NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed?

OBJECTIVES: Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS: A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS: Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION: The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.

Discharge planning and resource utilization for minimizing readmissions in acute exacerbation of chronic heart failure: Insights from an observational study in a community hospital.

This cross-sectional study analyzed discharge disposition in 1,584 readmitted patients aged 65 or older with acute exacerbation of chronic heart failure (AECHF) in a large community hospital from April 2021 to April 2022. The study aimed to explore the relationship between age (65-74, 75-85, and 85 or older) and gender (male, female) with discharge disposition. Results revealed that 55.6 % were discharged for home self-care, 27.3 % with external home health support, and 17.1 % to skilled nursing facilities. Logistic regression showed no significant differences in discharge between age groups. Gender also had no statistically significant effect on discharge disposition. Effective discharge planning emerged as a key factor in reducing readmissions for AECHF. Gender did not significantly impact disposition, suggesting other variables played a more pivotal role. Comprehensive discharge planning and resource allocation, tailored to patient needs, are recommended to enhance patient outcomes and lower AECHF readmission rates.

Sphingobium agri sp. nov., isolated from rhizospheric soil of eggplant.

A Gram-stain-negative, aerobic, short rod-shaped and motile bacterial strain, designated MAH-33T, was isolated from rhizospheric soil of eggplant. The colonies were observed to be yellow-coloured, smooth, spherical and 0.1-0.3 mm in diameter when grown on TSA agar medium for 2 days. Strain MAH-33T was found to be able to grow at 10-40 °C, at pH 5.0-10.0 and at 0-3.0 % NaCl (w/v). The strain was found to be positive for both oxidase and catalase tests. The strain was positive for hydrolysis of tyrosine and aesculin. According to the 16S rRNA gene sequence comparisons, the isolate was identified as a member of the genus Sphingobium and to be closely related to Sphingobium quisquiliarum P25T (98.4 % similarity), Sphingobium mellinum WI4T (97.8 %), Sphingobium fuliginis TKPT (97.3 %) and Sphingobium herbicidovorans NBRC 16415T (96.9 %). The novel strain MAH-33T has a draft genome size of 3 908 768 bp (28 contigs), annotated with 3689 protein-coding genes, 45 tRNA and three rRNA genes. The average nucleotide identity and digital DNA-DNA hybridization values between strain MAH-33T and closely related type strains were in the range of 79.8-81.6 % and 23.2-24.5 %, respectively. The genomic DNA G+C content was determined to be 62.2 %. The predominant isoprenoid quinone was ubiquinone 10. The major fatty acids were identified as C16 : 0 and summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c). The polar lipids identified in strain MAH-33T were phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, sphingoglycolipid, phosphatidylcholine; one unknown phospholipid and one unknown lipid. On the basis of digital DNA-DNA hybridization, ANI value, genotypic analysis, chemotaxonomic and physiological data, strain MAH-33T represents a novel species within the genus Sphingobium, for which the name Sphingobium agri sp. nov. is proposed, with MAH-33T (=KACC 19973T = CGMCC 1.16609T) as the type strain.

Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.

The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

Development of a Smart Signalization for Emergency Vehicles.

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.

Isolation and characterization of a novel glycoside hydrolase positive bacterial strain named Terrimonas ginsenosidimutans sp. nov. with ginsenoside-converting activity.

A novel yellow-pigmented catalase- and oxidase-positive bacterial strain (designated NA20T) was isolated from wetland soil and characterized. Results of 16S rRNA and draft genome sequence analysis placed strain NA20T within the genus Terrimonas of the family Chitinophagaceae. Strain NA20T showed ≤97.1 % sequence similarity to members of the genus Terrimonas and the highest sequence similarity was found to Terrimonas lutea DYT (97.1%). The draft genome of strain NA20T had a total length of 7 144 125 base pairs. A total of 5659 genes were identified, of which 5613 were CDS and 46 RNA genes were assigned a putative function. Mining the genomes revealed the presence of 225 carbohydrate genes out of 1334 genes. Strain NA20T contained iso-C15 : 0, iso-C15 : 0 G, iso-C17 : 0 3-OH and summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c) as major fatty acids. The predominant quinone was MK-7. The major polar lipids were phosphatidylethanolamine, one unknown polar lipid and one unknown aminophospholipid. Additionally, the functional analysis of NA20T showed the conversion of protopanaxatriol-mix type major ginsenosides (Rb1, Rc and Rd) to minor ginsenosides F2 and weak conversion of Rh2 and C-K within 24 h. As a result, the genotypic, phenotypic and taxonomic analyses support the affiliation of NA20T within the genus Terrimonas, for which the name Terrimonas ginsenosidimutans sp. nov. is proposed. The type strain is NA20T (=KACC 22218T=LMG 32198T).

Isolation and Characterization of Sphingomonas telluris, Sphingomonas caseinilyticus Isolated from Wet Land Soil.

Two novel bacterial strains, designated as SM33T and NSE70-1T, were isolated from wet soil in South Korea. To get the taxonomic positions, the strains were characterized. The genomic information (both 16S rRNA gene and draft genome sequence analysis) show that both novel isolates (SM33T and NSE70-1T) belong to the genus Sphingomonas. SM33T share the highest 16s rRNA gene similarity (98.2%) with Sphingomonas sediminicola Dae20T. In addition, NSE70-1T show 96.4% 16s rRNA gene similarity with Sphingomonas flava THG-MM5T. The draft genome of strains SM33T and NSE70-1T consist of a circular chromosome of 3,033,485 and 2,778,408 base pairs with DNA G+C content of 63.9, and 62.5%, respectively. Strains SM33T and NSE70-1T possessed the ubiquinone Q-10 as the major quinone, and a fatty acid profile with C16:0, C18:1 2-OH, C16:1 ω7c/C16:1 ω6c (summed feature 3) and C18:1 ω7c/C18:1 ω6c (summed feature 8) as major fatty acids. The major polar lipids of SM33T and NSE70-1T were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, sphingoglycolipid and phosphatidylcholine, respectively. Moreover, genomic, physiological, and biochemical results allowed the phenotypic and genotypic differentiation of strains SM33T and NSE70-1T from their closest and other species of the genus Sphingomonas with validly published names. Therefore, the SM33T and NSE70-1T represent novel species of the genus Sphingomonas, for which the name Sphingomonas telluris sp. nov. (type strain SM33T = KACC 22222T = LMG 32193T), and Sphingomonas caseinilyticus (type strain NSE70-1T = KACC 22411T = LMG 32495T).

FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis.

The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.

Ketonization of Ginsenoside C-K by Novel Recombinant 3-ß-Hydroxysteroid Dehydrogenases and Effect on Human Fibroblast Cells.

BACKGROUND AND OBJECTIVE: The ginsenoside compound K (C-K) (which is a de-glycosylated derivative of major ginsenosides) is effective in the treatment of cancer, diabetes, inflammation, allergy, angiogenesis, aging, and has neuroprotective, and hepatoprotective than other minor ginsenosides. Thus, a lot of studies have been focused on the conversion of major ginsenosides to minor ginsenosides using glycoside hydrolases but there is no study yet published for the bioconversion of minor ginsenosides into another high pharmacological active compound. Therefore, the objective of this study to identify a new gene (besides the glycoside hydrolases) for the conversion of minor ginsenosides C-K into another highly pharmacological active compound. METHODS AND RESULTS: Lactobacillus brevis which was isolated from Kimchi has showed the ginsenoside C-K altering capabilities. From this strain, a novel potent decarboxylation gene, named HSDLb1, was isolated and expressed in Escherichia coli BL21 (DE3) using the pMAL-c5X vector system. Recombinant HSDLb1 was also characterized. The HSDLb1 consists of 774 bp (258 amino acids residues) with a predicted molecular mass of 28.64 kDa. The optimum enzyme activity was recorded at pH 6.0-8.0 and temperature 30 °C. Recombinant HSDLb1 effectively transformed the ginsenoside C-K to 12-ß-hydroxydammar-3-one-20(S)-O-ß-D-glucopyranoside (3-oxo-C-K). The experimental data proved that recombinant HSDLb1 strongly ketonized the hydroxyl (-O-H) group at C-3 of C-K via the following pathway: C-K → 3-oxo-C-K. In vitro study, 3-oxo-C-K showed higher solubility than C-K, and no cytotoxicity to fibroblast cells. In addition, 3-oxo-C-K induced the inhibitory activity of ultraviolet A (UVA) against matrix metalloproteinase-1 (MMP-1) and promoted procollagen type I synthesis. Based on these expectations, we hypothesized that 3-oxo-C-K can be used in cosmetic products to block UV radiations and anti-ageing agent. Furthermore, we expect that 3-oxo-C-K will show higher efficacy than C-K for the treatment of cancer, ageing and other related diseases, for which more studies are needed.

Characterization of four novel bacterial species of the genus Sphingomonas, Sphingomonas anseongensis, Sphingomonas alba, Sphingomonas brevis and Sphingomonas hankyongi sp.nov., isolated from wet land.

Four novel bacterial strains, designated as RG327T, SE158T, RB56-2T and SE220T, were isolated from wet soil in the Republic of Korea. To determine their taxonomic positions, the strains were fully characterized. On the basis of genomic information (16S rRNA gene and draft genome sequences), all four isolates represent members of the genus Sphingomonas. The draft genomes of RG327T, SE158T, RB56-2T and SE220T consisted of circular chromosomes of 2 226 119, 2 507 338, 2 593 639 and 2 548 888 base pairs with DNA G+C contents of 64.6, 63.6, 63.0 and 63.1 %, respectively. All the isolates contained ubiquinone Q-10 as the predominant quinone compound and a fatty acid profile with C16 : 0, C17 : 1ω6c, C18 : 1 2-OH, summed feature 3 (C16 : 1ω7c/C16 : 1ω6c) and summed feature 8 (C18 : 1ω7c/C18 : 1ω6c) as the major fatty acids, supporting the affiliation of strains RG327T, SE158T, RB56-2T and SE220T to the genus Sphingomonas. The major identified polar lipids in all four novel isolates were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, sphingoglycolipid and phosphatidylcholine. Moreover, the physiological, biochemical results and low level of DNA-DNA relatedness and average nucleotide identity values allowed the phenotypic and genotypic differentiation of RG327T, SE158T, RB56-2T and SE220T from other species of the genus Sphingomonas with validly published names and indicated that they represented novel species of the genus Sphingomonas, for which the names Sphingomonas anseongensis sp. nov. (RG327T = KACC 22409T = LMG 32497T), Sphingomonas alba sp. nov. (SE158T = KACC 224408T = LMG 324498T), Sphingomonas brevis (RB56-2T = KACC 22410T = LMG 32496T) and Sphingomonas hankyongi sp. nov., (SE220T = KACC 22406T = LMG 32499T) are proposed.

Disaster preparedness and community helping behaviour in the wake of the 2020 Oregon wildfires.

Extreme weather events are increasing in frequency and severity owing to climate change. Individual-level behavioural responses-notably, disaster preparedness and community helping actions (such as donating and volunteering)-supplement government efforts to respond to such phenomena, but rarely have they been explored together. Using data from a survey administered soon after the 2020 Oregon wildfires, this paper compares a range of socio-demographic, experiential, attitudinal, and communication-related factors associated with these two individual-level behavioural responses. Findings indicate that respondents who reported experiencing a higher degree of harm and heightened concern about climate change after the wildfires were more likely to report disaster preparedness and community helping actions. Those who reported more frequent informal discussions about the wildfires, consulting more sources to seek information on them, and higher percentages of friends, neighbours, and community members taking actions to prepare for future wildfires also reported more disaster preparedness and community helping actions. Disaster preparedness actions were also positively associated with seeking information from formal/official sources.

Arthrobacter hankyongi sp. nov., Isolated From Wet Land.

A Gram-staining-positive, catalase positive and oxidase negative, non-motile, non-flagellated, and oval-shaped bacterium, was designated as I2-34 T, isolated from wetland in Soul South Korea. Colonies were round, entire, raised, and cream colored after two days of incubation on R2A agar plates at 25 °C. Based on genomes (both 16S rRNA gene and draft genome) sequence analysis, strain I2-34 T belongs to the genus Arthrobacter and was most closely related to Arthrobacter deserti YIM CS25T (98.0%). The strain I2-34 T had a circular genome with length of 5,186,447 base pairs (67 contigs) and 4830 total genes. Out of 4696 were protein-coding genes, 54 tRNA and 4 rRNA genes. The chemotaxonomic analysis indicates iso-C16:0, anteiso-C15:0, and anteiso-C17:0 as major fatty acids, phosphatidylglycerol (PG), diphosphatidylglycerol (DPG), and two unidentified glycolipids (GL1, GL2) as major polar lipids. The predominant quinone was MK-8(H2). The peptidoglycan type was A3α with an. L-Lys-L-Ala interpeptide bridge. Thus, the experimental data demonstrated here show that the novel isolate shares the similar major fatty acids, major polar lipid PG, DPG, and GLs, major and major quinone MK8-(H2) with the described members of the genus Arthrobacter. However, the low 16S rRNA gene sequence (98.0%), and some physiological and biochemical characteristics differentiate the I2-34 T from its closest phylogenetic neighbors. As a result, the isolate represents a novel species in within the genus Arthrobacter and family Micrococcaceae for which the name Arthrobacter hankyongi sp. nov. is proposed. The type strain is I2-34 T (= KACC 22217 T, LMG 32197 T).

Synergy of 1-MCP and hypobaric treatments prevent fermented flavour and improve consumers' acceptability of 'Shughri' pear.

Development of fermented flavour during storage reduces acceptability of Shughri pear. Therefore, the current study was designed to investigate the combined effect of 1-Methylcyclopropene (1-MCP) and hypobaric treatment on stability of Shughri pear during 120 days of storage. Fruit were treated individually or combinedly with 25, 50, and 75 kilo pascal hypobaric treatments for 4 h and 1-MCP (0.3 µLL-1 and 0.6 µLL-1) for 24 h, whereas control received no treatment. The pears were stored for 120 days at (0 ± 1 °C, 85 ± 5% RH), and were evaluated after every 30 days. After cold storage, pears were shifted to simulated retail conditions (20 ± 3 °C, 65 ± 5% RH). The combination of 25 kPa + 0.6 µLL-1 1-MCP significantly (P ≤ 0.05) delayed fruit ripening, reduced Alcohol dehydrogenase (ADH), and Pyruvate decarboxylase (PDC) activities, maintained the quality, and led to higher consumers' acceptability of the pear followed by 50 kPa + 0.6 µLL-1 and 25 kPa + 0.3 µLL-1. The control fruit were marketable for a week after storage with relatively less acceptability due to fermented flavour compared to treated fruit, marketable for more than two weeks. Among all the treatments, the synergy of 1-MCP and hypobaric treatment 25 kPa + 0.6 µLL-1 1-MCP improved the postharvest storage life and quality parameters, preventing development of fermented flavour in the pears. The experiment was conducted on pilot scale, for commercial application, the results of this study should be validated on large scale.

Repressing effect of transformed ginsenoside Rg3-mix against LPS-induced inflammation in RAW264.7 macrophage cells.

BACKGROUND: Rg3-ginsenoside, a protopanaxadiol saponin, is a well-known adaptogen used for the prevention of cancer and inflammation. However, despite its distinct biological activity, the concentration of Rg3 in the total ginseng extract is insufficient for therapeutic applications. This study aims to convert PPD-class of major ginsenosides into a mixture of minor ginsenoside, to analyze its immune-regulatory role in macrophage cells. RESULTS: Using heat and organic acid treatment, three major ginsenosides, Rc, Rd, and Rb1, were converted into a mixture of minor ginsenosides, GRg3-mix [Rg3(S), Rg3(R), Rg5, and Rk1]. Purity and content analysis of the transformed compound were performed using thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC), compared with their standards. Preceding with the anti-inflammatory activity of GRg3-mix, lipopolysaccharide (LPS)-stimulated murine RAW264.7 macrophage cells were treated with various concentrations of GRg3-mix (6.25, 12.5, 25, and 50 µg/mL). The cell viability assay revealed that the level of cell proliferation was increased, while the nitric oxide (NO) assay showed that NO production decreased dose-dependently in activated RAW264.7 cells. The obtained results were compared to those of pure Rg3(S) ≥ 98% (6.25, 12.5, and 25 µg/mL). Preliminary analysis of the CCK-8 and NO assay demonstrated that GRg3-mix can be used as an anti-inflammatory mediator, but mRNA and protein expression levels were evaluated for further confirmation. The doses of GRg3-mix significantly suppressed the initially upregulated mRNA and protein expression of inflammation-related enzymes and cytokines, namely inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nuclear transcription factor kappa B (NF-κB), tumor necrosis factor (TNF-α), and interleukins (IL-6 and IL1B), as measured by reverse transcription-polymerase chain reaction and western blotting. CONCLUSIONS: Our pilot data confirmed that the mixture of minor ginsenosides, namely GRg3-mix, has high anti-inflammatory activity and has an easy production procedure.

Glanzmann Thrombasthenia in Pakistani Patients: Identification of 7 Novel Pathogenic Variants in the Fibrinogen Receptor αIIbß3.

Glanzmann thrombasthenia (GT) is a rare autosomal recessive inherited platelet disorder occurring frequently in populations with high incidence of consanguineous marriages. GT is characterized by quantitative and/or qualitative defect of the platelet αIIbß3 (GPIIb/IIIa) receptor caused by pathogenic variants of the encoding genes: ITGA2B and ITGB3. Patients present with a moderate to severe bleeding tendency with normal platelet count. Platelets show reduced/absent aggregation for all agonists except ristocetin in light transmission aggregometry and reduced/absent αIIbß3 expression in flow cytometry (FC). In this study, we investigated a cohort of 20 Pakistani patients and 2 families collected from the National Institute of Blood Disease, Karachi and Chughtai's Lab, Lahore. Platelet aggregation studies, FC (platelet CD41, CD61, CD42a, CD42b) and direct sequencing of the candidate genes were performed. All patients showed altered platelet aggregation, but normal agglutination after stimulation with ristocetin. Absent/reduced αIIbß3 receptor expression was present in the platelets of 16 patients, in 4 patients expression was borderline/normal. Candidate gene sequencing identified pathogenic/likely pathogenic variants in 15 patients. Seven variants are novel. One patient with absent receptor expression remained without genetic finding. 13 (86.7%) of 15 patients stated consanguinity reflected by homozygosity finding in 14 (93.3%) patients.

Low-Complexity Beamforming Design for a Cooperative Reconfigurable Intelligent Surface-Aided Cell-Free Network.

Cell-free (CF) networks are proposed to suppress the interference among collocated cells by deploying several BSs without cell boundaries. Nevertheless, as installing several base stations (BSs) may require high power consumption, cooperative CF networks integrated with a reconfigurable intelligent surface (RIS)/metasurface can avoid this problem. In such cooperative RIS-aided MIMO networks, efficient beamforming schemes are essential to boost their spectral and energy efficiency. However, most of the existing available beamforming schemes to maximize spectral and energy efficiency are complex and entail high complexity due to the matrix inversions. To this end, in this work we present a computationally efficient stochastic optimization-based particle swarm optimization (PSO) algorithm to amplify the spectral efficiency of the cooperative RIS-aided CF MIMO system. In the proposed PSO algorithm, several swarms are generated, while the direction of each swarm is tuned in each iteration based on the sum-rate performance to obtain the best solution. Our simulation results show that our proposed scheme can approximate the performance of the existing solutions for both the performance metrics, i.e., spectral and energy efficiency, at a very low complexity.

An Enhanced Machine Learning Approach for Brain MRI Classification.

Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space's dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40-60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

Interest rate volatility and financing of Islamic banks.

Despite a direct ban on charging interest, interest-based benchmarks are used as a pricing reference by a majority of Islamic banks, due in part to the absence of stable and widely- published alternatives. Benchmarking interest rate exposes Islamic banks to the problems of conventional banks, particularly the interest rate risk. Against this backdrop, the present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banks. The empirical analysis is carried using evidence from the Islamic banking industry of Pakistan during the time period 2006-2020. The multivariate Johansen and Jusiles Co-integration test and Vector Error Correction Model (VECM) are used as the baseline econometric models. Moreover, the DCC-GARCH model is employed for robustness and ensuring the consistency of results. The results indicate that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. These findings suggest that paradoxical as it may seem, the financing of Islamic banks operating within a dual banking system is subject to interest rate risk, mainly due to benchmarking interest rate, which in-turn makes Islamic banks vulnerable to the rate of return risk and withdrawal risk. Moreover, corporate financing, in particular, is more vulnerable to interest rate risk.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.