The relationship between plant species richness and soil aggregate stability can depend on disturbance

Aims: Plant diversity has been shown to significantly increase topsoil aggregate stability of machine-graded ski slopes. We hypothesise that this effect is specific for these disturbed sites and that at sites of low and no disturbance the effect decreases. Methods: We determined plant species richness, cover percentage of five functional groups, root (length) density, and biomass as well as soil aggregate stability, gravimetric soil moisture, soil density, and particle size distribution at different levels of disturbance (i.e. graded and un-graded ski slopes and the surrounding area). Results: Plant species richness, vegetation cover, aggregate stability and soil moisture were significantly reduced on machine-graded slopes compared to control plots but hardly on un-graded slopes. On the contrary, machine-grading increased soil density and friction angle compared to un-graded ski slopes. The influence of species richness on aggregate stability was only positive on gravely soils and graded ski slopes. Aggregate stability increased linearly up to approximately eight plant species, 70% vegetation cover and 0.006g cm−3 root density. Conclusions: Our study showed that the relationship between plant diversity and aggregate stability was strongest on slopes with high disturbance and relatively low species numbers. We suggest that high plant diversity, vegetation cover and root density need to be established after major human disturbance such as gradin

Energetic Map Data Imputation: A Machine Learning Approach

Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profiles of 4.6 million kilometres of training and test traces. For evaluation, two test-scenarios capture the models’ performance for the analysed problem in two perspectives. First, we evaluate our ML models, followed by the problem-specific energetic evaluation perspective for better interpretability. From the latter, the results indicate energetic map data imputation performs promisingly better when using the regression instead of the classification model

Using Artificial Intelligence to Enhance Educational Opportunities and Student Services in Higher Education

Artificial intelligence (AI) technology is becoming the basis for business. Most businesses use it to improve the customer experience. The education community is just beginning to find ways to successfully implement AI for staff and students. Artificial Intelligence should be leveraged to create a better student experience. For example, Elon University uses AI to assist students with tracking previously taken courses and helps them apply the information to their course-planning (Gardner, 2018). Georgia State University uses Pounce, a chatbot built by AdmitHub, reducing summer melt by over 20% by reaching out to students via text when they have not completed tasks by certain dates (Page & Gehlbach, 2018). The use of this technology can range from help with admissions applications and FAFSA completion, class scheduling, and campus tours. Using AI within higher education will give faculty and staff the ability to be more effective and efficient when communicating with students

Analyses of nervous system patterning genes in the tardigrade Hypsibius exemplaris illuminate the evolution of panarthropod brains

Abstract Background Both euarthropods and vertebrates have tripartite brains. Several orthologous genes are expressed in similar regionalized patterns during brain development in both vertebrates and euarthropods. These similarities have been used to support direct homology of the tripartite brains of vertebrates and euarthropods. If the tripartite brains of vertebrates and euarthropods are homologous, then one would expect other taxa to share this structure. More generally, examination of other taxa can help in tracing the evolutionary history of brain structures. Tardigrades are an interesting lineage on which to test this hypothesis because they are closely related to euarthropods, and whether they have a tripartite brain or unipartite brain has recently been a focus of debate. Results We tested this hypothesis by analyzing the expression patterns of six3, orthodenticle, pax6, unplugged, and pax2/5/8 during brain development in the tardigrade Hypsibius exemplaris—formerly misidentified as Hypsibius dujardini. These genes were expressed in a staggered anteroposterior order in H. exemplaris, similar to what has been reported for mice and flies. However, only six3, orthodenticle, and pax6 were expressed in the developing brain. Unplugged was expressed broadly throughout the trunk and posterior head, before the appearance of the nervous system. Pax2/5/8 was expressed in the developing central and peripheral nervous system in the trunk. Conclusion Our results buttress the conclusion of our previous study of Hox genes—that the brain of tardigrades is only homologous to the protocerebrum of euarthropods. They support a model based on fossil evidence that the last common ancestor of tardigrades and euarthropods possessed a unipartite brain. Our results are inconsistent with the hypothesis that the tripartite brain of euarthropods is directly homologous to the tripartite brain of vertebrates

Remote Ischemic Preconditioning Neither Improves Survival nor Reduces Myocardial or Kidney Injury in Patients Undergoing Transcatheter Aortic Valve Implantation (TAVI)

BACKGROUND: Peri-interventional myocardial injury occurs frequently during transcatheter aortic valve implantation (TAVI). We assessed the effect of remote ischemic preconditioning (RIPC) on myocardial injury, acute kidney injury (AKIN) and 6-month mortality in patients undergoing TAVI. METHODS: We performed a prospective single-center controlled trial. Sixty-six patients treated with RIPC prior to TAVI were enrolled in the study and were matched to a control group by propensity-score. RIPC was applied to the upper extremity using a conventional tourniquet. Myocardial injury was assessed using high-sensitive troponin-T (hsTnT), and kidney injury was assessed using serum creatinine levels. Data were compared with the Wilcoxon-Rank and McNemar tests. Mortality was analysed with the log-rank test. RESULTS: TAVI led to a significant rise of hsTnT across all patients (p < 0.001). No significant inter-group difference in maximum troponin release or areas-under-the-curve was detected. Medtronic CoreValve and Edwards Sapien valves showed similar peri-interventional troponin kinetics and patients receiving neither valve did benefit from RIPC. AKIN occurred in one RIPC patient and four non-RIPC patients (p = 0.250). No significant difference in 6-month mortality was observed. No adverse events related to RIPC were recorded. CONCLUSION: Our data do not show a beneficial role of RIPC in TAVI patients for cardio- or renoprotection, or improved survival

Utility of the 3Di Short Version for the Diagnostic Assessment of Autism Spectrum Disorder and Compatibility with DSM-5

The Developmental Diagnostic Dimensional Interview-short version (3Di-sv) provides a brief standardized parental interview for diagnosing autism spectrum disorder (ASD). This study explored its validity, and compatibility with DSM-5 ASD. 3Di-sv classifications showed good sensitivity but low specificity when compared to ADOS-2-confirmed clinical diagnosis. Confirmatory factor analyses found a better fit against a DSM-5 model than a DSM-IV-TR model of ASD. Exploration of the content validity of the 3Di-sv for the DSM-5 revealed some construct underrepresentation, therefore we obtained data from a panel of 3Di-trained clinicians from ASD-specialized centers to recommend items to fill these gaps. Taken together, the 3Di-sv provides a solid basis to create a similar instrument suitable for DSM-5. Concrete recommendations are provided to improve DSM-5 compatibility

Neuronal correlates of ADHD in adults with evidence for compensation strategies – a functional MRI study with a Go/No-Go paradigm

Objective: Response inhibition impairment is one of the most characteristic symptoms of attention-deficit/hyperactivity disorder (ADHD). Thus functional magnetic resonance imaging (fMRI) during a Go/No-Go task seems to be an ideal tool for examining neuronal correlates of inhibitory control deficits in ADHD. Prior studies have shown frontostriatal abnormalities in children and adolescents. The aim of our study was to investigate whether adults with ADHD would still show abnormal brain activation in prefrontal brain regions during motor response inhibition tasks