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Abstract Autonomous Vehicles (AV) is one of the most evolving industries in the last decade. However, one of the bottlenecks of this evolution is providing data that contains different scenarios and scenes to improve the models without exposing the privacy and security of the edge vehicles. The authors of this research propose a secure and efficient novel solution for lane segmentation in AVs through the use of Federated Learning (FL). FedLane involves initial training of U-Net, ResUNet, and ResUNet++ models, followed by real-time inference in edge devices and the application of FL to update the server model using clients’ data. The study found that FL has enhanced the performance of lane segmentation significantly over baseline, enabling decentralized privacy-preserving collaborative optimization with increased dice coef from 0.9429 to 0.9794 for U-Net, from 0.9291 to 0.9854 for ResUNet and from 0.9079 to 0.9675 for ResUNet++. Additionally, the models show increased stability over the training iterations, highlighting the potential of FL to play a significant role in the future of automation in the AV industry.

Diabetes is a chronic condition that is characterized by high blood glucose levels and can cause damage to multiple organs over time. Continuous monitoring of glucose levels is essential for both diabetic and non-diabetic individuals. There have been major developments in glucose monitoring technology over the past decade, which have been driven by research and industry efforts. Despite these significant advancements, the area of glucose biosensors still faces significant challenges. This paper presents a comprehensive summary of the latest glucose monitoring technologies, including invasive, minimally invasive, and non-invasive methods. Subsequently, we bring together the electronic components, wireless communication technologies, and energy harvesting opportunities, along with the limitations and challenges associated with current glucose monitoring solutions. This is followed by highlighting the potential integration of health records generated by continuous glucose monitors and artificial intelligence (AI) techniques to define precise diabetes management protocols. This integration achieves accurate results with constrained prediction horizons employing a time series of continuous glucose readings. The paper emphasizes the need for further advancements in glucose monitoring technology to improve diabetes management and address the critical need in clinical practice for improved glucose monitoring technologies with translational implications.

Environmentally friendly technology is being used by industries all over the world, and engineers in the manufacturing and materials industry are embracing sustainable business models. In this paradigm, materials are processed using economically and environmentally sound methods. The use of microwave-absorbing materials (MAMs) in low-altitude observatory aircraft and the rise in electromagnetic pollution have brought them to light. The main aim of this study is to select an ideal MAM with excellent physical, electromagnetic, chemical, and thermal properties, which also fulfills sustainability aspects based on expert judgments. In this regard, we have proposed a new hybrid framework consisting of Modified Digital Logic (MDL), a subjective weighting method in combination with the measurement of alternatives and ranking according to compromise solution (MARCOS) under an interval-valued intuitionistic fuzzy (IVIF) environment to select an optimum MAM. Furthermore, this research work contributes to streamlining the selection process by consolidating the plethora of work available in the literature on the synthesis and characterization of MAMs. A database is created for 160 potential candidate materials in C, S, X, and Ku bands for carbon-based materials, including carbon nanotubes (CNT), graphene, reduced graphene oxide (rGO), carbon fibers, and biomass-derived materials. These materials are then passed through successive screening stages to shortlist 14 materials, which are ranked subsequently over a set of 15 crisp and ambiguous criteria. This comprehensive study simultaneously caters to quantitative and qualitative information extracted from experimental work, material resource packs, or expert evaluations. The findings highlight CNT/Fe (20 wt%, E) (Al1) as the most suitable candidate for MAM application with outstanding electromagnetic properties. Finally, the results are compared with extant approaches to check the reliability of the proposed framework. In addition, sensitivity analysis is carried out to establish the feasibility and robustness of the obtained results.

Abstract Power Line Communication technology uses power cables to transmit data. Knowing whether a node is working in advance without testing saves time and resources, leading to the proposed model. The model has been trained on three dominant features, which are SNR (Signal to Noise Ratio), RSSI (Received Signal Strength Indicator), and CINR (Carrier to Interference plus Noise Ratio). The dataset consisted of 1000 readings, with 90% in the training set and 10% in the testing set. In addition, 50% of the dataset is for class 1, which indicates whether the node readings are optimum. The model is trained with multi-layer perception, K-Nearest Neighbors, Support Vector Machine with linear and non-linear kernels, Random Forest, and adaptive boosting (ADA) algorithms to compare between statistical, vector-based, regression, decision, and predictive algorithms. ADA boost has achieved the best accuracy, F-score, precision, and recall, which are 87%, 0.86613, 0.9, 0.8646, respectively.

Abstract Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype.

The nearby radio galaxy M87 is a very high-energy (VHE) gamma-ray emitter established by observations with ground-based gamma-ray detectors. Here we report the long-term monitoring of M87 from 2021 to 2024 with the Large High Altitude Air Shower Observatory (LHAASO). M87 has been detected by LHAASO with a statistical significance ∼ 9 σ . The observed energy spectrum extends to 20 TeV, with a possible hardening at ∼20 TeV and then a clear softening at higher energies. Assuming that the intrinsic spectrum is described by a single power law up to 20 TeV, a tight upper bound on the extragalactic background light intensity is obtained. A strong VHE flare lasting 8 days, with a rise time of ${\tau }_{r}^{\mathrm{rise}}=1.05\pm 0.49$ days and decay time of ${\tau }_{d}^{\mathrm{decay}}=2.17\pm 0.58$ days, was found in early 2022. A possible GeV flare is seen also in Fermi Large Area Telescope data during the VHE flare period. The variability time as short as 1 day seen in the LHAASO data suggests a compact emission region with a size of ∼3 × 10 ^15 δ cm ( δ being the Doppler factor of the emitting region), corresponding to a few Schwarzschild radii of the central supermassive black hole in M87. The continuous monitoring of the source reveals a duty cycle of ∼1% for VHE flares with a flux above 10 ^−11 erg cm ^−2 s ^−1 .

The first source catalog of the Large High Altitude Air Shower Observatory (LHAASO) reported the detection of a very high energy gamma-ray source, 1LHAASO J1219+2915. This Letter presents a further detailed study of the spectral and temporal behavior of this pointlike source. The best-fit position of the TeV source (R.A. = 185.°05 ± 0.°04, decl. = 29.°25 ± 0.°03) is compatible with NGC 4278 within ∼0.°03. Variation analysis shows an indication of variability on a timescale of a few months in the TeV band, which is consistent with low-frequency observations. Based on these observations, we report the detection of TeV γ -ray emissions from this low-luminosity active galactic nucleus. The observation by LHAASO's Water Cherenkov Detector Array during the active period has a significance level of 8.8 σ with a best-fit photon spectral index Γ = 2.56 ± 0.14 and a flux f _1–10 TeV = (7.0 ± 1.1 _sta ± 0.35 _syst ) × 10 ^−13 photons cm ^−2 s ^−1 , or approximately 5% of the Crab Nebula. The discovery of VHE gamma-ray emission from NGC 4278 indicates that compact, weak radio jets can efficiently accelerate particles and emit TeV photons.

With surge irrigation, applying surges to an area is a cheap labor task. In industrialized nations, a variety of electronic valves for discontinuous water application (surges) are available; but their pricing prevents their usage in emerging regions. Additionally, these valves are too advanced for use by growers in underdeveloped nations. As a result, a basic Automatic Surge Gate was developed and tested in the labs to assess its effectiveness in terms of producing on-off surges. The reservoir was built to hold or collect low inflows for barrier functioning, and the gate was positioned on the suction side of the reservoir. Inflow rates, pinions, and poundage placements on the gate lever were the working parameters defined for the gate functioning. In the current investigation, three inflow rates 134, 169, and 187 l/s were employed. By leveraging the low inflows to the reservoir, the automatic surge gate demonstrated the possibility for automating the on-off action. The surge gate produced sizeable outflows even at the lowest inflow rate of 134 l/s into the reservoir. Power-law equations were discovered to be a good representation of the behavior in the statistical models that were also created using quasi statistical method.

Esophageal Cancer-Related Gene 2 (ECRG2), also known as Serine Peptidase Inhibitor Kazal type 7 (SPINK7), is a novel tumor suppressor gene from the SPINK family of genes that exhibits anticancer potential. ECRG2 was originally identified during efforts to discover genes involved in esophageal tumorigenesis. ECRG2 was one of those genes whose expression was absent or reduced in primary human esophageal cancers. Additionally, absent or reduced ECRG2 expression was also noted in several other types of human malignancies. ECRG2 missense mutations were identified in various primary human cancers. It was reported that a cancer-derived ECRG2 mutant (valine to glutamic acid at position 30) failed to induce cell death and caspase activation triggered by DNA-damaging anticancer drugs. Furthermore, ECRG2 suppressed cancer cell proliferation in cultured cells and grafted tumors in animals and inhibited cancer cell migration/invasion and metastasis. ECRG2 also was identified as a negative regulator of Hu-antigen R (HuR), an oncogenic RNA-binding protein that is known to regulate mRNA stability and the expression of transcripts corresponding to many cancer-related genes. ECRG2 function is important also for the regulation of inflammatory responses and the maintenance of epithelial barrier integrity in the esophagus. More recently, ECRG2 was discovered as one of the newest members of the pro-apoptotic transcriptional targets of p53. Two p53-binding sites (BS-1 and BS-2) were found within the proximal region of the ECRG2 gene promoter; the treatment of DNA-damaging agents in cancer cells significantly increased p53 binding to the ECRG2 promoter and triggered a strong ECRG2 promoter induction following DNA damage. Further, the genetic depletion of ECRG2 expression significantly impeded apoptotic cell death induced by DNA damage and wild-type p53 in cancer cells. These findings suggest that the loss of ECRG2 expression, commonly observed in human cancers, could play important roles in conferring anticancer drug resistance in human cancers. Thus, ECRG2 is a novel regulator in DNA damage-induced cell death that may also be a potential target for anticancer therapeutics.

Abstract Reduced numbers of online conventional generators, due to increasing shares of renewable energy sources, are increasing the requirement for a fast frequency response capability from reserve providers when a large infeed/outfeed trips. The commercial demand‐side sector, considering its size (MW/MWh) and existing communication and control infrastructure, makes it a potential valuable source of flexibility provision. Supermarket refrigeration systems are considered here as being representative of commercial end‐uses, with individual devices stochastically simulated under different ambient conditions, supermarket occupancy, food categories, and refrigeration performance. An aggregated fleet of supermarket loads are then created to provide a system‐wide reserve response. The all‐island power system of Northern Ireland and Republic of Ireland, with a horizon of 2030, is considered as a representative case study. Three distinct demand‐side frequency control methods, including static, droop, and hybrid control are presented and developed within a stability analysis platform. Year‐long simulation results show a general improvement in the frequency nadir and (maximum) rate of change of frequency when supermarkets are providing fast frequency response services to the grid, with the likelihood of severe transients (frequency nadir

Intro: Extensively drug-resistant (XDR) Salmonella Typhi has been reported in Sindh province of Pakistan since 2016. The potential for further spread is of serious concern as remaining treatment options are severely limited. We report the phenotypic and genotypic characterization of 27 XDR S. Typhi isolated from patients attending Jinnah Hospital, Lahore, Pakistan. Positive Blood Cultures for Salmonella Typhi raised the possibility that the Sindh province strain was circulating in Lahore, leading us to sequence 27 XDR S. Typhi isolates and compare SNPs in their whole genome sequences with those from Sindh. Methods: Isolates were identified by biochemical profiling; antimicrobial susceptibility was determined by a modified Kirby–Bauer method. These findings were confirmed using Illumina whole genome nucleotide sequence data. All sequences were compared to the outbreak strain from Southern Pakistan and typed using the S. Typhi genotyping scheme. All isolates were confirmed by sequence analysis to harbor an IncY plasmid and the CTX-M-15 ceftriaxone resistance determinant. All isolates were of the same genotypic background as the outbreak strain from Sindh province. Findings: All the XDR isolates differed by six SNPs from the MDR S. Typhi clade, the same level of variation as seen during the 2016 outbreak. Within the XDR clade, the Lahore isolates formed two clusters on the tree, one consisting of isolates XDR1, 21, 5, 28, and 27, and the other of XDR24, 4, 17, 6, 13, 10, 23, 19, 30, 26, 25, 15, 22, 35, 31, and 2, suggesting probable epidemiological linkage. Analysis of all 27 draft genome sequences from Lahore indicated the presence of both an IncY plasmid and the CTX-M-15 determinant associated with ceftriaxone resistance. Conclusion: In conclusion, the risk of single point mutations conferring resistance to azithromycin and of the horizontal transmission of carbapenemase-mediated resistance makes the ongoing threat of XDR typhoid fever very real in Pakistan and internationally.

COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods’ operability on such a problem. This work proposes integrating traditional computer vision techniques and deep learning methods to develop a reliable benchmarking framework for lane detection tasks in complex and dynamic road scenes. Firstly, an automatic segmentation algorithm based on a sequence of traditional computer vision techniques has been experimented. This algorithm precisely segments the semantic region of the host lane in the complex urban images of nuScenes dataset used in this framework; hence corresponding weak labels are generated. After that, the developed data is qualitatively evaluated to be used in training and benchmarking five state-of-the-art FCN-based architectures: SegNet, Modified SegNet, U-Net, ResUNet, and ResUNet++. The performance evaluation of the trained models is done visually and quantitatively by considering lane detection a binary semantic segmentation task. The output results show robust performance, especially ResUNet++, which outperforms all the other models while testing them in different complex road scenes with dynamic scenarios and various lighting conditions.

The aim of this paper is to investigate implicit viscosity iterative algorithm for nonexpansive mappings in the framework of CAT(0) spaces. We prove strong convergence theorems of such algorithm under some suitable assumptions on the sequences of parameters involved therein. We also proved that the presented algorithm is faster than a number of existing iteration processes in literature. Our results extend and improve some recent results of Ahmad et al.[J. Appl. Math. Informatics 35 (5-6)(2017), 423-438] and others.

In this paper, topology design, optimal routing, and gateways placement selection algorithms are proposed in Heterogeneous Cloud Radio Access Network (C-RAN) with exploiting Free Space Optical (FSO) communication. The proposed network consists of two tiers; the lower tier concerns with clustering Remote Radio Heads (RRHs) based on traffic demands. The upper tier consists of transceivers along with the Cluster Heads (CHs) and gateways. Algorithms are proposed to achieve the lowest number of edges and the highest possible throughput based on the presented optimization problem. Moreover, route optimization and gateway selection problems have been considered in two different traffic scenarios; static (maximum occupancy) or dynamic (Partial occupancy). Furthermore, a disaster recovery algorithm is proposed in case of failure of one of the nodes or edges to maintain the continuous connectivity of the network at the expense of the traffic. Our simulation results show that the number of gateways in a dynamic traffic operation is reduced by 33% in node size of 25, 36, and 42. Also, this reduction is increased to 41% if the node size increased to 49.

In this paper, a resource partitioning scheme combined with a new multi-carrier optical modulation technique for indoor visible light communication (VLC) system is proposed. In VLC systems, the coverage area is divided into multiple atto-cells. In each atto-cell, multiple LED arrays are used as access points (APs) serving the assigned users. The coverage area of APs might be overlapped to avoid service discontinuity for mobile users. The overlapped coverage zones result in co-channel interference (CCI). We develop a shared frequency reuse (SFR) technique combined with two resource allocation algorithms to minimize interference and maximize the system throughput. This technique divides the overall bandwidth into two parts: the shared and reused bands. The shared band serves the users in the interference area while the reused band serves users in the non-interfering area. Furthermore, we propose a new multi-carrier optical modulation technique called odd clipped optical-OFDM (OCO-OFDM). This technique applies the odd symmetry on the frequency-domain OFDM to enhance the spectral efficiency compared to the asymmetrical clipped optical OFDM (ACO-OFDM) which is currently used. Then we study and evaluate the system performance in terms of the signal-to-interference and noise ratio (SINR), total throughput, and the outage probability. The proposed system achieved total throughput of up to 800 Mbps with 40 dB SINR at the cell edge. Furthermore, the outage probability can be optimized to its minimum value when the receiver field-of-view (FOV) is taken by 40° when the minimum SINR is 10 dB.

A novel series of coumarin derivatives linked to the N-benzyl triazole group were synthesized and evaluated against 15-lipoxygenase (15-LOX), and acetyl- and butyrylcholinesterase (AChE and BuChE) to find the most potent derivative against Alzheimer’s disease (AD). Most of the compounds showed weak to moderate activity against ChEs. Among the most active BuChE and 15-LOX inhibitors, 8l and 8n exhibited an excellent neuroprotective effect, higher than the standard drug (quercetin) on the PC12 cell model injured by H2O2 and significantly reduced aggregation of amyloid Aβ1-42, with potencies of 1.44 and 1.79 times higher than donepezil, respectively. Compound 8l also showed more activity than butylated hydroxytoluene (BHT) as the reference antioxidant agent in reducing the levels of H2O2 activated by amyloid β in BV2 microglial cells. Kinetic and ligand–enzyme docking studies were also performed for better understanding of the mode of interaction between the best BuChE inhibitor and the enzyme. Considering the acceptable BuChE and 15-LOX inhibition activities as well as significant neuroprotection, and anti-amyloid aggregation activities, 8l and 8n could be considered as potential MTDLs for further modification and studies against AD.

Abstract This study aimed to identify the phylogenetic similarities among the muntjac (Muntiacus spp.). The phylogenetic similarities among seven major muntjac species were studied by comparing the nucleotide sequence of 16s rRNA and cytochrome b genome. Nucleotide sequences, retrieved from NCBI databases were aligned by using DNASTAR software. A phylogenetic tree was created for the selected species of muntjac by using the maximum likelihood method on MEGA7 software. The results of nucleotide sequences (16s rRNA) showed phylogenetic similarities between, the M. truongsonensis and M. rooseveltorum had the highest (99.2%) while the lowest similarities (96.8%) found between M. crinifrons and M. putaoensi. While the results of nucleotide sequences (Cty b) showed the highest similarity (100%) between M. muntjak and M. truongsonensis and the lowest s (91.5%) among M. putaoensis and M. crinifrons. The phylogenetic tree of muntjac species (16s rRNA gene) shows the main two clusters, the one including M. putaoensis, M. truongsonensis, M. rooseveltorum, and M. muntjak, and the second one including M. crinifrons and M. vuquangensis. The M. reevesi exists separately in the phylogenetic tree. The phylogenetic tree of muntjac species using cytochrome b genes shows that the M. muntjak and M. truongsonensis are clustered in the same group.

Abstract Background Recently, we have reported the characterization of a novel protein named Coiled-coil Helix Tumor and Metabolism 1 (CHTM1). CHTM1 localizes to both cytosol and mitochondria. Sequence corresponding to CHTM1 is also annotated in the database as CHCHD5. CHTM1 is deregulated in human breast and colon cancers and its deficiency in human cancer cells leads to defective lipid metabolism and poor growth under glucose/glutamine starvation. Methods Human cancer cell lines and tissue specimens were used. CHTM1 knockdown was done via lentiviral approach. CHTM1-expresssion constructs were developed and mutants were generated via site-directed mutagenesis approach. Western blotting, immunostaining, immunohistochemistry, cell fractionation and luciferase assays were performed. Reactive oxygen species and reactive nitrogen species were also measured. Results Here we report that CHTM1 deficiency sensitizes human lung cancer cells to metabolic stress-induced cell death mediated by glucose/glutamine deprivation and metformin treatment. CHTM1 interacts with Apoptosis Inducing Factor 1 (AIF1) that is one of the important death inducing molecules. CHTM1 appears to negatively regulate AIF1 by preventing AIF1 translocation to cytosol/nucleus and thereby inhibit AIF1-mediated caspase-independent cell death. Our results also indicate that p38, a stress kinase, plays a critical role in metabolic stress-induced cell death in CHTM1-deficient cells. Furthermore, p38 appears to enhance AIF1 translocation from mitochondria to cytosol particularly in metabolically stressed CHTM1-deficient cells and CHTM1 negatively regulates p38 kinase activity. The expression status of CHTM1 in lung cancer patient samples is also investigated and our results indicate that CHTM1 levels are increased in the majority of lung tumors when compared to their matching normal tissues. Conclusion Thus, CHTM1 appears to be an important metabolic marker that regulates cancer cell survival under metabolic stress conditions, and has the potential to be developed as a predictive tumor marker.

This data is related to the research article entitled “Germinal center humoral autoimmunity independently mediates progression of allograft vasculopathy” (Harper et al., 2016) [2]. The data presented here focuses on the humoral autoimmune response triggered by transferred allogeneic CD4 T cells and includes details on: (a) the recipient splenic germinal center (GC) response; (b) augmentation of humoral autoimmunity and accelerated heart allograft rejection following transplantation from donors primed against recipient; (c) flow cytometric analysis of donor and recipient CD4 T cells for signature markers of T follicular helper cell differentiation; (d) in vitro donor endothelial cell migration in response to column purified autoantibody from recipient sera; (e) analysis of development of humoral responses in recipients following adoptive transfer of donor CD4 T cells and; (f) the development of humoral autoimmunity in mixed haematopoietic chimeric mice.

The selection of an optimal building façade system is a challenging process that can be facilitated by using decision-analysis methods. However, current commonly-used decision-analysis tools in civil engineering cannot deal with the interactions among multiple design criteria. The Choquet integral is the only well-known method capable of accounting for such interactions. However, the process of assigning the fuzzy measures (importance weights) for this method is complex, particularly when there is a large number of criteria be considered. This paper proposes two supervised methods to estimate these fuzzy measures. The first method estimates the relative importance weights by using a statistical approach based on Principal Component Analysis, while the second method is elicited from a machine learning algorithm using Neural Networks. These two methods are used in an illustrative example to find the fuzzy measures related to façade design with respect to four criteria; and their merits and limitations are discussed.

Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes between normal and abnormal cases is comprised of Gray Level Co-occurrence Matrix (GLCM) as a feature extraction technique and random forest as a classifier, followed by the second classification stage which classifies the abnormal cases into benign or malignant cases and is comprised of Local Binary Patterns (LBP) as a feature extraction technique and random forest as a classifier. The classification accuracy for the first stage is 97% and an F1-score of 0.98 and 0.97 for normal and abnormal classes. While for the second stage, the classification accuracy is 75% and an F1-score of 0.76 and 0.74 for benign and malignant classes. The overall hierarchical classification system achieves a classification accuracy of 85%, Matthews correlation coefficient (MCC) of 0.76, and F1-score of 0.98, 0.7, and 0.74 for normal, benign, and malignant test cases.

We report the case of a 7-year old Hispanic boy who presented with acute, severe intermittent diffuse abdominal pain. There was no previous significant medical or surgical history. Physical examination revealed a soft abdomen, normoactive bowel sounds, and epigastric tenderness. Initial blood tests and abdominal imaging were non-contributory, except for focal loops of dilated small bowel in the mid-abdomen. In less than 24 hours, the patient had worsening symptoms and physical findings. Emergent laparotomy revealed colonic volvulus with infarction and proximal jejunal ischemia secondary to a congenital adhesive band. Volvulus reduction, band release, extended right hemicolectomy, terminal ileostomy, and Hartman closure of the splenic flexure were performed. Recombinant tissue plasminogen activator (tPA) was infused into partially ischemic jejunum mesenteric vessels, and second look laparotomy planned. During the second laparotomy, nitroglycerin was topically applied on the ischemic jejunum, tPA infused into affected vessels, and third look laparotomy planned. Interval improvement of the affected proximal jejunum led to a shorter resection during the third laparotomy.Congenital adhesive bands are rare abnormalities that can cause bowel obstruction in children. A high index of suspicion in the absence of previous trauma or operations may prevent delays in diagnosis and treatment, allowing for improved outcomes.

Background: COVID-19 has superseded all medical scientific challenges in 2020. It presented with a range of respiratory involvement from the mild upper respiratory tract to severe pneumonia with ARDS. A percentage of these patients manifested variable neurological presentations expanding the challenges of patients' assessment, care, and management. Objective: To discuss the neurological manifestations and the possible immunopathology of COVID-19. Methods: A literature search was performed in the PubMed database for the relevant articles published in English language between April 9 th , 2020 and June 1, 2020. Few papers were extracted from Google Scholar and pre-print material as well. The keywords used to perform search included “COVID-19,” “SARS Cov2,” “neurological manifestations,” and “immunology and pathology of COVID-19”. However, animal studies were excluded in the neurological manifestations, and the final number of literature search outcome was 27 articles. Results: The immunopathology involves angiotensin-converting enzyme (ACE) receptor 2 and spike protein S1. COVID-19 has a tremendous affinity to the ACE2 receptor. The status of secondary hemophagocytic lymphohistiocytosis (sHLH) and the cytokine storm lead to different organ damage and the corresponding clinical manifestations. The documented neurological manifestations mainly include acute ischemic stroke, hemorrhagic stroke, temporal lobe, thalamic encephalitis, and one case of Guillain-Barre Syndrome, in addition to agitation, headache, and seizures. Conclusion: Although the intimate relationship between the neurons and the immune activation is the basic concept of COVID-19 immunopathology, the definite routes of entry to the neuronal cells are yet to be disclosed. Acute stroke, myositis, headache, and meningoencephalitis are the most common types of complications of COVID-19 so far.

Accurate dose calculation in the treatment planning system (TPS) process is the most important step to succeed the radiation therapy. For this purpose, Monte Carlo method is a powerful tool for dose calculation. This study aims to validate Monte Carlo BEAMnrc model of Saturne43 Linac head to simulate 12 MV photon beam. To validate MC model, the dose distributions were calculated by BEAMnrc simulation and then the results obtained were compared against measurements. This requires to adjust the parameters of the initial electron beam incident on the target, such as mean energy, beam radius and mean angular spread. Our approach has been suggested to determine the initial electron beam parameters. The dose distribution (percent depth dose and lateral profile) has been calculated for 10 × 10 cm2 field size in a homogeneous water phantom of 40 × 40 × 40 cm3. The results obtained are compared with measured data using gamma index criteria which were fixed within 1.5%-1 mm accuracy. Using phase space technique as a sub-source allows us to reduce the simulation time by a factor of 6. Good agreement between calculated and measured dose has been achieved when the mean energy, beam width and mean angular spread were 11.8 MeV, 1.5 mm and 0.5°, respectively. So, Monte Carlo based- BEAMnrc code is suitable to be used in the process of treatment planning system for calculating dose distribution. Keywords: Monte Carlo, BEAMnrc, Dose distribution, Linac, Phase space

The role of radiations in textile processing is gaining attention due to its low cost, energy effectiveness and eco-friendly process. This study is concerned with the improvement in modulation of microwave assisted vat dyeing of cellulosic fiber. It was found that radiation treatment of both cotton fabric and dye solution for 1 min gives good color strength, while for redox reaction, 2.5 g of NaHSO3, 2 mL of CH3COOH, and 1.5 mL of H2O2 are the optimized conditions which show it is a cost-effective tool. Good color strength was obtained at 65 °C for 1 h dyeing using 50 mL of optimal solution in dye bath at pH 9. Finally, ISO standards for color fastness to light, washing, rubbing and perspiration were applied at 0.5–2.5% of shade at optimal conditions which showed that microwave treatment has enhanced the color characteristics. Hence, the technique can successfully and easily be employed for surface modification of fabric for good quality dyeing with various classes of dyes.

In the present study the 2-methylpropanamide and benzamide derivatives of carboxyterfenadine, have been synthesized by nucleophilic substitution at carboxylic acid moiety (C-33) of tertiary carbon C-19 substituted with benzene ring. The proficient and high yielding series was carried out in two steps by continuous monitoring with TLC. The carboxylic group was utilized in the formation of 2-methylpropanamide and benzamide derivatives of carboxyterfenadine. The derivatives were characterized by spectroscopic techniques including mass. The starting material (2-[4-[1-hydroxy-4-[4-(hydroxydiphenylmethyl)piperidino]butyl]phenyl]-2-methylpropanoic acid) itself is H1 receptor antagonist. Hence, all these compounds A–E were biologically evaluated for antihistaminic and anticholinergic activity, using isolated guinea pig ileum tissues.

Different profiles of alloantibody responses are observed in the clinic, with those that persist, often despite targeted treatment, associated with poorer long-term transplant outcomes. Although such responses would suggest an underlying germinal center (GC) response, the relationship to cellular events within the allospecific B cell population is unclear. Here we examine the contribution of germinal center (GC) humoral alloimmunity to chronic antibody mediated rejection (AMR). A murine model of chronic AMR was developed in which T cell deficient (Tcrbd−/−) C57BL/6 recipients were challenged with MHC-mismatched BALB/c heart allografts and T cell help provided by reconstituting with 103 “TCR75” CD4 T cells that recognize self-restricted allopeptide derived from the H-2Kd MHC class I alloantigen. Reconstituted recipients developed Ig-switched anti-Kd alloantibody responses that were slow to develop, but long-lived, with confocal immunofluorescence and flow cytometric characterization of responding H-2Kd-allospecific B cells confirming persistent splenic GC activity. This was associated with T follicular helper (TFH) cell differentiation of the transferred TCR75 CD4 T cells. Heart grafts developed progressive allograft vasculopathy, and were rejected chronically (MST 50 days), with explanted allografts displaying features of humoral vascular rejection. Critically, late alloantibody responses were abolished, and heart grafts survived indefinitely, in recipients reconstituted with Sh2d1a−/− TCR75 CD4 T cells that were genetically incapable of providing TFH cell function. The GC response was associated with affinity maturation of the anti-Kd alloantibody response, and its contribution to progression of allograft vasculopathy related principally to secretion of alloantibody, rather than to enhanced alloreactive T cell priming, because grafts survived long-term when B cells could present alloantigen, but not secrete alloantibody. Similarly, sera sampled at late time points from chronically-rejecting recipients induced more vigorous donor endothelial responses in vitro than sera sampled earlier after transplantation. In summary, our results suggest that chronic AMR and progression of allograft vasculopathy is dependent upon allospecific GC activity, with critical help provided by TFH cells. Clinical strategies that target the TFH cell subset may hold therapeutic potential.This work is composed of two parts, of which this is Part II. Please read also Part I: Alsughayyir et al., 2019.

Humoral alloimmunity is now recognized as a major determinant of transplant outcome. MHC glycoprotein is considered a typical T-dependent antigen, but the nature of the T cell alloresponse that underpins alloantibody generation remains poorly understood. Here, we examine how the relative frequencies of alloantigen-specific B cells and helper CD4 T cells influence the humoral alloimmune response and how this relates to antibody-mediated rejection (AMR). An MHC-mismatched murine model of cardiac AMR was developed, in which T cell help for alloantibody responses in T cell deficient (Tcrbd−/−) C57BL/6 recipients against donor H-2Kd MHC class I alloantigen was provided by adoptively transferred “TCR75” CD4 T cells that recognize processed H-2Kd allopeptide via the indirect-pathway. Transfer of large numbers (5 × 105) of TCR75 CD4 T cells was associated with rapid development of robust class-switched anti-H-2Kd humoral alloimmunity and BALB/c heart grafts were rejected promptly (MST 9 days). Grafts were not rejected in T and B cell deficient Rag2−/− recipients that were reconstituted with TCR75 CD4 T cells or in control (non-reconstituted) Tcrbd−/− recipients, suggesting that the transferred TCR75 CD4 T cells were mediating graft rejection principally by providing help for effector alloantibody responses. In support, acutely rejecting BALB/c heart grafts exhibited hallmark features of acute AMR, with widespread complement C4d deposition, whereas cellular rejection was not evident. In addition, passive transfer of immune serum from rejecting mice to Rag2−/− recipients resulted in eventual BALB/c heart allograft rejection (MST 20 days). Despite being long-lived, the alloantibody responses observed at rejection of the BALB/c heart grafts were predominantly generated by extrafollicular foci: splenic germinal center (GC) activity had not yet developed; IgG secreting cells were confined to the splenic red pulp and bridging channels; and, most convincingly, rapid graft rejection still occurred when recipients were reconstituted with similar numbers of Sh2d1a−/− TCR75 CD4 T cells that are genetically incapable of providing T follicular helper cell function for generating GC alloimmunity. Similarly, alloantibody responses generated in Tcrbd−/− recipients reconstituted with smaller number of wild-type TCR75 CD4 T cells (103), although long-lasting, did not have a discernible extrafollicular component, and grafts were rejected much more slowly (MST 50 days). By modeling antibody responses to Hen Egg Lysozyme protein, we confirm that a high ratio of antigen-specific helper T cells to B cells favors development of the extrafollicular response, whereas GC activity is favored by a relatively high ratio of B cells. In summary, a relative abundance of helper CD4 T cells favors development of strong extrafollicular alloantibody responses that mediate acute humoral rejection, without requirement for GC activity.This work is composed of two parts, of which this is Part I. Please read also Part II: Chhabra et al., 2019.

Chronic rejection of solid organ allografts remains the major cause of transplant failure. Donor-derived tissue-resident lymphocytes are transferred to the recipient during transplantation, but their impact on alloimmunity is unknown. Using mouse cardiac transplant models, we show that graft-versus-host recognition by passenger donor CD4 T cells markedly augments recipient cellular and humoral alloimmunity, resulting in more severe allograft vasculopathy and early graft failure. This augmentation is enhanced when donors were pre-sensitized to the recipient, is dependent upon avoidance of host NK cell recognition, and is partly due to provision of cognate help for allo-specific B cells from donor CD4 T cells recognizing B cell MHC class II in a peptide-degenerate manner. Passenger donor lymphocytes may therefore influence recipient alloimmune responses and represent a therapeutic target in solid organ transplantation.

Summary: MHC alloantigen is recognized by two pathways: “directly,” intact on donor cells, or “indirectly,” as self-restricted allopeptide. The duration of each pathway, and its relative contribution to allograft vasculopathy, remain unclear. Using a murine model of chronic allograft rejection, we report that direct-pathway CD4 T cell alloresponses, as well as indirect-pathway responses against MHC class II alloantigen, are curtailed by rapid elimination of donor hematopoietic antigen-presenting cells. In contrast, persistent presentation of epitope resulted in continual division and less-profound contraction of the class I allopeptide-specific CD4 T cell population, with approximately 10,000-fold more cells persisting than following acute allograft rejection. This expanded population nevertheless displayed sub-optimal anamnestic responses and was unable to provide co-stimulation-independent help for generating alloantibody. Indirect-pathway CD4 T cell responses are heterogeneous. Appreciation that responses against particular alloantigens dominate at late time points will likely inform development of strategies aimed at improving transplant outcomes. : The T cell allorecognition pathways governing rejection of organ allografts are poorly understood. Using a model of chronic heart graft rejection, Ali et al. show that continual presentation of MHC class I allopeptide drives late division in the responding alloreactive CD4 T cell population, resulting in a markedly augmented maintenance phase.

This paper presents the Monte Carlo simulation of the attenuation correction for Positron Emission Tomography (PET) data using MCNP6 code. Two attenuation correction maps have been generated, one for correcting the attenuation effect in a homogeneous phantom, which is a cylindrical volume of water and the other for correcting the attenuation effect in a heterogeneous phantom, which is a cylindrical volume of water within which, there are two small cylinders of bone-equivalent materials. These maps are derived from the data acquired as a result of transmission scans using a positron-emitting rod source. The attenuation map generated using this method does not need to be scaled because it is directly built for an energy of 511 keV. For each phantom, three types of simulations are done, one to estimate the radiotracer distribution in the phantom (emission scan) and two to estimate the distribution of attenuation coefficients in this phantom (transmission scans), the first with a blank field of view (FOV) and the second when the phantom exists in the FOV. From the transmission scans data, the attenuation map for each phantom is derived and after that it has been applied to the corresponding emission scan data during PET image reconstruction process to obtain the attenuation-corrected image. The images of the radiotracer distribution in each phantom reached in this study illustrate the quantitative and qualitative improvements in the image quality after attenuation correction than that before the attenuation correction.

Carboxyterfenadine, a primary metabolite of terfenadine, a second generation antihistaminic compound was introduced in therapy as a successor of terfenadine due to its cardiac arrhythmia. There are number of drug interactions of fexofenadine with erythromycin, ketoconazole and alike reported in the literature. In this paper, fexofenadine antacid interaction has been studied in presence of sodium bicarbonate, megaldrate, calcium carbonate, magnesium carbonate, aluminum hydroxide, magnesium hydroxide, magnesium trisilicate, simethicone (dimethylpolysiloxane) and calcium hydroxide by UV–Vis spectrophotometer and high performance liquid chromatography (HPLC). These in vitro fexofenadine–antacid interactions were carried out in simulated gastric and intestinal juices and in buffer of pH 7.4 (simulating blood pH) on BP 2005 dissolution apparatus. The results show non-concordant availability of fexofenadine envisaged due to formation of unstable charge transfer complexes.

We study a location-inventory problem in a three level supply chain network under uncertainty, which leads to risk. The (r,Q) inventory control policy is applied for this problem. Besides, uncertainty exists in different parameters such as procurement, transportation costs, supply, demand and the capacity of different facilities (due to disaster, man-made events and etc). We present a robust optimization model, which concurrently specifies: locations of distribution centers to be opened, inventory control parameters (r,Q), and allocation of supply chain components. The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on the basis of multi-objective particle swarm optimization for solving the proposed model. Eventually, computational results of different examples of the problem and sensitivity analysis are exhibited to show the model and algorithm's feasibility and efficiency.

The use of pulverised biomass as a replacement for coal in power generation is a major source of renewable electricity in the UK. However, there is little data on flame propagation rates for pulverised biomass and this makes it difficult to design burners and model these flames using the traditional burning velocity approach. AnISO 1 m3 dust explosion vessel was modified to enable constant pressure spherical expanding turbulent flamespeeds, ST, to be measured for pulverised biomass. The turbulence generated by the new dust injection system was calibrated using turbulent and laminar gas explosions and this enabled the dust laminar flame speed, SL, to be determined from ST. The burnt gas expansion ratio, EP, was determined from the peak pressure rise and this enabled the laminar burning velocity, Su, to be determined from the measured ST. The results showed for a range of biomass that the peak ST was in the range 3–5.5 m/s. The laminar burning velocity for 26 biomass and two coal samples and were very low at 0.1 – 0.2 m/s for the most reactive mixture, depending on the biomass particle size.

Present research work was conducted during rice growing season of 2013 at the paddy experimental field of Tsukuba International Center, Japan International Cooperation Agency (JICA) Japan. The experiment was laid out in a Randomized Complete Block Design with three replications. Three japonica and one indica varieties of rice viz. Koshihikari, Fukuhibiki, Hitomebore and IR-50 were screened out. High yielding and best performing varieties in respect to yield and yield components were identified. All varieties were transplanted at spacing of 30 cm x 15 cm using three seedlings per hill. The results showed that varieties IR-50, Koshihikari and Hitomebore performed better in terms of yield as compared to Fukuhibiki by producing 7.77, 6.96 and 6.75 tons ha-1 more yield, respectively. The variety IR-50 with high paddy grain yield also retained high number of panicles/m2, high ripening ratio and reasonable spikelets/m2. On the basis of these results, IR-50, Koshihikari and Hitomebore gave high biological and economical yield, therefore these varieties are recommended for cultivation in specific rice growing areas.

ABSTRACT: Dilated Cardiomyopathy (DCM) is a common problem and it is very important to differentiate its ischemic cause from nonischemic for management and prognostic purposes. OBJECTIVE: To evaluate rest gated myocardial perfusion scintigraphy (GMPS) in patients with ischemic and nonischemic DCM, categorized on the basis of coronary angiography, and to compare mean summed perfusion score (SPS), mean summed thickening score (STS) and mean extent of perfusion abnormality (EPA) between them. DESIGN: Descriptive case series. PATIENTS & METHODS: This study was conducted at Nuclear Cardiology department of Punjab Institute of Nuclear Medicine (PINUM), Faisalabad over a period of six months from 0101-2013 to 30-06-2013. Resting GMPS were performed in 102 known patients of DCM (aged 17 to 70 years with a mean age of 49.12 ± 11.8 years, 86 Male and 16 Female) by injecting 20 mCi of 99mTc-MIBI. Patients were subdivided into ischemic (n=74) and nonischemic subgroup (n=28) by using the coronary angiography results. Summed perfusion score (SPS), summed thickening score (STS) and extent of perfusion abnormality (EPA) were calculated by using twenty segment model. Results of GMPS were compared between ischemic DCM and nonischemic DCM by using independent samples t-test. P-value of

The maximal covering location problem (MCLP) seeks to locate a predefined number of facilities in order to maximize the number of covered demand points. In a classical sense, MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations are extended: the gradual cover models, the cooperative cover models, and the variable radius models. In this paper, we develop a special form of MCLP which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed problem has many applications such as locating cell phone towers. The model is formulated as a mixed integer non-linear programming (MINLP). In addition, a simulated annealing algorithm is used to solve the resulted problem and the performance of the proposed method is evaluated with a set of randomly generated problems.