Identification of QTNs, QTN-by-environment interactions, and their candidate genes for salt tolerance related traits in soybean.

BACKGROUND: Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits. RESULTS: Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes (GmCHX1, GsPRX9, Gm5PTase8, GmWRKY, GmCHX20a, GmNHX1, GmSK1, and GmLEA2-1) near 179 significant and 79 suggested QTNs and two salt tolerance genes (GmWRKY49 and GmSK1) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance (GmHDA13, GmPHO1, GmERF5, GmNAC06, GmbZIP132, and GmHsp90s) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150, were confirmed by qRT-PCR and are expected to be key players in responding to salt stress. CONCLUSIONS: Around the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR.

Determination of carbohydrate content in kenaf degumming wastewater and converting them to carbon dots.

The traditional textile degumming process produces abundant wastewater, which contains a lot of monosaccharides and oligosaccharides. It is of great economic and environmental significance to utilize these carbohydrates in high value. In this study, high performance liquid chromatography (HPLC) was used to analyze the carbohydrate components in kenaf degumming wastewater, and then the production of C-dots using the wastewater was explored. The results showed that the types and content in the degumming wastewater were monosaccharides (glucose, xylose and arabinose) and oligosaccharides (dextran, xylan and araban). The carbohydrate (mainly glucan and xylan) content in wastewater accounted for 91.16 % of the total carbohydrates weight loss in kenaf degumming process. By using hydrolysis and hydrothermal reaction on kenaf degumming wastewater, blue-green carbon dots (C-dots) with good performance were prepared and successfully applied to anti-counterfeiting printing. In particular, the as-prepared C-dots prepared from kenaf degumming wastewater with urea added (WUC-dots) showed an excitation-dependent photoluminescence (PL) spectrum and quantum yield (QY) of 2.4 % in aqueous solution. The fluorescent code exhibited a clear outline, excitation-tunable color and good stability, showing a great potential for anti-counterfeiting system.

Microwave-assisted solid-state pretreatment for fabrication of hemp fibres using ethanolamine at low temperature.

Conventional methods faced challenges in pretreating natural cellulose fibres due to their high energy consumption and large wastewater drainage. This research devised an efficient solid-state pretreatment method for pretreating hemp fibres using ethanolamine (ETA) assisted by microwave (MW) heating. This method produced a notable removal rate of lignin (85.4 %) with the highest cellulose content (83.0 %) at a high solid content (30 %) and low temperature (70 °C). Both FT-IR and XRD analyses indicated that the pretreatment did not alter the structure of cellulose within the hemp fibres but increased crystallinity as the CrI increased from 84 % in raw hemp fibre to 89 % in pretreated fibre. As a result, it produced hemp fibres with impressive fineness (4.6 dtex) and breaking strength (3.81 cN/dtex), meeting the requirement of textile fibre. In addition, an improvement in glucose concentration (15.6 %) was observed in enzymatic hydrolysis of the MW pretreated hemp fibres compared to the fibres pretreated without MW. Furthermore, the FT-IR and NMR data confirmed that the amination of lignin occurred even at low temperature, which contributed to the high lignin removal rate. Thus, this study presents a potentially effective energy-saving, and environmentally sustainable solid-state method for pretreating hemp fibres.

Investigation and characterization of rice dwarfing epidemic caused by southern rice black-streaked dwarf virus in Jiangsu in 2023.

During the field surveys in Jiangsu Province, China, contiguous patches of rice plants with varying degrees of dwarfing, wax-white or dark brown enations at the base of stems, and abnormal heading symptoms were observed in the fields located in Jiangning District in Nanjing City, Jurong County in Zhenjiang City, and Zhangjiagang County in Suzhou City. Through molecular analyses, the presence of southern rice black-streaked dwarf virus was confirmed in symptomatic rice plants. The infections of other rice viruses that cause dwarfing were also ruled out. Additionally, Koch's postulates were fulfilled, further validating SRBSDV as the causal agent for the observed dwarfing disease epidemic. Furthermore, the phylogenetic analyses revealed that the SRBSDV prevalent in Jiangsu in 2023 may originate from multiple regions in Vietnam. Our study has documented the emergence of an SRBSDV epidemic in Jiangsu in 2023, marking the first incidence of southern rice black-streaked dwarf disease in this region.

Transiently Induce RNA Silencing in Plants Using a Tobacco Necrosis Virus A (TNV-A)-Based dsRNA Production System.

Transgenic expression of hairpin RNA or artificial microRNA is widely used for genetic studies in plant science. However, induction of RNA silencing by transgenic method may have a problem when studying essential genes. Here, we provide an in planta transient double-stranded RNA (dsRNA) producing system using a tobacco necrosis virus A (TNV-A)-based replicon for efficiently inducing RNA silencing in plants. In this system, the target sequence is placed between the cauliflower mosaic virus 35S promoter and the 3'-terminal part of viral genomic RNA, while the C-terminal part of TNV-A RNA-dependent RNA polymerase (p82C) is expressed by a different promoter. The endogenous RNA polymerase-synthesized target sequence is recruited by p82C to produce dsRNA to induce RNA silencing.

Pseudo-Data Based Self-Supervised Federated Learning for Classification of Histopathological Images.

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contain both inherent and specific properties corresponding to the real images in this center, but do not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL) to pre-train the backbone of global mode. A multi-task SSL is then designed to effectively learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD models in each center by conducting model contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on four public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.

Drying without dying: A genome database for desiccation-tolerant plants and evolution of desiccation tolerance.

Desiccation is typically fatal, but a small number of land plants have evolved vegetative desiccation tolerance (VDT), allowing them to dry without dying through a process called anhydrobiosis. Advances in sequencing technologies have enabled the investigation of genomes for desiccation-tolerant plants over the past decade. However, a dedicated and integrated database for these valuable genomic resources has been lacking. Our prolonged interest in VDT plant genomes motivated us to create the "Drying without Dying" database, which contains a total of 16 VDT-related plant genomes (including 10 mosses) and incorporates 10 genomes that are closely related to VDT plants. The database features bioinformatic tools, such as blast and homologous cluster search, sequence retrieval, Gene Ontology term and metabolic pathway enrichment statistics, expression profiling, co-expression network extraction, and JBrowser exploration for each genome. To demonstrate its utility, we conducted tailored PFAM family statistical analyses, and we discovered that the drought-responsive ABA transporter AWPM-19 family is significantly tandemly duplicated in all bryophytes but rarely so in tracheophytes. Transcriptomic investigations also revealed that response patterns following desiccation diverged between bryophytes and angiosperms. Combined, the analyses provided genomic and transcriptomic evidence supporting a possible divergence and lineage-specific evolution of VDT in plants. The database can be accessed at http://desiccation.novogene.com. We expect this initial release of the "Drying without Dying" plant genome database will facilitate future discovery of VDT genetic resources.

Experimental and Numerical Investigation on the Aerosol Micro-Jet 3D Printing of Flexible Electronic Devices.

In this study, the optimal forming parameters for printing flexible circuits using aerosol jet printing technology are explored through numerical simulation and experiments. The printhead during the deposition process is numerically simulated. By employing the controlled variable method, the process parameters such as gas flow rate, working distance, nozzle diameter, and printing speed are selected to investigate their effects on the morphology of the printed lines. Accordingly, single-factor experiments are designed to validate the printing of flexible circuits on both planar and curved substrates. Laser micro-sintering is utilized to improve the conductivity of the printed lines and ultimately fabricate flexible strain sensors. Under the sheath gas flow rate of 400 sccm, carrier gas flow rate of 100 sccm, working distance of 3 mm, nozzle diameter of 500 µm, and printing speed of 10 mm/s, the optimal morphology of the printed lines is achieved with low linewidth characteristics. The variations in the focal ratio, working distance, nozzle diameter, and printing speed significantly affect the minimum feature line width and morphology of the printed lines.

A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model.

Objective.Epilepsy is a fairly common condition that affects the brain and causes frequent seizures. The sudden and recurring epilepsy brings a series of safety hazards to patients, which seriously affects the quality of their life. Therefore, real-time diagnosis of electroencephalogram (EEG) in epilepsy patients is of great significance. However, the conventional methods take in a tremendous amount of features to train the models, resulting in high computation cost and low portability. Our objective is to propose an efficient, light and robust seizure detecting and predicting algorithm.Approach.The algorithm is based on an interpretative feature selection method and spatial-temporal causal neural network (STCNN). The feature selection method eliminates the interference factors between different features and reduces the model size and training difficulties. The STCNN model takes both temporal and spatial information to accurately and dynamically track and diagnose the changing of the features. Considering the differences between medical application scenarios and patients, leave-one-out cross validation (LOOCV) and cross-patient validation (CPV) methods are used to conduct experiments on the dataset collected at the Children's Hospital Boston (CHB-MIT), Siena and Kaggle competition datasets.Main results.In LOOCV-based method, the detection accuracy and prediction sensitivity have been improved. A significant improvement is also achieved in the CPV-based method.Significance.The experimental results show that our proposed algorithm exhibits superior performance and robustness in seizure detection and prediction, which indicates it has higher capability to deal with different and complicated clinical situations.

Combining Multiple Plant Attributes to Reveal Differences in Community Structure in Two Distant Deserts in Central Asia.

International interest is growing in biodiversity conservation and sustainable use in drylands. Desert ecosystems across arid Central Asia are severely affected by global change. Understanding the changes in a plant community is an essential prerequisite to revealing the community assembly mechanism, vegetation conservation, and management. The knowledge of large-scale spatial variation in plant community structure in different Central Asian deserts is still limited. In this study, we selected the Taukum (TD, Kazakhstan) and the Gurbantunggut (GD, China) deserts as the research area, with similar latitudes despite being nearly 1000 km apart. Thirteen and 15 sampling plots were set up and thoroughly investigated. The differences in community structure depending on multiple plant attributes (individual level: plant height, canopy diameter, and plant volume, and community level: plant density, total cover, and total volume) were systematically studied. TD had a better overall environmental status than GD. A total of 113 species were found, with 68 and 74 in TD and GD, respectively. The number of species and plant attributes was unequally distributed across different families and functional groups between deserts. The values of several plant attributes, such as ephemerals, annuals, dicotyledons, and shrubs with assimilative branches in GD, were significantly lower than those in TD. The Motyka indices of six plant attributes (26.18-38.61%) were higher between the two deserts than the species similarity index (20.4%), indicating a more robust convergence for plant functional attributes. The community structures in the two deserts represented by different plant attribute matrices demonstrated irregular differentiation patterns in ordination diagrams. The most variance in community structure was attributed to soil and climatic factors, while geographic factors had the smallest proportion. Consequently, the community structures of the two distant deserts were both different and similar to an extent. This resulted from the long-term impacts of heterogeneous environments within the same region. Our knowledge is further deepened by understanding the variation in community structure in different deserts on a large spatial scale. This therefore provides valuable insights into conserving regional biodiversity in Central Asia.

Plants alter their aboveground and belowground biomass allocation and affect community-level resistance in response to snow cover change in Central Asia, Northwest China.

It is important to elucidate the changing distribution pattern of net primary productivity (NPP) to mechanistically understand the changes in aboveground and belowground ecosystem functions. In water-scarce desert environments, snow provides a crucial supply of water for plant development and the spread of herbaceous species. Yet uncertainty persists regarding how herbaceous plants' NPP allocation responds to variation in snow cover. The goal of this study was to investigate how variation in snow cover in a temperate desert influenced the NPP allocation dynamics of herbaceous species and their resistance to environmental change in terms aboveground and belowground productivity. In the Gurbantunggut Desert, wintertime snow cover depth was adjusted in plots by applying four treatments: snow removal (-S), ambient snow, double snow (+S), and triple snow (+2S). We examined their species richness, aboveground NPP (ANPP), belowground NPP (BNPP), and the resistance of ANPP and BNPP. We found that species diversity of the aboveground community increased significantly with increasing snow cover and decreased significantly Pielou evenness in plots. This resulted in greater ANPP with increasing snow cover; meanwhile, BNPP first increased and then decreased with increasing snow cover. However, this productivity in different soil layers responded differently to changed snow cover. In the 0-10 cm soil layer, productivity first rose and then declined, while it declined linearly in both the 10-20 cm and 20-30 cm soil layers, whereas in the 30-40 cm soil layer it showed an increasing trend. Belowground resistance would increase given that greater snow cover improved the BNPP in deeper soil and maintained the resource provisioning for plant growth, thus improving overall belowground stability. These results can serve as a promising research foundation for future work on how the functioning of desert ecosystems becomes altered due to changes in plant community expansion and, in particular, changes in snow cover driven by global climate change.

Biochemical fulvic acid derived amorphous carbon modified microcrystalline graphite as low-cost anode for potassium-ion storage.

Graphite anode has great potential toward potassium ion storage for abundant reserves, yet it suffers from the large volume expansion and slow diffusion rate. Herein, the low-cost biochemical fulvic acid-derived amorphous carbon (BFAC) is employed to modify the natural microcrystalline graphite (BFAC@MG) by a simple mixed carbonization strategy. The BFAC smooths the split layer and folds on the surface of microcrystalline graphite and builds the heteroatom-doped composite structure, which effectively alleviates the volume expansion caused by K+ electrochemical de-intercalation processes, together with improving electrochemical reaction kinetics. As expected, the optimized BFAC@MG-0.5 exhibits superior potassium-ion storage performance, which delivers a high reversible capacity (623.8 mAh g-1), excellent rate performance (147.8 mAh g-1 at 2 A g-1), and remarkable cycling stability (100.8 mAh g-1 after 1200 cycles). As a practical device application, the potassium-ion capacitors are assembled using the BFAC@MG-0.5 anode and commercial activated carbon cathode, which exhibits a maximum energy density of 126.48 Wh kg-1 and superior cycle stability. Significantly, this work demonstrates the potential of microcrystalline graphite as the host anode material for potassium-ion storage.

Room-Temperature Synthesis of Carbon Dot/TiO2 Composites with High Photocatalytic Activity.

Benefiting from the wide-range absorption and adjustable energy gap, carbon dots (C-dots) have attracted a great deal of attention and they have been used to sensitize semiconductor nanocomposites to boost the efficiency of energy conversion devices, while there is still a lack of fundamental understanding of the interaction between such materials and their influence on the catalytic activity on the reaction process. In this study, C-dots were used to modify TiO2 to form a direct Z-scheme (DZS) junction for enhancement of the photocatalytic activity. The C-dot/TiO2 composite was prepared by ultrasonication at room temperature through coupling between the Ti-O-C bond and electrostatic interaction. The C-dots can dramatically enhance the absorption of the composite by forming the DZS, and the composite is enabled to generate more free radicals, which facilitate ∼10 times higher photocatalytic activity compared to that of TiO2. As a proof of concept, the as-prepared C-dot/TiO2 was used for textile wastewater dye degradation. This study provides an efficient approach for room-temperature preparation of C-dot/TiO2 composites with high photocatalytic activity.

Effects of N and P additions on twig traits of wild apple (Malus sieversii) saplings.

BACKGROUND: Wild apple (Malus sieversii) is under second-class national protection in China and one of the lineal ancestors of cultivated apples worldwide. In recent decades, the natural habitation area of wild apple trees has been seriously declining, resulting in a lack of saplings and difficulty in population regeneration. Artificial near-natural breeding is crucial for protecting and restoring wild apple populations, and adding nitrogen (N) and phosphorous (P) is one of the important measures to improve the growth performance of saplings. In this study, field experiments using N (CK, N1, N2, and N3: 0, 10, 20, and 40 g m- 2 yr- 1, respectively), P (CK, P1, P2, and P3: 0, 2, 4, and 8 g m- 2 yr- 1, respectively), N20Px (CK, N2P1, N2P2, and N2P3: N20P2, N20P4 and N20P8 g m- 2 yr- 1, respectively), and NxP4 (CK, N1P2, N2P2, and N3P2: N10P4, N20P4, and N40P4 g m- 2 yr- 1, respectively) treatments (totaling 12 levels, including one CK) were conducted in four consecutive years. The twig traits (including four current-year stem, 10 leaf, and three ratio traits) and comprehensive growth performance of wild apple saplings were analyzed under different nutrient treatments. RESULTS: N addition had a significantly positive effect on stem length, basal diameter, leaf area, and leaf dry mass, whereas P addition had a significantly positive effect on stem length and basal diameter only. The combination of N and P (NxP4 and N20Px) treatments evidently promoted stem growth at moderate concentrations; however, the N20Px treatment showed a markedly negative effect at low concentrations and a positive effect at moderate and high concentrations. The ratio traits (leaf intensity, leaf area ratio, and leaf to stem mass ratio) decreased with the increase in nutrient concentration under each treatment. In the plant trait network, basal diameter, stem mass, and twig mass were tightly connected to other traits after nutrient treatments, indicating that stem traits play an important role in twig growth. The membership function revealed that the greatest comprehensive growth performance of saplings was achieved after N addition alone, followed by that under the NxP4 treatment (except for N40P4). CONCLUSIONS: Consequently, artificial nutrient treatments for four years significantly but differentially altered the growth status of wild apple saplings, and the use of appropriate N fertilizer promoted sapling growth. These results can provide scientific basis for the conservation and management of wild apple populations.

Genome-Wide Analysis and Functional Characterization of LACS Gene Family Associated with Lipid Synthesis in Cotton (Gossypium spp.).

Cotton (Gossypium spp.) is the fifth largest oil crop in the world, and cottonseed provides abundant vegetable oil resources and industrial bioenergy fuels for people; therefore, it is of practical significance to increase the oil content of cotton seeds for improving the oil yield and economic benefits of planting cotton. Long-chain acyl-coenzyme A (CoA) synthetase (LACS) capable of catalyzing the formation of acyl-CoAs from free fatty acids has been proven to significantly participate in lipid metabolism, of which whole-genome identification and functional characterization of the gene family have not yet been comprehensively analyzed in cotton. In this study, a total of sixty-five LACS genes were confirmed in two diploid and two tetraploid Gossypium species, which were divided into six subgroups based on phylogenetic relationships with twenty-one other plants. An analysis of protein motif and genomic organizations displayed structural and functional conservation within the same group but diverged among the different group. Gene duplication relationship analysis illustrates the LACS gene family in large scale expansion through WGDs/segmental duplications. The overall Ka/Ks ratio indicated the intense purifying selection of LACS genes in four cotton species during evolution. The LACS genes promoter elements contain numerous light response cis-elements associated with fatty acids synthesis and catabolism. In addition, the expression of almost all GhLACS genes in high seed oil were higher compared to those in low seed oil. We proposed LACS gene models and shed light on their functional roles in lipid metabolism, demonstrating their engineering potential for modulating TAG synthesis in cotton, and the genetic engineering of cottonseed oil provides a theoretical basis.

2D Cobalt Chalcogenide Heteronanostructures Enable Efficient Alkaline Hydrogen Evolution Reaction.

The development of high-efficiency non-precious metal electrocatalysts for alkaline electrolyte hydrogen evolution reactions (HER) is of great significance in energy conversion to overcome the limited supply of fossil fuels and carbon emission. Here, a highly active electrocatalyst is presented for hydrogen production, consisting of 2D CoSe2 /Co3 S4 heterostructured nanosheets along Co3 O4 nanofibers. The different reaction rate between the ion exchange reaction and redox reaction leads to the heterogeneous volume swelling, promoting the growth of 2D structure. The 2D/1D heteronanostructures enable the improved the electrochemical active area, the number of active sites, and more favorable H binding energy compared to individual cobalt chalcogenides. The roles of the different composition of the heterojunction are investigated, and the electrocatalysts based on the CoSe2 /Co3 S4 @Co3 O4 exhibited an overpotential as low as 165 mV for 10 mA cm-2 and 393 mV for 200 mA cm-2 in 1 m KOH electrolyte. The as-prepared electrocatalysts remained active after 55 h operation without any significant decrease, indicating the excellent long-term operation stability of the electrode. The Faradaic efficiency of hydrogen production is close to 100% at different voltages. This work provides a new design strategy toward Co-based catalysts for efficient alkaline HER.

A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r2) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs.

A sustainable layered nanofiber/sheet aerogels enabling repeated life cycles for effective oil/water separation.

Discarded oil-containing absorbents, which has been used in handling oil spills, are tricky to deal with and have rose global environmental concerns regarding release of microplastics. Herein, we developed a facile strategy to fabricate sustainable absorbents by a gas-inflating method, through which 2D electrospinning polycaprolactone nanofiber membranes were directly inflated into highly porous 3D nanofiber/sheet aerogels with layered long fiber structure. The membranes were inflated rapidly from a baseline porosity of 81.98% into 97.36-99.42% in 10-60 min. The obtained aerogels were further wrapped with -CH3 ended siloxane structures using CH3SiCl3. This hydrophobic absorbent (CA ≈ 145°) could rapidly trap oils from water with sorption range of 25.60-42.13 g/g and be recycled by simple squeeze due to its mechanical robustness. As-prepared aerogels also showed high separation efficiency to separate oils from both oil/water mixtures and oil-in-water emulsions (>96.4%). Interestingly, the oil-loaded absorbent after cleaning with absolute ethanol could be re-dissolved in selected solvents and promptly reconstituted by re-electrospinning and gas-inflation. The reconstituted aerogels were used as fire-new oil absorbents for repeated life cycles. The novel design, low cost and sustainability of the absorbent provides an efficient and environmentally-friendly solution for handling oil spills.