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The classical pathway of the complement system is activated by the binding of C1q in the C1 complex to the target activator, including immune complexes. Factor H is regarded as the key downregulatory protein of the complement alternative pathway. However, both C1q and factor H bind to target surfaces via charge distribution patterns. For a few targets, C1q and factor H compete for binding to common or overlapping sites. Factor H, therefore, can effectively regulate the classical pathway activation through such targets, in addition to its previously characterized role in the alternative pathway. Both C1q and factor H are known to recognize foreign or altered-self materials, e.g., bacteria, viruses, and apoptotic/necrotic cells. Clots, formed by the coagulation system, are an example of altered self. Factor H is present abundantly in platelets and is a well-known substrate for FXIIIa. Here, we investigated whether clots activate the complement classical pathway and whether this is regulated by factor H. We show here that both C1q and factor H bind to the fibrin formed in microtiter plates and the fibrin clots formed under in vitro physiological conditions. Both C1q and factor H become covalently bound to fibrin clots, and this is mediated via FXIIIa. We also show that fibrin clots activate the classical pathway of complement, as demonstrated by C4 consumption and membrane attack complex detection assays. Thus, factor H downregulates the activation of the classical pathway induced by fibrin clots. These results elucidate the intricate molecular mechanisms through which the complement and coagulation pathways intersect and have regulatory consequences.

This research explores the effects of wear on a 6063 aluminum alloy matrix combined with silicon carbide (SiC) and tungsten disulfide (WS2) particulates. Dry friction wear tests were conducted on three materials: pure 6063 aluminum alloy, a composite with 4 wt% SiC and 4 wt% WS2 reinforcement, and another composite with 6 wt% SiC and 4 wt% WS2 reinforcement. These materials were tested against AISI410 stainless steel discs under varying loads (10N, 20N, and 30N) and sliding rates (0.5, 1.0, and 1.5 ms−1) following a Taguchi L27 orthogonal experimental plan. Main effect graphs (S/N ratios) and ANOVA analysis were utilized to determine optimal parameter combinations, and confirmation tests were carried out to validate the Taguchi results. Scanning electron microscope (SEM) images of wear areas showed that incorporating SiC and WS2 significantly enhanced the wear resistance of the aluminum alloy. This indicates that incorporating SiC and WS2 reinforcement has the potential to enhance the wear resistance of aluminum alloys, making them suitable for applications such as piston rings in internal combustion engines, which is in line with the requirements of engine performance. Furthermore, potentiodynamic polarization studies evaluated the corrosion resistance of the Metal Matrix Composites AA6063 in 3.5% NaCl. Results indicated that the AA6063 + 6 wt% SiC + 4 wt% WS2 composite exhibited superior corrosion resistance compared to AA6063 alone.

The traditional approach for analyzing the quality of arecanuts is based on their external appearance. However, using machine learning and deep learning techniques, automated classifications were performed. But the true quality can only be analyzed when the internal structure of the arecanut is examined. Therefore, we use the X-ray imaging technique to determine the internal quality of arecanuts. We prepared a novel dataset of arecanut X-ray images and used a YOLOv5 based deep learning architecture for classification. The present study employs an adaptive genetic algorithm based approach for hyperparameter optimization to enhance the mean average precision (mAP) using a light weight model generated using a ghost network and a feature pyramid network (FPN). We have achieved the highest mAP of 97.84% using our method with a lower model size of 15 MB. Our method has excelled in detecting the arecanut compared to cutting-edge object detection algorithms such as YOLOv3, YOLOv4, Detetron, YOLOv6, YOLOv8, and YOLOX. We also acknowledged the performance enhancement using the adaptive genetic algorithm on the Pascal VOC 2007 image dataset. Despite of significant computational requirements for executing genetic algorithms, we proved that genetic algorithms can boost mAP. Additionally, the methodology developed in this investigation produced multiple models with the best mAP featuring optimized hyperparameters. This methodical strategy is helpful for the design of an automatic, non-destructive, integrated X-ray image based classification system. This system has the potential to revolutionize the quality assessment of arecanuts by offering a more efficient evaluation method.

By cultivating more sustainable, effective, and efficient corporate practices, artificial intelligence (AI) reinvents financial management. Contemporary finance includes AI as an existential component. The cornerstone technology of the industrial revolution and technological revolution is artificial intelligence. The rise of artificial intelligence (AI) is reshaping financial management by transforming how firms make choices, allocate resources and control risk. As companies face increasing pressure to integrate sustainability into their operations artificial intelligence (AI) has emerged as a key tool for resolving complex environmental concerns and improving financial performance. AI-powered analytics can help businesses detect and assess ecological issues, optimize resource allocation, and speed up financial processes like forecasting, auditing, and budgeting. This strengthens decision-making and increases operational efficiency by providing real-time insights on ESG (Environmental, Social, and Governance) factors. AI also helps businesses, investors, and creditors. This research paper explores the literature on the transformative Power of AI in financial management, examining its role, applications, benefits, and challenges. It investigates AI’s impact on financial forecasting, portfolio optimization, risk management and compliance.

In the past, the perspective of training programs has been used to understand employee motivation. However, there is a lack of normative and perceptive support that can guide the development of online training programs. Therefore, there is a need to understand employee motivation from the perspective of online training. This study aims to understand employee motivation and its relationship with online training in an organizational setting. The study adopted empirical research to investigate the relationship between training goals and trainee intentions to undergo online training, the role of self-efficacy in this relationship, and the expectancy of the learners from online training programs. The study tests the factors identified from the literature review for model validation. A total of 15 items were used in a survey of 812 randomly selected respondents from the organization in Belagavi, Karnataka, India. The structural equation modeling techniques were applied to explore and validate the model. The results show that factors such as learning goals, social interaction, time management and personalization have a great role to play in building training intention in the online environment, and the successful realization of the training goals. [ABSTRACT FROM AUTHOR]

Lung surfactant protein D (SP-D) and Dendritic cell-specific intercellular adhesion molecules-3 grabbing non-integrin (DC-SIGN) are pathogen recognising C-type lectin receptors. SP-D has a crucial immune function in detecting and clearing pulmonary pathogens; DC-SIGN is involved in facilitating dendritic cell interaction with naïve T cells to mount an anti-viral immune response. SP-D and DC-SIGN have been shown to interact with various viruses, including SARS-CoV-2, an enveloped RNA virus that causes COVID-19. A recombinant fragment of human SP-D (rfhSP-D) comprising of α-helical neck region, carbohydrate recognition domain, and eight N-terminal Gly-X-Y repeats has been shown to bind SARS-CoV-2 Spike protein and inhibit SARS-CoV-2 replication by preventing viral entry in Vero cells and HEK293T cells expressing ACE2. DC-SIGN has also been shown to act as a cell surface receptor for SARS-CoV-2 independent of ACE2. Since rfhSP-D is known to interact with SARS-CoV-2 Spike protein and DC-SIGN, this study was aimed at investigating the potential of rfhSP-D in modulating SARS-CoV-2 infection. Coincubation of rfhSP-D with Spike protein improved the Spike Protein: DC-SIGN interaction. Molecular dynamic studies revealed that rfhSP-D stabilised the interaction between DC-SIGN and Spike protein. Cell binding analysis with DC-SIGN expressing HEK 293T and THP- 1 cells and rfhSP-D treated SARS-CoV-2 Spike pseudotypes confirmed the increased binding. Furthermore, infection assays using the pseudotypes revealed their increased uptake by DC-SIGN expressing cells. The immunomodulatory effect of rfhSP-D on the DC-SIGN: Spike protein interaction on DC-SIGN expressing epithelial and macrophage-like cell lines was also assessed by measuring the mRNA expression of cytokines and chemokines. RT-qPCR analysis showed that rfhSP-D treatment downregulated the mRNA expression levels of pro-inflammatory cytokines and chemokines such as TNF-α, IFN-α, IL-1β, IL- 6, IL-8, and RANTES (as well as NF-κB) in DC-SIGN expressing cells challenged by Spike protein. Furthermore, rfhSP-D treatment was found to downregulate the mRNA levels of MHC class II in DC expressing THP-1 when compared to the untreated controls. We conclude that rfhSP-D helps stabilise the interaction between SARS- CoV-2 Spike protein and DC-SIGN and increases viral uptake by macrophages via DC-SIGN, suggesting an additional role for rfhSP-D in SARS-CoV-2 infection.

The complement system is a key component of the innate immune response to viruses and proinflammatory events. Exaggerated complement activation has been attributed to the induction of a cytokine storm in severe SARS-CoV-2 infection. However, there is also an argument for the protective role of complement proteins, given their local synthesis or activation at the site of viral infection. This study investigated the complement activation-independent role of C1q and C4b-binding protein (C4BP) against SARS-CoV-2 infection. The interactions of C1q, its recombinant globular heads, and C4BP with the SARS-CoV-2 spike and receptor binding domain (RBD) were examined using direct ELISA. In addition, RT-qPCR was used to evaluate the modulatory effect of these complement proteins on the SARS-CoV-2-mediated immune response. Cell binding and luciferase-based viral entry assays were utilised to assess the effects of C1q, its recombinant globular heads, and C4BP on SARS-CoV-2 cell entry. C1q and C4BP bound directly to SARS-CoV-2 pseudotype particles via the RBD domain of the spike protein. C1q via its globular heads and C4BP were found to reduce binding as well as viral transduction of SARS-CoV-2 spike protein expressing lentiviral pseudotypes into transfected A549 cells expressing human ACE2 and TMPRSS2. Furthermore, the treatment of the SARS-CoV-2 spike, envelope, nucleoprotein, and membrane protein expressing alphaviral pseudotypes with C1q, its recombinant globular heads, or C4BP triggered a reduction in mRNA levels of proinflammatory cytokines and chemokines such as IL-1β, IL-8, IL-6, TNF-α, IFN-α, and RANTES (as well as NF-κB) in A549 cells expressing human ACE2 and TMPRSS2. In addition, C1q and C4BP treatment also reduced SARS-CoV-2 pseudotype infection-mediated NF-κB activation in A549 cells expressing human ACE2 and TMPRSS2. C1q and C4BP are synthesised primarily by hepatocytes; however, they are also produced by macrophages, and alveolar type II cells, respectively, locally at the pulmonary site. These findings support the notion that the locally produced C1q and C4BP can be protective against SARS-CoV-2 infection in a complement activation-independent manner, offering immune resistance by inhibiting virus binding to target host cells and attenuating the infection-associated inflammatory response.

Binary transition metal chalcogenides and reduced graphene oxide exhibit significant potential for energy storage devices due to their superior electronic conductivity and capacity, surpassing that of single-metal sulfides, owing to their more extensive redox reactions. In this report, we introduce a novel synthesis method for producing a mixed-phase MnSe2/CoSe2 nanocomposite wrapped with reduced graphene oxide (rGO) sheets, designed specifically for supercapacitor applications. The MnSe2/CoSe2 hybrid material was synthesized using an ultrasonic assisted hydrothermal technique, followed by the preparation of the rGO/MnSe2/CoSe2 hybrid composite. The structural characterization was conducted employing x-ray diffraction (XRD), scanning electron microscopy (SEM), x-ray photoelectron spectroscopy (XPS) and Raman techniques. Brunauer–Emmett–Teller (BET) analysis demonstrated a significant specific surface area (66.5 m2/g) and a pore size distribution of 28.4 nm in rGO-MnSe2/CoSe2. The interconnected ultrathin rGO nanosheets and conductive carbon layer contributed to the exceptional conductivity and stability achieved by the rGO/MnSe2/CoSe2 composite electrode. The electrochemical performance was assessed using a three-electrode setup in a 3 M KOH solution, with nickel foam as the current collector. The working electrode, consisting of rGO/MnSe2/CoSe2, acetylene black and PVDF in an 80:15:5 weight ratio, demonstrated specific capacitance of 1214 F g−1 and cycling stability of 88% retention after 5000 cycles at 1 A g−1. An asymmetric supercapacitor, constructed using a tailored electrode composition, achieved energy density of 28.6 Wh kg−1 at 2100 W kg−1 and high power density of 888 W kg−1 at 49.7 Wh kg−1. [ABSTRACT FROM AUTHOR]

This study describes using carbon fibers coated with magnetic material as a filler in polyethylene for EMI shielding. It investigates how to utilize barium ferrite (BaFeO3) as a conducting filler in a polyethylene matrix by optimizing the conditions for its electroless deposition onto carbon fibers. Various polymer nanocomposite samples were prepared using varying concentrations of BFO@CF. Scanning electron microscopy is used to assess the surface appearance of the composite and the distribution of BFO@CF within the polymer matrix. Including MWCNT/graphene nanoplatelets exhibit superior shielding properties, and the polymer composite with the BFO@CF demonstrates enhanced mechanical properties. The 8.2 to 12.4 GHz frequency range is used to study the EMI shielding properties. For the composition ratio of LDPE: MWCNT: GNP: BFO@CF (50 : 5 : 47.5 : 2.5), a maximum shielding of 67 dB was attained. After BFO@CF was added to the matrix, the absorption mechanism rather than the reflection mechanism dominated the shielding mechanism. A thorough evaluation and discussion of the shielding mechanism and parameters are presented. [ABSTRACT FROM AUTHOR]

Abstract Background Gugguluthiktham Kashayam (GTK) is the decoction form of Panchatikta Guggulu Ghrita, a classical Ayurvedic formulation used for treating various diseases like skin disorders, ulcers, sinus, asthma, cardiac diseases, arthritis, and cancer. Results Tandem mass spectroscopic analysis of GTK was carried out by different ionization techniques such as electro spray ionization (ESI) and atmospheric pressure chemical ionization (APCI) in both positive and negative modes using Quadrupole Time-of-Flight (Q-TOF) mass spectroscopy. Data processing of molecular ions obtained by ESI and APCI mass fragmentation led to the identification of several phytoconstituents belonging to various classes of compounds such as phenolics, flavonoids, and coumarins. Conclusion The study concluded that GTK contains variety of phytochemicals with numerous biological properties that might be responsible for its various therapeutic effects.

With the exponential growth of data, it is difficult to efficiently store and retrieve data using traditional methods. There is a need to optimize the storage and to efficiently retrieve relevant data matching the user query. Traditional methods lack optimized storage and to effectively retrieve data. To overcome these limitations, in this project, we propose a distributed architecture framework to optimize memory usage and to effectively retrieve relevant data using Content-Based Image Retrieval (CBIR). The experimental results show that the proposed model enhances storage performance and retrieval time by 20%.

The complement system is designed to recognise and eliminate invading pathogens via activation of classical, alternative and lectin pathways. Human properdin stabilises the alternative pathway C3 convertase, resulting in an amplification loop that leads to the formation of C5 convertase, thereby acting as a positive regulator of the alternative pathway. It has been noted that human properdin on its own can operate as a pattern recognition receptor and exert immune functions outside its involvement in complement activation. Properdin can bind directly to microbial targets via DNA, sulfatides and glycosaminoglycans, apoptotic cells, nanoparticles, and well-known viral virulence factors. This study was aimed at investigating the complement-independent role of properdin against Influenza A virus infection. As one of the first immune cells to arrive at the site of IAV infection, we show here that IAV challenged neutrophils released properdin in a time-dependent manner. Properdin was found to directly interact with haemagglutinin, neuraminidase and matrix 1 protein Influenza A virus proteins in ELISA and western blot. Furthermore, modelling studies revealed that properdin could bind HA and NA of the H1N1 subtype with higher affinity compared to that of H3N2 due to the presence of an HA cleavage site in H1N1. In an infection assay using A549 cells, properdin suppressed viral replication in pH1N1 subtype while promoting replication of H3N2 subtype, as revealed by qPCR analysis of M1 transcripts. Properdin treatment triggered an anti-inflammatory response in H1N1-challenged A549 cells and a pro-inflammatory response in H3N2-infected cells, as evident from differential mRNA expression of TNF-α, NF-κB, IFN-α, IFN-β, IL-6, IL-12 and RANTES. Properdin treatment also reduced luciferase reporter activity in MDCK cells transduced with H1N1 pseudotyped lentiviral particles; however, it was increased in the case of pseudotyped H3N2 particles. Collectively, we conclude that infiltrating neutrophils at the site of IAV infection can release properdin, which then acts as an entry inhibitor for pandemic H1N1 subtype while suppressing viral replication and inducing an anti-inflammatory response. H3N2 subtype can escape this immune restriction due to altered haemagglutinin and neuraminindase, leading to enhanced viral entry, replication and pro-inflammatory response. Thus, depending on the subtype, properdin can either limit or aggravate IAV infection in the host.

Summary: Autonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a very famous network because of its many applications. IoT network has an integration of large-scale IoT devices that generate data. These IoT devices are very low power computing devices due to which they have a low level of communication. These devices construct data and transmit the data to the base station via intermediate IoT devices. The base station gathers and integrates the data and sends it to the administrator for further processing. The data attains the base station using various routing algorithms with the goal of low power consumption. When discussing low power IoT devices, power efficiency is an important performance measurement when creating a routing algorithm. This paper proposes a Minimum Power Consumption Routing (MPCR) algorithm using Hierarchical Fuzzy Logic Clustering (HFLC) algorithm for IoT networks. The MPCR with HFLC algorithm is an energy-efficient algorithm because of its lower power consumption for the cluster by aggregating the data within the cluster head and decreasing the number of data transmissions to the base station. In this paper, the cluster formation and cluster-head selection are explained, and a simulation has been conducted. In addition, the proposed algorithm is compared with the existing algorithms based on different metrics such as throughput, packet delivery ratio, and energy consumption of the network. The experimental results show that the proposed MPCR with the HFLC algorithm provides high throughput and packet delivery ratio and reduces energy consumption more efficiently than other existing algorithms. [ABSTRACT FROM AUTHOR]

Distributed processing systems play a crucial role in query search operations, where large-scale data are partitioned across multiple nodes using shard selection algorithms. However, the existing shard selection algorithms pose significant challenges, such as shard ranking, shard cut-off estimation, high latency, low throughput, and high processing costs. These limitations become more pronounced as the data size increases, affecting the efficiency and effectiveness of search operations. To address these challenges, the novel Hybrid Shard Selection Algorithm (HSSA) is proposed as a solution in this paper, designed specifically to enhance the effectiveness and efficiency of search operations within distributed processing systems. HSSA employs an advanced sharding approach that adeptly navigates and targets pertinent shards based on specific queries. This not only curtails search-related overhead but also enhances operational efficiency. Through rigorous testing using the Gov2 dataset, the HSSA algorithm has proven its merits. When set against well-established algorithms like CORI, Rank-S, and SHiRE, HSSA stands out, registering remarkable gains in average throughput by 21%, 16%, and 12%, while also slashing latency by 14.2%, 9.4%, and 8.2%, respectively. The insights gained from this research underscore HSSA's capability to effectively bridge the gaps inherent in traditional shard selection strategies. Furthermore, its exemplary efficacy with datasets of varied sizes amplifies its relevance for practical integration within distributed processing landscapes. [ABSTRACT FROM AUTHOR]

This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. The intention of this study is to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves best performance with test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes. [ABSTRACT FROM AUTHOR]