Clinical Outcomes in Hormone Replacement Therapy (HRT)-Frozen Embryo Transfer (FET) Protocol Increased by Administering Gonadotropin-Releasing Hormone Agonist (GnRH) in the Initial Stage of the Luteal Phase: A Retrospective Analysis.

OBJECTIVE: The objective of this study was to determine if gonadotropin-releasing hormone agonist (GnRH) administration supporting the luteal phase in frozen embryo transfer (FET) improves clinical outcomes Methods and materials This is a retrospective cohort study and we analyzed 3515 cycles of FET at the Department of Reproductive Medicine in our hospital from February 2018 through December 2021. Patients were divided into the GnRH (triptorelin+progesterone and human chorionic gonadotropin (hCG)) group and the non-GnRHa (existing treatment without triptorelin) group. There were 1033 and 2485 cases in the above groups, respectively. Live birth rates (LBR) and clinical pregnancy rates (CPR) were contrasted in the two groups. RESULTS: We found greater CPR (58.00% versus 48.40%, P-value = 0.003) and LBR (52.70% versus 45.60%, P-value = 0.001) for HRT-FET cycles, and found no clinical significance for natural cycle FET (NC-FET) (58.20% versus 52.90%, P-value = 0.364 and 54.40% versus 47.00%, P-value=0.211), GnRH+HRT-FET (53.00% versus 53.00%, P-value=0.176 and 46.20% versus 47.30%, P-value=0.794), and stimulation-FET (59.30% versus 52.90%, P-value=.00.566 and 59.30% versus 47.10%, P-value=.00.247) in terms of CPR and LBR in the two groups. There was a 47% increase in CPR in the GnRH group, and there was a 33% increase in LBR in the same group. CONCLUSION: During HRT-FET cycles, administering triptorelin three to four times in the existing luteal support can improve CPR and LBR, and administering triptorelin during the initial stage of the luteal phase can prove a new option for luteal support.

Incorporation of Cellulose-Based Aerogels into Textile Structures.

Given their exceptional attributes, aerogels are viewed as a material with immense potential. Being a natural polymer, cellulose offers the advantage of being both replenishable and capable of breaking down naturally. Cellulose-derived aerogels encompass the replenish ability, biocompatible nature, and ability to degrade naturally inherent in cellulose, along with additional benefits like minimal weight, extensive porosity, and expansive specific surface area. Even with increasing appreciation and acceptance, the undiscovered possibilities of aerogels within the textiles sphere continue to be predominantly uninvestigated. In this context, we outline the latest advancements in the study of cellulose aerogels' formulation and their diverse impacts on textile formations. Drawing from the latest studies, we reviewed the materials used for the creation of various kinds of cellulose-focused aerogels and their properties, analytical techniques, and multiple functionalities in relation to textiles. This comprehensive analysis extensively covers the diverse strategies employed to enhance the multifunctionality of cellulose-based aerogels in the textiles industry. Additionally, we focused on the global market size of bio-derivative aerogels, companies in the industry producing goods, and prospects moving forward.

Combined Use of Modal Analysis and Machine Learning for Materials Classification.

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

Geopolymers and Fiber-Reinforced Concrete Composites in Civil Engineering.

This paper discusses the influence of fiber reinforcement on the properties of geopolymer concrete composites, based on fly ash, ground granulated blast furnace slag and metakaolin. Traditional concrete composites are brittle in nature due to low tensile strength. The inclusion of fibrous material alters brittle behavior of concrete along with a significant improvement in mechanical properties i.e., toughness, strain and flexural strength. Ordinary Portland cement (OPC) is mainly used as a binding agent in concrete composites. However, current environmental awareness promotes the use of alternative binders i.e., geopolymers, to replace OPC because in OPC production, significant quantity of CO2 is released that creates environmental pollution. Geopolymer concrete composites have been characterized using a wide range of analytical tools including scanning electron microscopy (SEM) and elemental detection X-ray spectroscopy (EDX). Insight into the physicochemical behavior of geopolymers, their constituents and reinforcement with natural polymeric fibers for the making of concrete composites has been gained. Focus has been given to the use of sisal, jute, basalt and glass fibers.

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites.

This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input-output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.

Multiple Access-Enabled Relaying with Piece-Wise and Forward NOMA: Rate Optimization under Reliability Constraints.

The increasing proliferation of Internet-of-things (IoT) networks in a given space requires exploring various communication solutions (e.g., cooperative relaying, non-orthogonal multiple access, spectrum sharing) jointly to increase the performance of coexisting IoT systems. However, the design complexity of such a system increases, especially under the constraints of performance targets. In this respect, this paper studies multiple-access enabled relaying by a lower-priority secondary system, which cooperatively relays the incoming information to the primary users and simultaneously transmits its own data. We consider that the direct link between the primary transmitter-receiver pair uses orthogonal multiple access in the first phase. In the second phase, a secondary transmitter adopts a relaying strategy to support the direct link while it uses non-orthogonal multiple access (NOMA) to serve the secondary receiver. As a relaying scheme, we propose a piece-wise and forward (PF) relay protocol, which, depending on the absolute value of the received primary signal, acts similar to decode-and-forward (DF) and amplify-and-forward (AF) schemes in high and low signal-to-noise ratio (SNR), respectively. By doing so, PF achieves the best of these two relaying protocols using the adaptive threshold according to the transmitter-relay channel condition. Under PF-NOMA, first, we find the achievable rate region for primary and secondary receivers, and then we formulate an optimization problem to derive the optimal PF-NOMA time and power fraction that maximize the secondary rate subject to reliability constraints on both the primary and the secondary links. Our simulation results and analysis show that the PF-NOMA outperforms DF-NOMA and AF-NOMA-based relaying techniques in terms of achievable rate regions and rate-guaranteed relay locations.

Photocatalytic Behaviour of Zinc Oxide Nanostructures on Surface Activation of Polymeric Fibres.

Zinc oxide (ZnO) in various nano forms (nanoparticles, nanorods, nanosheets, nanowires and nanoflowers) has received remarkable attention worldwide for its functional diversity in different fields i.e., paints, cosmetics, coatings, rubber and composites. The purpose of this article is to investigate the role of photocatalytic activity (role of photogenerated radical scavengers) of nano ZnO (nZnO) for the surface activation of polymeric natural fibres especially cotton and their combined effect in photocatalytic applications. Photocatalytic behaviour is a crucial property that enables nZnO as a potential and competitive candidate for commercial applications. The confirmed features of nZnO were characterised by different analytical tools, i.e., scanning electron microscopy (SEM), field emission SEM (FESEM) and elemental detection spectroscopy (EDX). These techniques confirm the size, morphology, structure, crystallinity, shape and dimensions of nZnO. The morphology and size play a crucial role in surface activation of polymeric fibres. In addition, synthesis methods, variables and some of the critical aspects of nZnO that significantly affect the photocatalytic activity are also discussed in detail. This paper delineates a vivid picture to new comers about the significance of nZnO in photocatalytic applications.

Attitude toward personal genomics: A nation-wide survey of public and professionals in Pakistan.

With the emergence of modern genetic testing and profiling techniques, it has become imperative to assess the general public awareness and attitudes toward such developments. The public's perspective and possible responses are necessary for planning commercial, legal, medical, or healthcare initiatives. The purpose of this study was to assess the perception of the general public and professionals about the personal genome testing and genetic profiling. A questionnaire-based survey was conducted to investigate the attitudes of 2954 adults (56.4% male and 43.6% female) from more than 120 different educational, research, and professional organizations of Pakistan. The aim of the study was to assess interest in genomic testing. The findings of this survey will feed into the larger Genome Projects in Pakistan and will be helpful for the national bioethical committee, healthcare agencies, diagnostic companies, and other institutions for making policy decisions.

Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency.

Nitrogen is the main limiting nutrient after carbon, hydrogen and oxygen for photosynthetic process, phyto-hormonal, proteomic changes and growth-development of plants to complete its lifecycle. Excessive and inefficient use of N fertilizer results in enhanced crop production costs and atmospheric pollution. Atmospheric nitrogen (71%) in the molecular form is not available for the plants. For world's sustainable food production and atmospheric benefits, there is an urgent need to up-grade nitrogen use efficiency in agricultural farming system. The nitrogen use efficiency is the product of nitrogen uptake efficiency and nitrogen utilization efficiency, it varies from 30.2 to 53.2%. Nitrogen losses are too high, due to excess amount, low plant population, poor application methods etc., which can go up to 70% of total available nitrogen. These losses can be minimized up to 15-30% by adopting improved agronomic approaches such as optimal dosage of nitrogen, application of N by using canopy sensors, maintaining plant population, drip fertigation and legume based intercropping. A few transgenic studies have shown improvement in nitrogen uptake and even increase in biomass. Nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase and asparagine synthetase enzyme have a great role in nitrogen metabolism. However, further studies on carbon-nitrogen metabolism and molecular changes at omic levels are required by using "whole genome sequencing technology" to improve nitrogen use efficiency. This review focus on nitrogen use efficiency that is the major concern of modern days to save economic resources without sacrificing farm yield as well as safety of global environment, i.e. greenhouse gas emissions, ammonium volatilization and nitrate leaching.

A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation.

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges.

Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

High frequency and poor prognosis of late childhood BCR-ABL-positive and MLL-AF4-positive ALL define the need for advanced molecular diagnostics and improved therapeutic strategies in pediatric B-ALL in Pakistan.

BACKGROUND: Fusion oncogenes (FOs) resulting from chromosomal abnormalities have an important role in leukemogenesis in pediatric B cell acute lymphoblastic leukemia (ALL). The most common FOs are BCR-ABL, MLL-AF4, ETV6-RUNX1, and TCF3-PBX1, all of which have important prognostic and drug selection implications. Moreover, frequencies of FOs have ethnic variations. We studied Pakistani frequencies of FOs, clinical pattern, and outcome in pediatric B-ALL. METHODS: FOs were studied in 188 patients at diagnosis using reverse transcriptase-polymerase chain reaction (RT-PCR) and interphase fluorescent in situ hybridization (FISH). Data were analyzed using SPSS version 17 (SPSS Inc., Chicago, IL, USA). RESULTS: FOs were detected in 87.2 % of patients. Mean overall survival was 70.9 weeks, 3-year survival was 31.9 %, and 3-year relapse-free survival was 18.1 %. Four patients died of drug toxicities. ETV6-RUNX1 (19.14 %) had better survival (110.9 weeks; p = 0.03); TCF3-PBX1 (2.1 %) was associated with inferior outcome and higher central nervous system (CNS) relapse risk; MLL-AF4 (18.1 %) was more common in the 8- to 15-year age group (24/34; p = 0.001) and was associated with organomegaly, low platelet count, and poor survival; and BCR-ABL (47.9 %) was associated with older age (7-15 years, 52/90), lower remission rates, shorter survival (43.73 ± 4.24 weeks) and higher white blood cell count. Overall, MLL-AF4 and BCR-ABL were detected in 66 % of B-ALL, presented in later childhood, and were associated with poor prognosis and inferior survival. CONCLUSIONS: This study reports the highest ethnic frequency of BCR-ABL FO in pediatric ALL, and is consistent with previous reports from our region. Poor prognosis BCR-ABL and MLL-AF4 was detected in two-thirds of pediatric B-ALL and is likely to be the reason for the already reported poor survival of childhood ALL in South-East Asia. Furthermore, MLL-AF4, usually most common in infants, presented in later childhood in most of the ALL patients, which was one of the unique findings in our study. The results presented here highlight the need for mandatory inclusion of molecular testing for pediatric ALL patients in clinical decision making, together with the incorporation of tyrosine kinase inhibitors, as well as hematopoietic stem cell transplantation facilities, to improve treatment outcome for patients in developing countries.