Structural Features Research Articles

An Exploration of Organoid Technology: Present Advancements, Applications, and Obstacles.

Organoids are in vitro models that exhibit a three-dimensional structure and effectively replicate the structural and physiological features of human organs. This work examines the potential applications of organoid technology, as well as the challenges and future directions associated with its implementation. The manuscript was put together by conducting a comprehensive literature review, which involved an in-depth evaluation of globally renowned scientific research databases. The field of organoids has generated significant attention due to its potential applications in tissue development and disease modelling, as well as its implications for personalised medicine, drug screening, and cell-based therapies. The utilisation of organoids has proven to be effective in the examination of various conditions, encompassing genetic disorders, cancer, neurodevelopmental disorders and infectious diseases. The exploration of the wider uses of organoids is still in its early phases. Research shall be conducted to integrate 3D organoid systems as alternatives for current models, potentially improving both fundamental and clinical studies in the future.

Molecular Mechanism of Tau Misfolding and Aggregation: Insights from Molecular Dynamics Simulation.

Tau dysfunction has a close association with many neurodegenerative diseases, which are collectively referred to as tauopathies. Neurofibrillary tangles (NFTs) formed by misfolding and aggregation of tau are the main pathological process of tauopathy. Therefore, uncovering the misfolding and aggregation mechanism of tau protein will help to reveal the pathogenic mechanism of tauopathies. Molecular dynamics (MD) simulation is well suited for studying the dynamic process of protein structure changes. It provides detailed information on protein structure changes over time at the atomic resolution. At the same time, MD simulation can also simulate various conditions conveniently. Based on these advantages, MD simulations are widely used to study conformational transition problems such as protein misfolding and aggregation. Here, we summarized the structural features of tau, the factors affecting its misfolding and aggregation, and the applications of MD simulations in the study of tau misfolding and aggregation.

Evidence and Prospects of Lycopene as Powerful Red Superfood: Modern Approach to Food Science

Abstract: Lycopene is a plant-derived important nutrient belonging to a group of pigments identified as carotenoids. Lycopene has distinctive chemical and structural features contributing to definite biological properties. Lycopene is a powerful antioxidant and prevents cells and tissues from damage caused by singlet oxygen or free radicals. Research studies have shown the healthpromoting potential of lycopene or its metabolites in the prevention of chronic diseases like diabetes, cancer, asthma, metabolic syndrome, arthritis, and cardiovascular diseases. Lycopene is stable for processing like other natural compounds when present in the plant tissue matrix. Over the last few years, the global lycopene market has increased in different sectors like pharmaceuticals, cosmetics, food and dairy products, and other dietary supplements. The most important aspect of lycopene as a superfood is its synergistic nature with other nutrients. The main aim of this review article is to study chemistry, biosynthetic pathways, biotransformation, sources, and acceptable daily intake. Its characterization as a superfood, therapeutic potential, and wide range of applications in the food industry are also studied.

Computed tomography technologies to measure key structural features of polymeric biomedical implants from bench to bedside.

Implanted polymeric devices, designed to encourage tissue regeneration, require porosity. However, characterizing porosity, which affects many functional device properties, is non-trivial. Computed tomography (CT) is a quick, versatile, and non-destructive way to gain 3D structural information, yet various CT technologies, such as benchtop, preclinical and clinical systems, all have different capabilities. As system capabilities determine the structural information that can be obtained, seamless monitoring of key device features through all stages of clinical translation must be engineered intentionally. Therefore, in this study we tested feasibility of obtaining structural information in pre-clinical systems and high-resolution micro-CT (μCT) under physiological conditions. To overcome the low CT contrast of polymers in hydrated environments, radiopaque nanoparticle contrast agent was incorporated into porous devices. The size of resolved features in porous structures is highly dependent on the resolution (voxel size) of the scan. As the voxel size of the CT scan increased (lower resolution) from 5 to 50 μm, the measured pore size was overestimated, and percentage porosity was underestimated by nearly 50%. With the homogeneous introduction of nanoparticles, changes to device structure could be quantified in the hydrated state, including at high-resolution. Biopolymers had significant structural changes post-hydration, including a mean increase of 130% in pore wall thickness that could potentially impact biological response. By incorporating imaging capabilities into polymeric devices, CT can be a facile way to monitor devices from initial design stages through to clinical translation.

Spectral recomposition feature for optimizing seismic velocity model prediction with a neural network

An approach incorporating spectral recomposition results for training a neural network is proposed. The initial procedure is based on an inversion scheme for reconstructing the seismic spectrum of wavelets associated with a reflection event. This allows one to estimate each wavelet's temporal position within a seismogram. Since each calculated wavelet's temporal position is identified, this information is integrated into the seismogram as an additional feature for a trained neural network. A Fully Convolutional Network is trained with models that have features commonly found in offshore hydrocarbon reservoirs. The training step is based on composing a set of an acoustic velocity model set with its seismograms and their position matrixes at the time of each wavelet for each seismogram. Next, the network training with a specific number of sets is performed. This approach was able to predict acoustic velocity models more accurately, especially for predicting the shape of salt bodies and the interface of inclined layers, which makes the proposed approach an interesting means for estimating specific features in offshore hydrocarbon reservoir structures.

Zero group velocity feature in CFRP-Nomex honeycomb structure and its use for debonding detection

Carbon fiber reinforced plastic (CFRP) − Nomex adhesive honeycomb structures are widely used in aerospace due to their excellent properties. However, debonding defects pose a significant challenge to structural safety due to their hidden nature and high risk. In this work, to address the debonding detection in the CFRP-Nomex honeycomb structure, a method based on the zero group velocity (ZGV) feature is proposed. By calculating the dispersion curve of the structure, it is found that there is the ZGV on the S1 branch. Further analysis revealed that this ZGV feature is generated by the repulsion between the S2 and S1 branches. The simulation and experimental analysis of the acoustic propagation characteristics in the honeycomb structure confirmed the presence of the ZGV feature with its energy localized near the exciting point. When debonding defects occur in the honeycomb structure, this feature disappears. Therefore, the ZGV feature can be used for the debonding detection of CFRP-Nomex honeycomb structures. Compared with ultrasonic C-scan, the results demonstrate that the proposed method can accurately detect and locate debonding defects with a diameter of 20 mm. Due to its simplicity, the method does not require sophisticated equipment or complex signal processing procedures, making it suitable for in-service inspection.

An Expedition on Synthetic Methodology of FDA-approved Anticancer Drugs (2018-2021).

New drugs being established in the market every year produce specified structures for selective biological targeting. With medicinal insights into molecular recognition, these begot molecules open new rooms for designing potential new drug molecules. In this review, we report the compilation and analysis of a total of 56 drugs including 33 organic small molecules (Mobocertinib, Infigratinib, Sotorasib, Trilaciclib, Umbralisib, Tepotinib, Relugolix, Pralsetinib, Decitabine, Ripretinib, Selpercatinib, Capmatinib, Pemigatinib, Tucatinib, Selumetinib, Tazemetostat, Avapritinib, Zanubrutinib, Entrectinib, Pexidartinib, Darolutamide, Selinexor, Alpelisib, Erdafitinib, Gilteritinib, Larotrectinib, Glasdegib, Lorlatinib, Talazoparib, Dacomitinib, Duvelisib, Ivosidenib, Apalutamide), 6 metal complexes (Edotreotide Gallium Ga-68, fluoroestradiol F-18, Cu 64 dotatate, Gallium 68 PSMA-11, Piflufolastat F-18, 177Lu (lutetium)), 16 macromolecules as monoclonal antibody conjugates (Brentuximabvedotin, Amivantamab-vmjw, Loncastuximabtesirine, Dostarlimab, Margetuximab, Naxitamab, Belantamabmafodotin, Tafasitamab, Inebilizumab, SacituzumabGovitecan, Isatuximab, Trastuzumab, Enfortumabvedotin, Polatuzumab, Cemiplimab, Mogamulizumab) and 1 peptide enzyme (Erwiniachrysanthemi-derived asparaginase) approved by the U.S. FDA between 2018 to 2021. These drugs act as anticancer agents against various cancer types, especially non-small cell lung, lymphoma, breast, prostate, multiple myeloma, neuroendocrine tumor, cervical, bladder, cholangiocarcinoma, myeloid leukemia, gastrointestinal, neuroblastoma, thyroid, epithelioid and cutaneous squamous cell carcinoma. The review comprises the key structural features, approval times, target selectivity, mechanisms of action, therapeutic indication, formulations, and possible synthetic approaches of these approved drugs. These crucial details will benefit the scientific community for futuristic new developments in this arena.

Multi-View Subspace Clustering via Structured Multi-Pathway Network.

Recently, deep multi-view clustering (MVC) has attracted increasing attention in multi-view learning owing to its promising performance. However, most existing deep multi-view methods use single-pathway neural networks to extract features of each view, which cannot explore comprehensive complementary information and multilevel features. To tackle this problem, we propose a deep structured multi-pathway network (SMpNet) for multi-view subspace clustering task in this brief. The proposed SMpNet leverages structured multi-pathway convolutional neural networks to explicitly learn the subspace representations of each view in a layer-wise way. By this means, both low-level and high-level structured features are integrated through a common connection matrix to explore the comprehensive complementary structure among multiple views. Moreover, we impose a low-rank constraint on the connection matrix to decrease the impact of noise and further highlight the consensus information of all the views. Experimental results on five public datasets show the effectiveness of the proposed SMpNet compared with several state-of-the-art deep MVC methods.

One-pot synthesis of cysteine-rich peptide via ‘2,2’-dithiobispyridine’ mediated sulfur exchange reaction

The cyclization of polypeptides confers enhanced stability, specificity, binding affinity, and cell permeability, making it a common feature in the backbone structure of natural products. Disulfide bonds are widely found in active peptides and play a crucial role in maintaining their secondary structure. In this study, we present a sulfur exchange reaction-based method for constructing disulfide bonds using 2,2′-dithiobis(5-nitropyridine) as a mediator. This method exhibits excellent substrate adaptability and compatibility with conventional functional groups. It also demonstrates rapid reaction efficiency, allowing successful disulfide bond formation under various pH conditions. Moreover, the products and additives can be easily separated, showing the potential of this method for screening disulfide bond-rich peptides in future research.

Alkoxycarbonyl Groups in Metalloesters Showing Oxocarbenium‐like Structure and Alkylating Reactivity

AbstractIn contrast to the well‐documented acylating reactivity, the alkylating reactivity of the alkoxycarbonyl group, as signified by its oxocarbenium‐like resonance structure, remains almost unexplored. Herein, the first series of Co/Ni dinuclear metalloesters exhibiting the novel oxocarbenium‐like alkoxycarbonyl groups were synthesized and characterized. In these deformed alkoxycarbonyl groups, the Ccarbonyl−Oalkoxyl bonds were contracted to 1.177(11)~1.191(9) Å with the elongations of the Ccarbonyl=Ocarbonyl bonds to 1.368(13)~1.441(9) Å. Meanwhile, the O−Calkyl bonds were also elongated to 1.522(11) ~1.607(15) Å, and were by far the longest O−Calkyl bonds reported for alkoxycarbonyl groups. As triggered by the long O−Calkyl distances, the alkylating reactivity of the oxocarbenium‐like methoxycarbonyl group towards a series of C/N/O‐nucleophiles via the rare BAL2 mechanism at ambient conditions was examined. Furthermore, the homo‐etherifications of alcohols mediated by the Co/Ni dinuclear metalloesters were investigated. The yields followed the trend ethanol≫n‐propanol≫n‐butanol ≈n‐pentanol, that closely related to the structure features of the alkoxycarbonyl groups in corresponding metalloesters: while the ethoxycarbonyl group showed the reactive oxocarbenium‐like framework, the n‐propoxycarbonyl group displayed the dioxocarbenium‐like skeleton with a shorter O−Calkyl bond; In comparison, the classical frameworks with unactivated alkyl moieties were observed for n‐butoxycarbonyl and n‐pentoxycarbonyl groups.

Alkoxycarbonyl Groups in Metalloesters Showing Oxocarbenium-like Structure and Alkylating Reactivity.

In contrast to the well-documented acylating reactivity, the alkylating reactivity of the alkoxycarbonyl group, as signified by its oxocarbenium-like resonance structure, remains almost unexplored. Herein, the first series of Co/Ni dinuclear metalloesters exhibiting the novel oxocarbenium-like alkoxycarbonyl groups were synthesized and characterized. In these deformed alkoxycarbonyl groups, the Ccarbonyl-Oalkoxyl bonds were contracted to 1.177(11)~1.191(9) Å with the elongations of the Ccarbonyl=Ocarbonyl bonds to 1.368(13)~1.441(9) Å. Meanwhile, the O-Calkyl bonds were also elongated to 1.522(11) ~1.607(15) Å, and were by far the longest O-Calkyl bonds reported for alkoxycarbonyl groups. As triggered by the long O-Calkyl distances, the alkylating reactivity of the oxocarbenium-like methoxycarbonyl group towards a series of C/N/O-nucleophiles via the rare BAL2 mechanism at ambient conditions was examined. Furthermore, the homo-etherifications of alcohols mediated by the Co/Ni dinuclear metalloesters were investigated. The yields followed the trend ethanol≫n-propanol≫n-butanol ≈n-pentanol, that closely related to the structure features of the alkoxycarbonyl groups in corresponding metalloesters: while the ethoxycarbonyl group showed the reactive oxocarbenium-like framework, the n-propoxycarbonyl group displayed the dioxocarbenium-like skeleton with a shorter O-Calkyl bond; In comparison, the classical frameworks with unactivated alkyl moieties were observed for n-butoxycarbonyl and n-pentoxycarbonyl groups.

Gd(HB-DO3A)]: Equilibrium, Dissociation Kinetic and Structural Differences in a Simple Homolog of [Gd(HP-DO3A)] (Prohance®).

[Gd(HP-DO3A)] (gadoteridol) as an active compound of ProHance® is a widely employed contrast agent in clinical MRI scans in the last 30 years. Recent concerns about the long-term retention of gadolinium-based contrast agents (GBCAs) led to a deeper investigation of the structural features underlying the integrity of the paramagnetic metal complex. Several human and nonclinical studies have noted marked differences among the macrocyclic GBCAs, with the least retention of Gd traces and most rapid elimination consistently being reported for [Gd(HP-DO3A)]. It was deemed of interest to assess how minor structural/electronic changes associated to the ligand structure may affect basic properties of the metal complex with several [Gd(HP-DO3A)] analogues synthesized and characterized in the last years. We recently reported that the closest homolog of [Gd(HP-DO3A)], i. e.: [Gd(HB-DO3A)], in which a (±)-2-hydroxy-1-propyl pendant arm is replaced by a (±)-2-hydroxy-1-butyl moiety, showed a significantly different retention behaviour in the model interaction with collagen, despite the apparently very minor structural difference. In this paper we report a comprehensive study of the structural, thermodynamic, kinetic and relaxation properties of [Gd(HB-DO3A)], compared to the parent [Gd(HP-DO3A)] and to other closely related macrocyclic GBCAs to assess whether very minor structural changes can modulate the physico-chemical properties of Gd3+ complexes.

МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ПЕРЕХІДНИХ ПРОЦЕСІВ В ІЄРАРХІЧНИХ СТРУКТУРАХ ДЕРЖАВНОГО УПРАВЛІННЯ З НЕЛІНІЙНИМИ ХАРАКТЕРИСТИКАМИ

The last decades are characterized by a large number of studies of state and public figures, which can be called an era of deep (fundamental) and rapid changes. Society is experiencing qualitative changes and serious challenges, therefore there is a kind of paradox when the imperatives of a new era force the development of a theoretical basis in the semantic field of common sense, which is associated with permanence and qualitative changes in political, economic and social life [1, 2, 3] . In state administration and local self-government bodies, relations between people, rapid unpredictable changes in the situation in the country and in regions (political, economic, social, etc.) are non-linear management systems. Moreover, any physical system when examined in more detail is always nonlinear. The effectiveness of managing a nonlinear system, taking into account disturbing factors, depends on the accuracy of forecasting the dynamics of changes in individual elements of such a system. This is a multifaceted problem [15]. A characteristic feature of non-linear hierarchical structures (NIS) in the public service (state administration - DU and local self-government - MS) is a continuous and discrete change in the processes of development of events, which distinguishes them from other internal and external influences, such as those that cause the transformation of linear communication into non-linear, which ultimately leads to new changes. This is the first thing. Second, subject-object relationships can have uncoordinated actions caused by real-world circumstances. Such uncoordinated actions can occur even more in transitional processes of society (for example, the speed of their flow) due to the inertia of individual elements or the social system itself as a whole. As a result of these phenomena, the linear relationship between discrete changes is broken and the previous hierarchical structure changes, which leads to non-linear information relationships in the state administration (SU) and local self-government bodies (MS). For example, G. Pocheptsov and other scientists associate this nonlinearity with chaos, the growing complexity of social processes, globalization, with a threat to state security, with the manifestation of unpredictability and instability [1-5]. Strict requirements are imposed on the management process in the new state of DU and MS: - resilience of the management system; - the system should be described as continuous-discrete (its dynamics); - the management process must ensure the necessary accuracy of matching the indicators of the new state of the system with the previous one. A number of problems appear in newly formed non-linear hierarchical structures (NIS) in DU and MS: - lack of a priori information about processes with non-linear connections; - inaccuracy of structural identification; - inaccuracy in the choice of criteria for assessing the situation; - restructuring processes; - wrong choice of states of nature; - incorrect determination of the importance of the selected criteria according to the scale of importance; - incorrectly chosen dependence of alternatives on the criterion; - insufficient degree of awareness of the complexity of the situation. Solving the above problems requires experimental research. If the process of stabilization of the NIS in the DU is considered as an object of regulation, then it is enough to establish the dependence of this stability on the control action and the main disturbance, and all other actions are considered as disturbances on the object of study. Such a description will reflect the main dynamic characteristics of such a process and will be sufficient for the synthesis and development of an optimal regulation system with the aim of achieving stable (stable) processes in such systems (including DU and MS). In the future, we will approach the DU as a non-linear hierarchical system (NHS) and depict the NIS as a multidimensional continuous system, in which the state of nature is the regulated quantity, and the input is the action of managers in the DU and MS (management decisions made by them - PUR) regarding regulation , coordination and perturbations acting on the system.

Phenomenologies of loneliness: alone without you, alone without an us

Loneliness is not simply characterised by a lack of other people in one’s environment or lifeworld, but by a lack of specific forms of relationship, connection or belonging. In this paper I connect the conversation about different types of loneliness with debates happening in the philosophy of intersubjectivity, specifically the phenomenology of intersubjectivity. Philosophers of intersubjectivity tend to characterise experiences of direct encounter as having a second-personal ‘I-thou’ quality. By contrast, experiences of belonging tend to be characterised by first-person plural ‘we’ structures. In this paper I will compare and contrast some of the different phenomenological qualities that come with the absence or poverty of these two types of intersubjective structure respectively. These different types of loneliness are characterised by different phenomenological properties, but also by (the absence of) specific interpersonal and social structures. I argue that the absence of these different interpersonal structures respectively gives rise to loneliness as the experience of being unseen and loneliness as the experience of not-being-at-home. This is significant for loneliness studies, as understanding and combating loneliness is consequently likely to take variegated rather than homogeneous forms. This is particularly significant for a psychosocial approach to loneliness which seeks to understand, hold together and integrate both the structural features of loneliness (in this case the relevant interpersonal and social structures) and the lived experience of individuals and groups (in this case the phenomenology of loneliness).

SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis.

Benign and malignant classification of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT) is an essential task in computer-aided diagnosis. However, due to the anisotropic resolution of DBT, three-dimensional (3-D) convolutional neural network (CNN)-based methods cannot extract hierarchical features efficiently. Moreover, the sparse distribution of MC points in the cluster makes it difficult for the CNN to extract discriminative structural information for classification. To comprehensively address these challenges, we propose a novel structure-aware hierarchical network (SAH-Net) for benign and malignant classification of clustered MC in a DBT volume. Specifically, the two-dimensional (2-D) group convolution is used to extract intraslice features. The one-to-one correspondence between group convolutions and slices ensures the independence of hierarchical feature extraction. Then, a partial deformable Transformer-based 3-D structural feature learning module is proposed to capture the long-range dependency between MC points in the cluster. We evaluate the proposed method on an in-house dataset with 495 clustered MCs collected from 462 DBT images. Experimental results confirm the validity of our proposed modules. The results also show that the proposed SAH-Net outperforms several other representative methods on this topic, and achieves the best classification result, with an area under the receiver operation curve (AUC) of 86.87%. The implementation of the proposed model is available at https://github.com/sunhaotian130911/SAHNet.

Hard Carbon Derived From Different Precursors for Sodium Storage.

Due to the almost unlimited resource and acceptable performance, Sodium-ion batteries (SIBs) have been regarded as a promising alternative for lithium-ion batteries (LIBs) for grid-scale energy storage. As the key material of SIBs, hard carbon (HC) plays a decisive role in determining the batteries' performance. Nevertheless, the micro-structure of HCs is quite complex and the random organization of turbostratically stacked graphene layers, closed pores, and defects make the structure-performance relationship insufficiently revealed. On the other hand, the impending large-scale deployment of SIBs leads to producing HCs with low-cost and abundant precursors actively pursued. In this work, the recent progress of preparing HCs from different precursors including biomass, polymers, and fossil fuels is summarized with close attention to the influences of precursors on the structural evolution of HCs. After a brief introduction of the structural features of HCs, the recent understanding of the structure-performance relationship of HCs for sodium storage is summarized. Then, the main focus is concentrated on the progress of producing HCs from distinct precursors. After that, the pros and cons of HCs derived from different precursors are comprehensively compared to conclude the selection rules of precursors. Finally, the further directions of HCs are deeply discussed to end this review.

De novo GRIN variants in M3 helix associated with neurological disorders control channel gating of NMDA receptor

N-methyl-d-aspartate receptors (NMDARs) are members of the glutamate receptor family and participate in excitatory postsynaptic transmission throughout the central nervous system. Genetic variants in GRIN genes encoding NMDAR subunits are associated with a spectrum of neurological disorders. The M3 transmembrane helices of the NMDAR couple directly to the agonist-binding domains and form a helical bundle crossing in the closed receptors that occludes the pore. The M3 functions as a transduction element whose conformational change couples ligand binding to opening of an ion conducting pore. In this study, we report the functional consequences of 48 de novo missense variants in GRIN1, GRIN2A, and GRIN2B that alter residues in the M3 transmembrane helix. These de novo variants were identified in children with neurological and neuropsychiatric disorders including epilepsy, developmental delay, intellectual disability, hypotonia and attention deficit hyperactivity disorder. All 48 variants in M3 for which comprehensive testing was completed produce a gain-of-function (28/48) compared to loss-of-function (9/48); 11 variants had an indeterminant phenotype. This supports the idea that a key structural feature of the M3 gate exists to stabilize the closed state so that agonist binding can drive channel opening. Given that most M3 variants enhance channel gating, we assessed the potency of FDA-approved NMDAR channel blockers on these variant receptors. These data provide new insight into the structure–function relationship of the NMDAR gate, and suggest that variants within the M3 transmembrane helix produce a gain-of-function.

Studies on foamed concrete micropores and their effects on stress distribution and heat conduction

Further research on the pore structure of foamed concrete contribute to expand the application of energy-efficient buildings. In this paper, the micropore structure features of foamed concrete were characterized by nuclear magnetic resonance. It is found that the pore size of foamed concrete ranged from 1 nm to 74.84 μm. In order to understand the effect of micropores on the performance of foamed concrete, the models with (Model I) and without micropores (Model II) were established. Besides, the heat conduction and stress distribution of foamed concrete were simulated numerically. Meanwhile, the thermal conductivity and compressive strength were tested as the reference for numerical simulations. The results show that the heat conduction flux and temperature gradient of Model I were increased by 12%–13% and the thermal conductivity was increased by 2.63% compared to Model II. Thermal conductivity of Model I was closer to the experimental results. The stress of Model I was increased by 1.32%–54.66% compared to that of Model II. This indicates that the micropores can significantly increase the stress concentration in the foamed concrete.

Substantial composition and technological treatment of potassium rocks of the Upper Pechora salt basin

The material composition of potassium and potassium-magnesium salts of the Upper Pechora salt-bearing basin was studied, their lithological variability was established over the area of productive strata, depending on the tectonic features of the foundation. The main mining and geological conditions that complicate the possibility of their extraction and the environmental conditions for organizing mining and processing production of mineral salts were established. The basic physical and technological properties of sylvinite and carnallite rocks mined by the mine method were studied for their enrichment by halurgical and flotation methods. The objectives of the work were: determining the material composition, studying textural and structural features and assessing the technological processing of potassium rocks in the Upper Pechora salt-bearing basin. When solving the problems, the following methods were used: a study of the textural and structural features of sylvinites using the petrographic method; the chemical composition of samples and solutions was analyzed by AES spectrometry; the mineral composition of the water-insoluble residue is determined by X-ray quantitative phase analysis.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.