scholarly journals Prosecutorial Involvement in Exoneration

2020 ◽  
Vol 1 (1) ◽  
pp. 74-100
Author(s):  
Rachel Bowman ◽  
Jon Gould
Keyword(s):  
Performance Metrics ◽  
Organizational Factors ◽  
Wrongful Conviction ◽  
Institutional Barriers ◽  
Related Factors ◽  
Wrongful Convictions ◽  
Professional Performance ◽  
District Attorneys ◽  
Wide Range ◽  
Court Community

The current literature on wrongful convictions documents the legal, psychological, and institutional barriers that prosecutors face in considering post-conviction claims of innocence. However, less is known about how the local court context may relate to prosecutors’ decisions to engage in wrongful conviction investigations. To address this gap, the present study explores how characteristics of the local court community are related to the likelihood of prosecutors assisting, actively opposing, or remaining uninvolved in post-conviction claims of innocence. Specifically, we examine prosecutorial involvement in exonerations from three levels: case-factors, organizational factors, and county-context factors. Using archival data on the exonerations of factually innocent individuals (N = 75), we find that case-related factors are the strongest predictors of prosecutors’ involvement in exonerations. Broadly, our findings suggest that prosecutors are more willing to revisit, assist and even investigate potentially wrongful convictions when the stakes are lower (e.g. the offense is less severe, there is no alleged official misconduct, the district attorney is well-established in the role, etc.). Given the wide range of prosecutorial responses to wrongful conviction claims, we emphasize the importance of specialized conviction review units to help routinize the practice of post-conviction review. Secondly, we suggest that district attorneys explicitly define professional performance metrics to include corrective measures such as assisting in the review of wrongful conviction claims. Finally, we encourage states to adopt formal legal regulations to guide prosecutorial behavior in response to post-conviction claims of innocence.

scholarly journals Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

2021 ◽  
Vol 11 (13) ◽  
pp. 5859
Author(s):  
Fernando N. Santos-Navarro ◽  
Yadira Boada ◽  
Alejandro Vignoni ◽  
Jesús Picó
Keyword(s):  
Gene Expression ◽  
Gene Transcription ◽  
Performance Metrics ◽  
Cell Mass ◽  
Gene Copy ◽  
Substrate Uptake ◽  
Promoter Strength ◽  
Expression Levels ◽  
Trade Offs ◽  
Wide Range

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

scholarly journals Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dieter M. Tourlousse ◽  
Koji Narita ◽  
Takamasa Miura ◽  
Mitsuo Sakamoto ◽  
Akiko Ohashi ◽  
...  
Keyword(s):  
Dna Extraction ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Collaborative Study ◽  
Human Microbiome ◽  
Second Phase ◽  
Intermediate Precision ◽  
Fecal Microbiome ◽  
Library Construction ◽  
Wide Range

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

Abstract TP132: Selection and Activation of Sites in a Large Multi-Center Randomized Clinical Trial

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Bart M Demaerschalk ◽  
Robert D Brown ◽  
Virginia J Howard ◽  
MeeLee Tom ◽  
Mary E Longbottom ◽  
...  
Keyword(s):  
Site Selection ◽  
Performance Metrics ◽  
Interventional Neuroradiology ◽  
Interquartile Range ◽  
Carotid Revascularization ◽  
Training Requirements ◽  
Medical Centers ◽  
Wide Range ◽  
The Times ◽  
And Training

Introduction: Careful selection and timely activation of clinical sites in multicenter clinical trials is critical for successful enrollment, subject safety, and generalizability of results. Methods: In the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST-2), a multidisciplinary Site Selection Committee evaluated applicants referred via participation in CREST, CREST principal investigators (PIs) and other investigators, StrokeNet and industry partners. Data for consideration included performance metrics in CREST and other carotid trials and a site selection questionnaire containing information on the investigators as well as quantitative data on carotid procedures performed. Any FDA warning letters were reviewed. Results: The Committee met bi-weekly for 36 months (n=64 meetings). Applications from 176 sites between March 2014 and July 2016 were evaluated: 153 were approved, 7 are under Committee review, 5 were approved but withdrew, 5 were placed on a waiting list, and 6 were rejected. One-hundred-four sites have completed the regulatory and training requirements to randomize: 51 (49%) academic medical centers, 31 (30%) private hospital-based centers, 16 (15%) private office-based practices, and 6 (6%) Veterans Administration medical centers. The mean times from application-to- approval was 5.2 weeks (interquartile range, 1.9, 6.2), and from approval-to-randomization status was 46.7 weeks (interquartile range, 35.4, 51.7). Specialties of the 104 site PIs are vascular surgery for 35 (33.7%), cardiology for 30 (28.8%), neurology for 25 (24%), neurosurgery for 8 (7.7%), interventional radiology for 4 (3.8%), and interventional neuroradiology for 2 (1.9%). Conclusions: Careful site selection is time-consuming for prospective sites and for trial leadership. Times from application-to-site-approval were modest (mean = 5.2 weeks), in contrast to the times for completing regulatory and training requirements (mean = 46.7 weeks). However, subject enrollment by teams from a wide range of medical centers led by a multi-disciplinary cohort of PIs will promote the generalizability of trial results.

The Necessity of Performance Measures for Prosecutors

2021 ◽  
pp. 226-258
Author(s):  
Lauren-Brooke Eisen ◽  
Miriam Aroni Krinsky
Keyword(s):  
Performance Measures ◽  
Performance Metrics ◽  
Mass Incarceration ◽  
The United States ◽  
New Wave ◽  
Criminal Cases ◽  
Critical Data ◽  
District Attorneys ◽  
The Right ◽  
Individual Line

Local prosecutors are responsible for 95 percent of criminal cases in the United States—their charging decisions holding enormous influence over the number of people incarcerated and the length of sentences served. Performance metrics are a tool that can align the vision of elected prosecutors with the tangible actions of their offices’ line attorneys. The right metrics can provide clarity to individual line attorneys around the mission of the office and the goals of their job. Historically, however, prosecutor offices have relied on evaluation metrics that incentivize individual attorneys to prioritize more punitive responses and volume-driven activity—such as tracking the number of cases processed, indictments, guilty pleas, convictions, and sentence lengths. Under these past approaches, funding, budgeting, and promotional decisions are frequently linked to regressive measures that fail to account for just results. As more Americans have embraced the need to end mass incarceration, a new wave of reform-minded district attorneys have won elections. To ensure they are accountable to the voters who elected them into office and achieve the changes they championed, they must align measures of success with new priorities for their offices. New performance metrics predicated on the goals of reducing incarceration and enhancing fairness can shrink prison and jail populations, while improving public trust and promoting healthier and safer communities. The authors propose a new set of metrics for elected prosecutors to consider in designing performance evaluations, both for their offices and for individual attorneys. The authors also suggest that for these new performance measures to effectively drive decarceration practices, they must be coupled with careful, thoughtful implementation and critical data-management infrastructure.

The Criminology of Wrongful Conviction

2016 ◽  
Vol 33 (1) ◽  
pp. 82-106 ◽  
Author(s):  
Richard A. Leo
Keyword(s):  
Criminal Justice ◽  
Justice System ◽  
Wrongful Conviction ◽  
Wrongful Convictions ◽  
Cause Analysis ◽  
Academic Field ◽  
Root Cause ◽  
Improve Accuracy ◽  
Current Article ◽  
To Come

This article reflects on the author’s 2005 article, “Rethinking the Study of Miscarriages of Justice,” which sought to describe what scholars empirically knew at that time about the phenomenon, causes, and consequences of wrongful convictions in America. The 2005 article argued that the study of wrongful convictions constituted a coherent academic field of study and set forth a vision for a more sophisticated, insightful, and generalizable criminology of wrongful conviction. In this current article, the author revisits the ideas first developed in “Rethinking the Study of Miscarriages of Justice” to evaluate what scholars have learned about wrongful convictions in the last decade, and what challenges lie ahead for developing a more robust criminology of wrongful conviction. The article concludes that there have been significant theoretical, methodological, and substantive advances in the last decade, but that a root cause analysis of wrongful convictions has yet to come to fruition and urges empirical scholars to begin to study other sources of error and inaccuracy in the criminal justice system. Scholars should develop a criminology of erroneous outcomes, not just of erroneous conviction. By studying both sets of outcomes, scholars can improve accuracy and reduce errors across the board.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals White lie during patient care: A qualitative study of nurses’ perspectives

2020 ◽  
Author(s):  
Alireza Nikbakht nasrabadi ◽  
soodabeh joolaee ◽  
Elham Navab ◽  
Maryam esmaeilie ◽  
mahboobe shali
Keyword(s):  
Qualitative Study ◽  
Patient Care ◽  
Healthcare Providers ◽  
Cultural Knowledge ◽  
Hospital Nurses ◽  
Structured Interviews ◽  
Related Factors ◽  
Patient’S Rights ◽  
Wide Range ◽  
Data Saturation

Abstract Background: Keeping the patients well and fully informed about diagnosis, prognosis, and treatments is one of the patient’s rights in any healthcare system. Although all healthcare providers have the same viewpoint about rendering the truth in treatment process, sometimes the truth is not told to the patients; that is why the healthcare staff tell “white lie” instead. This study aimed to explore the nurses’ experience of white lies during patient care. Methods: This qualitative study was conducted from June to December 2018. Eighteen hospital nurses were recruited with maximum variation from ten state-run educational hospitals affiliated to Tehran University of Medical Sciences. Purposeful sampling was used and data were collected by semi-structured interviews that were continued until data saturation. Data were classified and analyzed by content analysis approach. Results: The data analysis in this study resulted in four main categories and eleven subcategories. The main categories included hope crisis, bad news, cultural diversity, and nurses’ limited professional competences. Conclusion: Results of the present study showed that, white lie told by nurses during patient care may be due to a wide range of patient, nurse and/or organizational related factors. Communication was the main factor that influenced information rendering. Nurses’ communication with patients should be based on mutual respect, trust and adequate cultural knowledge, and also nurses should provide precise information to patients, so that they can make accurate decisions regarding their health care.

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