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Abstract Background The bZIP transcription factor family, characterized by a bZIP domain, plays vital roles in plant stress responses and development. While this family has been extensively studied in various plant species, its specific functions in Camelina sativa (False Flax) remain underexplored. Methods and results This study identified 71 bZIP transcription factors in C. sativa, classified into nine distinct groups based on phylogenetic analysis. Subcellular localization predicted a nucleus-specific expression for these bZIPs. Analysis of GRAVY scores revealed a range from 0.469 to -1.256, indicating a spectrum from hydrophobic to hydrophilic properties. Motif analysis uncovered 10 distinct motifs, with one motif being universally present in all CsbZIPs. Conserved domain analysis highlighted several domains beyond the core bZIP domain. Protein-protein interaction predictions suggested a robust network involving CsbZIPs. Moreover, promoter analysis revealed over 60 types of cis-elements, including those responsive to stress. Expression studies through RNA-seq and Real-time RT-qPCR demonstrated high expression of CsbZIPs in roots, leaves, flowers, and stems. Specifically, CsbZIP01, CsbZIP02, CsbZIP44, and CsbZIP60 were consistently up-regulated under cold, salt, and drought stresses, whereas CsbZIP34 and CsbZIP35 were down-regulated. Conclusion This study presents the first comprehensive genome-wide profiling of bZIP transcription factors in Camelina sativa, providing novel insights into their roles in plant development and stress response mechanisms. By identifying and characterizing the bZIP gene family in C. sativa, this research offers new opportunities for improving stress tolerance and crop resilience through targeted genetic approaches, addressing key challenges in agriculture under changing environmental conditions.

OBJECTIVE: To investigate the relationship between repeated blood transfusions and growth retardation in children with transfusion-dependent Thalassemia in Peshawar, Pakistan. METHODS: This cross-sectional study was conducted on transfusion-dependent thalassemia patients receiving treatment at the Fatimid Foundation, Peshawar Pakistan, between February and August 2022. Participants were categorized by age (2 years) to assess and measure growth patterns over six months. RESULTS: Out of 93 children with thalassemia major, 55 (59.1%) males and 38 (40.9%) females. Mean age of the participants was 10.86 ± 5.72 years, and mean age at their first transfusion was 8.13 ± 5.78 months. Mean body mass index was 16.38 ± 1.82 kg/m². Short stature was observed in 49 patients (52.7%), of whom 57.1% (n=28) were male and 42.9% (n=21) were female. Serum ferritin levels were significantly elevated in patients with short stature, with 57.1% (n=28/49) having ferritin levels >4001 μg/L compared to 40.9% (n=18/44) with normal stature. Only 3.2% of patients had normal ferritin levels. The ROC analysis identified a ferritin cut-off of 1636 μg/L for predicting short stature (sensitivity 86%, specificity 68%). Growth assessment showed that 71% of the children were

Anterior mandible defects result in loss of support for the tongue, floor of the mouth and lower lip, resulting in impairment of airway, feeding, and speech. We treated four patients with these “Andy Gump” deformities. Reconstruction was performed with two free flaps: a fibula osteocutaneous flap for the anterior mandible and floor of the mouth, and a soft tissue free flap for the lip, chin, and anterior neck. The lower lip was suspended cranially with fascia or tendon grafts ± mini-temporalis turndown flaps. All flaps survived completely. All patients were tube feed-dependent before surgery; they all resumed an oral diet. All tracheostomies were decannulated. Lip competence was restored as evidenced by cessation of drooling. Speech improved from unintelligible to intelligible with frequent repetitions. Objective assessment was performed with the functional intraoral Glasgow scale; the mean FIGS score improved from 3.25 (range 3–4) to 11 (range 9–13). We conclude that composite anterior mandible and tongue defects have large tissue requirements that require multiple free flaps. Reconstruction leads to significant improvement in function.

Abstract In patients with relapsed DLBCL in complete remission (CR), autologous hematopoietic cell transplantation (auto-HCT) and CAR-T therapy are both effective, but it is unknown which modality provides superior outcomes. We compared the efficacy of auto-HCT vs. CAR-T in patients with DLBCL in a CR. A retrospective observational study comparing auto-HCT (2015–2021) vs. CAR-T (2018–2021) using the Center for International Blood & Marrow Transplant Research registry. Median follow-up was 49.7 months for the auto-HCT and 24.7 months for the CAR-T cohort. Patients ages 18 and 75 with a diagnosis of DLBCL were included if they received auto-HCT (n = 281) or commercial CAR-T (n = 79) while in a CR. Patients undergoing auto-HCT with only one prior therapy line and CAR-T patients with a previous history of auto-HCT treatment were excluded. Endpoints included Progression-free survival (PFS), relapse rate, non-relapse mortality (NRM) and overall survival (OS). In univariate analysis, treatment with auto-HCT was associated with a higher rate of 2-year PFS (66.2% vs. 47.8%; p

Abstract Background Treatment with regorafenib, a multiple-kinase inhibitor, to manage metastatic colorectal cancers (mCRCs) shows a modest improvement in overall survival but is associated with severe toxicities. Thus, to reduce regorafenib-induced toxicity, we used regorafenib at low concentration along with a dual JAK/HDAC small-molecule inhibitor (JAK/HDACi) to leverage the advantages of both JAK and HDAC inhibition to enhance antitumor activity. The therapeutic efficacy and safety of the combination treatment was evaluated with CRC models. Methods The cytotoxicity of JAK/HDACi, regorafenib, and their combination were tested with normal colonic and CRC cells exhibiting various genetic backgrounds. Kinomic, ATAC-seq, RNA-seq, cell cycle, and apoptosis analyses were performed to evaluate the cellular functions/molecular alterations affected by the combination. Efficacy of the combination was assessed using patient-derived xenograft (PDX) and experimental metastasis models of CRC. To evaluate the interplay between tumor, its microenvironment, and modulation of immune response, MC38 syngeneic mice were utilized. Results The combination therapy decreased cell viability; phosphorylation of JAKs, STAT3, EGFR, and other key kinases; and inhibited deacetylation of histone H3K9, H4K8, and alpha tubulin proteins. It induced cell cycle arrest at G0-G1 phase and apoptosis of CRC cells. Whole transcriptomic analysis showed that combination treatment modulated molecules involved in apoptosis, extracellular matrix-receptor interaction, and focal adhesion pathways. It synergistically reduces PDX tumor growth and experimental metastasis, and, in a syngeneic mouse model, the treatment enhances the antitumor immune response as evidenced by higher infiltration of CD45 and cytotoxic cells. Pharmacokinetic studies showed that combination increased the bioavailability of regorafenib. Conclusions The combination treatment was more effective than with regorafenib or JAK/HDACi alone, and had minimal toxicity. A clinical trial to evaluate this combination for treatment of mCRCs is warranted.

The Central Asian (CA) countries have been transitioning from a centrally planned economy to a market economy since they gained independence at the beginning of the 1990s. As per the official data for 2022, Uzbekistan, which is located in the center of the CA region, has the Russian Federation (18.6%), China (17.8%), Kazakhstan (9.2%), and Turkiye (6.4%) as its top trading partners. This study evaluates the technological progress by estimating the total factor productivity (TFP) of the CA countries. The study uses data from Penn World Table 10.0 (PWT10.0), covering the period from 1991 to 2019. The main findings are as follows: 1) CA countries have shown steady development and noteworthy TFP growth rates; 2) TFP growth was negative in all countries from 1992 to 1997, except in China and Turkiye. But in terms of TFP growth, they outpaced even China and Turkiye between 1998 and 2007, maintaining high rates from 2008 to 2016, demonstrating the catch-up effect; 3) our estimates of TFP growth rates are consistent with figures reported by several other studies. 4) the most suitable econometric model is found to be generalized least squares (GLS) compared with pooled OLS (pOLS), fixed effects (FE), or random effects (RE).

Cancer remains a leading cause of mortality, with non-small cell lung cancer (NSCLC) being a primary contributor to cancer-related deaths. Traditional treatment strategies such as chemotherapy, radiation, and hormone therapy often present challenges, including severe side effects, drug resistance, and toxicity. Recent advancements in nanotechnology aim to enhance the effectiveness of cancer therapies by targeting drugs selectively and specifically to tumor cells. Among these innovations, exosomes, or small extracellular vesicles (EVs), have emerged as promising carriers for drug delivery due to their natural origin and ability to encapsulate both small molecules and biologics. This study explores the use of exosomes derived from camel milk in Hail, Saudi Arabia, as a vehicle for delivering curcumin (CUR), a polyphenol with known chemopreventive properties but limited bioavailability. Camel milk was processed to isolate exosomes through differential centrifugation, followed by characterization using dynamic light scattering, zeta potential measurements, and Western blot analysis to confirm exosomal markers. The encapsulation of CUR into camel milk-derived exosomes demonstrated a 20% loading efficiency as analyzed by UPLC. In vitro antiproliferative assays revealed that the exosomal formulation of CUR (ExoCUR) significantly enhanced cytotoxicity against drug -sensitive (A549) and taxol-resistant (A549TR) lung cancer cells compared to free CUR. Molecular docking studies and molecular dynamics simulations indicated that CUR has a strong binding affinity for the epidermal growth factor receptor (EGFR), comparable to the established drug gefitinib. Furthermore, CUR effectively downregulated EGFR and STAT3 expression in cancer cells, suggesting its potential to disrupt key signaling pathways involved in tumor progression. Our findings highlight the potential of camel milk-derived exosomes as an effective and biocompatible delivery system for CUR, offering a promising strategy to overcome the limitations of current cancer therapies and enhance the therapeutic efficacy of chemopreventive agents.

The bromodomain and extraterminal (BET) family of proteins show altered expression across various cancers. The members of the bromodomain (BRD) family contain epigenetic reader domains that bind to acetylated lysine residues in both histone and non-histone proteins. Since BRD proteins are involved in cancer initiation and progression, therapeutic targeting of these proteins has recently been an area of interest. In experimental settings, JQ1, a commonly used BRD inhibitor, is the first known inhibitor to target BRD-containing protein 4 (BRD4), a ubiquitously expressed BRD and extraterminal family protein. BRD4 is necessary for a normal cell cycle, and its aberrant expression activates pro-inflammatory cytokines, leading to tumor initiation and progression. Various BRD4 inhibitors have been developed recently and tested in preclinical settings and are now in clinical trials. However, as with many targeted therapies, BRD inhibitor treatment can lead to resistance to treatment. Here, we investigated the kinases up-regulated on JQ1 treatment that may serve as target for combination therapy along with BRD inhibitors. To identify kinase targets, we performed a comparative analysis of gene expression data using RNA from BRD inhibitor-treated cells or BRD-modulated cells and identified overexpression of several kinases, including FYN, NEK9, and ADCK5. We further validated, by immunoblotting, the overexpression of FYN tyrosine kinase; NEK9 serine/threonine kinase and ADCK5, an atypical kinase, to confirm their overexpression after BRD inhibitor treatment. Importantly, our studies show that targeting FYN or NEK9 along with BRD inhibitor effectively reduces proliferation of cancer cells. Therefore, our research emphasizes a potential approach of utilizing inhibitors targeting some of the overexpressed kinases in conjunction with BRD inhibitors to enhance therapeutic effectiveness.

Alzheimer's is one of the brain syndromes that steadily affects the brain memory. The early stage of Alzheimer's disease (AD) is referred to as mild cognitive impairment (MCI), and the growth of Alzheimer's is not certain in patients with MCI. The premature detection of Alzheimer's is crucial for maintaining healthy brain function and avoiding memory loss. Different multi-neural network architectures have been proposed by researchers for efficient and accurate AD detection. The absence of improved feature extraction mechanisms and unexplored efficient optimizers in complex benchmark architectures lead to an inefficient and inaccurate AD classification. Moreover, the standard convolutional neural network (CNN)-based architectures for Alzheimer's diagnosis lack interpretability in their predictions. An interpretable, simplified, yet effective deep learning model is required for the accurate classification of AD. In this study, a generalized fractional order-based CNN classifier with explainable artificial intelligence (XAI) capabilities is proposed for accurate, efficient, and interpretable classification of AD diagnosis. The proposed study (a) classifies AD accurately by incorporating unexplored pooling technique with enhanced feature extraction mechanism, (b) provides fractional order-based optimization approach for adaptive learning and fast convergence speed, and (c) suggests an interpretable method for proving the transparency of the model. The proposed model outperforms complex benchmark architectures with regard to accuracy using standard ADNI dataset. The proposed fractional order-based CNN classifier achieves an improved accuracy of 99 % as compared to the state-of-the-art models.

Abstract Background The aetiology of delirium is not known, but pre-existing cognitive impairment is a predisposing factor. Here we explore the associations between delirium and cerebrospinal fluid (CSF) levels of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs), proteins with important roles in both acute injury and chronic neurodegeneration. Methods Using a 13-plex Discovery Assay®, we quantified CSF levels of 9 MMPs and 4 TIMPs in 280 hip fracture patients (140 with delirium), 107 cognitively unimpaired individuals, and 111 patients with Alzheimer’s disease dementia. The two delirium-free control groups without acute trauma were included to unravel the effects of acute trauma (hip fracture), dementia, and delirium. Results Here we show that delirium is associated with higher levels of MMP-2, MMP-3, MMP-10, TIMP-1, and TIMP-2; a trend suggests lower levels of TIMP-4 are also associated with delirium. Most delirium patients had pre-existing dementia and low TIMP-4 is the only marker associated with delirium in adjusted analyses. MMP-2, MMP-12, and TIMP-1 levels are clearly higher in the hip fracture patients than in both control groups and several other MMP/TIMPs are impacted by acute trauma or dementia status. Conclusions Several CSF MMP/TIMPs are significantly associated with delirium in hip fracture patients, but alterations in most of these MMP/TIMPs could likely be explained by acute trauma and/or pre-fracture dementia. Low levels of TIMP-4 appear to be directly associated with delirium, and the role of this marker in delirium pathophysiology should be further explored.

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts—with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks—significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Abstract Background Acacia nilotica Linn. is a widely distributed tree known for its applications in post-harvest and medicinal horticulture. However, its seed-based growth is relatively slow. Seed is a vital component for the propagation of A. nilotica due to its cost-effectiveness, genetic diversity, and ease of handling. Colchicine, commonly used for polyploidy induction in plants, may act as a pollutant at elevated levels. Its optimal concentration for Acacia nilotica's improved growth and development has not yet been determined, and the precise mechanism underlying this phenomenon has not been established. Therefore, this study investigated the impact of optimized colchicine (0.07%) seed treatment on A. nilotica's morphological, anatomical, physiological, fluorescent, and biochemical attributes under controlled conditions, comparing it with a control. Results Colchicine seed treatment significantly improved various plant attributes compared to control. This included increased shoot length (84.6%), root length (53.5%), shoot fresh weight (59.1%), root fresh weight (42.8%), shoot dry weight (51.5%), root dry weight (40%), fresh biomass (23.6%), stomatal size (35.9%), stomatal density (41.7%), stomatal index (51.2%), leaf thickness (11 times), leaf angle (2.4 times), photosynthetic rate (40%), water use efficiency (2.2 times), substomatal CO2 (36.6%), quantum yield of photosystem II (13.1%), proton flux (3.1 times), proton conductivity (2.3 times), linear electron flow (46.7%), enzymatic activities of catalase (25%), superoxide dismutase (33%), peroxidase (13.5%), and ascorbate peroxidase (28%), 2,2-diphenyl-1-picrylhydrazyl-radical scavenging activities(23%), total antioxidant capacity (59%), total phenolic (23%), and flavonoid content (37%) with less number of days to 80% germination (57.1%), transpiration rate (53.9%), stomatal conductance (67.1%), non-photochemical quenching (82.8%), non-regulatory energy dissipation (24.3%), and H2O2 (25%) and O−2 levels (30%). Conclusion These findings elucidate the intricate mechanism behind the morphological, anatomical, physiological, fluorescent, and biochemical transformative effects of colchicine seed treatment on Acacia nilotica Linn. and offer valuable insights for quick production of A. nilotica’s plants with modification and enhancement from seeds through an eco-friendly approach.

Abstract Background Potato serves as a major non-cereal food crop and income source for small-scale growers in Punjab, Pakistan. Unfortunately, improper fertilization practices have led to low crop yields, worsened by challenging environmental conditions and poor groundwater quality in the Cholistan region. To address this, we conducted an experiment to assess the impact of two fertilizer application approaches on potato cv. Barna using plant growth-promoting bacteria (PGPB) coated biofertilizers. The first approach, termed conventional fertilizer application (CFA), involved four split applications of PGPB-coated fertilizers at a rate of 100:75 kg acre–1 (N and P). The second, modified fertilizer application (MFA), employed nine split applications at a rate of 80:40 kg acre–1. Results The MFA approach significantly improved various plant attributes compared to the CFA. This included increased plant height (28%), stem number (45%), leaf count (46%), leaf area index (36%), leaf thickness (three-folds), chlorophyll content (53%), quantum yield of photosystem II (45%), photosynthetically active radiations (56%), electrochromic shift (5.6%), proton flux (24.6%), proton conductivity (71%), linear electron flow (72%), photosynthetic rate (35%), water use efficiency (76%), and substomatal CO2 (two-folds), and lowered non-photochemical quenching (56%), non-regulatory energy dissipation (33%), transpiration rate (59%), and stomatal conductance (70%). Additionally, the MFA approach resulted in higher tuber production per plant (21%), average tuber weight (21.9%), tuber diameter (24.5%), total tuber yield (29.1%), marketable yield (22.7%), seed-grade yield (9%), specific gravity (9.6%), and soluble solids (7.1%). It also reduced undesirable factors like goli and downgrade yields by 57.6% and 98.8%, respectively. Furthermore, plants under the MFA approach exhibited enhanced nitrogen (27.8%) and phosphorus uptake (40.6%), with improved N (26.1%) and P uptake efficiency (43.7%) compared to the CFA approach. Conclusion The use of PGPB-coated N and P fertilizers with a higher number of splits at a lower rate significantly boosts potato production in the alkaline sandy soils of Cholistan.

In many underdeveloped countries, textile industries discharge their effluents without any treatment. The untreated textile wastewater consists of both azo dyes and heavy metals which not only change the physicochemical and biological properties of soil and water but also affect human health. In recent years, copper-based nanomaterials have attained worldwide attention because of their unique properties and potential for decolorization of azo dyes and wastewater treatment. The present study demonstrates the bacterial synthesis of copper nanoparticles (Cu-NPs) using Bacillus flexus strain isolated from a textile wastewater followed by their application for photocatalytic degradation of various azo dyes and treatment of actual wastewater. The FT-IR analysis confirmed the presence of various functional groups including proteins on Cu-NPs which improved their stability. The scanning electron microscopy (SEM) images revealed the spherical shape of Cu-NPs with a size of range between 17–34 nm. Similarly, the XRD analysis of biosynthesized Cu-NPs showed various diffraction peaks at 44.5°, 51.5°, 74.75 which confirmed the crystalline nature of nanoparticles. While studying the photocatalytic decolorization of azo dyes by Cu-NPs, it was observed that 88.164 ± 0.19 %, 88.452 ± 1.89 %, 90.433 ± 1.81 %, 64.770 ± 1.02 %, 46.774 ± 1.61 %, and 67.274 ± 2.89 % of reactive black-5, congo red, malachite green, methylene blue, reactive red-2 and blue direct, respectively, were decolorized after 4 h of solar irradiation at 50 ppm concentration. Additionally, the biosynthesized nanoparticles also resulted in reduction of various parameters like EC, pH, TDS, COD, color intensity, sulphates, and phosphates in the textiles wastewater. The reduction of COD, sulfates, and phosphates was about 39.659 %, 43.157 %, and 49.493 %. The results of current work suggest that biosynthesized copper nanoparticles might serve as a potential green solution for the decolorization of various dyes including wastewater treatment.

Introduction The optimal surgical approach for malleolar fractures and distal tibiofibular syndesmotic (DTFS) disruption remains controversial. There is no uniform treatment protocol for this type of injury. The objective was to review modern surgical treatments of the pathology and determine the optimal option. Material and methods Articles of French, English, Uzbek, Kazakh, German, Danish, Japanese and Chinese authors were retrospectively reviewed. An internet search of MedLine; PubMed; Scopus; Web of Science, CINAHL, the Cochrane Central Register of Controlled Trials databases was performed. Results Comparative studies of dynamic fixation and static fixation of the DTFS showed advantages of the dynamic methods enabling precise, anatomical syndesmotic fixation and faster healing. Dynamic fixation methods would require no implant removal, while syndesmotic screw woul be taken off to reduce compression in the ankle joint and minimize a risk of malreduction facilitating mobility of the ankle joint. Dynamic methods are associated with greater stability and less complication rate. However, static methods have the advantages of being more accessible and less expensive, which can be an important factor choosing a treatment method. Static methods are a wide application and can be used in a wide range of clinical cases. Long-term results show no statistically significant differences between dynamic fixation and static fixation. Discussion Literature review indicates the dynamic method with suture-button, a combined method and titanium cable isotonic annular fixation system as the preferred technique for surgical stabilization of distal syndesmosis associated with ankle fractures with a lower risk of postoperative complications and the possibility of short-term rehabilitation. Conclusion The choice between dynamic and static methods of distal syndesmosis fixation depends on many factors, including the complexity of the injury, the availability and cost of implants and the experience of the surgeon.

The dynamic landscape of cloud computing design presents significant challenges regarding power consumption and quality of service (QoS). Virtual machine (VM) consolidation is essential for reducing power usage and enhancing QoS by relocating VMs between hosts. OpenStack Neat, a leading framework for VM consolidation, employs the Modified Best-Fit Decreasing (MBFD) VM placement technique, which faces issues related to energy consumption and QoS. To address these issues, we propose an Energy Efficient VM Consolidation (EEVMC) approach. Our method introduces a novel host selection criterion based on the incurred loss during VM placement to identify the most efficient host. For validation, we conducted simulations using real-time workload traces from Planet-Lab and Materna over ten days, leveraging the latest CloudSim toolkit to compare our approach with state-of-the-art techniques. For Planet-Lab’s workload, our EEVMC approach shows a reduction in energy consumption by 80.35%, 59.76%, 21.59%, and 7.40%, and fewer system-level agreement (SLA) violations by 94.51%, 94.85%, 47.17%, and 17.78% when compared to Modified Best-Fit Decreasing (MBFD), Power-Aware Best Fit Decreasing (PABFD), Medium Fit Power Efficient Decreasing (MFPED), and Power-Efficient Best-Fit Decreasing (PEBFD), respectively. Similarly, for Materna, EEVMC achieves a reduction in energy consumption by 16.10%, 61.0%, 4.94%, and 4.82%, and fewer SLA violations by 76.99%, 88.88%, 12.50%, and 48.65% against the same benchmarks. Additionally, Loss-Aware Performance Efficient Decreasing (LAPED) significantly reduces the total number of VM migrations and SLA time per active host, indicating a substantial improvement in cloud computing efficiency.

In present-day data communication, the imperative to safeguard information and shield it from a multitude of potential threats and attacks has gained significant prominence. One of the pivotal tools employed in the pursuit of data protection is the design of robust substitution boxes (S-Boxes). These S-Boxes often find their roots in the realm of chaotic maps, leveraging the inherent unpredictability and complexity of these mathematical constructs. An S-Box is an integral and critical component in the design of modern ciphers offering a means to enhance the security of data by introducing non-linearity and confusion into the encryption process. In this research endeavor, we propose the development of a novel chaotic map to create an S-Box that exhibits a notably high nonlinear score making it more formidable for potential adversaries to decrypt the ciphertext. To substantiate the efficacy of the proposed S-Box, a comprehensive analysis is done by comparing its performance using standard criteria including nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), presence of fixed points (FP), linear probability (LP), differential probability (DP), etc. to other state-of-the-art S-Boxes. The comparative analysis reinforces the security fortitude of the proposed S-Box in meeting the rigorous demands of modern data security and presents a vital contribution to the ever-evolving field of cryptographic techniques and data protection in the digital age.

With the advancements in space technology and the development of lightweight synthetic aperture radar (SAR) satellites by commercial companies, such as ICEYE, Capella Space and Umbra, SAR images have become available on a wide scale. Ship detection is a classic problem in the interpretation and analysis of satellite images and has its significance both in maritime as well as defense applications. In the case of SAR images, ship detection becomes even more challenging due to the presence of large-scale distortions as well as interclass similarity signature problem. Moreover, the state-of-the-art (SOTA) object detection models have weak generalization capability over SAR datasets. To overcome these challenges, we propose a You Only Look Once (YOLO)-based optimized ship detection model called YOLO-OSD. Our optimized ship detector is based on a hybrid data-model centric approach, which utilizes the statistical characteristics of the datasets under observation and has an efficient model architecture. We also carry out a detailed comparative analysis of our proposed model with other SOTA deep learning models on three well-known publicly available datasets. Our results show that the proposed YOLO-OSD outperforms YOLO5, YOLO7, and RetinaNet on all datasets under observation in terms of F1 score and mean average precision. YOLO-OSD also has approximately 16% fewer network parameters as compared with the original YOLO5. Moreover, our proposed model is at least 37.7% faster than YOLO7 and 41.02% faster than the YOLO8 model in terms of training time and thus suitable for real-time satellite-based SAR ship detection.

The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators. Our results showcase enhanced malware detection performance, yielding a 10% performance boost, thus affirming the efficacy of our approach compared to existing models.

Objective: To evaluate the mechanical, biological, and antibacterial properties of copper doped calcium phosphate (Cu-CaP) modified dental composites. Study Design: Cross-sectional study. Place and Duration of Study: The study was carried out at the Department of Dental Materials, Khyber Medical University Peshawar, Pakistan from February 2023 to January 2024. Methods: The process involved adding ethyl alcohol, distilled water, silane, and octyltriethoxysilane to powdered silica, refluxing, centrifuging, washing, and preparing copper oxide nanoparticles and copper doped calcium phosphate. Composite was prepared using TEGDMA i.e. Triethylene glycol dimethacrylate, camphor quinone, and dimethyl aminoethyl methacrylate. Results: The microscopic image of Copper oxide nanoparticles were gathered together with agglomerated structure, irregular shapes having voids, and size range of 80 nm to 5 μm. The shape was irregular with void spaces and composition was confirmed with Edx analysis. Scanning electron microscope image of copper doped calcium phosphate, shows a build-up of nanometric needles over entire base, with asymmetrical form and translucent appearance, ranging from 10 nm to 50 nm. The Energy dispersive X-ray (EDX) analysis demonstrated the presence of materials. The Fourier transmitted infrared microscopy of silica and salinized silica showed characteristics peaks of salinized silica at 1016 cm-1 and 1439 cm-1 with a main peak at 950 cm-1, while the Fourier Transform Infrared Spectroscopy of calcium phosphate, 2%, 4%, and 6% copper doped calcium phosphate in dental composite showed charcateristics peak at 1020 cm-1 which was due to increase of phosphates upon increasing copper doped calcium phosphate concentration. The compressive strength results of native dental composite and 2%, 4%, and 6% copper doped calcium phosphate in dental composite have values of 15.6 MPa, 18.72 MPa, 20.2 MPa, 49.15MPa while the flexural strength were of value 19.32MPa (dental composite) and that of 2%,4% and 6% have a value of 22.2MPa, 26.1MPa and 30.3MPa. The control group i.e. the dental composite had Vickers hardness of 14.55 MPa, while the experimental groups with 2% , 4%, and 6% copper doped calcium phosphate had values of 16.97 MPa, 21.49 MPa and 26.5 MPa respectively. Conclusion: The Vickers's hardness, flexural strength, and compressive strength of experimental dental composite increased with an increasing percentage of copper doped calcium phosphate added as a filler in a proportion of 2%, 4% and 6% with salinized silica in the dental composite. How to cite this: Ahmad W, Ahmad F, Babar BZ, Khan A, Khalid S, Ahmad SA. Effect of Copper Dope Calcium Phosphate on Mechanical Properties of Modified Dental Composites. Life and Science. 2024; 5(4): 530-537. doi: http://doi.org/10.37185/LnS.1.1.679

Background Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and Methodology This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. Results The proposed CNN model achieves high accuracy demonstrates the model’s effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model’s efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets. Conclusion Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.

Abstract Background Green chili is the predominant vegetable in tropical and subtropical regions with high economic value. However, after harvest, it exhibits vigorous metabolic activities due to the high moisture level, leading to a reduction in bioactive compounds and hence reduced shelf life and nutritional quality. Low temperature storage results in the onset of chilling injury symptoms. Therefore, developing techniques to increase the shelf life of green chilies and safeguard their nutritional value has become a serious concern for researchers. In this regard, an experiment was conducted to evaluate the impact of the alone or combined application of hot water treatment (HWT) (45 °C for 15 min) and eucalyptus leaf extract (ELE) (30%) on 'Golden Hot' chilies in comparison to the control. After treatment, chilies were stored at 20 ± 1.5 °C for 20 days. Results HWT + ELE-treated chilies had a significant reduction in fruit weight loss (14.6%), fungal decay index (35%), red chili percentage (41.2%), soluble solid content (42.9%), ripening index (48.9%), and reactive oxygen species production like H2O2 (55.1%) and O−2 (46.5%) during shelf in comparison to control, followed by the alone application of HWT and ELE. Furthermore, the combined use of HWT and ELE effectively improved the antioxidative properties of stored chilies including DPPH radical scavenging activities (54.6%), ascorbic acid content (28.4%), phenolic content (31.8%), as well as the enzyme activities of POD (103%), CAT (128%), SOD (26.5%), and APX (43.8%) in comparison to the control. Additionally, the green chilies underwent HWT + ELE treatment also exhibited higher chlorophyll levels (100%) and general appearance (79.6%) with reduced anthocyanin content (40.8%) and wrinkling (43%), leading to a higher marketable fruit (41.3%) than the control. Conclusion The pre-storage application of HWT and ELE could be used as an antimicrobial, non-chemical, non-toxic, and eco-friendly treatment for preserving the postharvest quality of green chilies at ambient temperature (20 ± 1.5 °C).

Breast cancer remains a leading cause of death worldwide. The Apiaceae plant family includes many culinary spices that have been shown to have medicinal properties. Many phytochemicals exhibit potent bioactivities but often suffer from poor uptake and oral bioavailability. Bovine milk and colostrum exosomes are a compelling drug delivery platform that could address this issue; these natural nanoparticles can be loaded with hydrophilic and lipophilic small molecules and biologics, resulting in lower doses needed to inhibit cancer growth. Ethanolic extracts of eight Apiaceae spices were examined for phytochemical content and antiproliferative potential. Acid hydrolysis (AH) was employed to remove glycosides, asses its impacts on extract efficacy, and evaluate its effects on exosome loading and subsequent formulation efficacy. Antiproliferative activity was assessed through MTT assays on T-47D, MDA-MB-231, and BT-474 breast cancer cells; all extracts exhibited broad antiproliferative activity. AH enhanced the bioactivity of cumin, caraway, and fennel in T-47D cells. Celery, cumin, anise, and ajwain showed the highest activity and were assayed in exosomal formulations, which resulted in reduced doses required to inhibit cellular proliferation for all extracts except AH-cumin. Apiaceae spice extracts demonstrated antiproliferative activities that can be improved with AH and further enhanced with exosomal delivery.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.

Abstract The development of an efficient, safe, and environment-friendly technique to terminate tuber dormancy in potatoes (Solanum tuberosum L.) is of great concern due to the immense scope of multiple cropping all over the globe. The breakage of tuber dormancy has been associated with numerous physiological changes, including a decline in the level of starch and an increase in the levels of sugars during storage of freshly harvested seed potatoes, although their consistency across genotypes and various dormancy-breaking techniques have not yet been fully elucidated. The purpose of the present research is to assess the efficacy of four different dormancy-breaking techniques, such as soaking in 90, 60, or 30 mg L−1 solutions of benzyl amino purine (BAP) and 30, 20, or 10 mg L−1 gibberellic acid (GA3) alone and in the combination of optimized concentrations; cold pre-treatment at 6, 4, or 2 °C; electric shock at 80, 60, 40, or 20 Vs; and irradiation at 3.5, 3, 2.5, 2, 1.5, or 1 kGy on the tuber dormancy period and sprout length of six genotypes. Furthermore, the changes that occurred in tuber weight and endogenous starch, sucrose, fructose, and glucose contents in experimental genotypes following the application of these techniques were also examined. Overall, the most effective technique to terminate tuber dormancy and hasten spout growth was the combined application of BAP and GA3, which reduced the length of dormancy by 9.6 days compared to the untreated control, following 6.7 days of electric current, 4.4 days of cold pre-treatment, and finally irradiation (3.3 days). The 60 mg L−1 solution of BAP greatly reduced the dormancy period in all genotypes but did not affect the sprout length at all. The genotypes showed a weak negative correlation (r = − 0.4) (P

Abstract Overview Dengue fever, a viral illness transmitted by the Aedes mosquito, is capable of causing a range of serious complications, including fulminant hepatic failure, renal dysfunction, encephalitis, encephalopathy, neuromuscular and ophthalmic disorders, seizures, and cardiomyopathy. Case description This report details the case of a 30-year-old lactating woman with no notable medical history who presented to the emergency department with symptoms of high-grade fever, altered mental status, and seizures. Upon imaging, bilateral infarcts in the thalami and cerebellar hemispheres were observed, consistent with cerebellitis and dengue encephalitis. Patient treatment and outcome The patient was admitted to the intensive care unit and received appropriate treatment. Following a critical phase and successful patient stabilization, she was transferred to a high dependency unit for a week before being discharged with recommendations for follow-up care. Conclusion This case illustrates the broad spectrum of complications that can arise as a result of dengue infection and the importance of timely diagnosis and management in improving patient outcomes. Further investigation is required to better understand the mechanisms underlying these complications and to formulate specific guidelines for the prevention and treatment of dengue shock syndrome.

Abstract Introduction Out‐of‐hospital cardiac arrest (OHCA) is defined as the loss of functional mechanical activity of the heart in association with an absence of systemic circulation, occurring outside of a hospital. Immediate coronary angiography (CAG) with percutaneous coronary intervention is recommended for OHCA with ST‐elevation. We aimed to evaluate the effect of early CAG on mortality and neurological outcomes in OHCA patients without ST‐elevation. Methods This meta‐analysis and systemic review was conducted as per principles of Preferred Reporting Items for Systematic Reviews and Meta‐analysis (PRISMA) group. A protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, Ref No. = CRD42022327833). A total of 674 studies were retrieved after scanning several databases (PubMed Central, EMBASE, Medline, and Cochrane Central Register of Controlled Trials). Results A total of 18 studies were selected for the final analysis, including 6 randomized control trials and 12 observational studies. Statistically, there was no significant difference in primary outcome, i.e., mortality, between early and delayed CAG. In terms of the grade of neurological recovery as a secondary outcome, early and delayed CAG groups also showed no statistically significant difference. Conclusion Early CAG has no survival benefits in patients with no ST elevations on ECG after OHCA.

Background: Fluorescence labeled DNA probes and in situ hybridization methods had shorter turn round time for results revolutionized their clinical application. Signals obtained from these probes are highly specific, yet they can produce fusion signals not necessarily representing fusion of actual genes due to other genes included in the probe design. In this study we evaluated discordance between cytogenetic, FISH and RNAseq results in 3 different patients with hematologic malignancies and illustrated the need to perform next generation sequencing (NGS) or RNASeq to accurately interpret FISH results. Methods: Bone marrow or peripheral blood karyotypes and FISH were performed to detect recurring translocations associated with hematologic malignancies in clinical samples routinely referred to our clinical cytogenetics laboratory. When required, NGS was performed on DNA and RNA libraries to detect somatic alterations and gene fusions in some of these specimens. Discordance in results between these methods is further evaluated. Results: For a patient with plasma cell leukemia standard FGFR3 / IGH dual fusion FISH assay detected fusion that was interpreted as FGFR3 -positive leukemia, whereas NGS/RNASeq detected NSD2::IGH . For a pediatric acute lymphoblastic leukemia patient, a genetic diagnosis of PDGFRB -positive ALL was rendered because the PDGFRB break-apart probe detected clonal rearrangement, whereas NGS detected MEF2D::CSF1R . A MYC -positive B-prolymphocytic leukemia was rendered for another patient with a cytogenetically identified t(8;14) and MYC::IGH by FISH, whereas NGS detected a novel PVT1::RCOR1 not previously reported. Conclusions: These are 3 cases in a series of several other concordant results, nevertheless, elucidate limitations when interpreting FISH results in clinical applications, particularly when other genes are included in probe design. In addition, when the observed FISH signals are atypical, this study illustrates the necessity to perform complementary laboratory assays, such as NGS and/or RNASeq, to accurately identify fusion genes in tumorigenic translocations.

Multidrug-resistant organisms are bacteria that are no longer controlled or killed by specific drugs. One of two methods causes bacteria multidrug resistance (MDR); first, these bacteria may disguise multiple cell genes coding for drug resistance to a single treatment on resistance (R) plasmids. Second, increased expression of genes coding for multidrug efflux pumps, which extrude many drugs, can cause MDR. Antibiotic resistance is a big issue since some bacteria may withstand almost all antibiotics. These bacteria can cause serious sickness, making them a public health threat. Methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), Multidrug resistant Mycobacterium tuberculosis (TB), and CRE are gut bacteria that resist antibiotics. Antimicrobial resistance is rising worldwide, increasing clinical and community morbidity and mortality. Superbugs have made antibiotic resistance in some environmental niches even harder to control. This study introduces new medicinal plants, gene-editing methods, nanomaterials, and bacterial vaccines that will fight MDR bacteria in the future.

Thyroid hormone receptor-interacting protein 13 (TRIP13) is involved in cancer progression, but its role in pancreatic ductal adenocarcinoma (PDAC) is unknown. Thus, we assessed the expression, functional role, and mechanism of action of TRIP13 in PDAC. We further examined the efficacy of TRIP13 inhibitor, DCZ0415, alone or in combination with gemcitabine on malignant phenotypes, tumor progression, and immune response. We found that TRIP13 was overexpressed in human PDACs relative to corresponding normal pancreatic tissues. TRIP13 knockdown or treatment of PDAC cells with DCZ0415 reduced proliferation and colony formation, and induced G2/M cell cycle arrest and apoptosis. Additionally, TRIP13 knockdown or targeting with DCZ0415 reduced the migration and invasion of PDAC cells by increasing E-cadherin and decreasing N-cadherin and vimentin. Pharmacologic targeting or silencing of TRIP13 also resulted in reduce expression of FGFR4 and STAT3 phosphorylation, and downregulation of the Wnt/β-catenin pathway. In immunocompromised mouse models of PDAC, knockdown of TRIP13 or treatment with DCZ0415 reduced tumor growth and metastasis. In an immunocompetent syngeneic PDAC model, DCZ0415 treatment enhanced the immune response by lowering expression of PD1/PDL1, increasing granzyme B/perforin expression, and facilitating infiltration of CD3/CD4 T-cells. Further, DCZ0415 potentiated the anti-metastatic and anti-tumorigenic activities of gemcitabine by reducing proliferation and angiogenesis and by inducing apoptosis and the immune response. These preclinical findings show that TRIP13 is involved in PDAC progression and targeting of TRIP13 augments the anticancer effect of gemcitabine.

Abstract Introduction Alzheimer’s disease (AD) can be characterised in vivo by biomarkers reflecting amyloid-β (Aβ) and tau pathology. However, there is a need for biomarkers reflecting additional pathological pathways. Matrix metalloproteinases (MMPs) have recently been highlighted as candidate biomarkers for sex-specific mechanisms and progression in AD. Methods In this cross-sectional study, we investigated nine MMPs and four tissue inhibitors of metalloproteinases (TIMPs) in the cerebrospinal fluid of 256 memory clinic patients with mild cognitive impairment or dementia due to AD and 100 cognitively unimpaired age-matched controls. We studied group differences in MMP/TIMP levels and examined the associations with established markers of Aβ and tau pathology as well as disease progression. Further, we studied sex-specific interactions. Results MMP-10 and TIMP-2 levels differed significantly between the memory clinic patients and the cognitively unimpaired controls. Furthermore, MMP- and TIMP-levels were generally strongly associated with tau biomarkers, whereas only MMP-3 and TIMP-4 were associated with Aβ biomarkers; these associations were sex-specific. In terms of progression, we found a trend towards higher MMP-10 at baseline predicting more cognitive and functional decline over time exclusively in women. Conclusion Our results support the use of MMPs/TIMPs as markers of sex differences and progression in AD. Our findings show sex-specific effects of MMP-3 and TIMP-4 on amyloid pathology. Further, this study highlights that the sex-specific effects of MMP-10 on cognitive and functional decline should be studied further if MMP-10 is to be used as a prognostic biomarker for AD.

Abiotic stresses e.g., cold, heat, and salinity affect the quality and yield of Rosa chinensis, a well-known ornamental and medicinal plant. Previously, several attempts have been made to identify the genes that confer resistance to oxidative stressors. Superoxide-dismutase (SOD) is a crucial member of the class metalloenzyme that responds to abiotic stresses and protects plants by countering the reactive-oxygen species (ROS). In this genome-wide association study, the SOD gene family has been investigated in R. chinensis. Seven SOD genes, including three Cu/ZnSODs, two MnSODs, and two FeSODs were identified. Phylogenetic analysis revealed that RcSODs are divided into three clades; (i) CSDs (Cu/ZnSODs) (ii) FSDs (FeSODs) and MSDs (MnSODs). Most of the RcSODs exhibited different exons/introns distribution patterns. Motifs 3 and 5 are conserved in all RcSODs genes. The RNA-seq data analysis and qRT-PCR-based expression profiling indicated that RcSODs exhibit diverse responses under salt stress conditions. The RcCSD1, RcCSD3, and RcFSD3 are significantly up-regulated under salt stress conditions in roots and down-regulated in leaves. This data provides valuable information for further application and function of SODs in R. chinensis under abiotic stresses.

The basic leucine zipper (bZIP) gene family is one of the largest transcription factors family in plants. These are involved in various biological processes including the regulation of light signaling, seed maturation, flower development, and the response to both biotic and abiotic stresses. Despite the crucial role of this gene family, no comprehensive investigation has been carried out to characterize bZIPs in sunflower. Therefore, an in silico analysis along with a wetlab experimental approach was adopted for bZIPs in sunflower plants. During the study, 73 bZIPs were identified in sunflower. Phylogenetic analysis involving bZIPs from A. thaliana, H. annuus, C. arabica, L. sativa, and V. vinifera divided these members into 11 groups and testified using conserved motif and gene structure analysis. Protein-Protein interaction analysis identified five clusters of HabZIPs. Intron diversity ranged from 0 to 12. The cis-regulatory elements analysis predicted various stress-responsive elements. RNA-seq data analysis reveiled that clusters of bZIPs were involved in stress response in roots and leaves during early or late stages of plant growth. Similatly, qRT-PCR analysis identified that HabZIP01, 04, 18, and 48 were significantly upregulated in response to mild or svere salt stress. Conversely, HabZIP08 was downregulated in response to both levels of salinity. Overall the study provides new insights into stress-responsive HabZIP genes in sunflower and provides a foundation for further research on their role in the plant's response to abiotic stress.

Potassium is a monovalent cation with an osmotic activity in plants. It comprises 10 to 11% of plant dry weight and has crucial importance in the development and stress mechanism of plants. A well-studied system of K+ channels and transporters is involved in the regulation and transport of potassium from soil to different parts of plants. However, the knowledge of this system in ornamental plants especially in roses is limited. In current research, various omics approaches were utilized to characterize the K+ transport system in China rose (Rosa chinensis). Genomewide analysis revealed that a total of 32 genes were candidates for K+ channels (21) and transporters (11). Based on their conserved domain and motif, these genes were further classified into sub-families as K+ channels: 6 Shakers, 5 TPKs, and K+ transporters: 2 HKTs, 5 KEAs, and 14 KUP/HAK/KTs. Phylogenetic and evolutionary studies revealed that segmental duplication may play an important role in the expansion of this gene family. The cis-elements in promoter region showed that these K+ transport-related genes may be involved in response to various abiotic stresses. RNA-seq analysis and its validation through qRT-PCR showed that a channel gene RcAKT1.2 and two transporter genes RcKUP/HAK/KT11 and RcKUP/HAK/KT13 were potentially involved in the regulation of plant stress response. This research explains the valuable vision for functional assays of K+ transport system in China rose.

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

This article describes a comprehensive Synthetic Aperture Radar (SAR) satellite based ships dataset for use in state of the art object detection algorithms. The dataset comprises 11,590 image tiles containing 27,885 ships examples. Each image tile has spatial dimensions of 512 × 512 pixels and is exported in JPEG format. The dataset contains a wide variety of inshore and offshore scenes under varying background settings and sea conditions to generate an all-inclusive understanding of the ship detection task in SAR satellite images. The dataset is generated using images from six different satellite sensors covering a wide range of electromagnetic spectrum including C, L and X band radar imaging frequencies. All the sensors have different resolutions and imaging modes. The dataset is randomly distributed into training, validation and test sets in the ratio of 70:20:10, respectively, for ease of comparison and bench-marking. The dataset was conceptualized, processed, labeled and verified at the Artificial Intelligence and Computer Vision (iVision) Lab at the Institute of Space Technology, Pakistan. To the best of our knowledge, this is the most diverse satellite based SAR ships dataset available in the public domain in terms of satellite sensors, radar imaging frequencies and background settings. The dataset can be used to train and optimize deep learning based object detection algorithms to develop generic models with high detection performance for any SAR sensor and background condition.

Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.

Biopsies are one of the most common ways to diagnose and make a prognosis of prostate cancer. Transrectal and Transperineal approaches are used to access the prostate for biopsy. Advancements in Magnetic Resonance Imaging (MRI) offer a clear visualization of soft tissues and improved volumetric clarity. Real time MRI guidance can significantly change the paradigm of soft tissue biopsy. Proposed is a device with the ability to collect the tissue samples taken by a biopsy needle during an MRI guided Prostate biopsy and store them temporarily. This could help avoid interruption during the process and make the entire process teleoperated. The proposed design consists of a double layer of paper, rolling over a set of pulleys. Paper collects and stores the core tissue sample. Experiments have been performed to optimize paper tension, speed, and size. Tissue collection performance improvement is noticed with optimum paper tension, speed, and size. A test has also been performed in an MRI environment to check MR compatibility of the device. The device is MRI conditional and demonstrates a maximum of 13.1% SNR reduction in MR images. The proposed device exhibits the potential to be integrated with an MRI safe or conditional robotic manipulator for real time MRI guided Prostate biopsy.

Malware has evolved to pose a major threat to information security. Efficient anti-malware software is essential in safeguarding confidential information from these threats. However, identifying malware continues to be a challenging task. Signature-based detection methods are quick but fail to detect unknown malware. Additionally, the traditional machine learning archetype requires a large amount of data to be effective, which hinders the ability of an anti-malware system to quickly learn about new threats with limited training samples. In a real-world setting, the majority of malware is found in the form of Portable Executable (PE) files. While there are various formats of PE files, samples of all formats such as ocx, acm, com, scr, etc., are not readily available in large numbers. Therefore, building a conventional Machine Learning (ML) model with greater generalization for data-scarce PE formats becomes a hefty task. Consequently, in such a scenario, Few-Shot learning (FSL) is helpful in detecting the presence of malware, even with a very small number of training samples. FSL techniques help to make predictions based on an insufficient number of samples. In this paper, we propose a novel architecture based on the Relation Network for FSL implementation. We propose a Discriminative Feature Embedder for feature extraction. These extracted features are passed to our proposed Relation Module (RM) for similarity measure. RM produces the relation scores that lead to improved classification. We use PE file formats, i.e., ocx, acm, com, and scr, after transforming them into images. We employ five-shot learning and then one-shot learning, which produces 94% accuracy with only one training instance. We observe that the proposed architecture outpaces the baseline method and provides enhanced accuracy by up to 94% with only one sample.

The smart grid relies on Advanced Metering Infrastructure (AMI) to function. Because of the significant packet loss and slow transmission rate of the wireless connection between smart meters in AMI, these infrastructures are considered Low-power and Lossy Networks (LLNs). The routing protocol in an AMI network is crucial for ensuring the availability and timeliness of data transfer. IPv6 Routing Protocol for Low-power and lossy networks (RPL) is an excellent routing option for the AMI communication configuration. However, it is highly at risk against many external and internal attacks, and its effectiveness may be severely diminished by Sybil assault. Different trust-based techniques have been suggested to mitigate internal attacks. However, existing trust systems have high energy consumption issues, which cause a reduction in the performance of LLNs due to complex calculations at the node level. Therefore, this paper presents a novel fog-enabled GINI index-based trust mechanism (GITM) to mitigate Sybil attacks using the forwarding behavior of legitimate member nodes. Regarding identifying and isolating Sybil assaults, our approach outperforms the state-of-the-art methods. GITM detects and isolates a more significant number of malicious network nodes compared to other techniques within a similar time frame. By using the proposed GITM framework, the Sybil attack detection rate increases by 4.48%, energy consumption reduces by 21%, and isolation latency reduces by 26.30% (concerning time). Furthermore, the end-to-end delay is merely 0.30% more in our case, and the number of control messages decreases by 28%.

The continuing demand for energy has led to the need for developing and improving the oil and gas infrastructure through the execution of various infrastructure capital projects in many parts of the world, including Pakistan. Oil and gas companies have vast experience implementing these infrastructure projects, yet many such projects face delays and cost overruns. This failure to meet project targets may be attributable to the lack of identifying and addressing the risks at the pre-execution stage in the contract documents. This study aims to identify the risks to address in the contract documentation developed for the infrastructure projects and allocate each factor to a particular stakeholder (Client, design consultant, legal consultant, and bidders) in Pakistan. Risks were identified through literature review and were validated using the Delphi technique applied through surveys. Twenty-four survey respondents were selected based on their qualifications and experience in Pakistan’s Oil and Gas sector. In the first round of the Delphi survey, the level of agreement authenticates the risks applicable to Pakistan's Oil and Gas sector. The level of agreement was reaffirmed in round two of the Delphi survey. It also included a check for stability of responses between rounds using the McNemar test. Results confirmed that sixty-one risk factors are the contractual risk for evaluation when developing contracts for Oil and Gas infrastructure projects in Pakistan.

Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the use of different datasets in previous studies, an extensive performance comparison of these algorithms is missing. In this paper, an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories. In addition, this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets. Results indicate that attribute and edge-preserving filtering-based detection (AED), local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW) and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD) perform better as indicated by the mean and median values of area under the receiver operating characteristic (ROC) curves. Finally, this paper studies the effect of various dimensionality reduction techniques on anomaly detection. Results indicate that reducing the number of components to around 20 improves the performance; however, any further decrease deteriorates the performance.

Due to rapid industrialization, and depletion of fossil fuels, alternative renewable resources are mandatory, where solar thermal energy is one of the promising alternate. In this study, an experimental investigation was conducted to analyze the thermal performance of different photovoltaic-modules under varying climate conditions. These include thin plate Copper indium diselenide, mono-crystalline silicon, micro crystalline silicone, amorphous silicon and poly-crystalline silicon. The analysis was concentrated on the evaluation of module efficiency, solar irradiance absorption rate, maximum power output, performance ratio, normalized power output efficiency and temperature effect on each module at real operational outdoor conditions. Mono-crystalline silicon module showed high average efficiency of 20.8% and average performance ratio 1.21 compared to the other PV modules. It was observed that all types of modules have higher average temperature in summer season and showed low performance ratio and low module efficiency as compared to winter season. Average normalized power out of mono-crystalline silicon 56.2% more efficient than the other modules. The increased thermal performance of mono-crystalline silicon was related with its high absorption rate and high conduction rate. Thus, mono-crystalline silicon PV module is the best potential candidate for solar capturing technique to be utilize in diverse solar thermal energy applications.

Purpose: This study investigates the many variables that support the expanding SCM-based literature published in the Web of Sciences till July 2023. Design/Methodology/Approach: This study separated the research front of Supply chain management using bibliometric coupling and then examined the conceptual structure of each element. SCM literature is highly published in 2021, with publication of Cleaner Production being the top publication for publishing SCM studies. The United Kingdom (UK) was the most often cited country for SCM research. Findings: The current study's aims are fourfold. To begin, we intend to investigate existing practices in the literature of SCM in provisions of key authors, fields, key journals, major institutes, associated nations, research kind, and economy. Second, we plan to identify important research tendencies in the field of SCM. Third, to comprehend the academic framework related to the research of SCM and how it has evolved over time Implications/Originality/Value: This study recommends a future research agenda for determining the rational formation, significant elements, and theoretical framework of literature based on SCM, as shown by the questions of the research suggested in this study. SCM stakeholders and organizations developing SCM might acquire helpful insights into the repercussions of SCM. Because of these unique traits, this work makes an important contribution to the burgeoning literature on SCM.

Arachis hypogaea (peanut) is a leading oil and protein-providing crop with a major food source in many countries. It is mostly grown in tropical regions and is largely affected by abiotic and biotic stresses. Cysteine-rich receptor-like kinases (CRKs) is a family of transmembrane proteins that play important roles in regulating stress-signaling and defense mechanisms, enabling plants to tolerate stress conditions. However, almost no information is available regarding this gene family in Arachis hypogaea and its progenitors. This study conducts a pangenome-wide investigation of A. hypogaea and its two progenitors, A. duranensis and A. ipaensis CRK genes (AhCRKs, AdCRKs, and AiCRKs). The gene structure, conserved motif patterns, phylogenetic history, chromosomal distribution, and duplication were studied in detail, showing the intraspecies structural conservation and evolutionary patterns. Promoter cis-elements, protein–protein interactions, GO enrichment, and miRNA targets were also predicted, showing their potential functional conservation. Their expression in salt and drought stresses was also comprehensively studied. The CRKs identified were divided into three groups, phylogenetically. The expansion of this gene family in peanuts was caused by both types of duplication: tandem and segmental. Furthermore, positive as well as negative selection pressure directed the duplication process. The peanut CRK genes were also enriched in hormones, light, development, and stress-related elements. MicroRNA (miRNA) also targeted the AhCRK genes, which suggests the regulatory association of miRNAs in the expression of these genes. Transcriptome datasets showed that AhCRKs have varying expression levels under different abiotic stress conditions. Furthermore, the multi-stress responsiveness of the AhCRK genes was evaluated using a machine learning-based method, Random Forest (RF) classifier. The 3D structures of AhCRKs were also predicted. Our study can be utilized in developing a detailed understanding of the stress regulatory mechanisms of the CRK gene family in peanuts and its further studies to improve the genetic makeup of peanuts to thrive better under stress conditions.

Wheat is an important food crop worldwide, providing substantial calories and nourishment. Genetic variability in wheat germplasm is crucial for the development of cultivars with desirable features. This two years study (2020–21 and 2021–22) was conducted to evaluate 13 diverse wheat genotypes factorially combined with foliar-applied zinc sulphate (0, 0.4, 0.6%) arranged in a triplicate randomized complete block design. Boxplot analysis revealed the significant (P

Purpose: Although there is an established role for microbiome dysbiosis in the pathobiology of colorectal cancer (CRC), CRC patients of various race/ethnicities demonstrate distinct clinical behaviors. Thus, we investigated microbiome dysbiosis in Egyptian, African American (AA), and European American (EA) CRC patients. Patients and methods: CRCs and their corresponding normal tissues from Egyptian (n = 17) patients of the Alexandria University Hospital, Egypt, and tissues from AA (n = 18) and EA (n = 19) patients at the University of Alabama at Birmingham were collected. DNA was isolated from frozen tissues, and the microbiome composition was analyzed by 16S rRNA sequencing. Differential microbial abundance, diversity, and metabolic pathways were identified using linear discriminant analysis (LDA) effect size analyses. Additionally, we compared these profiles with our previously published microbiome data derived from Kenyan CRC patients. Results: Differential microbiome analysis of CRCs across all racial/ethnic groups showed dysbiosis. There were high abundances of Herbaspirillum and Staphylococcus in CRCs of Egyptians, Leptotrichia in CRCs of AAs, Flexspiria and Streptococcus in CRCs of EAs, and Akkermansia muciniphila and Prevotella nigrescens in CRCs of Kenyans (LDA score >4, adj. p-value