Pay-for-performance (PFP) is a relatively new approach to agricultural conservation that attaches an incentive payment to quantified reductions in nutrient runoff from a participating farm. Similar to a payment for ecosystem services approach, PFP lends itself to providing incentives for the most beneficial practices at the field level. To date, PFP conservation in the U.S. has only been applied in small pilot programs. Because monitoring conservation performance for each field enrolled in a program would be cost-prohibitive, field-level modeling can provide cost-effective estimates of anticipated improvements in nutrient runoff. We developed a PFP system that uses a unique application of one of the leading agricultural models, the USDA’s Soil and Water Assessment Tool, to evaluate the nutrient load reductions of potential farm practice changes based on field-level agronomic and management data. The initial phase of the project focused on simulating individual fields in the River Raisin watershed in southeastern Michigan. Here we present development of the modeling approach and results from the pilot year, 2015-2016. These results stress that (1) there is variability in practice effectiveness both within and between farms, and thus there is not one “best practice” for all farms, (2) conservation decisions are made most effectively at the scale of the farm field rather than the sub-watershed or watershed level, and (3) detailed, field-level management information is needed to accurately model and manage on-farm nutrient loadings.

Supplemental information mentioned in the article is attached as a separate document.

The emerging field of neuroprosthetics is focused on the development of new therapeutic interventions that will be able to restore some lost neural function by selective electrical stimulation or by harnessing activity recorded from populations of neurons. As more and more patients benefit from these approaches, the interest in neural interfaces has grown significantly and a new generation of penetrating microelectrode arrays are providing unprecedented access to the neurons of the central nervous system (CNS). These microelectrodes have active tip dimensions that are similar in size to neurons and because they penetrate the nervous system, they provide selective access to these cells (within a few microns). However, the very long-term viability of chronically implanted microelectrodes and the capability of recording the same spiking activity over long time periods still remain to be established and confirmed in human studies. Here we review the main responses to acute implantation of microelectrode arrays, and emphasize that it will become essential to control the neural tissue damage induced by these intracortical microelectrodes in order to achieve the high clinical potentials accompanying this technology.

In this study, a low-cycle fatigue experiment was conducted on printed wiring boards (PWB). The Weibull regression model and computational Bayesian analysis method were applied to analyze failure time data and to identify important factors that influence the PWB lifetime. The analysis shows that both shape parameter and scale parameter of Weibull distribution are affected by the supplier factor and preconditioning methods Based on the energy equivalence approach, a 6-cycle reflow precondition can be replaced by a 5-cycle IST precondition, thus the total testing time can be greatly reduced. This conclusion was validated by the likelihood ratio test of two datasets collected under two different preconditioning methods Therefore, the Weibull regression modeling approach is an effective approach for accounting for the variation of experimental setting in the PWB lifetime prediction.

Studies about the data quality of National Bridge Inventory (NBI) reveal missing, erroneous, and logically conflicting data. Existing data quality programs lack a focus on detecting the logical inconsistencies within NBI and between NBI and external data sources. For example, within NBI, the structural condition ratings of some bridges improve over a period while having no improvement activity or maintenance funds recorded in relevant attributes documented in NBI. An example of logical inconsistencies between NBI and external data sources is that some bridges are not located within 100 meters of any roads extracted from Google Map. Manual detection of such logical errors is tedious and error-prone. This paper proposes a systematical “hypothesis testing” approach for automatically detecting logical inconsistencies within NBI and between NBI and external data sources. Using this framework, the authors detected logical inconsistencies in the NBI data of two sample states for revealing suspicious data items in NBI. The results showed that about 1% of bridges were not located within 100 meters of any actual roads, and few bridges showed improvements in the structural evaluation without any reported maintenance records.

Quorum-sensing networks enable bacteria to sense and respond to chemical signals produced by neighboring bacteria. They are widespread: over 100 morphologically and genetically distinct species of eubacteria are known to use quorum sensing to control gene expression. This diversity suggests the potential to use natural protein variants to engineer parallel, input-specific, cell–cell communication pathways. However, only three distinct signaling pathways, Lux, Las, and Rhl, have been adapted for and broadly used in engineered systems. The paucity of unique quorum-sensing systems and their propensity for crosstalk limits the usefulness of our current quorum-sensing toolkit. This review discusses the need for more signaling pathways, roadblocks to using multiple pathways in parallel, and strategies for expanding the quorum-sensing toolbox for synthetic biology.

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale.

Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Climatic changes have the potential to impact electricity generation in the U.S. Southwest and methods are needed for estimating how cities will be impacted. This study builds an electricity vulnerability risk index for two Southwest cities (Phoenix and Los Angeles) based on climate-related changes in electricity generation capacity. Planning reserve margins (PRM) are used to estimate the potential for blackouts and brownouts under future climate scenarios. Reductions in PRM occur in both cities in 2016 with the most significant reductions occurring in regions relying more heavily on hydropower.

Background: Robotic devices have been utilized in gait rehabilitation but have only produced moderate results when compared to conventional physiotherapy. Because bipedal walking requires neural coupling and dynamic interactions between the legs, a fundamental understanding of the sensorimotor mechanisms of inter-leg coordination during walking, which are not well understood but are systematically explored in this study, is needed to inform robotic interventions in gait therapy.

Methods: In this study we investigate mechanisms of inter-leg coordination by utilizing novel sensory perturbations created by real-time control of floor stiffness on a split-belt treadmill. We systematically alter the unilateral magnitude of the walking surface stiffness and the timing of these perturbations within the stance phase of the gait cycle, along with the level of body-weight support, while recording the kinematic and muscular response of the unperturbed leg. This provides new insight into the role of walking surface stiffness in inter-leg coordination during human walking. Both paired and unpaired unadjusted t-tests at the 95 % confidence level are used in the appropriate scenario to determine statistical significance of the results.

Results: We present results of increased hip, knee, and ankle flexion, as well as increased tibialis anterior and soleus activation, in the unperturbed leg of healthy subjects that is repeatable and scalable with walking surface stiffness. The observed response was not impacted by the level of body-weight support provided, which suggests that walking surface stiffness is a unique stimulus in gait. In addition, we show that the activation of the tibialis anterior and soleus muscles is altered by the timing of the perturbations within the gait cycle.

Conclusions: This paper characterizes the contralateral leg’s response to ipsilateral manipulations of the walking surface and establishes the importance of walking surface stiffness in inter-leg coordination during human walking.

In recent years, concerns have grown over the risks posed by climate change on the U.S. electricity grid. The availability of water resources is integral to the production of electric power, and droughts are expected to become more frequent, severe, and longer-lasting over the course of the twenty-first century. The American Southwest, in particular, is expected to experience large deficits in streamflow. Studies on the Colorado River anticipate streamflow declines of 20-45% by 2050. Other climactic shifts—such as higher water and air temperatures—may also adversely affect power generation. As extreme weather becomes more common, better methods are needed to assess the impact of climate change on power generation. This study uses a physically-based modeling system to assess the vulnerability of power infrastructure in the Southwestern United States at a policy-relevant scale.

Thermoelectric power—which satisfies a majority of U.S. electricity demand—is vulnerable to drought. Thermoelectric power represents the backbone of the U.S. power sector, accounting for roughly 91% of generation. Thermoelectric power also accounts for roughly 39% of all water withdrawals in the U.S.—roughly equivalent to the amount of water used for agriculture. Water use in power plants is primarily dictated by the needs of the cooling system. During the power generation process, thermoelectric power plants build up waste heat, which must be discharged in order for the generation process to continue. Traditionally, water is used for this purpose, because it is safe, plentiful, and can absorb a large amount of heat. However, when water availability is constrained, power generation may also be adversely affected. Thermoelectric power plants are particularly susceptible to changes in streamflow and water temperature. These vulnerabilities are exacerbated by environmental regulations, which govern both the amount of water withdrawn, and the temperatures of the water discharged. In 2003, extreme drought and heat impaired the generating capacity of more than 30 European nuclear power plants, which were unable to comply with environmental regulations governing discharge temperatures. Similarly, many large base-load thermoelectric facilities in the Southeastern United States were threatened by a prolonged drought in 2007 and 2008. During this period, the Tennessee Valley Authority (TVA) reduced generation at several facilities, and one major facility was shut down entirely. To meet demand, the TVA was forced to purchase electricity from the grid, causing electricity prices to rise.

Although thermoelectric power plants currently produce most of the electric power consumed in the United States, other sources of power are also vulnerable to changes in climate. Renewables are largely dependent on natural resources like rain, wind, and sunlight. As the quantity and distribution of these resources begins to change, renewable generation is also likely to be affected. Hydroelectric dams represent the largest source of renewable energy currently in use throughout the United States. Under drought conditions, when streamflow attenuates and reservoir levels drop, hydroelectric plants are unable to operate at normal capacity. In 2001, severe drought in California and the Pacific Northwest restricted hydroelectric power generation, causing a steep increase in electricity prices. Although blackouts and brownouts were largely avoided, the Northwest Power and Conservation Council estimated a regional economic impact of roughly $2.5 to $6 billion. In addition to hydroelectric power, it has also been theorized that solar energy resources may also be susceptible to predicted increases in surface temperature and atmospheric albedo. One study predicts that solar facilities in the Southwestern U.S. may suffer losses of 2-5%.

The aim of this study is to estimate the extent to which climate change may impact power generation in the Southwestern United States. This analysis will focus on the Western Interconnection, which comprises the states of Washington, Oregon, California, Idaho, Nevada, Utah, Arizona, Colorado, Wyoming, Montana, South Dakota, New Mexico and Texas. First, climactic and hydrologic parameters relevant to power generation are identified for five types of generation technologies. A series of functional relationships are developed such that impacts to power generation can be estimated directly from changes in certain meteorological and hydrological parameters. Next, climate forcings from the CMIP3 multi-model ensemble are used as inputs to a physically-based modeling system (consisting of a hydrological model, an offline routing model, and a one-dimensional stream temperature model). The modeling system is used to estimate changes in climactic and hydrologic parameters relevant to electricity generation for various generation technologies. Climactic and hydrologic parameters are then combined with the functional relationships developed in the first step to estimate impacts to power generation over the twenty-first century.

The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint as another special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0-1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.