Active Visual Localization in Partially Calibrated Environments

Humans can robustly localize themselves without a map after they get lost following prominent visual cues or landmarks. In this work, we aim at endowing autonomous agents the same ability. Such ability is important in robotics applications yet very challenging when an agent is exposed to partially calibrated environments, where camera images with accurate 6 Degree-of-Freedom pose labels only cover part of the scene. To address the above challenge, we explore using Reinforcement Learning to search for a policy to generate intelligent motions so as to actively localize the agent given visual information in partially calibrated environments. Our core contribution is to formulate the active visual localization problem as a Partially Observable Markov Decision Process and propose an algorithmic framework based on Deep Reinforcement Learning to solve it. We further propose an indoor scene dataset ACR-6, which consists of both synthetic and real data and simulates challenging scenarios for active visual localization. We benchmark our algorithm against handcrafted baselines for localization and demonstrate that our approach significantly outperforms them on localization success rate.Comment: https://www.youtube.com/watch?v=DIH-GbytCPM&feature=youtu.b

Altered Functional Connectivity in Resting State Networks in Tourette’s Disorder

Introduction: Brain regions are anatomically and functionally interconnected in order to facilitate important functions like cognition and movement. It remains incompletely understood how brain connectivity contributes to the pathophysiology of Tourette’s disorder (TD). By using resting-state functional MRI, we aimed to identify alterations in the default mode network (DMN), frontal-parietal network (FPN), sensori-motor network (SMN), and salience network (SN) in TD compared with healthy control (HC) subjects.Method: In 23 adult TD patients and 22 HC, 3T-MRI resting-state scans were obtained. Independent component analysis was performed comparing TD and HC to investigate connectivity patterns within and between resting-state networks.Results: TD patients showed higher involvement of the dorsal medial prefrontal cortex in the connectivity of the DMN and less involvement of the inferior parietal cortex in the connectivity of the FPN when compared to HC. Moreover, TD patients showed a stronger coupling between DMN and left FPN than HC. Finally, in TD patients, functional connectivity within DMN correlated negatively with tic severity.Conclusion: We tentatively interpret the increased functional connectivity within DMN in TD patients as compensatory to the lower functional connectivity within left FPN. The stronger coupling between DMN and left FPN, together with the finding that higher DMN intrinsic connectivity is associated with lower tic severity would indicate that DMN is recruited to exert motor inhibition

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Long short-term memory (LSTM) networks have demonstrated successful applications in accurately and efficiently predicting reservoir releases from hydrometeorological drivers including reservoir storage, inflow, precipitation, and temperature. However, due to its black-box nature and lack of process-based implementation, we are unsure whether LSTM makes good predictions for the right reasons. In this work, we use an explainable machine learning (ML) method, called SHapley Additive exPlanations (SHAP), to evaluate the variable importance and variable-wise temporal importance in the LSTM model prediction. In application to 30 reservoirs over the Upper Colorado River Basin, United States, we show that LSTM can accurately predict the reservoir releases with NSE ≥ 0.69 for all the considered reservoirs despite of their diverse storage sizes, functionality, elevations, etc. Additionally, SHAP indicates that storage and inflow are more influential than precipitation and temperature. Moreover, the storage and inflow show a relatively long-term influence on the release up to 7 days and this influence decreases as the lag time increases for most reservoirs. These findings from SHAP are consistent with our physical understanding. However, in a few reservoirs, SHAP gives some temporal importances that are difficult to interpret from a hydrological point of view, probably because of its ignorance of the variable interactions. SHAP is a useful tool for black-box ML model explanations, but the hydrological processes inferred from its results should be interpreted cautiously. More investigations of SHAP and its applications in hydrological modeling is needed and will be pursued in our future study

Binge drinking is associated with higher cortisol and lower hippocampal and prefrontal gray matter volume: Prospective association with future alcohol intake

Background: Cortisol is a significant driver of the biological stress response that is potently activated by acute alcohol intake and increased with binge drinking. Binge drinking is associated with negative social and health consequences and risk of developing alcohol use disorder (AUD). Both cortisol levels and AUD are also associated with changes in hippocampal and prefrontal regions. However, no previous research has assessed structural gray matter volume (GMV) and cortisol concurrently to examine BD effects on hippocampal and prefrontal GMV and cortisol, and their prospective relationship to future alcohol intake. Methods: Individuals who reported binge drinking (BD: N = 55) and demographically matched non-binge moderate drinkers (MD: N = 58) were enrolled and scanned using high-resolution structural MRI. Whole brain voxel-based morphometry was used to quantify regional GMV. In a second phase, 65% of the sample volunteered to participate in prospective daily assessment of alcohol intake for 30 days post-scanning. Results: Relative to MD, BD showed significantly higher cortisol and smaller GMV in regions including hippocampus, dorsal lateral prefrontal cortex (dlPFC), prefrontal and supplementary motor, primary sensory and posterior parietal cortex (FWE, p < 0.05). GMV in bilateral dlPFC and motor cortices were negatively associated with cortisol levels, and smaller GMV in multiple PFC regions was associated with more subsequent drinking days in BD. Conclusion: These findings indicate neuroendocrine and structural dysregulation associated with BD relative to MD. Notably, BD-associated lower GMV regions were those involved in stress, memory and cognitive control, with lower GMV in cognitive control and motor regions also predicting higher levels of future alcohol intake in BD

Overexpression of <i>ZmDHN15</i> Enhances Cold Tolerance in Yeast and Arabidopsis

Maize (Zea mays L.) originates from the subtropical region and is a warm-loving crop affected by low-temperature stress. Dehydrin (DHN) protein, a member of the Group 2 LEA (late embryogenesis abundant proteins) family, plays an important role in plant abiotic stress. In this study, five maize DHN genes were screened based on the previous transcriptome sequencing data in our laboratory, and we performed sequence analysis and promoter analysis on these five DHN genes. The results showed that the promoter region has many cis-acting elements related to cold stress. The significantly upregulated ZmDHN15 gene has been further screened by expression pattern analysis. The subcellular localization results show that ZmDHN15 fusion protein is localized in the cytoplasm. To verify the role of ZmDHN15 in cold stress, we overexpressed ZmDHN15 in yeast and Arabidopsis. We found that the expression of ZmDHN15 can significantly improve the cold resistance of yeast. Under cold stress, ZmDHN15-overexpressing Arabidopsis showed lower MDA content, lower relative electrolyte leakage, and less ROS (reactive oxygen species) when compared to wild-type plants, as well as higher seed germination rate, seedling survival rate, and chlorophyll content. Furthermore, analysis of the expression patterns of ROS-associated marker genes and cold-response-related genes indicated that ZmDHN15 genes play an important role in the expression of these genes. In conclusion, the overexpression of the ZmDHN15 gene can effectively improve the tolerance to cold stress in yeast and Arabidopsis. This study is important for maize germplasm innovation and the genetic improvement of crops

Overexpression of Maize Glutathione S-Transferase <i>ZmGST26</i> Decreases Drought Resistance of <i>Arabidopsis</i>

Drought stress critically endangers the growth and development of crops. Glutathione S-transferase plays a vital role in response to abiotic stress. However, there are few studies on the role of glutathione S-transferase in maize drought stress. In this study, the significantly downregulated expression of ZmGST26 in roots under drought stress was analyzed by qRT-PCR. Promoter analyses showed that there were several cis-acting elements related to drought stress and that were involved in oxidative response in the promoter region of ZmGST26. Subcellular localization results showed that ZmGST26 was localized in the nucleus. The transgenic lines of the Arabidopsis over-expressing ZmGST26 were more sensitive to drought stress and ABA in seed germination and inhibited ABA-mediated stomatal closure. Under drought stress, phenotypic analyses showed that the germination rate, root length and survival rate of ZmGST26 overexpressing lines were significantly lower than those of wild-type lines. The determination of physiological and biochemical indexes showed that the water loss rate, malondialdehyde, O2− and H2O2 of the overexpression lines significantly increased compared with wild-type Arabidopsis, but the antioxidant enzyme activities (CAT, SOD and POD), and proline and chlorophyll contents were significantly reduced. Subsequently, the qRT-PCR analysis of drought stress-related gene expression showed that, under drought stress conditions, the expression levels of DREB2A, RD29A, RD29B and PP2CA genes in ZmGST26 overexpression lines were significantly lower than those in wild-type Arabidopsis. In summary, ZmGST26 reduced the drought resistance of plants by aggravating the accumulation of reactive oxygen species in Arabidopsis