Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advancement of nanosponges in pharmaceutical formulation for drug delivery systems

Through nanotechnology, nanosponges (NS) have a great impetus to develop ongoing research in drug delivery systems. In several pharmaceutical preparations, concern about the effects and regulation of transporters on drug effects can significantly contribute to our ability to predict drug variations. An ideal drug delivery system will solubilize the active medicament at the target site to decrease or cure the disease stage. NS is solid, porous tiny sponges filled with various drug molecules in their cavities for excellent drug delivery systems that play a significant role in drug delivery to specific target sites. It can improve the aqueous solubility and penurious bioavailability of drugs as they can load water and lipid-soluble drug molecules and reduce their side effects, in various dosage forms for controlled drug delivery such as oral, parenteral, topical, rectal, and inhalational dosage forms. It can also be employed as a biocatalyst carrier in drug delivery by developing drug delivery systems for enzymes, proteins, vaccines, and antibodies. The current review describes the methods of preparation of NS, types of NS, characterization, statistical design for the development of the formulation, their applications, in vitro cytotoxicity studies, recent products, patents filed in this area, and some marketed formulations of NS are all highlighted in this study.
Function of the R2R3-MYB Transcription Factors in Dalbergia odorifera and Their Relationship with Heartwood Formation

R2R3-MYB transcription factors (TFs) form one of the most important TF families involved in regulating various physiological functions in plants. The heartwood of Dalbergia odorifera is a kind of high-grade mahogany and valuable herbal medicine with wide application. However, the role of R2R3-MYB genes in the growth and development of D. odorifera, especially their relevance to heartwood formation, has not been revealed. A total of 126 R2R3-MYBs were screened from the D. odorifera genome and named DodMYB1-126 based on their location on 10 chromosomes. The collinearity results showed that purification selection was the main driving force for the evolution of the R2R3-MYB TFs family, and whole genome/fragment replication event was the main form for expanding the R2R3-MYB family, generating a divergence of gene structure and function. Comparative phylogenetic analysis classified the R2R3-MYB TFs into 33 subfamilies. S3-7,10,12-13,21 and N4-7 were extensively involved in the metabolic process; S9,13,16-19,24-25 and N1-3,8 were associated with the growth and development of D. odorifera. Based on the differential transcriptional expression levels of R2R3-MYBs in different tissues, DodMYB32, DodMYB55, and DodMYB89 were tentatively screened for involvement in the regulatory process of heartwood. Further studies have shown that the DodMYB89, localized in the nucleus, has transcriptional activation activity and is involved in regulating the biosynthesis of the secondary metabolites of heartwood by activating the promoters of the structural genes DodI2’H and DodCOMT. This study aimed to comprehensively analyze the functions of the R2R3-MYB TFs and screen for candidate genes that might be involved in heartwood formation of D. odorifera.
Synthesis and Evaluation of Some Sulfonamide-Substituted of 1,3,5-Triphenyl Pyrazoline Derivatives as Tyrosinase Enzyme Inhibitors

Pyrazoline is well-known as heterocycles compound that can exhibit many biological effects. In this work, we synthesized a series of sulfonamide-substituted 1,3,5-triphenyl pyrazoline compounds as a promising tyrosinase inhibitor agent. These compounds prepared by multicomponent reaction of corresponding aldehyde, ketone, and hydrazine using seal-vessel reactor. Pyrazolines compound were tested for their tyrosinase inhibitor activity through in vitro assay. The test result found that compound 4c, 4d, and 4e possessed better tyrosinase inhibitory activity compared to the reference drug, kojic acid. Compound 4c exhibited the strongest tyrosinase inhibitory effect with an IC50 value of 30.14 µM. The results suggested that hydroxyl and methoxy substituent at para position are preferable. Furthermore, molecular docking studies result match the pattern of in vitro assay where the compound will provide a stronger binding interaction and lower binding free energies.
Application of Pulsar-Based Navigation for Deep-Space CubeSats

This paper investigates the use of pulsar-based navigation for deep-space CubeSats. A novel approach for dealing with the onboard computation of navigational solutions and timekeeping capabilities of a spacecraft in a deep-space cruise is shown, and the related implementation and numerical simulations are discussed. The pulsar’s signal detection, processing, and exploitation are simulated for navigation onboard a spacecraft, thus showing the feasibility of autonomous state estimation in deep space even for miniaturized satellites.
Depressive and Other Adverse CNS Effects of Fluoroquinolones

Fluoroquinolones (FQs) are widely used drugs around the world. This is a result of their broad spectrum of antibacterial activity, high bioavailability, and known efficacy. Since they appeared on the market, their prescribing frequency has gradually increased. In 2011, FQs became the third most prescribed class of antibiotics in the US. Widespread use of these drugs resulted in an increasing number of reported side effects. In 2016, the FDA warned about significant side effects, including mental disorders in the form of anxiety, psychotic symptoms, insomnia, and depression. Psychiatric adverse reactions to FQs occur with a frequency of 1 to 4.4% and the mechanism of their formation is not entirely clear. It is believed that the antagonistic effect of FQs on the GABA receptor or interaction with the main receptor for the glutamatergic system—NMDA—is responsible for this. The paper is a structured review of 68 selected publications and the latest summary of CNS adverse effects that occur during FQ use. Prescribers should be aware of the risk factors for FQ toxicity, including elderly patients with underlying medical conditions or receiving concomitant medication; however, these adverse events may also occur in other groups of patients.
Genetic Algorithm Approach for Cutting Stock Problems in Construction Industries

Cutting Stock Problem (CSP) is a classic problem involving cutting long stocks into smaller products with certain quantities. The optimization is to find cutting patterns with minimum waste. In construction industries, CSP applies to steel bar cutting. The steel bar is an important element in making reinforced concrete. The length of the steel bars from the steel manufacturers is fixed, while the requirements for the constructions are varying. The problem is to find optimized way to cut long, fixed-length steel bars into smaller, varying length bars required in the constructions. The requirements are different from building to building, both in the lengths and quantities. Many studies have been extensively done on the subject, from Brute Force, Greedy Search to Linear Programming. In this paper the study focuses on Genetic Algorithm approach. The results look promising for Fitness Function 1 where the focus is to minimize waste. Waste ranges from 2.03% to 4.31%. Fitness function 2 and 3 do not emphasize merely on minimizing the waste, but also on contiguity. Therefore the residues are more, ranges from 2.21% to 4.91% for Fitness Function 2 and from 2.03% to 30.7% for Fitness Function 3
Addressing Beliefs in the Implementation of Artifical Intelligence in Visual Communication Design: Theory of Planned Behaviour Perspectives

The current situation of social development demands digitalization in all aspects of public life. The use of technology in education has some potential for improving cognitive abilities that emerge as essential benefits in the form of emotional and social perspectives. In this regard, as a foundation of cultural and learning process, education may serve as a starting point for the competent and aware deployment of Artificial Intelligence (AI). The descriptive qualitative study used to explore lecturers’ beliefs in the use of artificial intelligence for higher education especially in Visual Communication Design educational context. The data was collected through a semi structured interview with seven Visual Communication Design lecturers from state University in Surakarta. The result showed that the lecturers had positive beliefs towards the use of Artificial Intelligence in Education. According to the findings, the lecturers found that there were possibilities to have both advantages and disadvantages in the process. In doing so, the participants believed that AI was eligible to support cognitive skills although several factors can facilitate and hinder the implementation of the use of Ai in education.
A multimodal meta-analysis of gray matter alterations in trigeminal neuralgia

Background Brain gray matter alterations in patients with trigeminal neuralgia (TN) have been detected in prior neuroimaging studies, but the results are heterogeneous. The current study conducted coordinate-based meta-analyses across neuroimaging studies, aiming to find the pattern of brain anatomic and functional alterations in patients with TN. Methods We performed a systematic literature search of PubMed, Embase, and Web of Science to identify relevant publications. A multimodal meta-analysis for whole-brain voxel-based morphometry (VBM) studies and functional imaging studies in TN was performed using anisotropic effect size-based signed differential mapping. Results The meta-analysis comprised 10 VBM studies with 398 TN patients and 275 healthy controls, and 13 functional magnetic resonance imaging studies with 307 TN patients and 264 healthy controls. The multimodal meta-analysis showed conjoint structural and functional brain alterations in the right fusiform gyrus and inferior temporal gyrus, bilateral thalamus, left superior temporal gyrus, left insula, and inferior frontal gyrus. The unimodal meta-analysis showed decreased gray matter volume alone in the left putamen, left postcentral gyrus, and right amygdala as well as only functional abnormalities in the left cerebellum, bilateral precuneus, and left middle temporal gyrus. Conclusion This meta-analysis revealed overlapping anatomic and functional gray matter abnormalities in patients with TN, which may help provide new insights into the neuropathology and potential treatment biomarkers of TN.
The Canadian National Seismograph Network

The Canadian National Seismograph Network currently consists of 5 very-broadband (VBB) and 15 broadband (BB) stations across Canada, supplemented by 6 short period (SP) stations. When it is completed by the end of 1995, a further 1 VBB, 12 BB and over 40 SP stations will have been added. Data from all sites are telemetered in real time to twin network acquisition, processing and archiving centres in Eastern and Western Canada. All data are continuously archived in SEED format on optical disk and access to the most recent three days of data is provided through a mail-based AutoDRM system. Continuous data from the VBB sites are sent to the FDSN Data Management Centre approximately one month after being recorded.
Development of Web-Based Plant Sensor Tool Data Processing Application

The rapid development of technology in the 4.0 era brings changes in the use of technology. The industrial 4.0 era makes IoT (Internet of Things) media a technology that can support all sectors of activity including the agriculture or farming sector. IoT media takes place in the form of plant sensor device that can be used to measure changes in environmental parameters. These changes include light intensity, temperature, humidity, soil fertility, are the supporting factors for plant growth. This study aims to design a website-based application that can read plant sensor data. Sensor device data is collected and downloaded in CSV (Comma Separated Values) format. Data processing requires the Naïve Bayes algorithm as a method for grouping parameter data. The results of the study are in the form of a website design that can help analyze the ideal environmental parameters for farming activities.
AMPA and GABAA receptor nanodomains assemble in the absence of synaptic neurotransmitter release

Postsynaptic neurotransmitter receptors and their associated scaffolding proteins assemble into discrete, nanometer-scale subsynaptic domains (SSDs) within the postsynaptic membrane at both excitatory and inhibitory synapses. Intriguingly, postsynaptic receptor SSDs are mirrored by closely apposed presynaptic active zones. These trans-synaptic molecular assemblies are thought to be important for efficient neurotransmission because they concentrate postsynaptic receptors near sites of presynaptic neurotransmitter release. While previous studies have characterized the role of synaptic activity in sculpting the number, size, and distribution of postsynaptic SSDs at established synapses, it remains unknown whether neurotransmitter signaling is required for their initial assembly during synapse development. Here, we evaluated synaptic nano-architecture under conditions where presynaptic neurotransmitter release was blocked prior to, and throughout synaptogenesis with tetanus neurotoxin (TeNT). In agreement with previous work, neurotransmitter release was not required for the formation of excitatory or inhibitory synapses. The overall size of the postsynaptic specialization at both excitatory and inhibitory synapses was reduced at chronically silenced synapses. However, both AMPARs and GABAARs still coalesced into SSDs, along with their respective scaffold proteins. Presynaptic active zone assemblies, defined by RIM1, were smaller and more numerous at silenced synapses, but maintained alignment with postsynaptic AMPAR SSDs. Thus, basic features of synaptic nano-architecture, including assembly of receptors and scaffolds into trans-synaptically aligned structures, are intrinsic properties that can be further regulated by subsequent activity-dependent mechanisms.
Characterization and genomic analysis of an oceanic cyanophage infecting marine Synechococcus reveal a novel genus

Cyanophages play a crucial role in the biogeochemical cycles of aquatic ecosystems by affecting the population dynamics and community structure of cyanobacteria. In this study, a novel cyanophage, Nanhaivirus ms29, that infects Synechococcus sp. MW02 was isolated from the ocean basin in the South China Sea. It was identified as a T4-like phage using transmission electron microscopy. Phylogenetic analysis demonstrated that this cyanophage is distinct from other known T4-like cyanophage, belonging to a novel genus named Nanhaivirus within the family Kyanoviridae, according to the most recent classification proposed by the International Committee on Taxonomy of Viruses (ICTV). The genome of this novel cyanophage is composed of 178,866 bp of double-stranded DNA with a G + C content of 42.5%. It contains 217 potential open reading frames (ORFs) and 6 tRNAs. As many as 30 auxiliary metabolic genes (AMGs) were identified in the genome, which related to photosynthesis, carbon metabolism, nutrient uptake and stress tolerance, possibly reflecting a genomic adaption to the oligotrophic environment. Read-mapping analysis showed that Nanhaivirus ms29 mainly distributed in temperate and tropical epipelagic waters. This study enriches of the virus gene database of cyanophages and provides valuable insights into the phylogeny of cyanophages and their interactions with their hosts.
Unleashing the Potential of Bacterial Isolates from Apple Tree Rhizosphere for Biocontrol of Monilinia laxa: A Promising Approach for Combatting Brown Rot Disease

Monilinia laxa, a notorious fungal pathogen responsible for the devastating brown rot disease afflicting apples, wreaks havoc in both orchards and storage facilities, precipitating substantial economic losses. Currently, chemical methods represent the primary means of controlling this pathogen in warehouses. However, this study sought to explore an alternative approach by harnessing the biocontrol potential of bacterial isolates against brown rot in apple trees. A total of 72 bacterial isolates were successfully obtained from the apple tree rhizosphere and subjected to initial screening via co-cultivation with the pathogen. Notably, eight bacterial isolates demonstrated remarkable efficacy, reducing the mycelial growth of the pathogen from 68.75 to 9.25%. These isolates were subsequently characterized based on phenotypic traits, biochemical properties, and 16S rRNA gene amplification. Furthermore, we investigated these isolates’ production capacity with respect to two enzymes, namely, protease and chitinase, and evaluated their efficacy in disease control. Through phenotypic, biochemical, and 16S rRNA gene-sequencing analyses, the bacterial isolates were identified as Serratia marcescens, Bacillus cereus, Bacillus sp. , Staphylococcus succinus, and Pseudomonas baetica. In dual culture assays incorporating M. laxa, S. marcescens and S. succinus exhibited the most potent degree of mycelial growth inhibition, achieving 68.75 and 9.25% reductions, respectively. All the bacterial isolates displayed significant chitinase and protease activities. Quantitative assessment of chitinase activity revealed the highest levels in strains AP5 and AP13, with values of 1.47 and 1.36 U/mL, respectively. Similarly, AP13 and AP6 exhibited the highest protease activity, with maximal enzyme production levels reaching 1.3 and 1.2 U/mL, respectively. In apple disease control assays, S. marcescens and S. succinus strains exhibited disease severity values of 12.34% and 61.66% (DS), respectively, highlighting their contrasting efficacy in mitigating disease infecting apple fruits. These findings underscore the immense potential of the selected bacterial strains with regard to serving as biocontrol agents for combatting brown rot disease in apple trees, thus paving the way for sustainable and eco-friendly alternatives to chemical interventions.
Current Biomarkers for Carotid Artery Stenosis: A Comprehensive Review of the Literature

Carotid artery stenosis (CAS), an atherosclerotic disease of the carotid artery, is one of the leading causes of transient ischemic attacks (TIA) and cerebrovascular attacks (CVA). The atherogenic process of CAS affects a wide range of physiological processes, such as inflammation, endothelial cell function, smooth muscle cell migration and many more. The current gold-standard test for CAS is Doppler ultrasound; however, there is yet to be determined a strong, clinically validated biomarker in the blood that can diagnose patients with CAS and/or predict adverse outcomes in such patients. In this comprehensive literature review, we evaluated all of the current research on plasma and serum proteins that are current contenders for biomarkers for CAS. In this literature review, 36 proteins found as potential biomarkers for CAS were categorized in to the following nine categories based on protein function: (1) Inflammation and Immunity, (2) Lipid Metabolism, (3) Haemostasis, (4) Cardiovascular Markers, (5) Markers of Kidney Function, (6) Bone Health, (7) Cellular Structure, (8) Growth Factors, and (9) Hormones. This literature review is the most up-to-date and current comprehensive review of research on biomarkers of CAS, and the only review that demonstrated the several pathways that contribute to the initiation and progression of the disease. With this review, future studies can determine if any new markers, or a panel of the proteins explored in this study, may be contenders as diagnostic or prognostic markers for CAS.
DLVisor: Dynamic Learning Hypervisor for Software Defined Network

Software Defined Network (SDN) is one of the modern networking technologies that provide network flexibility and simplifies network management. Virtual SDN (vSDN) enhances the flexibility of sharing physical networking resources by multiple slices representing multiple tenants or services where each tenant has control over their services or applications over the Virtual Network (VN). Network virtualization gives service providers more flexibility to offer new and innovative services with extra efficiency and reliability. Running multiple virtual networks over a given infrastructure creates challenges for efficient resource allocation mechanisms to avoid congestion and resource starvation and to maintain Service Level Agreement (SLA), where resource management in vSDN is carried out by hypervisors. Few studies have addressed dynamic resource allocation in the vSDN domain. Therefore, to efficiently utilize the resources of the virtualized networking infrastructure, network hypervisors must be proactive with self-reconfiguration capabilities to assign the physical resources and be highly adaptable and react to changing vSDN future demands. Thus, dynamic learning-based hypervisors are to improve hypervisor operations. Based on that, this study aims to enhance the vSDN technology to provide an enhanced proactive dynamic slice resource allocation mechanism, to improve traffic delivery and resource utilization. This can be fulfilled by proposing an enhanced intelligent forecasting model for vSDN slice resource utilization based on improved statistical and Machine Learning (ML) techniques. The proposed model will react dynamically to the concept drifts and then be utilized to develop a resource allocation mechanism for vSDN slice resource allocation. The improved dynamic forecasting resource allocation mechanism is verified through available real network traces datasets from various sources. The DLVisor with its Dynamic Learning Framework (DLF) can reduce overutilization and, consequently, resource starvation by 100% compared to the related benchmark.
Clustering-Based Sector Investing

Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors. JEL classification: G12, C55, C58