Effects of Natural Zeolites on Bioavailability and Leachability of Heavy Metals in the Composting Process of Biodegradable Wastes

The bioavailability and leachability of heavy metals play an important role in the toxicity of heavy metals in the final compost followed by land application. This chapter examines the effects of natural zeolite on bioavailability of heavy metals (Zn, Cu, Mn, Fe, Ni, Pb, Cd, and Cr) in the form of water soluble and diethylenetriaminepentaacetic acid (DTPA) extractable. The toxicity characteristic leaching procedure (TCLP) test was performed to examine the leachability of heavy metals. Water solubility, DTPA extractability, leachability, and most bioavailable fractions were reduced during agitated pile composting (APC) and rotary drum composting (RDC) of water hyacinth with zeolite addition. The addition of the natural zeolite (clinoptilolite) during the composting process led to an increase in Na, Ca, and K concentrations and significantly reduced the water solubility and DTPA and TCLP extractability of heavy metals. The addition of an appropriate amount of natural zeolite during the composting process enhanced the organic matter degradation, thereby increasing the conversion into the most stabilized organic matter and reducing the bioavailability and leachability of heavy metals

Deep Cross-Modal Steganography Using Neural Representations

Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neural Networks (DNNs) have recently been utilized in steganography. However, existing deep steganography techniques are limited in scope, as they focus on specific data types and are not effective for cross-modal steganography. Therefore, We propose a deep cross-modal steganography framework using Implicit Neural Representations (INRs) to hide secret data of various formats in cover images. The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions. Experiments on various secret datasets of diverse types demonstrate that the proposed approach is expandable and capable of accommodating different modalities.Comment: ICIP 202

Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning

Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation. In this paper, we propose Self-Distillation for Fine-Tuning diffusion models (SDFT), a methodology to address these challenges by leveraging diverse features from diffusion models pretrained on large source datasets. SDFT distills more general features (shape, colors, etc.) and less domain-specific features (texture, fine details, etc) from the source model, allowing successful knowledge transfer without disturbing the training process on target datasets. The proposed method is not constrained by the specific architecture of the model and thus can be generally adopted to existing frameworks. Experimental results demonstrate that SDFT enhances the expressiveness of the diffusion model with limited datasets, resulting in improved generation capabilities across various downstream tasks.Comment: WACV 202

Taurine in drinking water recovers learning and memory in the adult APP/PS1 mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a lethal progressive neurological disorder affecting the memory. Recently, US Food and Drug Administration mitigated the standard for drug approval, allowing symptomatic drugs that only improve cognitive deficits to be allowed to accelerate on to clinical trials. Our study focuses on taurine, an endogenous amino acid found in high concentrations in humans. It has demonstrated neuroprotective properties against many forms of dementia. In this study, we assessed cognitively enhancing property of taurine in transgenic mouse model of AD. We orally administered taurine via drinking water to adult APP/PS1 transgenic mouse model for 6 weeks. Taurine treatment rescued cognitive deficits in APP/PS1 mice up to the age-matching wild-type mice in Y-maze and passive avoidance tests without modifying the behaviours of cognitively normal mice. In the cortex of APP/PS1 mice, taurine slightly decreased insoluble fraction of Aβ. While the exact mechanism of taurine in AD has not yet been ascertained, our results suggest that taurine can aid cognitive impairment and may inhibit Aβ-related damages.MIT International Science and Technology InitiativesKorea Health Industry Development Institute (H14C04660000)Korea Institute of Science and Technology (Open Research 2E24582)Korea Institute of Science and Technology (Flagship 2E25023

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

Conservation strategies for jerangau merah (Boesenbergia stenophylla) using DNA profiling and micropropagation

Jerangau merah (Boesenbergia stenophylla) is highly endemic to the highland of Borneo. Their medicinal value attracts many plant collectors which raise up to the concern on their population size. A study was carried out to establish the conservation approaches for this species. The objectives of this study are to determine the genetic variations among accession from Bario and to develop the in vitro culturing protocol for productions of seedlings. Genetic variation studies were done using simple sequence repeats (SSR) and random amplified polymorphic DNA markers (RAPD). Micropropagation of shoot tips was carried out using BAP and NAA plant growth regulator supplemented in MS media. The genetic variation studies using SSR and RAPD marker show no variations among accession and three sub populations. Two steps protocol was recommended for the tissue culture of B. stenophylla. But it start with culturing using shoot tips in MS media containing 0.2 mg/L NAA for shoot induction followed by sub–culturing to MS media with 2 mg/L BAP +0.4 mg/L NAA for rapid shoot elongation. This study suggests that their conservation should remain as in situ and seedling production under optimum nursery conditions should be carried out near to their natural populations