Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds.

This study utilizes machine learning (ML) algorithms to develop a robust total organic carbon (TOC) prediction model for river waters in the Geumho River sub-basins, South Korea, considering both non-rain and rain events. The model incorporates geospatial parameters such as land use, slope, flow rate, and basic water quality metrics including biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). A key aspect of this research is examining how land use information enhances the model's predictive accuracy. We compared two ML algorithms-extreme gradient boosting (XGBoost) and deep neural networks (DNN)-with a traditional multiple linear regression (MLR) approach. XGBoost outperformed the others, achieving an R2 value between 0.61 and 0.68 in the test dataset and demonstrating significant improvement during rain events with an R2 of 0.77 when including land use data. In contrast, this enhancement was not observed with the MLR model. Feature importance analysis using Shapley values highlighted COD as the primary predictor for non-rain events, while during rain events, COD, TP, TN, SS and agricultural land collectively influenced TOC levels. This study significantly advances understanding of TOC variability across different land use scenarios in river systems and underscores the importance of integrating geospatial and water quality parameters to enhance TOC prediction, particularly during rain events. This methodology provides a valuable framework for developing river management strategies and monitoring long-term TOC trends, especially in scenarios with gaps in essential monitoring data.

HOXA­AS3 induces tumor progression through the epithelial­mesenchymal transition pathway in epithelial ovarian cancer.

HOXA cluster antisense RNA 3 (HOXA­AS3) is considered to be involved in several malignancies, however, its biological function in the progression of epithelial ovarian cancer (EOC) remains unclear. The present study compared the expression of HOXA­AS3 in ovarian cancer and normal ovarian tissues and analyzed the association between the expression of HOXA­AS3 and the survival outcomes of patients with ovarian cancer. RNA interference was used to suppress HOXA­AS3 expression in ovarian cancer cell lines in order to demonstrate the function of HOXA­AS3 in ovarian cancer progression. The associations between HOXA­AS3 and epithelial­mesenchymal transition (EMT) markers were explored to verify the mechanism of action of HOXA­AS3 in ovarian cancer. The results of the present study revealed that ovarian cancer tissues exhibited higher HOXA­AS3 expression than normal ovarian tissues. Clinical data indicated that HOXA­AS3 was a significant predictor of progression­free survival and overall survival. Patients with high HOXA­AS3 expression had a poorer prognosis than patients with low HOXA­AS3 expression. In vitro experiments using HOXA­AS3­knockdown ovarian cancer cell lines demonstrated that HOXA­AS3 knockdown inhibited cell proliferation and migration. HOXA­AS3 was a potent inducer and modulator of the expression of EMT pathway­related markers and interacted with both the mRNA and protein forms of HOXA3. Collectively, the findings of the present study demonstrated that HOXA­AS3 expression is associated with ovarian cancer progression and thus, may be employed as a prognostic marker and therapeutic target in EOC.

Flocculation kinetics and mechanisms of microalgae- and clay-containing suspensions in different microalgal growth phases.

Interplays between microalgae and clay minerals enhance biologically mediated flocculation, thereby affecting the sedimentation and transportation of suspended particulate matter (SPM) in water and benthic environments. This interaction forms larger flocs with a higher settling velocity and enhances SPM sinking. The aim of this study was to investigate the flocculation kinetics of microalgae and clay in suspension and to elucidate the mechanisms associated with such interactions. Standard jar test experiments were conducted using various mixtures of kaolinite and microalgal samples from batch cultures (Chlorella vulgaris) to estimate biologically mediated flocculation kinetics. The organic matter (OM) composition secreted by the microalgae was characterized using a liquid chromatography - organic carbon detection system, and quantitative analysis of transparent exopolymer particles was conducted separately. A two-class flocculation kinetic model, based on the interaction between flocculi and flocs, was also adopted to quantitatively analyze the experimental data from flocculation. Results from the flocculation kinetic tests and OM analyses, in association with other data analyses (i.e., floc size distribution and flocculation kinetic model), showed that flocculation increased with OM concentration during the growth phase (10-20 d). However, on day 23 during the early stationary phase, flocculation kinetics started decreasing and substantially declined on day 30, even though the amount of OM (mainly biopolymers) continued to increase. Our results indicate that an adequate quantity of biopolymers produced by the microalgal cells in the growth phase enhanced floc-to-floc attachment and hence flocculation kinetics. In contrast, an excessive quantity of biopolymers and humic substances in the stationary phase enhanced the formation of polymeric backbone structures and flocculation via scavenging particles but simultaneously increased steric stabilization with the production of a large number of fragmented particles.

Long Non-coding RNA LOC285194 Promotes Epithelial Ovarian Cancer Progression via the Apoptosis Signaling Pathway.

BACKGROUND/AIM: To explore the molecular mechanism and clinical significance of a newly identified lncRNA LOC285194 in epithelial ovarian cancer (EOC). MATERIALS AND METHODS: LOC285194 transcript levels were analyzed in EOC cells compared to normal cells. Small interfering RNAs were used to suppress LOC285194 expression. Levels of apoptosis-related proteins were determined by western blot. LOC285194 expression in ovarian cancer and non-tumor tissues were compared with clinicopathologic and survival data. RESULTS: Knockdown of LOC285194 decreased cell migration and proliferation, enhanced reactive oxygen species production and resulted in increased levels of proteins of the extrinsic apoptotic signaling pathway. LOC285194 expression level was higher in ovarian cancer tissues compared to control. Overall survival was significantly shorter in patients with high LOC285194 expression. Lymph node metastasis and high LOC285194 expression were significant prognostic factors of mortality (HR=4.614 and 5.880; p=0.026 and p=0.002, respectively). CONCLUSION: LOC285194 can promote the progression of EOC via an anti-apoptotic mechanism. It may serve as a novel biomarker for predicting prognosis of EOC.

Metal ions affect the formation and stability of amyloid ß aggregates at multiple length scales.

Amyloid ß (Aß) aggregates, which are a hallmark for neurodegenerative disease, are formed through a self-assembly process such as aggregation of Aß peptide chains. This aggregation process depends on the solvent conditions under which the proteins are aggregated. Nevertheless, the underlying mechanism of the ionic effect on the formation and stability of amyloid aggregates has not been fully understood. Here, we report how metal ions play a role in the formation and stability of Aß aggregates at different length scales, i.e. oligomers and fibrils. It is shown that the metal (i.e. zinc or copper) ion increases the stability of Aß oligomers, whereas the metal ion reduces the stability of Aß fibrils. In addition, we found that zinc ions are able to more effectively destabilize fibril structures than copper ions. Metal ion-mediated (de)stabilization of Aß oligomers (or fibrils) is attributed to the critical effect of the metal ion on the ß-sheet rich crystalline structure of the amyloid aggregate and the status of hydrogen bonds within the aggregate. Our study sheds light on the role of the metal ion in stabilizing the amyloid oligomers known as a toxic agent (to functional cells), which is consistent with clinical observation that high concentrations of metal ions are found in patients suffering from neurodegenerative diseases.

Mechanical features of various silkworm crystalline considering hydration effect via molecular dynamics simulations.

Silk materials are receiving significant attention as base materials for various functional nanomaterials and nanodevices, due to its exceptionally high mechanical properties, biocompatibility, and degradable characteristics. Although crystalline silk regions are composed of various repetitive motifs with differing amino acid sequences, how the effect of humidity works differently on each of the motifs and their structural characteristics remains unclear. We report molecular dynamics (MD) simulations on various silkworm fibroins composed of major motifs (i.e. (GAGAGS)n, (GAGAGA)n, and (GAGAGY)n) at varying degrees of hydration, and reveal how each major motifs of silk fibroins change at each degrees of hydration using MD simulations and their structural properties in mechanical perspective via steered molecular dynamics simulations. Our results explain what effects humidity can have on nanoscale materials and devices consisting of crystalline silk materials.

Extraction of natural colorant from purple sweet potato and dyeing of fabrics with silver nanoparticles for augmented antibacterial activity against skin pathogens.

The main objective of this study was to extract natural colorant from purple sweet potato powder (PSPP) via a water bath and ultrasound water bath using acidified ethanol (A. EtOH) as the extraction solvent. When optimizing the colorant extraction conditions of the solvents, acidified ethanol with ultrasound yielded a high extraction capacity and color intensity at pH2, temperature of 80°C, 20mL of A. EtOH, 1.5g of PSPP, time of 45min, and ultrasonic output power of 75W. Subsequently, the colorant was extracted using the optimized conditions for dyeing of textiles (leather, silk, and cotton). This natural colorant extraction technique can avoid serious environmental pollution during the extraction and is an alternative to synthetic dyes, using less solvent and simplified abstraction procedures. The extracted purple sweet potato natural colorant (PSPC) was used to dye leather, silk, and cotton fabrics in an eco-friendly approach with augmented antibacterial activity by in situ synthesis of silver nanoparticles (AgNPs) and dyeing. The optimal dyeing conditions for higher color strength (K/S) values were pH2 and 70°C for 45min. The colorimetric parameters L∗, a∗, b∗, C, and H were measured to determine the depth of the color. The Fourier transform infrared spectroscopy (FTIR) spectra of undyed control, dyed with PSPC and dyed with blend of PSPC and AgNPs treated leather, silk and cotton fabric were investigated to study the interaction among fiber type, nanoparticles, and dye. The structural morphology of leather and silk and cotton fabrics and the anchoring of AgNPs with elemental compositions were investigated by scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDS). The dry and wet rubbing fastness for dye alone and dye with nanoparticles were grade 4-5 and 4, respectively. Thus, the results of the present study clearly suggest that in situ synthesis of AgNPs along with dyeing should be considered in the development of antimicrobial textile finishes.

Mechanical and vibrational characterization of amyloid-like HET-s nanosheets based on the skewed plate theory.

Pathological amyloidogenic prion proteins have a toxic effect on functional cells in the human cerebrum because of poor degradability and the tendency to accumulate in an uncontrolled manner under physiological conditions. HET-s, a fungal prion protein, is known to undergo conformational variations from fibrillar to nanosheet structures during a change from low to high pH conditions. It has been said that this conformational change can lead to self-propagation by nucleating on the lateral surface of singlet fibrils. Efforts have been made toward the mechanical characterization of fibrillar amyloids, but a global understanding of amyloid-like HET-s nanosheet structures is lacking. In this study, we analyzed the mechanical and vibrational characteristics of the skewed HET-s nanosheet structures that developed under neutral pH conditions by performing various molecular dynamics simulations. By applying the skewed plate theory to HET-s nanosheets for various length scales with numerous pores inside the structures, we found that the skewed HET-s nanosheet structure has mechanical properties comparable to those of previously reported biological film materials and nanomaterials. Considering the inherent characteristics of structural stability, our observation provides valuable and detailed structural information on skewed amyloid-like HET-s nanosheets.

Steered molecular dynamics analysis of the role of cofilin in increasing the flexibility of actin filaments.

Cofilin is one of the most essential regulatory proteins and participates in the process of disassembling actin filaments. Cofilin induces conformational changes to actin filaments, and both the bending and torsional rigidity of the filament. In this study, we investigate the effects of cofilin on the mechanical properties of actin filaments using computational methods. Three models defined by their number of bound cofilins are constructed using coarse-grained MARTINI force field, and they are then extended with steered molecular dynamics simulation. After obtaining the stress-strain curves of the models, we calculate their Young's moduli and other mechanical properties that have not yet been determined for actin filaments. We analyze the cause of the different behaviors of the three models based on their atomistic geometrical differences. Finally, it is demonstrated that cofilin binding causes changes in the distances, angles, and stabilities of the residues in actin filaments.

Biophysical characterization of cofilin-induced extension-torsion coupling in actin filaments.

Cofilin makes the actin filament flexible and thermally unstable by disassembling the filament and inducing bending and torsional compliance. Actin monomers bound to cofilin are able to chemically and mechanically interact in response to external forces. In this study, we performed two molecular dynamics tensile tests for actin and cofilactin filaments under identical conditions. Surprisingly, cofilactin filaments were found to be twisted, generating shear stress caused by torsion. Additionally, analysis by plane stress assumption indicated that the extension-torsion coupling effect increases the amount of principal stress by 10%. Using elasticity and solid mechanics theories, our study elucidates the role of cofilin in the disassembly of actin filaments under tensile forces.

Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence.

Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences.

Conformational changes of Aß (1-42) monomers in different solvents.

Amyloid proteins are known to be the main cause of numerous degenerative and neurodegenerative diseases. In general, amyloids are misfolded from monomers and they tend to have ß-strand formations. These misfolded monomers are then transformed into oligomers, fibrils, and plaques. It is important to understand the forming mechanism of amyloids in order to prevent degenerative diseases to occur. Aß protein is a highly noticeable protein which causes Alzheimer's disease. It is reported that solvents affect the forming mechanism of Aß amyloids. In this research, Aß1-42 was analyzed using an all-atom MD simulation with the consideration of effects induced by two disparate solvents: water and DMSO. As a result, two different conformation changes of Aß1-42 were exhibited in each solvent. It was found that salt-bridge of Asp23 and Lys28 in Aß1-42 was the key for amyloid folding based on the various analysis including hydrogen bond, electrostatic interaction energy and salt-bridge distance. Since this salt-bridge region plays a crucial role in initiating the misfolding of Aß1-42, this research may shed a light for studies related in amyloid folding and misfolding.

Morphology and mechanical properties of multi-stranded amyloid fibrils probed by atomistic and coarse-grained simulations.

Amyloid fibrils are responsible for pathogenesis of various diseases and exhibit the structural feature of an ordered, hierarchical structure such as multi-stranded helical structure. As the multi-strandedness of amyloid fibrils has recently been found to be highly correlated with their toxicity and infectivity, it is necessary to study how the hierarchical (i.e. multi-stranded) structure of amyloid fibril is formed. Moreover, although it has recently been reported that the nanomechanics of amyloid proteins plays a key role on the amyloid-induced pathogenesis, a critical role that the multi-stranded helical structure of the fibrils plays in their nanomechanical properties has not fully characterized. In this work, we characterize the morphology and mechanical properties of multi-stranded amyloid fibrils by using equilibrium molecular dynamics simulation and elastic network model. It is shown that the helical pitch of multi-stranded amyloid fibril is linearly proportional to the number of filaments comprising the amyloid fibril, and that multi-strandedness gives rise to improving the bending rigidity of the fibril. Moreover, we have also studied the morphology and mechanical properties of a single protofilament (filament) in order to understand the effect of cross-ß structure and mutation on the structures and mechanical properties of amyloid fibrils. Our study sheds light on the underlying design principles showing how the multi-stranded amyloid fibril is formed and how the structure of amyloid fibrils governs their nanomechanical properties.

Effects of lysine residues on structural characteristics and stability of tau proteins.

Pathological amyloid proteins have been implicated in neuro-degenerative diseases, specifically Alzheimer's, Parkinson's, Lewy-body diseases and prion related diseases. In prion related diseases, functional tau proteins can be transformed into pathological agents by environmental factors, including oxidative stress, inflammation, Aß-mediated toxicity and covalent modification. These pathological agents are stable under physiological conditions and are not easily degraded. This un-degradable characteristic of tau proteins enables their utilization as functional materials to capturing the carbon dioxides. For the proper utilization of amyloid proteins as functional materials efficiently, a basic study regarding their structural characteristic is necessary. Here, we investigated the basic tau protein structure of wild-type (WT) and tau proteins with lysine residues mutation at glutamic residue (Q2K) on tau protein at atomistic scale. We also reported the size effect of both the WT and Q2K structures, which allowed us to identify the stability of those amyloid structures.

Cofilin reduces the mechanical properties of actin filaments: approach with coarse-grained methods.

An actin filament is an essential cytoskeleton protein in a cell. Various proteins bind to actin for cell functions such as migration, division, and shape control. ADF/cofilin is a protein that severs actin filaments and is related to their dynamics. Actin is known to have excellent mechanical properties. Binding cofilin reduces its mechanical properties, and is related to the severing process. In this research, we applied a coarse-grained molecular dynamics simulation (CGMD) method to obtain actin filaments and cofilin-bound actin (cofilactin) filaments. Using these two obtained models, we constructed an elastic network model-based structure and conducted a normal mode analysis. Based on the low-frequency normal modes of the filament structure, we applied the continuum beam theory to calculate the mechanical properties of the actin and cofilactin filaments. The CGMD method provided structurally accurate actin and cofilactin filaments in relation to the mechanical properties, which showed good agreement with the established experimental results.

Identification of tail binding effect of kinesin-1 using an elastic network model.

Kinesin is a motor protein that delivers cargo inside a cell. Kinesin has many different families, but they perform basically same function and have same motions. The walking motion of kinesin enables the cargo delivery inside the cell. Autoinhibition of kinesin is important because it explains how function of kinesin inside a cell is stopped. Former researches showed that tail binding is related to autoinhibition of kinesin. In this work, we performed normal mode analysis with elastic network model using different conformation of kinesin to determine the effect of tail binding by considering four models such as functional form, autoinhibited form, autoinhibited form without tail, and autoinhibited form with carbon structure. Our calculation of the thermal fluctuation and cross-correlation shows the change of tail-binding region in structural motion. Also strain energy of kinesin showed that elimination of tail binding effect leads the structure to have energetically similar behavior with the functional form.

Role of sequence and structural polymorphism on the mechanical properties of amyloid fibrils.

Amyloid fibrils playing a critical role in disease expression, have recently been found to exhibit the excellent mechanical properties such as elastic modulus in the order of 10 GPa, which is comparable to that of other mechanical proteins such as microtubule, actin filament, and spider silk. These remarkable mechanical properties of amyloid fibrils are correlated with their functional role in disease expression. This suggests the importance in understanding how these excellent mechanical properties are originated through self-assembly process that may depend on the amino acid sequence. However, the sequence-structure-property relationship of amyloid fibrils has not been fully understood yet. In this work, we characterize the mechanical properties of human islet amyloid polypeptide (hIAPP) fibrils with respect to their molecular structures as well as their amino acid sequence by using all-atom explicit water molecular dynamics (MD) simulation. The simulation result suggests that the remarkable bending rigidity of amyloid fibrils can be achieved through a specific self-aggregation pattern such as antiparallel stacking of ß strands (peptide chain). Moreover, we have shown that a single point mutation of hIAPP chain constituting a hIAPP fibril significantly affects the thermodynamic stability of hIAPP fibril formed by parallel stacking of peptide chain, and that a single point mutation results in a significant change in the bending rigidity of hIAPP fibrils formed by antiparallel stacking of ß strands. This clearly elucidates the role of amino acid sequence on not only the equilibrium conformations of amyloid fibrils but also their mechanical properties. Our study sheds light on sequence-structure-property relationships of amyloid fibrils, which suggests that the mechanical properties of amyloid fibrils are encoded in their sequence-dependent molecular architecture.

Domain decomposition-based structural condensation of large protein structures for understanding their conformational dynamics.

Normal mode analysis (NMA) with coarse-grained model, such as elastic network model (ENM), has allowed the quantitative understanding of protein dynamics. As the protein size is increased, there emerges the expensive computational process to find the dynamically important low-frequency normal modes due to diagonalization of massive Hessian matrix. In this study, we have provided the domain decomposition-based structural condensation method that enables the efficient computations on low-frequency motions. Specifically, our coarse-graining method is established by coupling between model condensation (MC; Eom et al., J Comput Chem 2007, 28, 1400) and component mode synthesis (Kim et al., J Chem Theor Comput 2009, 5, 1931). A protein structure is first decomposed into substructural units, and then each substructural unit is coarse-grained by MC. Once the NMA is implemented to coarse-grained substructural units, normal modes and natural frequencies for each coarse-grained substructural unit are assembled by using geometric constraints to provide the normal modes and natural frequencies for whole protein structure. It is shown that our coarse-graining method enhances the computational efficiency for analysis of large protein complexes. It is clearly suggested that our coarse-graining method provides the B-factors of 100 large proteins, quantitatively comparable with those obtained from original NMA, with computational efficiency. Moreover, the collective behaviors and/or the correlated motions for model proteins are well delineated by our suggested coarse-grained models, quantitatively comparable with those computed from original NMA. It is implied that our coarse-grained method enables the computationally efficient studies on conformational dynamics of large protein complex.

Large Protein Dynamics Described by Hierarchical-Component Mode Synthesis.

Protein dynamics has played a pivotal role in understanding the biological function of protein. For investigation of such dynamics, normal-mode analysis (NMA) has been broadly employed with atomistic model and/or coarse-grained models such as elastic network model (ENM). For large protein complexes, NMA with even ENM encounters the expensive computational process such as diagonalization of Hessian (stiffness) matrix. Here, we suggest the hierarchical-component mode synthesis (hCMS), which allows the fast computation of low-frequency normal modes related to conformational change. Specifically, a large protein structure is regarded as a combination of several structural units, for which the eigen-value problem is utilized for obtaining the frequencies and their normal modes for each structural unit, and consequently, such frequencies and normal modes are assembled with geometrical constraint for interface between structural units in order to find the low-frequency normal modes of a large protein complex. It is shown that hCMS is able to provide the normal modes with accuracy, quantitatively comparable to those of original NMA. This implies that hCMS may enable the computationally efficient analysis of large protein dynamics.