Moderate-to-High Intensity Physical Exercise in Patients with Alzheimer's Disease:A Randomized Controlled Trial

Background: Studies of physical exercise in patients with Alzheimer’s disease (AD) are few and results have been inconsistent. Objective: To assess the effects of a moderate-to-high intensity aerobic exercise program in patients with mild AD. Methods: In a randomized controlled trial, we recruited 200 patients with mild AD to a supervised exercise group (60-min sessions three times a week for 16 weeks) or to a control group. Primary outcome was changed from baseline in cognitive performance estimated by Symbol Digit Modalities Test (SDMT) in the intention-to-treat (ITT) group. Secondary outcomes included changes in quality of life, ability to perform activities of daily living, and in neuropsychiatric and depressive symptoms. Results: The ITT analysis showed no significant differences between intervention and control groups in change from baseline of SDMT, other cognitive tests, quality of life, or activities of daily living. The change from baseline in Neuropsychiatric Inventory differed significantly in favor of the intervention group (mean: –3.5, 95% confidence interval (CI) –5.8 to –1.3, p = 0.002). In subjects who adhered to the protocol, we found a significant effect on change from baseline in SDMT as compared with the control group (mean: 4.2, 95% CI 0.5 to 7.9, p = 0.028), suggesting a dose-response relationship between exercise and cognition. Conclusions: This is the first randomized controlled trial with supervised moderate-to-high intensity exercise in patients with mild AD. Exercise reduced neuropsychiatric symptoms in patients with mild AD, with possible additional benefits of preserved cognition in a subgroup of patients exercising with high attendance and intensity.</jats:p

Practical guidelines for the registration and monitoring of serious traffic injuries, D7.1 of the H2020 project SafetyCube

BACKGROUND AND OBJECTIVES Crashes also cause numerous serious traffic injuries, resulting in considerable economic and human costs. Given the burden of injury produced by traffic, using only fatalities as an indicator to monitor road safety gives a very small picture of the health impact of traffic crashes, just the tip of the iceberg. Moreover, in several countries during the last years the number of serious traffic injuries has not been decreasing as fast as the number of fatalities. In other countries the number of serious traffic injuries has even been increasing (Berecki-Gisolf et al., 2013; IRTAD Working Group on Serious Road Traffic Casualties, 2010; Weijermars et al., 2015).Therefore, serious traffic injuries are more commonly being adopted by policy makers as an additional indicator of road safety. Reducing the number of serious traffic injuries is one of the key priorities in the road safety programme 2011-2020 of the European Commission (EC, 2010). To be able to compare performance and monitor developments in serious traffic injuries across Europe, a common definition of a serious road injury was necessary. In January 2013, the High Level Group on Road Safety, representing all EU Member States, established the definition of serious traffic injuries as road casualties with an injury level of MAIS ≥ 3. The Maximum AIS represents the most severe injury obtained by a casualty according to the Abbreviated Injury Scale (AIS). Traditionally the main source of information on traffic accidents and injuries has been the police registration. This provides the official data for statistics at national and European level (CARE Database). Data reported by police usually is very detailed about the circumstances of the crash particularly if there are people injured or killed. But on the other hand police cannot assess the severity of injuries in a reliable way, due, obviously to their training. Therefore, police based data use to classify people involved in a crash as fatality, severe injured if hospitalised more than 24 hours and slight injured if not hospitalised. Moreover, it is known that even a so clear definition as a fatality is not always well reported and produces underreporting. This is due to several factors such as lack of coverage of police at the scene or people dying at hospital not followed by police (Amoros et al., 2006; Broughton et al., 2007; Pérez et al., 2006). Hospital records of patients with road traffic injuries usually include very little information on circumstances of the crash but it does contain data about the person, the hospitalisation (date of hospitalisation and discharge, medical diagnosis, mechanism or external cause of injury, and interventions). Hospital inpatient Discharge Register (HDR) offers an opportunity to complement police data on road traffic injuries. Medical diagnoses can be used to derive information about severity of injuries. Among others, one of the possible scales to measure injury severity is the Abbreviated Injury Scale (AIS). The High Level group identified three main ways Member States can collect data on serious traffic injuries (MAIS ≥ 3): 1) by applying a correction on police data, 2) by using hospital data and 3) by using linked police and hospital data. Once one of these three ways is selected, several additional choices need to be made. In order to be able to compare injury data across different countries, it is important to understand the effects of methodological choices on the estimated numbers of serious traffic injuries. A number of questions arise: How to determine the correction factors that are to be applied to police data? How to select road traffic casualties in the hospital data and how to derive MAIS ≥ 3 casualties? How should police and hospital data be linked and how can the number of MAIS ≥ 3 casualties be determined on the basis of the linked data sources? Currently, EU member states use different procedures to determine the number of MAIS ≥ 3 traffic injuries, dependent on the available data. Given the major differences in the procedures being applied, the quality of the data differs considerably and the numbers are not yet fully comparable between countries. In order to be able to compare injury data across different countries, it is important to understand the effects of methodological choices on the estimated numbers of serious traffic injuries. Work Package 7 of SafetyCube project is dedicated to serious traffic injuries, their health impacts and their costs. One of the aims of work package 7 is to assess and improve the estimation of the number of serious traffic injuries. The aim of this deliverable (D7.1) is to report practices in Europe concerning the reporting of serious traffic injuries and to provide guidelines and recommendations applied to each of the three main ways to estimate the number of road traffic serious injuries. Specific objectives for this deliverable are to: Describe the current state of collection of data on serious traffic injuries across Europe Provide practical guidelines for the estimation of the number of serious traffic injuries for each of the three ways identified by the High Level Group Examine how the estimated number of serious traffic injuries is affected by differences in methodology

Deduced consensus sequence of Sindbis virus strain AR339: mutations contained in laboratory strains which affect cell culture and in vivo phenotypes.

The consensus sequence of the Sindbis virus AR339 isolate, the prototype alphavirus, has been deduced. THe results presented here suggest (i) that a substantial proportion of the sequence divergence evident between the consensus sequence and sequences of laboratory strains of AR339 has resulted from selection for efficient growth in cell culture, (ii) that many of these changes affect the virulence of the virus in animal models, and (iii) that such modified genetic backgrounds present in laboratory strains can exert a significant influence on genetic studies of virus pathogenesis and host range. A laboratory strain of Sindbis virus AR339 was sequenced and cloned as a cDNA (pTRSB) from which infectious virus (TRSB) could be derived. The consensus sequence was deduced from the complete sequences of pTRSB and HRsp (E. G. Strauss, C. M. Rice, and J. H. Strauss, Virology 133:92-110, 1984), from partial sequences of the glycoprotein genes of three other AR339 laboratory strains, and by comparison with the sequences of the glycoprotein genes of three other AR339 sequence. HRsp differed form the consensus sequence by eight coding changes, and TRSB differed by three coding changes. In the 5' untranslated region, HRsp differed from the consensus sequence at nucleotide (nt) 5. These differences were likely the result of cell culture passage of the original AR339 isolate. At three of the difference loci (one in TRSB and two in HRsp), selection of cell-culture-adaptive mutations was documented with Sindbis virus or other alphaviruses. Selection in cell culture often results in attenuation of virulence in animals. Considering the TRSB and HRsp sequences together, one noncoding difference from the consensus (an A-for-G substitution in the 5' untranslated region at nt 5) and six coding differences in the glycoprotein genes (at E2 amino acids 1, 3, 70, and 172 and at E1 amino acids 72 and 237) were at loci which, either individually or in combination, significantly affected alphavirus virulence in mice. Although the levels of virulence of isogenic strains containing either nt 5 A or nt 5 G did not differ significantly in neonatal mice, the presence of nt 5 A greatly enhanced the effect of a second attenuating mutation in the E2 gene. These results suggest that minimal differences in the "wild type" genetic background into which an additional mutation is introduced can have a dramatic effect on apparent virulence and pathogenesis phenotypes. A cDNA clone of the consensus AR339 sequence, a sequence devoid of occult attenuating mutations introduced by cell culture passage, will allow the molecular genetic examination of cell culture and in vivo phenotypes of a virus which may best reflect the sequence of Sindbis virus AR339 at the time of its isolation

The global biogeography of tree leaf form and habit

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

The global biogeography of tree leaf form and habit

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling

Native diversity buffers against severity of non-native tree invasions

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species$^{1,2}$. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies$^{3,4}$. Here, leveraging global tree databases$^{5,6,7}$, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions

The global biogeography of tree leaf form and habit

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling

The global biogeography of tree leaf form and habit.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling