Search

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

Governing transitions have assumed increasing significance in managing change with respect to climate change and rapid periurbanization. In this respect, evidence from sustainability transitions research in South Asia stipulates the need for institutional innovations that are suitable to the governance context, which is characterized by a fuzzy field of “speculative frontiers,” and strong hierarchically structured mechanisms. This paper focuses on Chennai, India to ascertain the potentials of “adaptive governance” in this periurban region of the Global South. We approach this by (a) defining the periurban space from a bio-regional, socio-ecological systems perspective, (b) complementing it with a zonal classification considering disaster and climate change risks, and (c) presenting an adaptive governance framework. Based on analyzing existing governance structures, the paper discusses identified syndromes, synergistic potentials and activities for adaptive governance, especially highlighting water and agroecology-related pathways. Our integrated analytical approach is (a) multi-dimensional deploying the adaptive governance framework, (b) multi-level considering local, intermediate and state governance, and (c) multi-scalar applying macro, meso and micro scales. The implications underscore that transformative interventions, sooner or later, will have to challenge the system that produces such climate vulnerabilities. In spite of several barriers, there are noteworthy indications of adaptive governance practices discernible in numerous niche projects in the Chennai region that demonstrate diverse embryonic forms to build more strategic and conscientious approaches.

The National River Ganga, home to the National aquatic animal, Gangetic dolphin along with species of critically endangered otters, gharial crocodile, turtles, waterbirds and numerous aquatic and semi aquatic life forms is of conservation importance to safeguard the biodiversity it harbors. Meanwhile, River Ganga is also the source of highly productive agricultural yield that satiates the nation’s food demands. The last few years have echoed demands for ‘e-flow’ regulations to be formalized and regularized to maintain healthy river conditions with adequate amount of water retained in the rivers while meeting the populations demand for water supply. Though e flow estimations are a complex science in itself, the simplest way is visualization. In this context, there needs to be a transparent mechanism to picturise and depict the ground conditions of the river stretches. Remote sensing provides ‘eyes in the sky’. The river receives water in the form of precipitation, snow melt and ground water recharge and the hydrological flux is evident in the surface water. Thus delineating the water spread area can provide a crude estimate of the water in the river. The Google Earth Engine platform provides a collection of satellite imageries, server side data processing and algorithms. The Landsat and Sentinel data catalogue offers good coverage of the river and its basin. Though sentinel data provides high ground resolution of 10m, Landsat provides multi temporal historic coverage at a good 30 m resolution, that works well for the main stem river. From the various available data processing methods, spectral decomposition technique of Modified Normalised Differential Water Index MNDWI was found to be most effective in delineating surface water. Data was filtered for the three major seasons of Pre monsoon, Monsoon and Post monsoon periods and MNDWI for the same was visualized. While Middle Ganga region through Uttar Pradesh shows seasonal flooding during the monsoon period, the Lower Ganga after the confluence of major tributaries of Son, Ghaghra, Gandak and Kosi flows in full bank width in the Post monsoon period. During the pre monsoon period many stretches of Ganga particularly the Upper middle stretch showed thin shallow channel and intermittent deep pools disconnected by dry sand beds. These regions result in disruption in the river flow and are priority sites to implement e flow regulations. Another key factor is the regulation of flow in the river from barrages. Sudden release of water can cause flooding resulting in disturbance to the life cycle of species that are tuned to the natural annual hydrological pulse of the river. This is because different aquatic species have different river width and depth requirements. Thus monitoring the seasonal variations in the water spread area helps to note the longitudinal and lateral connectivity of the river which is a prime characteristic for the health of a river. This study intended to create an application that offers to visualize the seasonal water spread in the Ganga river for the past years that can assist decision makers to monitor the river and make informed regulatory measures.

We present a driving scheme for solid-state quantum emitters, referred to as Notch-filtered Adiabatic Rapid Passage (NARP), that utilizes frequency-swept pulses containing a spectral hole resonant with the optical transition in the emitter. NARP enables high-fidelity state inversion and exhibits robustness to variations in the laser pulse parameters, benefits that are derived from the insensitivity of the condition for adiabatic evolution. NARP also offers the advantage of immunity to phonon-mediated excitation-induced dephasing when positively chirped control pulses are used. Our resonant driving approach could be combined with spectral filtering of the scattered pump light and photonic devices for enhanced collection efficiency to realize simultaneous high indistinguishability and brightness in single photon source applications.

Energy demand is projected to increase throughout the globe in near future which necessitates the development of carbon capture and storage (CCS) technologies to avoid the harmful impacts of climate change. Direct mineral carbonation of alkaline solid wastes is one such technology that provides the benefit of being a permanent, cost effective and a promising on-site option, while improving the environmental quality of the waste. The present work attempts to study direct mineral carbonation of red mud (RM) with CO2 (concentration as in flue gas) by wet and dry route employing response surface methodology (RSM). Maximum reduction of about 18.5% in the concentration of CO2 was attained in wet carbonation conditions (65.1 ºC, 42.1 bar, L/S ratio of 0.53, 1 h) and the sequestration capacity of RM was found to be 15.5 g of CO2/kg, correspondingly. The models were found to be statistically significant (R2 value > 98.5%). Temperature and reaction time were found to influence the dry route. Meanwhile in wet route, temperature, pressure, liquid to solid (L/S) ratio and reaction time were all significant. The results demonstrated that RM could significantly sequester CO2 even from flue gas. The carbonation process was evidenced using Fourier-Transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) analysis and scanning electron microscopy (SEM).

Amphibians represent a diverse group of tetrapods, marked by deep divergence times between their three systematic orders and families. Studying amphibian biology through the genomics lens increases our understanding of the features of this animal class and that of other terrestrial vertebrates. The need for amphibian genomic resources is more urgent than ever due to the increasing threats to this group. Amphibians are one of the most imperiled taxonomic groups, with approximately 41% of species threatened with extinction due to habitat loss, changes in land use patterns, disease, climate change, and their synergistic effects. Amphibian genomic resources have provided a better understanding of ontogenetic diversity, tissue regeneration, diverse life history and reproductive modes, anti-predator strategies, and resilience and adaptive responses. They also serve as essential models for studying broad genomic traits, such as evolutionary genome expansions and contractions, as they exhibit the widest range of genome sizes among all animal taxa and possess multiple mechanisms of genetic sex determination. Despite these features, genome sequencing of amphibians has significantly lagged behind that of other vertebrates, primarily due to the challenges of assembling their large, repeat-rich genomes and the relative lack of societal support. The emergence of long-read sequencing technologies, combined with advanced molecular and computational techniques that improve scaffolding and reduce computational workloads, is now making it possible to address some of these challenges. To promote and accelerate the production and use of amphibian genomics research through international coordination and collaboration, we launched the Amphibian Genomics Consortium (AGC, https://mvs.unimelb.edu.au/amphibian-genomics-consortium ) in early 2023. This burgeoning community already has more than 282 members from 41 countries. The AGC aims to leverage the diverse capabilities of its members to advance genomic resources for amphibians and bridge the implementation gap between biologists, bioinformaticians, and conservation practitioners. Here we evaluate the state of the field of amphibian genomics, highlight previous studies, present challenges to overcome, and call on the research and conservation communities to unite as part of the AGC to enable amphibian genomics research to leap to the next level.

Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Despite their role as innate sentinels, macrophages can serve as cellular reservoirs of chikungunya virus (CHIKV), a highly-pathogenic arthropod-borne alphavirus that has caused large outbreaks among human populations. Here, with the use of viral chimeras and evolutionary selection analysis, we define CHIKV glycoproteins E1 and E2 as critical for virion production in THP-1 derived human macrophages. Through proteomic analysis and functional validation, we further identify signal peptidase complex subunit 3 (SPCS3) and eukaryotic translation initiation factor 3 subunit K (eIF3k) as E1-binding host proteins with anti-CHIKV activities. We find that E1 residue V220, which has undergone positive selection, is indispensable for CHIKV production in macrophages, as its mutation attenuates E1 interaction with the host restriction factors SPCS3 and eIF3k. Finally, we show that the antiviral activity of eIF3k is translation-independent, and that CHIKV infection promotes eIF3k translocation from the nucleus to the cytoplasm, where it associates with SPCS3. These functions of CHIKV glycoproteins late in the viral life cycle provide a new example of an intracellular evolutionary arms race with host restriction factors, as well as potential targets for therapeutic intervention.

In recent days, unmanned aerial vehicles (UAVs) becomes more familiar because of its versatility, automation abilities, and low cost. Dynamic scene classification gained significant interest among the UAV-based surveillance systems, e.g., high-voltage power line and forest fire monitoring, which facilitate the object detection, tracking process and drastically enhances the outcome of visual surveillance. This paper proposes a new optimal deep learning-based scene classification model captured by UAVs. The proposed model involves a residual network-based features extraction (RNBFE) which extracts features from the diverse convolution layers of a deep residual network. In addition, the several parameters in RNBFE lead to many configuration errors due to manual parameter tuning. So, self-adaptive global best harmony search (SGHS) algorithm is employed for tuning the parameters of the RNBFE. The resultant feature vectors undergo classification by the use of latent variable support vector machine (LVSVM) model. The presented optimal RNBFE (ORNBFE) model has been tested using two open access datasets namely UC Merced (UCM) Land Use Dataset and WHU-RS Dataset. The presented technique attains maximum scene classification accuracy over the other recently proposed methods.

Recently, the increase in inexpensive and compact unmanned aerial vehicles (UAVs) and light-weight imaging sensors has led to an interest in using them in various remote sensing applications. The processes of collecting, calibrating, registering, and processing data from miniature UAVs and interpreting the data semantically are time-consuming. In UAV aerial imagery, learning effective image representations is central to the scene classification process. Earlier approaches to the scene classification process depended on feature coding methods with low-level hand-engineered features or unsupervised feature learning. These methods could produce mid-level image features with restricted representational abilities, which generally yielded mediocre results. The development of convolutional neural networks (CNNs) has made image classification more efficient. Due to the limited resources in UAVs, it is hard to fine-tune the hyperparameters and the trade-offs between classifier results and computation complexity. This paper introduces a new multi-objective optimization model for evolving state-of-the-art deep CNNs for scene classification, which generates the non-dominant solutions in an automated way at the Pareto front. We use a set of two benchmark datasets to test the performance of the scene classification model and make a detailed comparative study. The proposed method attains a very low computational time of 80 sec and maximum accuracy of 97.88% compared to all other methods. The proposed method is found to be appropriate for the effective scene classification of images captured by UAVs.

IntroductionWe investigated the associations of leptin markers with cognitive function and magnetic resonance imaging (MRI) measures of brain atrophy and vascular injury in healthy middle-aged adults.MethodsWe included 2262 cognitively healthy participants from the Framingham Heart Study with neuropsychological evaluation; of these, 2028 also had available brain MRI. Concentrations of leptin, soluble leptin receptor (sOB-R), and their ratio (free leptin index [FLI]), indicating leptin bioavailability, were measured using enzyme-linked immunosorbent assays. Cognitive and MRI measures were derived using standardized protocols.ResultsHigher sOB-R was associated with lower fractional anisotropy (FA, β = -0.114 ± 0.02, p

INTRODUCTION: The apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimers disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid-beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex-dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research. METHODS: In 2023, the Alzheimers Association convened multidisciplinary researchers at the AAIC Advancements: APOE conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE-targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function. RESULTS: This manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field. DISCUSSION: Understanding apoEs multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine. HIGHLIGHTS: APOE is a central player in the pathogenesis of Alzheimers disease. APOE exerts a numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.

Vitamin B12 is critical for hematopoiesis and myelination. Deficiency can cause neurologic deficits including loss of coordination and cognitive decline. However, diagnosis relies on measurement of vitamin B12 in the blood, which may not accurately reflect the concentration in the brain. Using programmable phage display, we identified an autoantibody targeting the transcobalamin receptor (CD320) in a patient with progressive tremor, ataxia, and scanning speech. Anti-CD320 impaired cellular uptake of cobalamin (B12) in vitro by depleting its target from the cell surface. Despite a normal serum concentration, B12 was nearly undetectable in her cerebrospinal fluid (CSF). Immunosuppressive treatment and high-dose systemic B12 supplementation were associated with increased B12 in the CSF and clinical improvement. Optofluidic screening enabled isolation of a patient-derived monoclonal antibody that impaired B12 transport across an in vitro model of the blood-brain barrier (BBB). Autoantibodies targeting the same epitope of CD320 were identified in seven other patients with neurologic deficits of unknown etiology, 6% of healthy controls, and 21.4% of a cohort of patients with neuropsychiatric lupus. In 132 paired serum and CSF samples, detection of anti-CD320 in the blood predicted B12 deficiency in the brain. However, these individuals did not display any hematologic signs of B12 deficiency despite systemic CD320 impairment. Using a genome-wide CRISPR screen, we found that the low-density lipoprotein receptor serves as an alternative B12 uptake pathway in hematopoietic cells. These findings dissect the tissue specificity of B12 transport and elucidate an autoimmune neurologic condition that may be amenable to immunomodulatory treatment and nutritional supplementation.

The fact that neutrinos carry a non-vanishing rest mass is evidence ofphysics beyond the Standard Model of elementary particles. Their absolute massbears important relevance from particle physics to cosmology. In this work, wereport on the search for the effective electron antineutrino mass with theKATRIN experiment. KATRIN performs precision spectroscopy of the tritium$\beta$-decay close to the kinematic endpoint. Based on the first fiveneutrino-mass measurement campaigns, we derive a best-fit value of $m_u^{2} ={-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_u <{0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics ofprevious data sets, amounting to 36 million electrons collected in 259measurement days, a substantial reduction of the background level and improvedsystematic uncertainties, this result tightens KATRIN's previous bound by afactor of almost two.

Rationale & objectiveTubulointerstitial damage is a feature of early chronic kidney disease (CKD), but current clinical tests capture it poorly. Urine biomarkers of tubulointerstitial health may identify risk of CKD.Study designProspective cohort (Atherosclerosis Risk in Communities [ARIC]) and case-cohort (Multi-Ethnic Study of Atherosclerosis [MESA] and Reasons for Geographic and Racial Differences in Stroke [REGARDS]).Setting & participantsAdults with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and without diabetes in the ARIC, REGARDS, and MESA studies.ExposuresBaseline urine monocyte chemoattractant protein-1 (MCP-1), alpha-1-microglobulin (α1m), kidney injury molecule-1, epidermal growth factor, and chitinase-3-like protein 1.OutcomeIncident CKD or end-stage kidney disease.Analytical approachMultivariable Cox proportional hazards regression for each cohort; meta-analysis of results from all 3 cohorts.Results872 ARIC participants (444 cases of incident CKD), 636 MESA participants (158 cases), and 924 REGARDS participants (488 cases) were sampled. Across cohorts, mean age ranged from 60 ± 10 to 63 ± 8 years, and baseline eGFR ranged from 88 ± 13 to 91 ± 14 mL/min/1.73 m2. In ARIC, higher concentrations of urine MCP-1, α1m, and kidney injury molecule-1 were associated with incident CKD. In MESA, higher concentration of urine MCP-1 and lower concentration of epidermal growth factor were each associated with incident CKD. In REGARDS, none of the biomarkers were associated with incident CKD. In meta-analysis of all 3 cohorts, each 2-fold increase α1m concentration was associated with incident CKD (HR, 1.19; 95% CI, 1.08-1.31).LimitationsObservational design susceptible to confounding; competing risks during long follow-up period; meta-analysis limited to 3 cohorts.ConclusionsIn 3 combined cohorts of adults without prevalent CKD or diabetes, higher urine α1m concentration was independently associated with incident CKD. 4 biomarkers were associated with incident CKD in at least 1 of the cohorts when analyzed individually. Kidney tubule health markers might inform CKD risk independent of eGFR and albuminuria.