Key Issues in Essential Tremor Genetics Research: Where Are We Now and How Can We Move Forward?

Background: Genetics research is an avenue towards understanding essential tremor (ET). Advances have been made in genetic linkage and association: there are three reported ET susceptibility loci, and mixed but growing data on risk associations. However, causal mutations have not been forthcoming. This disappointing lack of progress has opened productive discussions on challenges in ET and specifically ET genetics research, including fundamental assumptions in the field. Methods: This article reviews the ET genetics literature, results to date, the open questions in ET genetics and the current challenges in addressing them. Results: Several inherent ET features complicate genetic linkage and association studies: high potential phenocopy rates, inaccurate tremor self-reporting, and ET misdiagnoses are examples. Increasing use of direct examination data for subjects, family members, and controls is one current response. Smaller moves towards expanding ET phenotype research concepts into non-tremor features, clinically disputed ET subsets, and testing phenotype features instead of clinical diagnosis against genetic data are gradually occurring. The field has already moved to considering complex trait mechanisms requiring detection of combinations of rare genetic variants. Hypotheses may move further to consider novel mechanisms of inheritance, such as epigenetics. Discussion: It is an exciting time in ET genetics as investigators start moving past assumptions underlying both phenotype and genetics experimental contributions, overcoming challenges to collaboration, and engaging the ET community. Multicenter collaborative efforts comprising rich longitudinal prospective phenotype data and neuropathologic analysis combined with the latest in genetics experimental design and technology will be the next wave in the field

Red Giant stars in the Large Magellanic Cloud clusters

We present deep J,H,Ks photometry and accurate Color Magnitude Diagrams down to K ~18.5, for a sample of 13 globular clusters in the Large Magellanic Cloud. This data set combined with the previous sample of 6 clusters published by our group gives the opportunity to study the properties of giant stars in clusters with different ages (ranging from ~80 Myr up to ~3.5 Gyr). Quantitative estimates of star population ratios (by number and luminosity) in the Asymptotic Giant Branch, the Red Giant Branch and the He-clump, have been obtained and compared with theoretical models in the framework of probing the so-called phase transitions. The AGB contribution to the total luminosity starts to be significant at ~200 Myr and reaches its maximum at ~5-600 Myr, when the RGB Phase Transition is starting. At ~900 Myr the full developing of an extended and well populated RGB has been completed. Both the occurrence of the AGB and RGB Phase Transitions are sharp events, lasting a few hundreds Myr only. These empirical results agree very well with the theoretical predictions of simple stellar population models based on canonical tracks and the fuel-consumption approach.Comment: 32 pages, 11 figures, accepted to Ap

Probing the RGB-phase transition: Near-IR photometry of six intermediate age LMC clusters

This is the first of a series of papers devoted to a global study of the photometric properties of the red stellar sequences in a complete sample of the Large Magellanic Cloud clusters, by means of near infrared array photometry. Deep J,H,Ks photometry and accurate Color Magnitude Diagrams down to K=18.5, i.e. 1.5 mag below the red He-clump, for six intermediate age clusters (namely NGC1987, NGC2108, NGC2190, NGC2209, NGC2231, NGC2249) are presented. A quantitative estimate of the population ratios (by number and luminosity) between Red Giant Branch and He-clump stars for each target cluster is provided and discussed in the framework of probing the so-called Red Giant Branch phase transition (RGB Ph-T). By using the Elson & Fall s-parameter as an age indicator, the observed RGB population shows a sharp enhancement (both in number and luminosity) at s=36. Obviously, the corresponding absolute age strictly depends on the details of theoretical models adopted to calibrate the s-parameter. Curiously, the currently available calibrations of the s-parameter in term of age based on canonical (by Elson & Fall 1988) and overshooting (Girardi et al. 1995) models provide ages that well agree within 10%, suggesting that the full development of the Red Giant Branch occurs at t=700 Myr and be a relatively fast event (delta t=300 Myr). However, the RGB Ph-T epoch derived from the overshooting calibration of the s-parameter turns out to be significantly earlier than the epoch provided by the recent evolutionary tracks by Girardi et al. (2000). A new calibration of the s-parameter based on high quality Color Magnitude Diagrams and updated models is urged to address the origin of this discrepancy and finally establish the epoch of the RGB Ph-T.Comment: ApJ, in pres

Progressive Cognitive Deficit, Motor Impairment and Striatal Pathology in a Transgenic Huntington Disease Monkey Model from Infancy to Adulthood

One of the roadblocks to developing effective therapeutics for Huntington disease (HD) is the lack of animal models that develop progressive clinical traits comparable to those seen in patients. Here we report a longitudinal study that encompasses cognitive and motor assessment, and neuroimaging of a group of transgenic HD and control monkeys from infancy to adulthood. Along with progressive cognitive and motor impairment, neuroimaging revealed a progressive reduction in striatal volume. Magnetic resonance spectroscopy at 48 months of age revealed a decrease of N-acetylaspartate (NAA), further suggesting neuronal damage/loss in the striatum. Postmortem neuropathological analyses revealed significant neuronal loss in the striatum. Our results indicate that HD monkeys share similar disease patterns with HD patients, making them potentially suitable as a preclinical HD animal model

A two years longitudinal study of a transgenic Huntington disease monkey

BACKGROUND: A two-year longitudinal study composed of morphometric MRI measures and cognitive behavioral evaluation was performed on a transgenic Huntington’s disease (HD) monkey. rHD1, a transgenic HD monkey expressing exon 1 of the human gene encoding huntingtin (HTT) with 29 CAG repeats regulated by a human polyubiquitin C promoter was used together with four age-matched wild-type control monkeys. This is the first study on a primate model of human HD based on longitudinal clinical measurements. RESULTS: Changes in striatal and hippocampal volumes in rHD1 were observed with progressive impairment in motor functions and cognitive decline, including deficits in learning stimulus-reward associations, recognition memory and spatial memory. The results demonstrate a progressive cognitive decline and morphometric changes in the striatum and hippocampus in a transgenic HD monkey. CONCLUSIONS: This is the first study on a primate model of human HD based on longitudinal clinical measurements. While this study is based a single HD monkey, an ongoing longitudinal study with additional HD monkeys will be important for the confirmation of our findings. A nonhuman primate model of HD could complement other animal models of HD to better understand the pathogenesis of HD and future development of diagnostics and therapeutics through longitudinal assessment

Searching for Intragroup Light in Deep U-band Imaging of the COSMOS Field

We present the results of deep, ground based U-band imaging with the Large Binocular Telescope of the Cosmic Evolution Survey (COSMOS) field as part of the near-UV imaging program, UVCANDELS. We utilize a seeing sorted stacking method along with night-to-night relative transparency corrections to create optimal depth and optimal resolution mosaics in the U-band, which are capable of reaching point source magnitudes of AB 26.5 mag at 3 sigma. These ground based mosaics bridge the wavelength gap between the HST WFC3 F27W and ACS F435W images and are necessary to understand galaxy assembly in the last 9-10 Gyr. We use the depth of these mosaics to search for the presence of U-band intragroup light (IGrL) beyond the local Universe. Regardless of how groups are scaled and stacked, we do not detect any U-band IGrL to unprecedented U-band depths of 29.1-29.6 mag/arcsec2, which corresponds to an IGrL fraction of less than 1% of the total group light. This stringent upper limit suggests that IGrL does not contribute significantly to the Extragalactic Background Light at short wavelengths. Furthermore, the lack of UV IGrL observed in these stacks suggests that the atomic gas observed in the intragroup medium (IGrM) is likely not dense enough to trigger star formation on large scales. Future studies may detect IGrL by creating similar stacks at longer wavelengths or by pre-selecting groups which are older and/or more dynamically evolved similar to past IGrL observations of compact groups and loose groups with signs of gravitational interactions.Comment: Accepted to PAS

Health-Related Quality of Life in Long-Term Survivors of Relapsed Childhood Acute Lymphoblastic Leukemia

BACKGROUND: Relapses occur in about 20% of children with acute lymphoblastic leukemia (ALL). Approximately one-third of these children can be cured. Their risk for late effects is high because of intensified treatment, but their health-related quality of life (HRQOL) was largely unmeasured. Our aim was to compare HRQOL of ALL survivors with the general population, and of relapsed with non-relapsed ALL survivors. METHODOLOGY/PRINCIPAL FINDINGS: As part of the Swiss Childhood Cancer Survivor Study (SCCSS) we sent a questionnaire to all ALL survivors in Switzerland who had been diagnosed between 1976-2003 at age <16 years, survived ≥5 years, and were currently aged ≥16 years. HRQOL was assessed with the Short Form-36 (SF-36), which measures four aspects of physical health and four aspects of mental health. A score of 50 corresponded to the mean of a healthy reference population. We analyzed data from 457 ALL survivors (response: 79%). Sixty-one survivors had suffered a relapse. Compared to the general population, ALL survivors reported similar or higher HRQOL scores on all scales. Survivors with a relapse scored lower in general health perceptions (51.6) compared to those without (55.8;p=0.005), but after adjusting for self-reported late effects, this difference disappeared. CONCLUSION/SIGNIFICANCE: Compared to population norms, ALL survivors reported good HRQOL, even after a relapse. However, relapsed ALL survivors reported poorer general health than non-relapsed. Therefore, we encourage specialists to screen for poor general health in survivors after a relapse and, when appropriate, specifically seek and treat underlying late effects. This will help to improve patients' HRQOL

Indici per la valutazione della qualit? ecologica dei laghi

Collection of methods to evaluate lake quality using biological element

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