Insulin and TOR pathways regulate cellular and organismal growth through the myc oncogene in Drosophila

A large body of literature documents in both mice and Drosophila the involvement of Insulin pathway in growth regulation, probably due to its role in glucose and lipid import, nutrient storage, and translation of RNAs implicated in ribosome biogenesis (Vanhaesebroeck et al. 2001). Moreover several lines of evidence implicate this pathway as a causal factor in cancer (Sale, 2008; Zeng and Yee 2007; Hursting et al., 2007; Chan et al., 2008). With regards to Myc, studies in cell culture have implied this family of transcription factors as regulators of the cell cycle that are rapidly induced in response to growth factors. Myc is a potent oncogene, rearranged and overexpressed in a wide range of human tumors and necessary during development. Its conditional knock-out in mice results in reduction of body weight due to defect in cell proliferation (Trumpp et al. 2001). Evidence from in vivo studies in Drosophila and mammals suggests a critical function for myc in cell growth regulation (Iritani and Eisenman 1999; Johnston et al. 1999; Kim et al. 2000; de Alboran et al. 2001; Douglas et al. 2001). This role is supported by our analysis of Myc target genes in Drosophila, which include genes involved in RNA binding, processing, ribosome biogenesis and nucleolar function (Orain et al 2003, Bellosta et al., 2005, Hulf et al, 2005). The fact that Insulin signaling and Myc have both been associated with growth control suggests that they may interact with each other. However, genetic evidence suggesting that Insulin signaling regulates Myc in vivo is lacking. In this work we were able to show, for the first time, a direct modulation of dMyc in response to Insulin stimulation/silencing both in vitro and in vivo. Our results suggest that dMyc up-regulation in response to DILPs signaling occurs both at the mRNA and potein level. We believe dMyc protein accumulation after Insulin signaling activation is conditioned to AKT-dependent GSK3β/sgg inactivation. In fact, we were able to demonstate that dMyc protein stabilization through phosphorylation is a conserved feature between Drosophila and vertebrates and requires multiple events. The final phosphorylation step, that results in a non-stable form of dMyc protein, ready to be degraded by the proteasome, is performed by GSK3β/sgg kinase (Sears, 2004). At the same time we demonstrated that CKI family of protein kinase are required to prime dMyc phosphorylation. DILPs and TOR/Nutrient signalings are known to communicate at several levels (Neufeld, 2003). For this reason we further investigated TOR contribution to dMyc-dependent growth regulation. dMyc protein accumulates in S2 cells after aminoacid stimulation, while its mRNA does not seem to be affected upon TORC1 inhibition, suggesting that the Nutrient pathway regulates dMyc mostly post-transcriptionally. In support to this hypothesis, we observed a TORC1-dependent GSK3β/sgg inactivation, further confirming a synergic effect of DILPs and Nutrients on dMyc protein stability. On the other hand, our data show that Rheb but not S6K, both downstream of the TOR kinase, contributes to the dMyc-induced growth of the eye tissue, suggesting that Rheb controls growth independently of S6K.. Moreover, Rheb seems to be able to regulate organ size during development inducing cell death, a mechanism no longer occurring in absence of dmyc. These observations suggest that Rheb might control growth through a new pathway independent of TOR/S6K but still dependent on dMyc. In order to dissect the mechanism of dMyc regulation in response to these events, we analyzed the relative contribution of Rheb, TOR and S6K to dMyc expression, biochemically in S2 cells and in vivo in morphogenetic clones and we further confirmed an interplay between Rheb and Myc that seems to be indipendent from TOR. In this work we clarified the mechanisms that stabilize dMyc protein in vitro and in vivo and we observed for the first time dMyc responsiveness to DILPs and TOR. At the same time, we discovered a new branch of the Nutrient pathway that appears to drive growth through dMyc but indipendently from TOR. We believe our work shed light on the mechanisms cells use to grow or restrain growth in presence/absence of growth promoting cues and for this reason it contributes to understand the physiology of growth control.

Frequent Arousal from Hibernation Linked to Severity of Infection and Mortality in Bats with White-Nose Syndrome

White-nose syndrome (WNS), an emerging infectious disease that has killed over 5.5 million hibernating bats, is named for the causative agent, a white fungus (Geomyces destructans (Gd)) that invades the skin of torpid bats. During hibernation, arousals to warm (euthermic) body temperatures are normal but deplete fat stores. Temperature-sensitive dataloggers were attached to the backs of 504 free-ranging little brown bats (Myotis lucifugus) in hibernacula located throughout the northeastern USA. Dataloggers were retrieved at the end of the hibernation season and complete profiles of skin temperature data were available from 83 bats, which were categorized as: (1) unaffected, (2) WNS-affected but alive at time of datalogger removal, or (3) WNS-affected but found dead at time of datalogger removal. Histological confirmation of WNS severity (as indexed by degree of fungal infection) as well as confirmation of presence/absence of DNA from Gd by PCR was determined for 26 animals. We demonstrated that WNS-affected bats aroused to euthermic body temperatures more frequently than unaffected bats, likely contributing to subsequent mortality. Within the subset of WNS-affected bats that were found dead at the time of datalogger removal, the number of arousal bouts since datalogger attachment significantly predicted date of death. Additionally, the severity of cutaneous Gd infection correlated with the number of arousal episodes from torpor during hibernation. Thus, increased frequency of arousal from torpor likely contributes to WNS-associated mortality, but the question of how Gd infection induces increased arousals remains unanswered.

The angiotensin-converting enzyme insertion/deletion polymorphism and its associations with carotid artery wall thickness and coronary heart disease: The atherosclerosis risk in communities study

Coronary heart disease (CHD) is the leading cause of death in the United States. Recently, renin-angiotensin system (RAS) was found associated with atherosclerosis formation, with angiotensin II inducing vascular smooth muscle cell growth and migration, platelet activation and aggregation, and stimulation of plasminogen activator inhibitor-1. Angiotensin II is converted from angiotensin I by angiotensin I-converting enzyme (ACE) and this enzyme is mainly genetically determined. The ACE gene has been assigned to chromosome 17q23 and an insertion/deletion (I/D)polymorphism has been characterized by the presence/absence of a 287 bp fragment in intron 16 of the gene. The two alleles form three genotypes, namely, DD, ID and II and the DD genotype has been linked to higher plasma ACE levels and cell ACE activity.^ In this study, the association between the ACE I/D polymorphism and carotid artery wall thickness measured by B-mode ultrasound was investigated in a biracial sample, and the association between the gene and incident CHD was investigated in whites and if the gene-CHD association in whites, if any, was due to the gene effect on atherosclerosis. The study participants are from the prospective Atherosclerosis Risk in Communities (ARIC) Study, including adults aged 45 to 65 years. The present dissertation used a matched case-control design for studying the associations of the ACE gene with carotid artery atherosclerosis and an unmatched case-control design for the association of the gene with CHD. A significant recessive effect of the D allele on carotid artery thickness was found in blacks (OR = 3.06, 95% C.I: 1.11-8.47, DD vs. ID and II) adjusting for age, gender, cigarette smoking, LDL-cholesterol and diabetes. No similar associations were found in whites. The ACE I/D polymorphism is significantly associated with coronary heart disease in whites, and while stratifying data by carotid artery wall thickness, the significant associations were only observed in thin-walled subgroups. Assuming a recessive effect of the D allele, odds ratio was 2.84 (95% C.I:1.17-6.90, DD vs. ID and II) and it was 2.30 (95% C.I:1.22-4.35, DD vs. ID vs. II) assuming a codominant effect of the D allele. No significant associations were observed while comparing thick-walled CHD cases with thin-walled controls. Following conclusions could be drawn: (1) The ACE I/D polymorphism is unlikely to confer appreciable increase in the risk of carotid atherosclerosis in US whites, but may increases the risk of carotid atherosclerosis in blacks. (2) ACE I/D polymorphism is a genetic risk factor for incident CHD in US whites and this effect is separate from the chronic process of atherosclerosis development. Finally, the associations observed here are not causal, since the I/D polymorphism is in an intron, where no ACE proteins are encoded. ^

Proximity of premolar roots to maxillary sinus: a radiographic survey using cone-beam computed tomography.

INTRODUCTION The proximity of the roots of the posterior maxillary teeth to the maxillary sinus is a constant challenge to the dental practitioner. Because the majority of studies have assessed the relationship regarding molars, the present study focused on premolars. METHODS Cone-beam computed tomographic images of 192 patients were reconstructed in sagittal, coronal, and axial planes to quantify the distances between the root apices of the maxillary premolars and the adjacent maxillary sinus. Measurements were taken for each root, and data were correlated with age, sex, side, and presence of both or absence of 1 of the 2 premolars. RESULTS A total of 296 teeth (177 first and 119 second premolars) were evaluated. The mean distances from buccal roots of the first premolars to the border of the maxillary sinus in the sagittal, coronal, and axial planes ranged from 5.15 ± 2.99 to 8.28 ± 6.27 mm. From palatal roots, the mean distances ranged from 4.20 ± 3.69 to 7.17 ± 6.14 mm. The mean distances of second premolars were markedly shorter in buccal roots between 2.32 ± 2.19 and 3.28 ± 3.17 mm and in palatal roots between 2.68 ± 3.58 and 3.80 ± 3.71 mm, respectively. The frequency of a premolar root protrusion into the maxillary sinus was very low in first premolars (0%-7.2%) but higher in second premolars (2.5%-13.6%). Sex, age, side, and presence/absence of premolars failed to significantly influence the mean distances between premolar roots and the maxillary sinus. CONCLUSIONS Based on the calculated mean distances of the present study, only few premolars (and if so second premolars) would present a risk of violating the border of the maxillary sinus during conventional or surgical endodontic treatment or in case of tooth extraction.

Esthetic outcomes following immediate and early implant placement in the anterior maxilla: a systematic review.

PURPOSE The objectives of this systematic review are (1) to quantitatively estimate the esthetic outcomes of implants placed in postextraction sites, and (2) to evaluate the influence of simultaneous bone augmentation procedures on these outcomes. MATERIALS AND METHODS Electronic and manual searches of the dental literature were performed to collect information on esthetic outcomes based on objective criteria with implants placed after extraction of maxillary anterior and premolar teeth. All levels of evidence were accepted (case series studies required a minimum of 5 cases). RESULTS From 1,686 titles, 114 full-text articles were evaluated and 50 records included for data extraction. The included studies reported on single-tooth implants adjacent to natural teeth, with no studies on multiple missing teeth identified (6 randomized controlled trials, 6 cohort studies, 5 cross-sectional studies, and 33 case series studies). Considerable heterogeneity in study design was found. A meta-analysis of controlled studies was not possible. The available evidence suggests that esthetic outcomes, determined by esthetic indices (predominantly the pink esthetic score) and positional changes of the peri-implant mucosa, may be achieved for single-tooth implants placed after tooth extraction. Immediate (type 1) implant placement, however, is associated with a greater variability in outcomes and a higher frequency of recession of > 1 mm of the midfacial mucosa (eight studies; range 9% to 41% and median 26% of sites, 1 to 3 years after placement) compared to early (type 2 and type 3) implant placement (2 studies; no sites with recession > 1 mm). In two retrospective studies of immediate (type 1) implant placement with bone graft, the facial bone wall was not detectable on cone beam CT in 36% and 57% of sites. These sites had more recession of the midfacial mucosa compared to sites with detectable facial bone. Two studies of early implant placement (types 2 and 3) combined with simultaneous bone augmentation with GBR (contour augmentation) demonstrated a high frequency (above 90%) of facial bone wall visible on CBCT. Recent studies of immediate (type 1) placement imposed specific selection criteria, including thick tissue biotype and an intact facial socket wall, to reduce esthetic risk. There were no specific selection criteria for early (type 2 and type 3) implant placement. CONCLUSIONS Acceptable esthetic outcomes may be achieved with implants placed after extraction of teeth in the maxillary anterior and premolar areas of the dentition. Recession of the midfacial mucosa is a risk with immediate (type 1) placement. Further research is needed to investigate the most suitable biomaterials to reconstruct the facial bone and the relationship between long-term mucosal stability and presence/absence of the facial bone, the thickness of the facial bone, and the position of the facial bone crest.

The role of workplace absence as an organizational mechanism in predicting employee alcohol and drug disorders

Background. EAP programs for airline pilots in companies with a well developed recovery management program are known to reduce pilot absenteeism following treatment. Given the costs and safety consequences to society, it is important to identify pilots who may be experiencing an AOD disorder to get them into treatment. ^ Hypotheses. This study investigated the predictive power of workplace absenteeism in identifying alcohol or drug disorders (AOD). The first hypothesis was that higher absenteeism in a 12-month period is associated with higher risk that an employee is experiencing AOD. The second hypothesis was that AOD treatment would reduce subsequent absence rates and the costs of replacing pilots on missed flights. ^ Methods. A case control design using eight years (time period) of monthly archival absence data (53,000 pay records) was conducted with a sample of (N = 76) employees having an AOD diagnosis (cases) matched 1:4 with (N = 304) non-diagnosed employees (controls) of the same profession and company (male commercial airline pilots). Cases and controls were matched on the variables age, rank and date of hire. Absence rate was defined as sick time hours used over the sum of the minimum guarantee pay hours annualized using the months the pilot worked for the year. Conditional logistic regression was used to determine if absence predicts employees experiencing an AOD disorder, starting 3 years prior to the cases receiving the AOD diagnosis. A repeated measures ANOVA, t tests and rate ratios (with 95% confidence intervals) were conducted to determine differences between cases and controls in absence usage for 3 years pre and 5 years post treatment. Mean replacement costs were calculated for sick leave usage 3 years pre and 5 years post treatment to estimate the cost of sick leave from the perspective of the company. ^ Results. Sick leave, as measured by absence rate, predicted the risk of being diagnosed with an AOD disorder (OR 1.10, 95% CI = 1.06, 1.15) during the 12 months prior to receiving the diagnosis. Mean absence rates for diagnosed employees increased over the three years before treatment, particularly in the year before treatment, whereas the controls’ did not (three years, x = 6.80 vs. 5.52; two years, x = 7.81 vs. 6.30, and one year, x = 11.00cases vs. 5.51controls. In the first year post treatment compared to the year prior to treatment, rate ratios indicated a significant (60%) post treatment reduction in absence rates (OR = 0.40, CI = 0.28, 0.57). Absence rates for cases remained lower than controls for the first three years after completion of treatment. Upon discharge from the FAA and company’s three year AOD monitoring program, case’s absence rates increased slightly during the fourth year (controls, x = 0.09, SD = 0.14, cases, x = 0.12, SD = 0.21). However, the following year, their mean absence rates were again below those of the controls (controls, x = 0.08, SD = 0.12, cases, x¯ = 0.06, SD = 0.07). Significant reductions in costs associated with replacing pilots calling in sick, were found to be 60% less, between the year of diagnosis for the cases and the first year after returning to work. A reduction in replacement costs continued over the next two years for the treated employees. ^ Conclusions. This research demonstrates the potential for workplace absences as an active organizational surveillance mechanism to assist managers and supervisors in identifying employees who may be experiencing or at risk of experiencing an alcohol/drug disorder. Currently, many workplaces use only performance problems and ignore the employee’s absence record. A referral to an EAP or alcohol/drug evaluation based on the employee’s absence/sick leave record as incorporated into company policy can provide another useful indicator that may also carry less stigma, thus reducing barriers to seeking help. This research also confirms two conclusions heretofore based only on cross-sectional studies: (1) higher absence rates are associated with employees experiencing an AOD disorder; (2) treatment is associated with lower costs for replacing absent pilots. Due to the uniqueness of the employee population studied (commercial airline pilots) and the organizational documentation of absence, the generalizability of this study to other professions and occupations should be considered limited. ^ Transition to Practice. The odds ratios for the relationship between absence rates and an AOD diagnosis are precise; the OR for year of diagnosis indicates the likelihood of being diagnosed increases 10% for every hour change in sick leave taken. In practice, however, a pilot uses approximately 20 hours of sick leave for one trip, because the replacement will have to be paid the guaranteed minimum of 20 hour. Thus, the rate based on hourly changes is precise but not practical. ^ To provide the organization with practical recommendations the yearly mean absence rates were used. A pilot flies on average, 90 hours a month, 1080 annually. Cases used almost twice the mean rate of sick time the year prior to diagnosis (T-1) compared to controls (cases, x = .11, controls, x = .06). Cases are expected to use on average 119 hours annually (total annual hours*mean annual absence rate), while controls will use 60 hours. The cases’ 60 hours could translate to 3 trips of 20 hours each. Management could use a standard of 80 hours or more of sick time claimed in a year as the threshold for unacceptable absence, a 25% increase over the controls (a cost to the company of approximately of $4000). At the 80-hour mark, the Chief Pilot would be able to call the pilot in for a routine check as to the nature of the pilot’s excessive absence. This management action would be based on a company standard, rather than a behavioral or performance issue. Using absence data in this fashion would make it an active surveillance mechanism. ^

Distancias genéticas entre perfiles moleculares obtenidos desde marcadores multilocus multialélicos

Para expresar la magnitud de la identidad genética (similaridad) o su complemento (distancia) entre dos individuos caracterizados molecularmente a través de marcadores del tipo microsatélites (SSR), que son multilocusmultialélicos, es necesario elegir una métrica acorde con la naturaleza multivariada de los datos. Comúnmente, las métricas de distancias genéticas son diseñadas para expresar, en un único número, la diferencia genética entre dos poblaciones y son expresadas como función de la frecuencia alélica poblacional. Dichas métricas pueden también ser utilizadas para calcular la distancia entre perfiles individuales, pero las frecuencias alélicas no son continuas en este caso. Alternativamente, se pueden usar distancias geométricas obtenidas como el complemento del índice de similaridad para datos binarios que indican la presencia/ ausencia de cada alelo en un individuo. El objetivo de este trabajo fue evaluar simultáneamente el desempeño de ambos tipos de métricas para ordenar y clasificar individuos en una base de datos generadas a partir de loci de marcadores microsatélites SSR. Se calcularon 11 métricas de distancias a partir de 17 loci SSR obtenidos desde 17 introducciones de un banco de germoplasma de soja [Glycine max (L.) Merr.]. Se evaluó el consenso de los resultados obtenidos para la clasificación de los 17 perfiles moleculares desde varias métricas. Los resultados sugieren que los diferentes tipos de métricas producen información similar para comparar individuos. No obstante, se realizó una clasificación de las métricas que responden a diferencias entre los núcleos de las expresiones de cálculo.

Whale sightings, group sizes and krill biomass in West Greenland in 2005

This study combined data on fin whale Balaenoptera physalus, humpback whale Megaptera novaeangliae, minke whale B. acutorostrata, and sei whale B. borealis sightings from large-scale visual aerial and ship-based surveys (248 and 157 sightings, respectively) with synoptic acoustic sampling of krill Meganyctiphanes norvegica and Thysanoessa sp. abundance in September 2005 in West Greenland to examine the relationships between whales and their prey. Krill densities were obtained by converting relationships of volume backscattering strengths at multiple frequencies to a numerical density using an estimate of krill target strength. Krill data were vertically integrated in 25 m depth bins between 0 and 300 m to obtain water column biomass (g/m**2) and translated to density surfaces using ordinary kriging. Standard regression models (Generalized Additive Modeling, GAM, and Generalized Linear Modeling, GLM) were developed to identify important explanatory variables relating the presence, absence, and density of large whales to the physical and biological environment and different survey platforms. Large baleen whales were concentrated in 3 focal areas: (1) the northern edge of Lille Hellefiske bank between 65 and 67°N, (2) north of Paamiut at 63°N, and (3) in South Greenland between 60 and 61° N. There was a bimodal pattern of mean krill density between depths, with one peak between 50 and 75 m (mean 0.75 g/m**2, SD 2.74) and another between 225 and 275 m (mean 1.2 to 1.3 g/m**2, SD 23 to 19). Water column krill biomass was 3 times higher in South Greenland than at any other site along the coast. Total depth-integrated krill biomass was 1.3 x 10**9 (CV 0.11). Models indicated the most important parameter in predicting large baleen whale presence was integrated krill abundance, although this relationship was only significant for sightings obtained on the ship survey. This suggests that a high degree of spatio-temporal synchrony in observations is necessary for quantifying predator-prey relationships. Krill biomass was most predictive of whale presence at depths >150 m, suggesting a threshold depth below which it is energetically optimal for baleen whales to forage on krill in West Greenland.

(Table A1) Species list of the high Antarctic Weddell Sea food web

Human-induced habitat destruction, overexploitation, introduction of alien species and climate change are causing species to go extinct at unprecedented rates, from local to global scales. There are growing concerns that these kinds of disturbances alter important functions of ecosystems. Our current understanding is that key parameters of a community (e.g. its functional diversity, species composition, and presence/absence of vulnerable species) reflect an ecological network's ability to resist or rebound from change in response to pressures and disturbances, such as species loss. If the food web structure is relatively simple, we can analyse the roles of different species interactions in determining how environmental impacts translate into species loss. However, when ecosystems harbour species-rich communities, as is the case in most natural systems, then the complex network of ecological interactions makes it a far more challenging task to perceive how species' functional roles influence the consequences of species loss. One approach to deal with such complexity is to focus on the functional traits of species in order to identify their respective roles: for instance, large species seem to be more susceptible to extinction than smaller species. Here, we introduce and analyse the marine food web from the high Antarctic Weddell Sea Shelf to illustrate the role of species traits in relation to network robustness of this complex food web. Our approach was threefold: firstly, we applied a new classification system to all species, grouping them by traits other than body size; secondly, we tested the relationship between body size and food web parameters within and across these groups and finally, we calculated food web robustness. We addressed questions regarding (i) patterns of species functional/trophic roles, (ii) relationships between species functional roles and body size and (iii) the role of species body size in terms of network robustness. Our results show that when analyzing relationships between trophic structure, body size and network structure, the diversity of predatory species types needs to be considered in future studies.

Effects of reduced and business-as-usual CO2 emission scenarios on the algal territories of the damselfish Pomacentrus wardi (Pomacentridae)

Turf algae are a very important component of coral reefs, featuring high growth and turnover rates, whilst covering large areas of substrate. As food for many organisms, turf algae have an important role in the ecosystem. Farming damselfish can modify the species composition and productivity of such algal assemblages, while defending them against intruders. Like all organisms however, turf algae and damselfishes have the potential to be affected by future changes in seawater (SW) temperature and pCO2. In this study, algal assemblages, in the presence and absence of farming Pomacentrus wardi were exposed to two combinations of SW temperature and pCO2 levels projected for the austral spring of 2100 (the B1 "reduced" and the A1FI "business-as-usual" CO2 emission scenarios) at Heron Island (GBR, Australia). These assemblages were dominated by the presence of red algae and non-epiphytic cyanobacteria, i.e. cyanobacteria that grow attached to the substrate rather than on filamentous algae. The endpoint algal composition was mostly controlled by the presence/absence of farming damselfish, despite a large variability found between the algal assemblages of individual fish. Different scenarios appeared to be responsible for a mild, species specific change in community composition, observable in some brown and green algae, but only in the absence of farming fish. Farming fish appeared unaffected by the conditions to which they were exposed. Algal biomass reductions were found under "reduced" CO2 emission, but not "business-as-usual" scenarios. This suggests that action taken to limit CO2 emissions may, if the majority of algae behave similarly across all seasons, reduce the potential for phase shifts that lead to algal dominated communities. At the same time the availability of food resources to damselfish and other herbivores would be smaller under "reduced" emission scenarios.

Conflicting effects of predator cue and ocean acidification on the mussel Mytilus coruscus byssus production

Understanding the impact of ocean acidification and warming on communities and ecosystems is a researcher priority. This can only be achieved through a combination of experimental and field approaches that would allow developing a mechanistic understanding of impacts across level of biological organizations. Surprisingly, most published studies are still focusing on single species responses with little consideration for interspecific interactions. In this study, the impacts of a 3 days exposure to three parameters (temperature, pH, and presence/absence of the predator cue of the crab Charybdis japonica) and their interactions on an ecologically important endpoint were evaluated: the byssus production of the mussel Mytilus coruscus. Tested temperatures (25°C and 30°C) were within the present range of natural variability whereas pH (8.1, 7.7, and 7.4) covered present as well as near-future natural variability. As expected, the presence of the crab cue induced an antipredator response in Mytilus coruscus (significant 10% increase in byssus secretion rate, 22% increase in frequency of shed byssus, and 30% longer byssus). Decreased pH but not temperature had a significant negative impact on the same endpoints (up to a 17% decrease in byssus secretion rate, 40% decrease in frequency of shed byssus, and 10% shorter byssus at pH 7.3 as compared with pH 8.1) with no significant interactions between the three tested parameters. In this study, it has been hypothesized that pH and predator cue have different modes of action and lead to conflicting functional responses (escape response versus stronger attachment). Functional consequences for ecosystem dynamics still need to be investigated.

EPOCA Svalbard 2009 benthic experiment: Serripes study, 2009

Ocean acidification influences sediment/water nitrogen fluxes, possibly by impacting on the microbial process of ammonia oxidation. To investigate this further, undisturbed sediment cores collected from Ny Alesund harbour (Svalbard) were incubated with seawater adjusted to CO2 concentrations of 380, 540, 760, 1,120 and 3,000 µatm. DNA and RNA were extracted from the sediment surface after 14 days' exposure and the abundance of bacterial and archaeal ammonia oxidising (amoA) genes and transcripts quantified using quantitative polymerase chain reaction. While there was no change to the abundance of bacterial amoA genes, an increase to 760 µatm pCO2 reduced the abundance of bacterial amoA transcripts by 65 %, and this was accompanied by a shift in the composition of the active community. In contrast, archaeal amoA gene and transcript abundance both doubled at 3,000 µatm, with an increase in species richness also apparent. This suggests that ammonia oxidising bacteria and archaea in marine sediments have different pH optima, and the impact of elevated CO2 on N cycling may be dependent on the relative abundances of these two major microbial groups. Further evidence of a shift in the balance of key N cycling groups was also evident: the abundance of nirS-type denitrifier transcripts decreased alongside bacterial amoA transcripts, indicating that NO3 ? produced by bacterial nitrification fuelled denitrification. An increase in the abundance of Planctomycete-specific 16S rRNA, the vast majority of which grouped with known anammox bacteria, was also apparent at 3,000 µatm pCO2. This could indicate a possible shift from coupled nitrification-denitrification to anammox activity at elevated CO2.

Table 1: Soil chemical properties of the analysed depth profiles in the Venice lagoon

Visual traces of iron reduction and oxidation are linked to the redox status of soils and have been used to characterise the quality of agricultural soils.We tested whether this feature could also be used to explain the spatial pattern of the natural vegetation of tidal habitats. If so, an easy assessment of the effect of rising sea level on tidal ecosystems would be possible. Our study was conducted at the salt marshes of the northern lagoon of Venice, which are strongly threatened by erosion and rising sea level and are part of the world heritage 'Venice and its lagoon'. We analysed the abundance of plant species at 255 sampling points along a land-sea gradient. In addition, we surveyed the redox morphology (presence/absence of red iron oxide mottles in the greyish topsoil horizons) of the soils and the presence of disturbances. We used indicator species analysis, correlation trees and multivariate regression trees to analyse relations between soil properties and plant species distribution. Plant species with known sensitivity to anaerobic conditions (e.g. Halimione portulacoides) were identified as indicators for oxic soils (showing iron oxide mottles within a greyish soil matrix). Plant species that tolerate a low redox potential (e.g. Spartina maritima) were identified as indicators for anoxic soils (greyish matrix without oxide mottles). Correlation trees and multivariate regression trees indicate the dominant role of the redox morphology of the soils in plant species distribution. In addition, the distance from the mainland and the presence of disturbances were identified as tree-splitting variables. The small-scale variation of oxygen availability plays a key role for the biodiversity of salt marsh ecosystems. Our results suggest that the redox morphology of salt marsh soils indicates the plant availability of oxygen. Thus, the consideration of this indicator may enable an understanding of the heterogeneity of biological processes in oxygen-limited systems and may be a sensitive and easy-to-use tool to assess human impacts on salt marsh ecosystems.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.