The benefits of organic farming for biodiversity

Previous studies suggest widespread positive responses of biodiversity to organic farming. Many of these studies, however, have been small-scale. This project tested the generality of habitat and biodiversity differences between matched pairs of organic and non-organic farms containing cereal crops in lowland England on a large-scale across a range of taxa including plants, insects, birds and bats. The extent of both cropped and un-cropped habitats together with their composition and management on a range of scales were also compared. Organic farms was likely to favour higher levels of biodiversity and indeed organic farms tended to support higher numbers of species and overall abundance across most taxa. However, the magnitude of the response differed strikingly; plants showed stronger and more consistent responses than other taxa. Some, but not all, differences in biodiversity between systems appear to be a consequence of differences in habitat quantity

Organic farming: biodiversity impacts can depend on dispersal characteristics and landscape context

Organic farming, a low intensity system, may offer benefits for a range of taxa, but what affects the extent of those benefits is imperfectly understood. We explored the effects of organic farming and landscape on the activity density and species density of spiders and carabid beetles, using a large sample of paired organic and conventional farms in the UK. Spider activity density and species density were influenced by both farming system and surrounding landscape. Hunting spiders, which tend to have lower dispersal capabilities, had higher activity density, and more species were captured, on organic compared to conventional farms. There was also evidence for an interaction, as the farming system effect was particularly marked in the cropped area before harvest and was more pronounced in complex landscapes (those with little arable land). There was no evidence for any effect of farming system or landscape on web-building spiders (which include the linyphiids, many of which have high dispersal capabilities). For carabid beetles, the farming system effects were inconsistent. Before harvest, higher activity densities were observed in the crops on organic farms compared with conventional farms. After harvest, no difference was detected in the cropped area, but more carabids were captured on conventional compared to organic boundaries. Carabids were more species-dense in complex landscapes, and farming system did not affect this. There was little evidence that non-cropped habitat differences explained the farming system effects for either spiders or carabid beetles. For spiders, the farming system effects in the cropped area were probably largely attributable to differences in crop management; reduced inputs of pesticides (herbicides and insecticides) and fertilisers are possible influences, and there was some evidence for an effect of non-crop plant species richness on hunting spider activity density. The benefits of organic farming may be greatest for taxa with lower dispersal abilities generally. The evidence for interactions among landscape and farming system in their effects on spiders highlights the importance of developing strategies for managing farmland at the landscape-scale for most effective conservation of biodiversity

Identifying parkinsonism in mild cognitive impairment.

Introduction Clinical parkinsonism is a core diagnostic feature for mild cognitive impairment with Lewy bodies (MCI-LB) but can be challenging to identify. A five-item scale derived from the Unified Parkinson’s Disease Rating Scale (UPDRS) has been recommended for the assessment of parkinsonism in dementia. This study aimed to determine whether the five-item scale is effective to identify parkinsonism in MCI. Methods Participants with MCI from two cohorts (n=146) had a physical examination including the UPDRS and [123I]-FP-CIT SPECT striatal dopaminergic imaging. Participants were classified as having clinical parkinsonism (P+) or no parkinsonism (P-), and with abnormal striatal dopaminergic imaging (D+) or normal imaging (D-). The five-item scale was the sum of UPDRS tremor at rest, bradykinesia, action tremor, facial expression, and rigidity scores. The ability of the scale to differentiate P+D+ and P-D- participants was examined. Results The five-item scale had an AUROC of 0.92 in Cohort 1, but the 7/8 cut-off defined for dementia had low sensitivity to identify P+D+ participants (sensitivity 25%, specificity 100%). Optimal sensitivity and specificity was obtained at a 3/4 cut-off (sensitivity 83%, specificity 88%). In Cohort 2, the five-item scale had an AUROC of 0.97, and the 3/4 cut-off derived from Cohort 1 showed sensitivity of 100% and a specificity of 82% to differentiate P+D+ from P-D- participants. The five-item scale was not effective in differentiating D+ from D- participants. Conclusions The five-item scale is effective to identify parkinsonism in MCI, but a lower threshold must be used in MCI compared with dementia

Neuropsychological Impairments and their cognitive architecture in Mild Cognitive Impairment (MCI) with Lewy Bodies and MCI-Alzheimer’s Disease

This is the final version. Available on open access from Cambridge University Press via the DOI in this recordObjective: The present study aimed to clarify the neuropsychological profile of the emergent diagnostic category of Mild Cognitive Impairment with Lewy bodies (MCI-LB) and determine whether domain-specific impairments such as in memory were related to deficits in domain-general cognitive processes (executive function or processing speed). Method: Patients (n=83) and healthy age- and sex-matched controls (n=34) underwent clinical and imaging assessments. Probable MCI-LB (n=44) and MCI-AD (n=39) were diagnosed following National Institute on Aging-Alzheimer’s Association (NIA-AA) and DLB consortium criteria. Neuropsychological measures included cognitive and psychomotor speed, executive function, working memory, and verbal and visuospatial recall. Results: MCI-LB scored significantly lower than MCI-AD on processing speed (Trail Making Test B: p=0.03, g=0.45; Digit Symbol Substitution Test [DSST]: p=0.04, g=0.47; DSST Error Check: p.05) Conclusions: MCI-LB was characterised by executive dysfunction and slowed processing speed but did not show the visuospatial dysfunction expected, whilst MCI-AD displayed an amnestic profile. However, there was considerable neuropsychological profile overlap and processing speed mediated performance in both MCI subtypes.Alzheimer’s Research UKMedical Research Council (MRC)GE HealthcareAlzheimer’s SocietyNational Institute for Health Research (NIHR

Organic farming: Biodiversity impacts can depend on dispersal characteristics and landscape context

Organic farming, a low intensity system, may offer benefits for a range of taxa, but what affects the extent of those benefits is imperfectly understood.We explored the effects of organic farming and landscape on the activity density and species density of spiders and carabid beetles, using a large sample of paired organic and conventional farms in the UK. Spider activity density and species density were influenced by both farming system and surrounding landscape. Hunting spiders, which tend to have lower dispersal capabilities, had higher activity density, and more species were captured, on organic compared to conventional farms. There was also evidence for an interaction, as the farming system effect was particularly marked in the cropped area before harvest and was more pronounced in complex landscapes (those with little arable land). There was no evidence for any effect of farming system or landscape on web-building spiders (which include the linyphiids, many of which have high dispersal capabilities). For carabid beetles, the farming system effects were inconsistent. Before harvest, higher activity densities were observed in the crops on organic farms compared with conventional farms. After harvest, no difference was detected in the cropped area, but more carabids were captured on conventional compared to organic boundaries. Carabids were more species-dense in complex landscapes, and farming system did not affect this. There was little evidence that non-cropped habitat differences explained the farming system effects for either spiders or carabid beetles. For spiders, the farming system effects in the cropped area were probably largely attributable to differences in crop management; reduced inputs of pesticides (herbicides and insecticides) and fertilisers are possible influences, and there was some evidence for an effect of non-crop plant species richness on hunting spider activity density. The benefits of organic farming may be greatest for taxa with lower dispersal abilities generally. The evidence for interactions among landscape and farming system in their effects on spiders highlights the importance of developing strategies for managing farmland at the landscape-scale for most effective conservation of biodiversity

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Do obese but metabolically normal women differ in intra-abdominal fat and physical activity levels from those with the expected metabolic abnormalities? A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Obesity remains a major public health problem, associated with a cluster of metabolic abnormalities. However, individuals exist who are very obese but have normal metabolic parameters. The aim of this study was to determine to what extent differences in metabolic health in very obese women are explained by differences in body fat distribution, insulin resistance and level of physical activity.</p> <p>Methods</p> <p>This was a cross-sectional pilot study of 39 obese women (age: 28-64 yrs, BMI: 31-67 kg/m²) recruited from community settings. Women were defined as 'metabolically normal' on the basis of blood glucose, lipids and blood pressure. Magnetic Resonance Imaging was used to determine body fat distribution. Detailed lifestyle and metabolic profiles of participants were obtained.</p> <p>Results</p> <p>Women with a healthy metabolic profile had lower intra-abdominal fat volume (geometric mean 4.78 l [95% CIs 3.99-5.73] vs 6.96 l [5.82-8.32]) and less insulin resistance (HOMA 3.41 [2.62-4.44] vs 6.67 [5.02-8.86]) than those with an abnormality. The groups did not differ in abdominal subcutaneous fat volume (19.6 l [16.9-22.7] vs 20.6 [17.6-23.9]). A higher proportion of those with a healthy compared to a less healthy metabolic profile met current physical activity guidelines (70% [95% CIs 55.8-84.2] vs 25% [11.6-38.4]). Intra-abdominal fat, insulin resistance and physical activity make independent contributions to metabolic status in very obese women, but explain only around a third of the variance.</p> <p>Conclusion</p> <p>A sub-group of women exists who are metabolically normal despite being very obese. Differences in fat distribution, insulin resistance, and physical activity level are associated with metabolic differences in these women, but account only partially for these differences. Future work should focus on strategies to identify those obese individuals most at risk of the negative metabolic consequences of obesity and on identifying other factors that contribute to metabolic status in obese individuals.</p