膏盐岩-碳酸盐岩共生层系岩石微相及储层特征——以阿布扎比B油田侏罗系Arab组为例

为探究膏盐岩–碳酸盐岩共生层系强非均质性问题,基于岩芯及测井资料,探究阿布扎比B油田Arab组岩石微相类型,分析各类微相的储层特征及优质储层主控因素。Arab组可识别出12种微相类型(MF1~MF12),微相类型及组合指示其为局限–蒸发背景下萨布哈潮坪–潟湖–障壁滩沉积体系。微相类型控制储层品质,其中MF2及MF9~MF12孔喉较粗,连通性好,孔隙度和渗透率较高,是储层发育有利微相类型。MF2和MF10发育白云岩储层,储集空间以晶间孔、残余粒间孔及粒内溶孔为主;MF9,MF11和MF12发育颗粒灰岩储层,储集空间以粒间(溶)孔、铸模孔及粒内溶孔为主。相对海平面的震荡性变化导致各沉积相带在纵向上的有序叠置,不同沉积相带之间或同一沉积相带内微相类型及成岩作用的差异性是Arab组储层强非均质性的根本原因。障壁滩和潮上带是优质储层发育的有利相带,其中障壁滩相优质储层原生粒间孔保持较好,并叠加显著的早期暴露溶蚀,导致次生孔隙的产生和孔隙结构的改善;潮上带优质储层的发育受控于早期白云石化和准同生溶蚀作用,白云石化改善孔隙结构,有利于早期孔隙保存,分散状硬石膏的早期溶蚀产生大量次生孔隙,显著地改善了储层物性。

Uncovering the epidemiology of bladder cancer in the Arab world: A review of risk factors, molecular mechanisms, and clinical features

Objective:Bladder cancer(BC)is a significant public health concern in the Middle East and North Africa,but the epidemiology and clinicopathology of the disease and contributors to high mortality in this region remain poorly understood.The aim of this systematic review was to investigate the epidemiological features of BC in the Arab world and compare them to those in Western countries in order to improve the management of this disease.Methods:An extensive electronic search of the PubMed/PMC and Cochrane Library databases was conducted to identify all articles published until May 2022,following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.A total of 95 articles were included in the final analysis after title,abstract,and full-text screening,with additional data obtained from the GLOBOCAN and WHO 2020 databases.

AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format for subsequent processing.Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle.The use of convolutional neural network(CNN)in recent developments has notably advanced HCR,leveraging the ability to extract discriminative features from extensive sets of raw data.Because of the absence of pre-existing datasets in the Kurdish language,we created a Kurdish handwritten dataset called(KurdSet).The dataset consists of Kurdish characters,digits,texts,and symbols.The dataset consists of 1560 participants and contains 45,240 characters.In this study,we chose characters only from our dataset.We utilized a Kurdish dataset for handwritten character recognition.The study also utilizes various models,including InceptionV3,Xception,DenseNet121,and a customCNNmodel.To show the performance of the KurdSet dataset,we compared it to Arabic handwritten character recognition dataset(AHCD).We applied the models to both datasets to show the performance of our dataset.Additionally,the performance of the models is evaluated using test accuracy,which measures the percentage of correctly classified characters in the evaluation phase.All models performed well in the training phase,DenseNet121 exhibited the highest accuracy among the models,achieving a high accuracy of 99.80%on the Kurdish dataset.And Xception model achieved 98.66%using the Arabic dataset.

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Enhancement of the Antigenotoxic and Antioxidant Actions of Eugenol from Spice Clove and the Stabilizer Gum Arabic on Colorectal Carcinogenesis

Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the distal segment and entire colon, as well as the number of ACF with at least 5 crypts in the entire colon. Thus, our results also demonstrate the synergistic effects of 10% gum arabic together with eugenol (from cloves), with antioxidant, antigenotoxic and anticarcinogenic actions (prevention and treatment) at the doses and durations studied, in the colon of rats submitted to colorectal carcinogenesis induced by dimethylhydrazine.

Effect of Gum Arabic from Acacia senegal var. kerensis as an Improver on the Rheological Properties of Wheat Flour Dough

Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to increasing consumer awareness, thus contributing to the rising demand for natural hydrocolloids. Gum Arabic from Acacia senegal var. kerensis is a natural gum exhibiting excellent water binding and emulsification capacity. However, very little is reported on how it affects the rheological properties of wheat dough. The aim of this study was therefore, to determine the rheological properties of wheat dough with partial additions of gum Arabic as an improver. Six treatments were analyzed comprising of: flour-gum blends prepared by adding gum Arabic to wheat flour at different levels (1%, 2% and 3%), plain wheat flour (negative control), commercial bread flour and commercial chapati flour (positive controls). The rheological properties were determined using Brabender Farinograph, Brabender Extensograph and Brabender Viscograph. Results showed that addition of gum Arabic significantly (p chapati. These findings support the need to utilize gum Arabic from Acacia senegal var. kerensis as a dough improver.

Physico-Chemical, and Sensory Properties of Mayonnaise Substitute Prepared from Chia Mucilage (Salvia hispanica L.) and Gum Arabic from Acacia senegal var. kerensis

Gum Arabic (GA) from Acacia senegal var. kerensis has been approved as an emulsifier, stabilizer, thickener, and encapsulator in food processing industry. Chia mucilage, on the other hand, has been approved to be used as a fat and egg yolk mimic. However, both chia mucilage and gum Arabic are underutilized locally in Kenya;thus, marginal reports have been published despite their potential to alter functional properties in food products. In this study, the potential use of chia mucilage and gum Arabic was evaluated in the development of an eggless fat-reduced mayonnaise (FRM). The mayonnaise substitute was prepared by replacing eggs and partially substituting sunflower oil with chia mucilage at 15%, 30%, 45%, and 60% levels and gum Arabic at 3% while reducing the oil levels to 15%, 30%, 45%, and 60%. The effect of different concentrations of oil and chia mucilage on the physicochemical properties, for example, pH, emulsion stability, moisture content, protein, carbohydrate, fats, calories, ash, and titratable acidity using AOAC methods and sensory properties for both consumer acceptability and quantitative descriptive analysis of mayonnaise were evaluated and compared to the control with eggs and 75% sunflower oil. The results indicated that all fat-reduced mayonnaises had significantly lower energy to 493 kcal/100g and 20% fat content but higher water content of 0.74 than the control with 784 Kcal/100g calories, 77% fat and 0.39 moisture. These differences increased with increasing substitution levels of chia mucilage, as impacted on pH, carbohydrate, and protein. There was no significant difference between ash content for both fat-reduced mayonnaise and control. Sensory evaluation demonstrated that mayonnaises substituted with chia seeds mucilage and gum Arabic were accepted. All the parameters are positively correlated to overall acceptability, with flavor having the strongest correlation of r = 0.78. Loadings from principal component analysis (PCA) of 16 sensory attributes of mayonnaise showed that approximately over 66% of the variations in sensory attributes were explained by the first six principal components. This study shows good potential for chia mucilage and gum Arabic to be used as fat and egg mimetics and stabilizers, respectively, in mayonnaise with functional properties.

The Hui Medicine Application in Rapid Rehabilitation after Orthopedic Surgery: An Overview

Orthopedic surgeries often require a long recovery period, but Hui medicine offers promising strategies for rapid rehabilitation. This paper explores the integration of Hui medicine into postoperative care, focusing on herbal remedies, physical therapies, and dietary adjustments. It uses a variety of methods, such as pasting, Tazi, acupuncture, diet therapy, medicine therapy, etc., to provide comprehensive treatment for various bone diseases. In the field of Hui medicine, through in-depth research and clinical verification, more therapies and drugs with unique curative effects have been discovered. Orthopedic rehabilitation and fumigation therapy play an important role in the rehabilitation stage of the disease, helping patients recover limb function, reduce pain, and improve quality of life. This paper elaborates on the spread and influence of Islamic Arab medical civilization in China and introduces the outstanding achievements of Hui medicine department and Hui medicine as important branches of this medical system. Through the in-depth study of relevant literature and historical data, the brilliant achievements of Hui medicine in inheritance and innovation are further revealed. In addition, the article also discusses the combination of modern science, technology, and traditional medicine--which has injected new vitality into the development of traditional medicine. Hui medicine, with its unique theoretical system and therapeutic methods, offers promising approaches to enhance the recovery process. Rehabilitation, acupuncture and fumigation treatment are typical representatives of the Sinicization of Islamic Arab medical civilization, and have made important contributions to the development of traditional Chinese medicine. At the same time, the rich experience and unique therapy of Hui medicine provide useful reference and inspiration for modern medicine. This paper overviews the effectiveness of Hui medicine in promoting rapid rehabilitation after orthopedic surgery.

An Assessment of English Instruction for Elementary-Level Students in Jeddah Governorate, as Perceived by English Supervisors and Educators

This study investigated the perceptions of English educators and supervisors in Jeddah Governorate regarding the process of teaching English to elementary students.A survey was conducted using a sample size of 94 educators and 10 supervisors.The data indicate that respondents considered English instruction at the elementary level essential for expanding kids’perspectives,improving academic performance,and promoting international involvement.The main advantages cited are the development of English language skills and the promotion of early education.Although not as easily noticeable,the disadvantages include potential negative impacts on an individual’s proficiency in Arabic and their sense of national identification.The highlighted challenges encompass insufficient teacher training,student reluctance towards English,limited resources,and school disparities.The proposed techniques focused on prioritizing English instructors’training,ensuring the use of appropriate content,utilizing technology,and promoting awareness of students and educators.The current research found different obstacles in teaching English at elementary stages.To overcome these obstacles,it will be essential to enhance teacher competencies,develop efficient teaching methods,get the backing of stakeholders,assign adequate resources,and carry out continuous evaluations.Further research can also contribute to a better understanding of how early English learning impacts on Arabic identity and proficiency.

Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

Arabic Dialect Identification in Social Media:A Comparative Study of Deep Learning and Transformer Approaches

Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.

The Enforcement of Occupational Safety and Health Requirements in Public and Private Sectors in the Emirate of Abu Dhabi, the United Arab Emirates

Research Problem: In Abu Dhabi, limited implementation of OSH Regulations contributes to the general unawareness among employees and workers about occupational hazards and safety measures, resulting in slow responsiveness toward enforcement measures and a lack of self-regulatory approaches within companies. Purpose: The purpose of this study is to examine the implementation methods practised in Abu Dhabi with those in developed countries with established OSH regulatory bodies. Methodology: Qualitative and quantitative research methods were employed to gather primary research data. Workers from various industries in Abu Dhabi were sampled on purpose and asked to respond to questionnaires and interviews on OSH protocol awareness and implementation, and circumstances of workplace incidence. Results: The findings of this study showed that the enforcement of OSH requirements in UAE positively correlated to a reduction in the rate of work-related injury and improved business performance. The quantitative research data showed that the energy sector had the highest score (15) while the tourism sector had the lowest score (5.3) in occupational health systems and improvements in business efficiency and productivity. Implications: The outcomes of this study shed light on the importance of implementing OSH Guidelines for companies to empower their safety managers to fully enforce OSH requirements in their organisations. In conclusion, effective OSH enforcement requires cooperation between general workers and OSH managers and facilitation from business owners.

Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

Hydrothermal synthesis of silver nanoparticles in Arabic gum aqueous solutions

Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperature, mass ratio of AG to AgNO3, and reaction time, silver nanoparticles with different morphological characteristics could be obtained. The products were characterized by UV-Vis, FTIR, TEM, SEM, and XRD measurements. It was found that temperature and AG played an important role in the synthesis of mono-disperse silver nanoparticles. Well dispersed and quasispherical silver nanoparticles were obtained under the optimal synthesis conditions of 10 mmol/L AgNO3, m（AG）/m（AgN03）= l：1, 160 ℃ and 3 h.

波斯湾盆地Arab组—Hith组成藏组合地质特征及油气成藏规律

Arab组—Hith组成藏组合为波斯湾盆地重要的成藏组合之一,侏罗系Arab组碳酸盐岩储集层和Hith组层间硬石膏是成藏组合的基本储盖要素。侏罗系Tuwaiq Mountain组、Hanifa组和Diyab组烃源岩为该成藏组合提供了绝大部分的烃类,烃源岩有机质含量高,在始新世中晚期大面积成熟生油,此时阿尔卑斯构造运动Ⅰ幕已形成众多圈闭,油气生成之后即可直接运移至圈闭中。Arab组储集层分布面积广,以颗粒灰岩为主,成岩阶段早期原生孔隙破坏少,后期普遍经受白云岩化和淋滤作用,储集层物性优越。Hith组硬石膏厚度大,分布面积广,遭受断层的破坏程度较弱,油气很少能穿越它进入上部储集层,仅在相变尖灭带或大型断裂处造成油气垂向逸散。由于侏罗系烃源岩生成的油气大多属近源成藏,油气侧向运移的距离短,最有潜力的区域为侏罗系台内盆地分布区,Arab组储集层的油气丰度主要取决于Hith组盖层的封盖能力。

卡塔尔上侏罗系ARAB“D”储集层的新沉积模式及层序地层学解释

Arab组是沉积在由Hanifa和Jubaila组组成的阿拉伯台地之上．浅水相鲕粒和海岸相藻类、珊瑚、有孔虫等沉积在地台边缘，形成了一个礁堤，把一系列内陆盆地和开阔的古地中海隔开．在启莫里阶(J1)低水位期，石膏楔形层沉积于该盆地，由于水面的轻微变化，礁堤“时隐时现”，形成了Arab组的自然旋回沉积．Arab组的旋回和Vail型的第四旋回的频率相同，但是沉积厚度比典型的Vail型第三旋回厚，引起厚度差异可能是由于第四旋回的洪水事件填满了该盆地的几十米深的预先存在的适宜空间．超盐度卤水的迁移导致了溶蚀孔和白云化的广泛发育．在卡塔尔Arab组中石油的生油岩可能是Hanifa＼Jubaila组的纹层状富含有机质含沥青泥灰岩，主要的储集岩类型为鲕粒＼球粒灰岩和含云灰岩．

Involvement of sphingoid bases in mediating reactive oxygen intermediate production and programmed cell death in Arabidopsis

Sphingolipids have been suggested to act as second messengers for an array of cellular signaling activities in plant cells, including stress responses and programmed cell death （PCD）. However, the mechanisms underpinning these processes are not well understood. Here, we report that an Arabidopsis mutant, fumonisin B1 r_esistant11-1 （/br11-1）, which fails to generate reactive oxygen intermediates （ROIs）, is incapable of initiating PCD when the mutant is challenged by fumonisin B l （FB0, a specific inhibitor of ceramide synthase. Molecular analysis indicated that FBR11 encodes a long-chain base 1 （LCB 1） subunit of serine palmitoyltransferase （SPT）, which catalyzes the first rate-limiting step of de novo sphingolipid synthesis. Mass spectrometric analysis of the sphingolipid concentrations revealed that whereas the fbr11-1 mutation did not affect basal levels of sphingoid bases, the mutant showed attenuated formation of sphingoid bases in response to FBl. By a direct feeding experiment, we show that the free sphingoid bases dihydrosphingosine, phytosphingosine and sphingosine efficiently induce ROI generation followed by cell death. Conversely, ROI generation and cell death induced by dihydrosphingosine were specifically blocked by its phosphorylated form dihydrosphingosine- 1-phosphate in a dosedependent manner, suggesting that the maintenance of homeostasis between a free sphingoid base and its phosphorylated derivative is critical to determining the cell fate. Because alterations of the sphingolipid level occur prior to the ROI production, we propose that the free sphingoid bases are involved in the control of PCD in Arabidopsis, presumably through the regulation of the ROI level upon receiving different developmental or environmental cues.