Criteria and indicators for assessing sustainability of smallholder tree plantations in selected regions in The Philippines

A study was conducted in three regions in the Philippines to identify a set of criteria and indicators for assessing sustainability of smallholder tree plantations. The research method involved a combination of key informant interviews, focus group discussions and consultations as well as an examination of existing records, documents and forest regulatory policies at both national and local levels. Six criteria and 41 indicators were selected for assessing sustainability of smallholder tree plantations. The 41 indicators are distributed among the six criteria identified as follows: Criterion 1 − Enabling conditions for sustainable forest management (SFM) with 10 indicators; Criterion 2 − Forest Resource Security with 8 indicators; Criterion 3 − Forest Ecosystem Health and Condition with 2 indicators; Criterion 4 − Flow of Forest Products Produce with 9 indicators; Criterion 5 − Soil and water conservation with 2 indicators; and Criterion 6 − Economic, Social and Cultural aspects with 10 indicators. These criteria and indicators for smallholder tree plantations are designed to provide a comprehensive framework which will guide smallholder tree farmers and the Philippine government towards sustainable forest management

Management and decision support system for smallholder tree plantation developers

This paper reports experiences and inferences in the development of a decision support system (DSS) for smallholder tree plantation development in Regions 2, 10, and 13 in the Philippines. It was found out that farmers in these regions are not practicing intensive plantation management. Silvicultural practices and cultural management including weeding, fertilization, pruning and thinning, if practiced, are done arbitrarily. Choice of species is mainly based on seed availability, and time for harvest is selected based on gut feeling and more often dictated by the need to convert crops to liquid assets. Although the farmers claimed they are realizing positive returns from investment, analysis showed that the production from plantations can be increased. A DSS is being developed to show these realities to the farmers and to allow them to evaluate options to increase production. The main component of the system is a database on silvicultural regimes and management requirements of mahogany, mangium and yemane in plantations. Data were gathered from reported results of previous studies. The DSS also has a database of the various policies affecting the decisions that farmers take. The other component of the system is a dynamic growth model that simulates plantation performance. With the databases and the dynamic models put together in a DSS, a decision-support tool is developed that allows the developers to conduct a trial and error or ‘what if’ analysis on a proposed or existing plantation, prior to implementation of an activity or treatment. The DSS is being developed to assist smallholder tree plantation developers in making technical and business decisions, from choice of species to conduct of cultural management activities to final harvest to transport of raw materials

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Quantitative 18F-AV1451 Brain Tau PET Imaging in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Patients

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results:18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Integrated yield forecasting and harvest scheduling in a tropical pine plantation in Fiji

This thesis reports on enhancements of two planning method components aimed at improving management and planning of forest plantations in the tropics. The two modular planning models subjected to detailed study are growth and yield modelling and harvest scheduling. A case study relating to Caribbean pine in Fiji is used to demonstrate the refined capability. Growth and yield modelling has been improved by applying modern statistical and computer techniques to solve non-linear equations that describe growth of stands appropriately. Further improvements have been achieved by developing diameter distribution growth and yield models solved by a combination of parameter recovery and prediction method thereby ensuring compatibility between average stand values and diameter distribution values. In conducting improvements in growth and yield models, data manipulation and data validation procedures are described and reviewed in detail to emphasize their importance, particularly for non-linear regression fitting of equations, growth, yield and diameter distribution projection modelling. Various growth projection equations were tested before final stand average functional forms for basal area per hectare, standard deviation of diameter at breast height outside bark, maximum diameter at breast height outside bark and survival per hectare were identified and then integrated into the growth and yield model. The precision of the equations was assessed through graphs and statistics relating to residuals. The stand simultaneous growth and yield equations solved and used in the model consist of modified forms of different growth projection functions, such as the Gompertz, exponential and Schumacher, which were then used to derive a diameter distribution based on the Reverse Weibull probability density function. The diameter distribution growth and yield model was prepared as a simulation model to predict stand average values then, in conjunction with existing stem volume and taper equations, to derive stand and stock tables that allow disaggregation of diameter classes into log types. Three simulation models were created, one in Vax Fortran, one in PC Fortran and the other in spreadsheet format to enhance the models's portability. The harvest scheduling model developed is a spreadsheet based LP model which is able to schedule harvests from a number of stands within a medium-term planning horizon using different logging methods with the log harvest to be delivered to different ports or utilization plants. A Fiji case study provided a demonstration of the modelling capability for fifteen stands, seven years, four logging methods and two ports. This new kind of LP harvest scheduling model was developed with a deliberate intention to facilitate the running of it with the input from the improved growth and yield model. In developing this harvest scheduling model, the nature of LP in general was first reviewed and compared to other tools of harvest scheduling like binary search and simulation. LP harvest scheduling was found in this review to be a widely used tool and solution algorithms for which abound. A major problem with most solutions was the need to cater for sophisticated report writing and matrix generation. These two concerns were specifically addressed in the model developed as part of this study. The use of a spreadsheet as input to the LP was seen to be an efficient way of overcoming some of the major criticisms levelled at LP by potential users. The methodology developed was also advantageous because of its capability to facilitate the integration of growth and yield outputs with harvest scheduling. It was concluded that forest planning models can be readily improved with software and hardware that developing countries can easily afford. The models reported here harness the capabilities of the now commonly employed spreadsheet as a powerful tool for easier routine input, output and sensitivity analysis, to assist decision making for harvest scheduling and to simplify managerial planning and control

Análisis espacial del paisaje como base para muestreos dendrocronológicos: El caso de la Reserva de la Biosfera Mariposa Monarca, México

Landscape spatial analyses using Remote Sensing (RS) and Geographic Information Systems (GIS) has been scarcely used to indentify sites with dendrochronological potential. For this study, we designed a protocol to identify areas with climatically sensitive trees, based on the spatial analysis of landscape biophysical features. The protocol included the analyses of slope angles, slope aspect, and the distribution of Land Cover and Land Use (LCLU) using RS and GIS tools. The protocol was validated through dendrochronological sampling in two adjacent sites. Analyses of tree ring widths were done using COFECHA and dplR. Results suggest that sensitive trees grow in 18% of the Monarch Butterfly Biosphere Reserve. Trees from the two sites differed in age, diameter, and mean ring width. Our protocol allowed us to identify sites with long-lived and sensitive trees as represented by the high inter-annual variation found in tree ring widths. The spatial analysis of biophysical variables prior to sampling enables the identification of sites with sensitive trees implying the elimination of less samples. Our proposed protocol is efficient and can be used in other areas.El análisis espacial del paisaje, con técnicas de Percepción Remota (PR) y herramientas de Sistemas de Información Geográfica (SIG), es un procedimiento poco usado en la identificación de sitios potencialmente idóneos para el muestreo dendrocronológico. En el presente trabajo se diseñó un protocolo para identificar áreas con árboles climáticamente sensibles, empleando un análisis espacial de variables biofísicas. El diseño consistió en el análisis de la pendiente del terreno, la orientación de laderas y la distribución de Cobertura Vegetal y Uso de Suelo (CVUS), mediante el uso de técnicas de PR y herramientas de SIG. El protocolo se validó mediante un muestreo dendrocronológico en dos sitios adyacentes. Los análisis estadísticos del ancho de anillos de crecimiento se realizaron mediante los programas COFECHA y dplR. Los resultados indicaron que 18% de la Reserva de la Biosfera Mariposa Monarca presenta características aptas para colectar muestras dendrocronológicas sensibles a variación climática. Los análisis estadísticos indican que existen diferencias entre los dos sitios en edad de árboles, diámetro y ancho de los anillos. La propuesta del diseño de muestreo permitió ubicar sitios con árboles longevos y sensibles, lo que se traduce en alta variación interanual en el ancho de anillos. El análisis espacial de variables biofísicas previas al muestreo dendrocronológico, contribuye a la identificación de sitios con árboles sensibles y a eliminar menos muestras. El protocolo propuesto es eficiente y puede ser replicable en otras áreas