe link between air pollution and poor public health is well known and has been further documented during the COVID-19 pandemic,but EPA has outdated methods and rules to detect air emissions. Enforcing existing environmental regulations presents challenges because the detection and monitoring technologies identified in the regulations, or the regulation language itself, may not sufficiently identify environmental pollution, let alone complex environmental fraud.This Article recommends that all stakeholders work together to propose new detection methods and remedial technologies that EPA may use to collect evidence for enforcement actions and to resolve noncompliance

Lipid droplet degradation by autophagy connects mitochondria metabolism to Prox1-driven expression of lymphatic genes and lymphangiogenesis.

Autophagy has vasculoprotective roles, but whether and how it regulates lymphatic endothelial cells (LEC) homeostasis and lymphangiogenesis is unknown. Here, we show that genetic deficiency of autophagy in LEC impairs responses to VEGF-C and injury-driven corneal lymphangiogenesis. Autophagy loss in LEC compromises the expression of main effectors of LEC identity, like VEGFR3, affects mitochondrial dynamics and causes an accumulation of lipid droplets (LDs) in vitro and in vivo. When lipophagy is impaired, mitochondrial ATP production, fatty acid oxidation, acetyl-CoA/CoA ratio and expression of lymphangiogenic PROX1 target genes are dwindled. Enforcing mitochondria fusion by silencing dynamin-related-protein 1 (DRP1) in autophagy-deficient LEC fails to restore LDs turnover and lymphatic gene expression, whereas supplementing the fatty acid precursor acetate rescues VEGFR3 levels and signaling, and lymphangiogenesis in LEC-Atg5-/- mice. Our findings reveal that lipophagy in LEC by supporting FAO, preserves a mitochondrial-PROX1 gene expression circuit that safeguards LEC responsiveness to lymphangiogenic mediators and lymphangiogenesis.We thank K. Rillaerts, J. Souffreau, and A. Bouche, for expert technical support and Dr. A. Luttun and Dr. A. Zijsen for sharing tools and advices. P.A. is supported by grants from the Flemish Research Foundation (FWO-Vlaanderen; G076617N, G049817N, G070115N), the EOS MetaNiche consortium N degrees 40007532, Stichting tegen Kanker (FAF-F/2018/1252) and the iBOF/21/053 ATLANTIS consortium with G.B. D.H. is the recipient of an FWO Doctoral Fellowship from the Flemish Research Foundation (FWO-Vlaanderen, 1186019N), Belgium. M.B. is supported by the `Fonds voor Wetenschappelijk Onderzoek' (FWO). K.J. is the recipient of an FWO Postdoctoral Fellowship from the Flemish Research Foundation (FWO-Vlaanderen). P.C. is supported by Methusalem funding by the Flemish government, and by an ERC Advanced Research Grant (EU-ERC269073).S

PSR J1024-0719:A Millisecond Pulsar in an Unusual Long-Period Orbit

PSR J1024-0719 is a millisecond pulsar that was long thought to be isolated. However, puzzling results concerning its velocity, distance, and low rotational period derivative have led to a reexamination of its properties. We present updated radio timing observations along with new and archival optical data which show that PSR J1024-0719 is most likely in a long-period (2-20 kyr) binary system with a low-mass (approximate to 0.4 M-circle dot), low-metallicity (Z approximate to -0.9 dex) main-sequence star. Such a system can explain most of the anomalous properties of this pulsar. We suggest that this system formed through a dynamical exchange in a globular cluster that ejected it into a halo orbit, which is consistent with the low observed metallicity for the stellar companion. Further astrometric and radio timing observations such as measurement of the third period derivative could strongly constrain the range of orbital parameters

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.Jon Agirre is a Royal Society University Research Fellow (UF160039 and URF\R\221006). Mihaela Atanasova is funded by the UK Engineering and Physical Sciences Research Council (EPSRC; EP/R513386/1). Haroldas Bagdonas is funded by The Royal Society (RGF/R1/181006). Jose´ Javier Burgos-Ma´rmol and Daniel J. Rigden are supported by the BBSRC (BB/S007105/1). Robbie P. Joosten is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871037 (iNEXTDiscovery) and by CCP4. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation: MRC file reference No. MC_UP_A025_1012 to Garib N. Murshudov, which also funded Keitaro Yamashita, Paul Emsley and Fei Long. Robert A. Nicholls is funded by the BBSRC (BB/S007083/1). Soon Wen Hoh is funded by the BBSRC (BB/T012935/1). Kevin D. Cowtan and Paul S. Bond are funded in part by the BBSRC (BB/S005099/1). John Berrisford and Sameer Velankar thank the European Molecular Biology Laboratory–European Bioinformatics Institute, who supported this work. Andrea Thorn was supported in the development of AUSPEX by the German Federal Ministry of Education and Research (05K19WWA and 05K22GU5) and by Deutsche Forschungsgemeinschaft (TH2135/2-1). Petr Kolenko and Martin Maly´ are funded by the MEYS CR (CZ.02.1.01/0.0/0.0/16_019/0000778). Martin Maly´ is funded by the Czech Academy of Sciences (86652036) and CCP4/STFC (521862101). Anastassis Perrakis acknowledges funding from iNEXT (grant No. 653706), iNEXT-Discovery (grant No. 871037), West-Life (grant No. 675858) and EOSC-Life (grant No. 824087) funded by the Horizon 2020 program of the European Commission. Robbie P. Joosten has been the recipient of a Veni grant (722.011.011) and a Vidi grant (723.013.003) from the Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman, Robbie P. Joosten and Anastassis Perrakis thank the Research High Performance Computing facility of the Netherlands Cancer Institute for providing and maintaining computation resources and acknowledge the institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon is funded by the BBSRC (BB/S007040/1). Randy J. Read is supported by a Principal Research Fellowship from the Wellcome Trust (grant 209407/Z/17/Z). Atlanta G. Cook is supported by a Wellcome Trust SRF (200898) and a Wellcome Centre for Cell Biology core grant (203149). Isabel Uso´n acknowledges support from STFC-UK/CCP4: ‘Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW’ and Spanish MICINN/AEI/FEDER/UE (PID2021-128751NB-I00). Pavol Skubak and Navraj Pannu were funded by the NWO Applied Sciences and Engineering Domain and CCP4 (grant Nos. 13337 and 16219). Bernhard Lohkamp was supported by the Ro¨ntgen A˚ ngstro¨m Cluster (grant 349-2013-597). Nicholas Pearce is currently funded by the SciLifeLab and Wallenberg Data Driven Life Science Program (grant KAW 2020.0239) and has previously been funded by a Veni Fellowship (VI.Veni.192.143) from the Dutch Research Council (NWO), a Long-term EMBO fellowship (ALTF 609-2017) and EPSRC grant EP/G037280/1. David M. Lawson received funding from BBSRC Institute Strategic Programme Grants (BB/P012523/1 and BB/P012574/1). Lucrezia Catapano is the recipient of an STFC/CCP4-funded PhD studentship (Agreement No: 7920 S2 2020 007).Peer reviewe

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates