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To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends. The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya's domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback. Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8-14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife. This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

Kochia [Bassia scoparia (L.) A.J. Scott] is an invasive tumbleweed in the North American Great Plains that is difficult to manage in croplands and ruderal areas due to widespread resistance to up to four herbicide sites of action, including auxin mimics (Herbicide Resistance Action Committee [HRAC] Group 4) and inhibitors of acetolactate synthase (HRAC Group 2), photosystem II (HRAC Group 5), and 5-enolpyruvylshikimate-3-phosphate synthase (HRAC Group 9). Poor B. scoparia control with protoporphyrinogen oxidase (PPO)-inhibiting (HRAC Group 14) herbicides was noted in a brown mustard [Brassica juncea (L.) Czern.] field near Kindersley, SK, Canada, in 2021. Similar observations were made in a sunflower (Helianthus annuus L.) field near Mandan, ND, USA, and in research plots near Minot, ND, USA, in 2022. Whole-plant dose–response experiments were conducted to determine whether these B. scoparia accessions were resistant to the PPO-inhibiting herbicides saflufenacil and carfentrazone and the level of resistance observed. All three B. scoparia accessions were highly resistant to foliar-applied saflufenacil and carfentrazone compared with two locally relevant susceptible accessions. The Kindersley accession exhibited 57- to 87-fold resistance to saflufenacil and 97- to 121-fold resistance to carfentrazone based on biomass dry weight at 21 d after treatment (DAT). Similarly, the Mandan accession exhibited 204- to 321-fold resistance to saflufenacil and 111- to 330-fold resistance to carfentrazone, while the Minot accession exhibited 45- to 71-fold resistance to saflufenacil and 88- to 264-fold resistance to carfentrazone. Substantial differences in visible control at 7 and 21/28 DAT were also observed between the putative-resistant and susceptible accessions. This study represents the first confirmations of PPO inhibitor–resistant B. scoparia globally and the fifth herbicide site of action to which B. scoparia has evolved resistance. It also documents this issue present at three locations in the Northern Great Plains region that occur up to 790 km apart and on both sides of the Canada/U.S. border.

As the use of simulation increases across many different application domains, the need for high-fidelity three-dimensional virtual representations of real-world environments has never been greater. This need has driven the research and development of both faster and easier methodologies for creating such representations. In this research, we present two different inference-based geometric modeling techniques that support the automatic construction of complex cluttered environments. The first method we present is a surface reconstruction-based approach that is capable of reconstructing solid models from a point cloud capture of a cluttered environment. Our algorithm is capable of identifying objects of interest amongst a cluttered scene, and reconstructing complete representations of these objects even in the presence of occluded surfaces. This approach incorporates a predictive modeling framework that uses a set of user provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several synthetic and real-world datasets containing heavy clutter and occlusion. The second method we present is a generative modeling-based approach that can construct a wide variety of diverse models based on user provided templates. This technique leverages an inference-based construction algorithm for developing solid models from these template objects. This algorithm samples and extracts surface patches from the input models, and develops a Petri net structure that is used by our algorithm for properly fitting these patches in a consistent fashion. Our approach uses this generated structure, along with a defined parameterization (either user-defined through a simple sketch-based interface or algorithmically defined through various methods), to automatically construct objects of varying sizes and configurations. These variations can include arbitrary articulation, and repetition and interchanging of parts sampled from the input models. Finally, we affirm our motivation by showing an application of these two approaches. We demonstrate how the constructed environments can be easily used within a physically-based simulation, capable of supporting many different application domains.

Abstract Background To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to demonstrate its robustness and ability to track disease trends. Methods The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya’s domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback. Results Over 95% of trained domestic animal officers downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 10-fold increase in number of disease reports when compared the previous year (p<0.05), and reports were more spatially distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife. Conclusions This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases. Authors Summary Taking advantage of a recently developed freely downloadable disease reporting application in the United States, we customized it for android smartphones to collect and submit domestic and wild animal disease data in real-time in Kenya. To enhance user friendliness, the Kenya Animal Biosurveillance System (KABS) was installed with disease reporting tools currently used by the animal sector and tailored to collected data on transboundary animal disease important for detecting zoonotic endemic and emerging diseases. The KABS database was housed by the government of Kenya, providing important assurance on its security. The application had a feedback module that performed basics analysis to provide feedback to the end-user in real-time. Rolling out of KABS resulted in >70% of domestic and wildlife disease surveillance officers using it to report, resulting in exponential increase in frequency and spatial distributions of reports regions. Utility of the system was demonstrated by successful detected a Rift Valley fever outbreak in livestock in 2018, resulting in early response and prevention of widespread human infections. For the wildlife sector in Eastern Africa, the application provided the first disease surveillance system developed. This open-source system is ideal for rolling out in other countries in sub-Saharan Africa to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

Groundwater conservation districts in the THP region have already enacted restrictions on groundwater pumping to prolong the usable economic lifetime of the Ogallala Aquifer. [...]IT-based decision support tools are needed for cotton producers in these regions to efficiently apply limited irrigation water and optimize crop yield, while complying with groundwater pumping restrictions. The app is organized into three major components: (1) data input from the user on initial field conditions, cultivar used, tillage, planting details (e.g., date, row spacing, and seeding rate), chemicals used, and harvest date; (2) data processing and computation to create input files and for execution of the DSSAT and economic models; and (3) generation and presentation of the results, including irrigation schedules, water use efficiency, and expected yield and net return. To ensure the reliability of weather data, idCROP combines weather data from four sources: (1) historic weather data until the day before the date of simulation (or app use) obtained from a nearby weather station; (2) real-time data from an on-site or nearby weather station; (3) daily short-term forecasted weather data for a 16day period obtained from the Global Forecast System (GFS); and (4) daily seasonal forecasted weather data for six months obtained from the North American Multi-Model Ensemble (NMME) forecasting system. The next steps Compared to other irrigation decision support systems, idCROP has several distinct benefits, including: (1) more reliable estimation of weather parameters for crop model simulation by using a combination of historic, real-time, and shortterm forecasted weather data; (2) improvement in the site-specific accuracy of irrigation schedules by combining crop model-based irrigation scheduling with on-farm, non-contact sensor data; and (3) calculation of net returns, enabling users to choose an irrigation strategy that best fits their well capacities, yield goals, and economic goals.

Early-onset colorectal cancer (EOCRC) is defined as a diagnosis of colorectal cancer (CRC) in individuals younger than 50 years of age. While overall CRC rates in the United States (US) decreased between 2001 and 2018, EOCRC rates have increased. This research project aims to evaluate the feasibility and acceptability of Time-Restricted Eating (TRE), Mindfulness, or TRE combined with Mindfulness among young to middle-aged adults at risk of EOCRC. Forty-eight participants will be randomly assigned to one of four groups: TRE, Mindfulness, TRE and Mindfulness, or Control. Data on feasibility, adherence, and acceptability will be collected. Measures assessed at baseline and post-intervention will include body weight, body composition, dietary intake, physical activity, sleep behavior, circulating biomarkers, hair cortisol, and the gut microbiome. The effects of the intervention on the following will be examined: (1) acceptability and feasibility; (2) body weight, body composition, and adherence to TRE; (3) circulating metabolic, inflammation, and oxidative stress biomarkers; (4) intestinal inflammation; and (5) the gut microbiome. TRE, combined with Mindfulness, holds promise for stress reduction and weight management among individuals at risk of EOCRC. The results of this pilot study will inform the design and development of larger trials aimed at preventing risk factors associated with EOCRC.

ThinkVantage Technologies bring your PCs one step closer to being self-configured, self-optimizing, self-protecting, and self-healing, to help save you time and money throughout the life of your systems. In short, ThinkVantage Technologies let you focus your attention on your business, rather than on your computer.This IBM Redbooks publication helps you maintain, recover, and secure the ThinkVantage Technologies on Lenovo, IBM, and third-party desktops, and mobile computers.ThinkVantage Technologies covered in the book include:Rescue and Recovery with Antidote Delivery Manager V3.0System Information Center V1.2Software Delivery Center V1.1ThinkVantage Productivity Center V1.01Client Security Software V5.4Client Security Solution V6Fingerprint reader V4.6ThinkVantage System Update V1 This is Volume 2 of a two-volume set of ThinkVantage Technologies Redbooks. Be sure to look for the first volume: Using ThinkVantage Technologies Volume 1: Creating and Deploying Client Systems, SG24-7106.