Fractal Description of Soil Fragmentation for Various Tillage Methods and Crop Sequences

Soil structure has been difficult to quantify and, at best, has been studied semiquantitatively. Fractal representation of soil fragmentation can provide an indication of soil structure. The purpose of our study was to use fractal analysis to quantify soil fragmentation under various tillage and crop sequence treatments at different times during the growing season. We collected soil samples from four tillage treatments (established 10 yr earlier) of chisel, disk, no-till, and moldboard plow in factorial arrangement with two crop sequences of corn (Zea mays L.)-soybean [Glycine max (L.) Merr.]-corn (C-SC), and soybean- cornaoybean, (S-C-S) on a Sharpsburg (fine, montmorillonitic, mesic Typic Argiudoll) soil. Aggregate-size distribution was used to calculate fractal dimension (D) for each treatment. Higher D values indicate greater soil fragmentation and a soil dominated by smaller aggregates. The opposite is true for lower D values. Differences in soil fragmentation observed for tillage treatments after autumn tillage became even greater over winter. Soil fragmentation increased over autumn and winter, with D increasing in the order of plow \u3e chisel \u3e disk \u3e no-till. Formation of larger soil aggregates increased during the growing season for all tillage systems. The D values for C S C were smaller than S-C-S in the no-till, indicating that the previous year\u27s corn in CS-C provided more large aggregates. Soybean appears to have negative effects on large-aggregate formation in no-till. Aggregate densities, averaged across tillage and crop sequence, increased from 1.25 to 1.77 Mg m-3 as the aggregate diameter decreased from 6.38 to 0.162 mm. Fractal analysis was found to be useful in determining soil fragmentation differences due to different tillage methods and crop sequences

Fractal Description of Soil Fragmentation for Various Tillage Methods and Crop Sequences

Soil structure has been difficult to quantify and, at best, has been studied semiquantitatively. Fractal representation of soil fragmentation can provide an indication of soil structure. The purpose of our study was to use fractal analysis to quantify soil fragmentation under various tillage and crop sequence treatments at different times during the growing season. We collected soil samples from four tillage treatments (established 10 yr earlier) of chisel, disk, no-till, and moldboard plow in factorial arrangement with two crop sequences of corn (Zea mays L.)-soybean [Glycine max (L.) Merr.]-corn (C-SC), and soybean- cornaoybean, (S-C-S) on a Sharpsburg (fine, montmorillonitic, mesic Typic Argiudoll) soil. Aggregate-size distribution was used to calculate fractal dimension (D) for each treatment. Higher D values indicate greater soil fragmentation and a soil dominated by smaller aggregates. The opposite is true for lower D values. Differences in soil fragmentation observed for tillage treatments after autumn tillage became even greater over winter. Soil fragmentation increased over autumn and winter, with D increasing in the order of plow \u3e chisel \u3e disk \u3e no-till. Formation of larger soil aggregates increased during the growing season for all tillage systems. The D values for C S C were smaller than S-C-S in the no-till, indicating that the previous year\u27s corn in CS-C provided more large aggregates. Soybean appears to have negative effects on large-aggregate formation in no-till. Aggregate densities, averaged across tillage and crop sequence, increased from 1.25 to 1.77 Mg m-3 as the aggregate diameter decreased from 6.38 to 0.162 mm. Fractal analysis was found to be useful in determining soil fragmentation differences due to different tillage methods and crop sequences

Exploring the Role of Artificial Intelligence in Recruitment: A Comprehensive Perspective

This study aims to explore the impact of AI on the recruitment process and its implications for recruiters and job seekers. The findings highlight that AI has significantly improved recruitment practices, including efficiency, candidate experience, and accuracy in candidate selection. AI-powered tools have revolutionized traditional recruitment, enabling effective talent identification and streamlined hiring decisions

Smart Retention: Leveraging Machine Learning to Enhance Employee Engagement in Remote Work

With the rise of remote work, organizations face challenges in employee engagement. AI and machine learning can enhance human resource management by optimizing workforce planning and predicting needs. This study evaluates how machine learning improves employee engagement in virtual environments, offering strategies for smart retention and highlighting the need for further research

Microglial responses around intrinsic CNS neurons are correlated with axonal regeneration

<p>Abstract</p> <p>Background</p> <p>Microglia/macrophages and lymphocytes (T-cells) accumulate around motor and primary sensory neurons that are regenerating axons but there is little or no microglial activation or T-cell accumulation around axotomised intrinsic CNS neurons, which do not normally regenerate axons. We aimed to establish whether there was an inflammatory response around the perikarya of CNS neurons that were induced to regenerate axons through a peripheral nerve graft.</p> <p>Results</p> <p>When neurons of the thalamic reticular nucleus (TRN) and red nucleus were induced to regenerate axons along peripheral nerve grafts, a marked microglial response was found around their cell bodies, including the partial enwrapping of some regenerating neurons. T-cells were found amongst regenerating TRN neurons but not rubrospinal neurons. Axotomy alone or insertion of freeze-killed nerve grafts did not induce a similar perineuronal inflammation. Nerve grafts in the corticospinal tracts did not induce axonal regeneration or a microglial or T-cell response in the motor cortex.</p> <p>Conclusions</p> <p>These results strengthen the evidence that perineuronal microglial accumulation (but not T-cell accumulation) is involved in axonal regeneration by intrinsic CNS and other neurons.</p

Diagnostic pitfalls in fine needle aspiration of solitary pulmonary nodules: two cases with radio-cyto-histological correlation

BACKGROUND: Fine needle aspiration is an important tool for diagnosis and preoperative evaluation of solitary nodules of the lung. It provides a definitive diagnosis in most patients at low cost with minimal trauma. However, because of the nature of the study and the presentation of the cells in a more distorted and incomplete tissue structure than a histological slide, false positive results can occur. Prior detailed clinical knowledge about the patient, procedures and methods of radiology in obtaining the aspirate specimen is extremely useful in the accurate interpretation of fine needle cytological specimens. CASE PRESENTATION: We report two cases of solitary pulmonary nodules in two elderly females, which were initially diagnosed as malignant by fine needle aspiration biopsy. Both cases subsequently underwent pulmonary lobectomy in which, one turned out to be a pulmonary hamartoma and the other appeared to be a middle lobe syndrome of the right lung with liver tissue contamination at the time of fine needle aspiration of the lung. CONCLUSIONS: We are now strong believers that much care must be taken in the interpretation of fine needle aspiration of solitary nodules of the lung. Complete study of the entire specimen, including the cell block, is warranted, since what one interprets as malignant, could have different features in another part of the sample. Last but not the least, prior knowledge of the complete clinical history of the patient together with the salient radiological findings would greatly facilitate the cytopathologist to reach an accurate diagnosis

A novel method for measuring bowel motility and velocity with dynamic magnetic resonance imaging in two and three dimensions

Increasingly, dynamic MRI has potential as a non-invasive and accessible tool for diagnosing and monitoring gastrointestinal motility in healthy and diseased bowel. However, current MRI methods of measuring bowel motility have limitations: requiring bowel preparation or long acquisition times; providing mainly surrogate measures of motion; and estimating bowel-wall movement in just two dimensions. In this proof-of-concept study we apply a method that provides a quantitative measure of motion within the bowel, in both 2D and 3D, using existing, vendor-implemented MRI pulse sequences with minimal bowel-preparation. This method uses a minimised cost function to fit linear vectors in the spatial and temporal domains. It is sensitised to the spatial scale of the bowel and aims to address issues relating to the low signal-to-noise in high-temporal resolution dynamic MRI scans, previously compensated for by performing thick-slice (10 mm) 2D coronal scans. We applied both 2D and 3D scanning protocols in two healthy volunteers. For 2D scanning, analysis yielded bi-modal velocity peaks, with a mean antegrade motion of 5.5 mm/s and an additional peak at ~9 mm/s corresponding to longitudinal peristalsis, as supported by intra-operative data from the literature. Furthermore, 3D scans indicated a mean forward motion of 4.7 mm/s, and degrees of ante- and retrograde motion were also established. These measures show promise for the non-invasive assessment of bowel motility, and have the potential to be tuned to particular regions of interest and behaviours within the bowel