The Role of Policy Makers and Institutions in the Energy Sector: The Case of Energy Infrastructure Governance in Nigeria

This paper focuses on investigating the linkages and consequences of the policy decision process in the governance of energy infrastructure in Nigeria. It attempts to gain a better understanding of the role of policy makers and institutions in the provision of energy infrastructure in Nigeria. Using a combination of semi-structured interviews and documentary evidences from published literature, this study reveals three essential areas where the policy-making processes (and therefore policy makers) intervene in the provision of energy infrastructure. These are: (1) granting access to historical data; (2) regulations; and (3) permitting/issuance of licenses. This study also reveals three major unintended consequences of the policy decision processes and institutions in the governance of energy infrastructure provisions in Nigeria, which are: (1) government financing corruption in the energy sector; (2) economic delusion; and (3) uncontrolled growth in energy demand driven more by export and not local internal demand

Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures

Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients

Promoter methylation of Wnt-antagonists in polypoid and nonpolypoid colorectal adenomas.

BACKGROUND: Nonpolypoid adenomas are a subgroup of colorectal adenomas that have been associated with a more aggressive clinical behaviour compared to their polypoid counterparts. A substantial proportion of nonpolypoid and polypoid adenomas lack APC mutations, APC methylation or chromosomal loss of the APC locus on chromosome 5q, suggesting the involvement of other Wnt-pathway genes. The present study investigated promoter methylation of several Wnt-pathway antagonists in both nonpolypoid and polypoid adenomas. METHODS: Quantitative methylation-specific PCR (qMSP) was used to evaluate methylation of four Wnt-antagonists, SFRP2, WIF-1, DKK3 and SOX17 in 18 normal colorectal mucosa samples, 9 colorectal cancer cell lines, 18 carcinomas, 44 nonpolypoid and 44 polypoid adenomas. Results were integrated with previously obtained data on APC mutation, methylation and chromosome 5q status from the same samples. RESULTS: Increased methylation of all genes was found in the majority of cell lines, adenomas and carcinomas compared to normal controls. WIF-1 and DKK3 showed a significantly lower level of methylation in nonpolypoid compared to polypoid adenomas (p < 0.01). Combining both adenoma types, a positive trend between APC mutation and both WIF-1 and DKK3 methylation was observed (p < 0.05). CONCLUSIONS: Methylation of Wnt-pathway antagonists represents an additional mechanism of constitutive Wnt-pathway activation in colorectal adenomas. Current results further substantiate the existence of partially alternative Wnt-pathway disruption mechanisms in nonpolypoid compared to polypoid adenomas, in line with previous observations

Promoter methylation of Wnt-antagonists in polypoid and nonpolypoid colorectal adenomas

BACKGROUND: Nonpolypoid adenomas are a subgroup of colorectal adenomas that have been associated with a more aggressive clinical behaviour compared to their polypoid counterparts. A substantial proportion of nonpolypoid and polypoid adenomas lack APC mutations, APC methylation or chromosomal loss of the APC locus on chromosome 5q, suggesting the involvement of other Wnt-pathway genes. The present study investigated promoter methylation of several Wnt-pathway antagonists in both nonpolypoid and polypoid adenomas. METHODS: Quantitative methylation-specific PCR (qMSP) was used to evaluate methylation of four Wnt-antagonists, SFRP2, WIF-1, DKK3 and SOX17 in 18 normal colorectal mucosa samples, 9 colorectal cancer cell lines, 18 carcinomas, 44 nonpolypoid and 44 polypoid adenomas. Results were integrated with previously obtained data on APC mutation, methylation and chromosome 5q status from the same samples. RESULTS: Increased methylation of all genes was found in the majority of cell lines, adenomas and carcinomas compared to normal controls. WIF-1 and DKK3 showed a significantly lower level of methylation in nonpolypoid compared to polypoid adenomas (p < 0.01). Combining both adenoma types, a positive trend between APC mutation and both WIF-1 and DKK3 methylation was observed (p < 0.05). CONCLUSIONS: Methylation of Wnt-pathway antagonists represents an additional mechanism of constitutive Wnt-pathway activation in colorectal adenomas. Current results further substantiate the existence of partially alternative Wnt-pathway disruption mechanisms in nonpolypoid compared to polypoid adenomas, in line with previous observations

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Long Lamai community ICT4D E‐commerce system modelling: an agent oriented role‐based approach

This paper presents the post‐mortem report upon completion of the Long Lamai e‐commerce development project. Some weaknesses with regards to the current software modelling approach are identified and an alternative role‐based approach is proposed. We argue that the existing software modelling technique is not suitable for modelling, making it difficult to establish a good contract between stakeholders causing delays in the project delivery. The role‐based approach is able to explicitly highlight the responsibilities among stakeholders, while also forming the contract agreement among them leading towards sustainable ICT4D

Replication data for: The Etiology of Public Support for the Designated Hitter Rule

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Poster: Real-time Markerless Kinect based Finger Tracking and Hand Gesture Recognition for HCI

Hand gestures are intuitive ways to interact with a variety of user interfaces. We developed a real-time finger tracking technique using the Microsoft Kinect as an input device and compared its results with an existing technique that uses the K-curvature algorithm. Our technique calculates feature vectors based on Fourier descriptors of equidistant points chosen on the silhouette of the detected hand and uses template matching to find the best match. Our preliminary results show that our technique performed as well as an existing k-curvature algorithm based finger detection technique