Molecular characterization, polymorphism of growth differentiation factor 5 gene and association with ultrasound measurement traits in native Chinese cattle breeds

Growth differentiation factor 5 (GDF5), involved in the development and maintenance of bone andcartilage, is a n important candidate gene for growth and carcass traits selection through markerassisted selection (MAS). Genomic structural analysis showed that bovine GDF5 shares much similarity with human GDF5. The latest findings demonstrate that the single nucleotide polymorphism (SNP) T586C in exon 1 is significantly associated with ultrasound marbling score (UMAR) and ultrasound backfat thickness (UBF). Furthermore, the analysis of T586C SNP marker shows there are significant effects on the UBF (P = 0.0498) and on the UMAR (P = 0.0058) in 465 individuals. These results clearly suggest that the GDF5 gene is among target genes for carcass traits in bovine reproduction and breeding.Keywords: Cattle, GDF5 gene, ultrasound measurement, polymorphism, association analysisAfrican Journal of Biotechnology Vol. 9(33), pp. 5269-5273, 16 August, 201

Towards Sustainable Ultrawide Bandgap Van der Waals Materials: An ab initio Screening Effort

The sustainable development of next-generation device technology is paramount in the face of climate change and the looming energy crisis. Tremendous efforts have been made in the discovery and design of nanomaterials that achieve device-level sustainability, where high performance and low operational energy cost are prioritized. However, many of such materials are composed of elements that are under threat of depletion and pose elevated risks to the environment. The role of material-level sustainability in computational screening efforts remains an open question thus far. Here we develop a general van der Waals materials screening framework imbued with sustainability-motivated search criteria. Using ultrawide bandgap (UWBG) materials as a backdrop -- an emerging materials class with great prospects in dielectric, power electronics, and ultraviolet device applications, we demonstrate how this screening framework results in 25 sustainable UWBG layered materials comprising only of low-risks elements. Our findings constitute a critical first-step towards reinventing a more sustainable electronics landscape beyond silicon, with the framework established in this work serving as a harbinger of sustainable 2D materials discovery.Comment: 15 pages, 8 figure

Development of an Ocean Hazards Classification Scheme (OHCS) for Projecting Future Scenario Vulnerability Ranking on Coastal Built Infrastructure

From many sources, we develop an ocean hazard classification scheme (OHCS) based on the collection of historical and projected ocean hazards data at 302 locations along Hawaii’s state coastal highways. The OHCS identifies ocean hazards impacting coastal built infrastructure, i.e. roadways. In the OHCS, we first rank the vulnerability of: sea level rise; waves; shoreline change; tsunami; and storm surge. Next, using our developed OHCS, provide the vulnerability ranking for all five variables combined. We find the highest OHCS to be on Molokai, the island that has the highest OHCS numbers for most of the island. For the majority of state highway locations in Hawaii, we find the highest vulnerability is from storm surge, with tsunami threat being the second largest contributor. Sea level rise should also be considered a contributor since higher sea levels contribute to more extreme storm surge and tsunami inundation. Although the OHCS is applied towards roads in our study, our method can be applied towards any coastal island-based built infrastructure vulnerability scheme. This is an important tool in planning for future construction projects or identifying which hazards to focus on in more detailed assessments, such a probabilistic risk assessment in a more localized location

Stormwater Drainage Design and Best Management Practices with Applications to Roadways and Climate Change [Brief]

USDOT Grant 69A3551747109The objective of this project is to develop a 5-hour training course that is intended to prepare participants to describe and analyze stormwater runoff problems and select and design appropriate stormwater Best Management Practices (BMPs), with applications to roadways and climate change

The GPS Geodetic Infrastructure in the Puerto Rico and Virgin Islands Region and Its Applications for Faulting, Landslide, and Sea-Level Change Study

The Puerto Rico and Virgin Islands (PRVI) are located within the complex plate boundary zone between the North American and Caribbean plates in the northeastern Caribbean Sea. The interaction of geophysical risk and human settlement makes the region especially vulnerable to natural hazards, such as earthquakes, tsunamis, landslides, hurricanes, and flooding. This region is one of the earliest places that employed high-accuracy Global Positioning System (GPS) technology to study plate tectonics and natural hazards. A dense Continuously Operating Reference Stations (CORS) network with 24 permanent GPS stations is currently jointly operated by academic, government, and local land surveying communities. We summarize the current GPS geodetic infrastructure in the PRVI region: a dense CORS network, a stable local reference frame, and sophisticated GPS data processing software packages. We established a stable Puerto Rico and Virgin Islands reference frame (PRVI14), which is essential to precisely delineate local ground deformation in the space and time domains. The current geodetic infrastructure in the PRVI region can distinguish ground deformation as slow as 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. Our study shows the island of St. Croix is moving away from the PRVI region toward southeast at a steady velocity of 1.7 mm/year; the Lajas Valley in southwestern Puerto Rico is experiencing a north-south extension (1.5 mm/year) and a minor right-lateral strike slip (0.4 mm/year) with respect to PRVI14. We developed a workflow for conducting millimeter-accuracy landslide monitoring without installing any reference stations in the field, which is more efficient than the conventional differential positioning method. We performed a GPS, tide gauge, and satellite altimeter integrated sea-level change study in the PRVI and the whole Caribbean region. Our results show that current absolute sea-level rise rate along PRVI coasts is 1.7±0.3 mm/year, according to the tide gauge dataset from 1955 to 2015. The sea-level change rates have considerable temporal and spatial variabilities, and estimates may be subject to the techniques used and observation periods in the Caribbean Sea. It is expected that our study will promote applications of GPS techniques in natural hazards research and mitigation in the PRVI region and in other areas prone to natural hazards.Earth and Atmospheric Sciences, Department o

Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring

Is There Deep-Seated Subsidence in the Houston-Galveston Area?

Long-term continuous groundwater level and land subsidence monitoring in the Houston-Galveston area indicates that, during the past two decades (1993–2012), the groundwater head has been increasing and the overall land subsidence rate has been decreasing. Assuming that the hydraulic head in the aquifer will reach or exceed the preconsolidation level in the near future, will subsidence in the Houston-Galveston area eventually cease? The key to answer this question is to identify if there is deep-seated subsidence in this area. This study investigated the recent subsidence observed at different depths in the Houston-Galveston area. The subsidence was recorded by using 13 borehole extensometers and 76 GPS antennas. Four of the GPS antennas are mounted on the deep-anchored inner pipes of borehole extensometers. We conclude that recent subsidence (1993–2012) in the Houston-Galveston area was dominated by the compaction of sediments within 600 m below the land surface. Depending on the location of specific sites, the compaction occurred within the Chicot aquifer and part or all of the Evangeline aquifer. No measurable compaction was observed within the Jasper aquifer or within deeper strata. Deep-seated subsidence is not likely occurring in the Houston-Galveston area

A fatigue assessment method based on attention mechanism and surface electromyography

Surface electromyography (sEMG) signals can be used to quantitatively assess muscle fatigue, thereby directly and objectively reflecting the functional state of neuromuscular activity. Effective fatigue diagnosis can prevent muscle damage, thereby improving the safety of rehabilitation exercise. Traditional fatigue diagnosis has certain limitations, including strong subjectivity and poor accuracy. This paper designs a sEMG signals acquisition circuit and collects the sEMG signals of the upper limb biceps brachii and triceps brachii in the force-relaxation state in a dual-channel form. Muscle fatigue classification assessment using Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) and three deep learning algorithms. The experimental results show that compared with traditional machine learning algorithms, deep learning algorithm can achieve higher accuracy and time efficiency. In addition, this study introduces an attention mechanism to dynamically and reasonably assign network weights to achieve high level feature learning. The Attention-Long Short-Term Memory (Attention Based LSTM) neural network achieves 93.5% assessment accuracy with a time overhead of only 3.73s, allowing for real-time assessment of muscle fatigue

Adaptive Hierarchical Collocation Method for Solving Fractional Population Diffusion Model

The fractional population diffusion model is crucial for pest prevention. This paper presents an adaptive hierarchical collocation method for solving this model, enhancing the efficiency of algorithms based on Low-Complexity Shannon-Cosine wavelet derived from combinatorial identity theory. This function, an improvement over previous constructs, mitigates the need for iterative computation of parameters and boasts advantages like interpolation, symmetry, and compact support. The method’s extension to other time-fractional partial differential equations (PDEs) is also possible. The algorithm’s complexity analysis illustrates the concise function’s efficiency advantage over the original expression when solving time-fractional PDEs. Comparatively, the method exhibits superior numerical performance to alternative wavelet spectral methods like the Shannon–Gabor wavelet