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"Ali, Akhtar"
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Philosophers, sufis, and caliphs : politics and authority from Cordoba to Cairo and Baghdad
What was the relationship between government and religion in Middle Eastern history? In a world of caliphs, sultans, and judges, who exercised political and religious authority? In this book, Ali Humayun Akhtar investigates debates about leadership that involved ruling circles and scholars of jurisprudence and theology. At the heart of this story is a medieval rivalry between three caliphates: the Umayyads of Cordoba, the Fatimids of Cairo, and the Abbasids of Baghdad. In a fascinating revival of Late Antique Hellenism, Aristotelian and Platonic notions of wisdom became a key component of how these caliphs debated their authority as political leaders. By tracing how these political debates impacted the theological scholars and their own conception of communal guidance, Akhtar offers a new picture of premodern political authority and the connections between Western and Islamic civilizations. It will be of use to students and specialists of the premodern and modern Middle East.
Book
Aflatoxin Contamination, Its Impact and Management Strategies: An Updated Review
Aflatoxin, a type of mycotoxin, is mostly produced by Aspergillus flavus and Aspergillus parasiticus. It is responsible for the loss of billions of dollars to the world economy, by contaminating different crops such as cotton, groundnut, maize, and chilies, and causing immense effects on the health of humans and animals. More than eighteen different types of aflatoxins have been reported to date, and among them, aflatoxins B1, B2, G1, and G2 are the most prevalent and lethal. Early detection of fungal infection plays a key role in the control of aflatoxin contamination. Therefore, different methods, including culture, chromatographic techniques, and molecular assays, are used to determine aflatoxin contamination in crops and food products. Many countries have set a maximum limit of aflatoxin contamination (2–20 ppb) in their food and agriculture commodities for human or animal consumption, and the use of different methods to combat this menace is essential. Fungal infection mostly takes place during the pre- and post-harvest stage of crops, and most of the methods to control aflatoxin are employed for the latter phase. Studies have shown that if correct measures are adopted during the crop development phase, aflatoxin contamination can be reduced by a significant level. Currently, the use of bio-pesticides is the intervention employed in many countries, whereby atoxigenic strains competitively reduce the burden of toxigenic strains in the field, thereby helping to mitigate this problem. This updated review on aflatoxins sheds light on the sources of contamination, and the on occurrence, impact, detection techniques, and management strategies, with a special emphasis on bio-pesticides to control aflatoxins.
Journal Article
Italy and the Islamic world : from Caesar to Mussolini
'Italy and the Islamic World' tells the story of how Italian cities have been centres of international exchange for centuries, linking Europe with the most storied marketplaces of the Middle East and North Africa. From the Ancient Roman period and the Renaissance to the rise of the Italian Republic, Italy has been a global crossroads for more than two millennia. In Ali Humayun Akhtar's new picture of European history, Italy's debates about trade with its southern neighbours evoke an earlier era of encounters - one that sheds light on where the EU is heading today.
BOOK
An advanced deep learning models-based plant disease detection: A review of recent research
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
Journal Article
Privilege-resistant policies in the Middle East and North Africa : measurement and operational implications
\"Renewing the social contract--one of the pillars of the new World Bank Group strategy for the Middle East and North Africa--requires a new development model built on greater trust; openness, transparency, inclusive and accountable service delivery; and a stronger private sector that can create jobs and opportunities for the youth of the region. Recent analytic work trying to explain weak job creation and insufficient private sector dynamism in the region point to formal and informal barriers to entry and competition. These barriers privilege a few (often unproductive) incumbents who enjoy a competition edge due to their connections or ability to influence policy making and delivery. Policy recommendations to date in the field of governance for private sector policymaking have been too general and too removed from concrete, actionable policy outcomes. This report proposes--for the first time--to fill this policy and operational gap by answering the following question: What good governance features should be instilled in the design of economic policies and institutions to help shield them from capture, discretion and arbitrary implementation? Privilege-Resisitant Policies in the Middle East and North Africa benchmarks eight countries on a number of policy areas with regard to their vulnerability to privilege-seeking. The book offers various operational and technical entry points to enhance privilege-resistant policy making in a concrete way that is politically tractable in different country contexts.\"--Page 4 of cover.
Book
Mitochondrial regulation in the tumor microenvironment: targeting mitochondria for immunotherapy
Mitochondrial regulation plays a crucial role in cancer immunity in the tumor microenvironment (TME). Infiltrating immune cells, including T cells, natural killer (NK) cells, and macrophages, undergo mitochondrial metabolic reprogramming to survive the harsh conditions of the TME and enhance their antitumor activity. On the other hand, immunosuppressive cells like myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), mast cells, and tumor-associated macrophages (TAMs) rely on mitochondrial regulation to maintain their function as well. Additionally, mitochondrial regulation of cancer cells facilitates immune evasion and even hijacks mitochondria from immune cells to enhance their function. Recent studies suggest that targeting mitochondria can synergistically reduce cancer progression, especially when combined with traditional cancer therapies and immune checkpoint inhibitors. Many mitochondrial-targeting drugs are currently in clinical trials and have the potential to enhance the efficacy of immunotherapy. This mini review highlights the critical role of mitochondrial regulation in cancer immunity and provides lists of mitochondrial targeting drugs that have potential to enhance the efficacy of cancer immunotherapy.
Journal Article
Desensitization of ABA-Signaling: The Swing From Activation to Degradation
Abscisic acid (ABA) is a key plant stress-signaling hormone that accumulates upon osmotic stresses such as drought and high salinity. Several proteins have been identified that constitute the ABA-signaling pathway. Among them ABA receptors (PYR/PYL/RCAR), co-receptor PP2Cs (protein phosphatases), SnRK2 kinases (SNF1-related protein kinases) and ABI5/ABFs (transcription factors) are the major components. Upon ABA signal, PYR/PYL receptors interact with and recruit PP2Cs, releasing SnRK2s kinases from sequestration with PP2Cs. This allows SnKR2s to promote the activation of downstream transcription factors of ABA pathway. However, apart from activation, ubiquitination and degradation of core proteins in the ABA pathway by the ubiquitin proteasome system is less explored. In this review we will focus on the recent findings about feedback regulation of ABA signaling core proteins through degradation, which is emerging as a critical step that modulates and eventually ceases the signal relay. Additionally, we also discuss the importance of the recently identified effector protein HOS15, which negatively regulate ABA-signaling through degradation of OST1.
Journal Article
Ppb-Level Hydrogen Sulfide Gas Sensor Based on the Nanocomposite of MoS2 Octahedron/ZnO-Zn2SnO4 Nanoparticles
Hydrogen sulfide (H2S) detection is extremely necessary due to its hazardous nature. Thus, the design of novel sensors to detect H2S gas at low temperatures is highly desirable. In this study, a series of nanocomposites based on MoS2 octahedrons and ZnO-Zn2SnO4 nanoparticles were synthesized through the hydrothermal method. Various characterizations such as X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectrum (XPS) have been used to verify the crystal phase, morphology and composition of synthesized nanocomposites. Three gas sensors based on the nanocomposites of pure ZnO-Zn2SnO4 (MS-ZNO-0), 5 wt% MoS2-ZnO-Zn2SnO4 (MS-ZNO-5) and 10 wt% MoS2-ZnO-Zn2SnO4 (MS-ZNO-10) were fabricated to check the gas sensing properties of various volatile organic compounds (VOCs). It showed that the gas sensor of (MS-ZNO-5) displayed the highest response of 4 to 2 ppm H2S and fewer responses to all other tested gases at 30 °C. The sensor of MS-ZNO-5 also displayed humble selectivity (1.6), good stability (35 days), promising reproducibility (5 cycles), rapid response/recovery times (10 s/6 s), a limit of detection (LOD) of 0.05 ppm H2S (Ra/Rg = 1.8) and an almost linear relationship between H2S concentration and response. Several elements such as the structure of MoS2, higher BET-specific surface area, n-n junction and improvement in oxygen species corresponded to improving response.
Journal Article
Genome-wide identification of heavy-metal ATPases genes in Areca catechu: investigating their functionality under heavy metal exposure
Heavy-metal ATPases (HMAs) play a vital role in plants, helping to transport heavy metal ions across cell membranes.However, insufficient data exists concerning HMAs genes within the
Arecaceae
family.In this study, 12
AcHMA
genes were identified within the genome of
Areca catechu
, grouped into two main clusters based on their phylogenetic relationships.Genomic distribution analysis reveals that the
AcHMA
genes were unevenly distributed across six chromosomes. We further analyzed their physicochemical properties, collinearity, and gene structure.Furthermore, RNA-seq data analysis exhibited varied expressions in different tissues of
A. catechu
and found that
AcHMA1
,
AcHMA2
, and
AcHMA7
were highly expressed in roots, leaves, pericarp, and male/female flowers. A total of six
AcHMA
candidate genes were selected based on gene expression patterns, and their expression in the roots and leaves was determined using RT-qPCR under heavy metal stress. Results showed that the expression levels of
AcHMA1
and
AcHMA3
genes were significantly up-regulated under Cd2 + and Zn2 + stress. Similarly, in response to Cu
2+,
the
AcHMA5
and
AcHMA8
revealed the highest expression in roots and leaves, respectively. In conclusion, this study will offer a foundation for exploring the role of the
HMAs
gene family in dealing with heavy metal stress conditions in
A. catechu
.
Journal Article
Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems
Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient’s prognosis and the accuracy of the initial diagnosis. Clinical examination of skin lesions is a key aspect that is important in the assessment of skin disease but comes with some drawbacks mainly with interpretational aspects, time-consuming and healthare expenditure. Skin cancer if detected early and treated in time can be controlled and its deadly impacts arrested completely. Algorithms applied in convolutional neural network ( CNN ) could lead to an enhanced speed of identifying and distinguishing a disease, which in turn leads to early detection and treatment. So as to eliminate these challenges, optimized CNN prediction models for cancer skin classification is studied in this researche. The objectives of this study were to develop reliable optimized CNN prediction models for skin cancer classification, to handle the severe class imbalance problem where skin cancer class was found to be much smaller than the healthy class. To evaluate model interpretability and to develop an end-to-end smart healthcare system using explainable AI ( XAI ) such as Grad-CAM and Grad-CAM++ . In this researche new activation function namely NGNDG-AF was offered specifically to enhance the capabilities of network fitting and generalization ability, convergence rate and reduction in mathematical computational cost. A research used an optimized CNN and ResNet152V2 with the HAM10000 dataset to differentiate between the seven forms of skin cancer. Model training involved the use of two optimization functions ( RMSprop and Adam ) and NGNDG-AF activation functions. Cross validation technique the holdout validation is used to estimate of the model’s generalization performance for unseed data. Optimized CNN is performing well as compare to ResNet152V2 for unseen data. The efficacy of the optimized CNN method with NGNDG-AF was examined by a comparative study wirh popular CNN with various activation functions shows that better performance of NGNDG-AF , achieving the classification accuracy rates that are as high as 99 % in training and 98 % in the validation. The recommended system also involves the integration of the smart healthcare application as a central component to give the doctors as well as the healthcare providers diagnosing and tools that would assist in the early detection of skin cancer hence leading to better outcomes of the treatment.
Journal Article