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The approach presented in this study is about the calculation of the similarities among languages by using the new feature template to be obtained from the syntactic analysis phase. Studies were conducted on 6 different language sets from two different language families in order to show the calculability of similarity of languages with the help of the recommended new feature template. In the first study, triplet-pattern template which is obtained from the syntactic analysis of Turkey, Kazakh, and Uyghur Turkish languages from Turkic languages families belonging to the Ural-Altaic linguistic family, could be formed automatically through developed software, and also similarity analysis of the desired languages could be made thanks to a different module developed within the same software. Consequently, not only similar structural relations of the languages from the same language family but also structural differences among the languages can also be revealed. Even if the first aim is to determine the similarities among languages when developing an approach, the real aim of the desired structure is to form a system independent from the language. In order to show that the formed system has a structure independent from the language, another study was carried out. In the second study, the similarities among the languages were determined by using treebanks of English, Swedish and Norwegian from the Germen language family. When the language family is Turkic and the metrics are Jaccard, Dice, and Similarity Matching, the highest similarity is Turkish-Uyghur, and the values of the metrics are 25.21%, 40.27%, and 50.42%, respectively. When the language family is Germen, the highest similarity is Norwegian-Swedish, and the values of the metrics are 37.15%, 54.17%, and 74.3, respectively.

Mobile data collectors that can be used without a central control mechanism currently have common use in many fields. Because they do not need a central unit, each node in a network can move independently. The field literature offers various group- or entity-based models to define the functioning of mobile data collectors. In this study, a random entity mobility model (REMM) research was performed. The study was based on the models random walk (RW) and random waypoint (RWP), used in several former studies mentioned in the literature. Furthermore, the models random point (RP) and random journey (RJ) proposed by Bilgin [1] for two-dimensional (2D) space were transferred to three-dimensional (3D–cubic) to be used in the study. Study findings obtained by defining a various number of fixed nodes in areas of various sizes were analyzed using 4 different metrics. It was observed that 4 different metric values decreased for 4 REMMs when the cubic area was enlarged by increasing the edge lengths (150–200–250 pixel) of the cubic. When the cubic’s edge length is 150-200-250 pixel, respectively, connected node ratio (CNR) metric value is 98.04%–95.8%–91.34% for RP and 96.83%-83.23%-70% for RJ. Provided that the cubic area remains constant, the increases in the number of nodes generally tend to increase, although there are slight fluctuations on the results. When the cubic edge is 200 and the node numbers are 4–6–8–10, the message delay is 13.345–16.566–27.386–40.050 seconds for RW and 6.579–9.124–11.431–13.456 seconds for RWP. In the comparisons made by taking the average of the values obtained according to the size of the cubic area and the number of nodes, the RP model reached the highest values for all metrics. For example, the visited node ratio (VNR) metric average for the cubic edge 200 pixels is 98.76% for RP and 94.68%–87.38%–94.78% for RW–RWP–RJ. The VNR metric for the cubic edge 250 is 96.55%–93.7%–87.45%–51.27% for the RP–RW–RWP–RJ. Similarly, the average values obtained for other metrics prove this situation. In addition, when the results of the study are examined, it has been measured that the RP model can deliver the message to the base with less delay than other models. The average delay for the cubic edge 150 is 2.933–27.667–23.236–5.698 second for the RP–RW–RWP–RJ and 2.846–24.337–10.148-4.293 second when the edge is 200. When the average results obtained were examined, the success ranking in the delay metric was RP–RJ–RWP and RW, while the other metrics were formed as RP–RJ–RW–RWP. Considering all the obtained results, it was seen that the proposed two models achieved better results than the existing models in 3D after 2D.

In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. Humans and animals learn much better when gradually presented in a meaningful order showing more concepts and complex samples rather than randomly presenting the information. The use of such training strategies in the context of artificial neural networks is called curriculum learning. In this study, a strategy was developed for curriculum learning. Using the CIFAR-10 and CIFAR-100 training sets, the last few layers of the pre-trained on ImageNet Xception model were trained to keep the training set knowledge in the model’s weight. Finally, a much smaller model was trained with the sample sorting methods presented using these difficulty levels. The findings obtained in this study show that the accuracy value generated when trained by the method we provided with the accuracy value trained with randomly mixed data was more than 1% for each epoch. Keywords: Curriculum learning, model distillation, deep learning, academia, neural networks.

It has become possible to collect data from geographically large areas with smart devices that are prevalently used today. Sensors that are integrated into smart devices make it possible for these devices to receive and transmit data wirelessly. The most important problem of this model that is known as mobile crowd sensing and that allows inferences on the data obtained from its users is lack of data. The main reason for this problem is the lack of sufficient usage of the sensors on devices by the user. To increase the amount of data collected, while users may be incentivized in various ways, the amount and accuracy of the collected data may be increased by developing random entity mobility models REMMs . In this study, two new models random point and random journey were proposed as alternatives to existing REMMs. In the experiment environment that was created to measure the performances of the proposed models, their performances were compared to those that are currently used prevalently random waypoint RWP , random walk RW , and random direction RD . In the experiment environment, the performances were compared in terms of three different metrics visiting rates of nodes, rates of reaching the basis, and the number of messages they carried to the basis . The greatest increase in differently sized areas and at different numbers of nodes in the RP model in terms of rates of reaching the basis was 2.6% compared to RWP, 7% compared to RW, and 46.34% compared to RD, while these values for the number of nodes that were visited were 3% compared to RWP, 1.5% compared to RW, and 17.67% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1465.4, 2933.46, and 7260.12 more messages than those in respectively RWP, RW, and RD. The greatest increase in differently sized areas and at different numbers of nodes in the RJ model in terms of reaching the basis was 1% compared to RWP, 3.5% compared to RW, and 25% compared to RD, while these values for the number of nodes that were visited were 0.75% compared to RWP, 2% compared to RW, and 21.4% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1109.56, 1534.26, and 4488.5 more messages than those in RWP, RW, and RD, respectively.

In this study, we discuss the solution to the vehicle routing problem for a heterogeneous fleet with a depot and a time window satisfied by meeting customer demands with various constraints. A 3-stage hierarchical method consisting of transportation, routing, and linear correction steps is proposed for the solution. In the first stage, customer demands have the shortest routing. They were clustered using the annealing simulation algorithm and assigned vehicles of appropriate type and equipment. In the second stage, a genetic algorithm was used to find the optimal solution that satisfies both the requirements of the transported goods and the customer requirements. In the third stage, an attempt was made to increase the optimality by linear correction of the optimal solution found in the second stage. The unique feature of the application is the variety of constraints addressed by the problem and the close proximity to real logistics practice.

Object detection applications started to take their places in our lives with the improvements in technology and artificial intelligence. In daily life, some applications such as license plate detection, optical character recognition become indispensable. In parallel with ongoing technological developments today, the technologies which soon will be a part of our daily life such as detection of suspicious situations through security cameras and autonomous cars, are improving rapidly. The studies in the object detection area are have generally focused on opaque objects. The number of studies on transparent objects is very limited. In this study, a system which is trained by the dataset that contains transparent and non-transparent glasses, is used to detect transparency of these glasses. Recently introduced capsule networks are used to develop a proposed system. To compare the obtained results, LeNet, AlexNet, and ResNet are trained and tested with the same dataset. When the result of the study is evaluated, it was seen that CapsNet got better results on classification accuracy than other deep learning methods which are used in this study. The obtained test accuracy, precision, recall, and F-score values for CapsNet are 91.58%, 91.61%, 91.58%, and 91.56%, respectively. The best obtained test accuracies for LeNet, AlexNet, and ResNet are 84.58%, 88.58%, and 87.58%, respectively. Thus, AlexNet got the second-best result on classification accuracy. While the ResNet took third place, LeNet stayed behind all of them.

Named entity recognition is a subject, inside of information retrieval which is a subdomain of natural processing. It pertains to identifying and labeling of location, person, organization, etc., inside of text content. Named entity recognition provides identifying and classifying of person, area, etc. inside of formal and informal text content and it can be used for different purposes as question answering systems and removal of the relation between events. In this work, named entity recognition is performed and one method is suggested and results are discussed for assignment to unlabeled name entities by using OpenNLP library with the help of KNIME program in the data set.

İnternetin günlük hayatımızdaki artan kullanımı ile beraber sosyal medya organlarının gelişimi de paralellik göstermektedir. Mikroblog adı verilen facebook ve twitter benzeri uygulamaları ile anlık duyguları ve düşünceleri ifade etmek son derece yaygın bir hale gelmiştir. Mikroblog sitelerinin en yaygın kullanıma sahip olanlarından birisi de Twitter uygulamasıdır. Twitter üzerinden paylaşılan mesajlar bir ürün ya da hizmet hakkında olabileceği gibi bir kişiyle ilgili bir yorumda olabilmektedir. Yapılan yorumun belirtmek istediği anlamı ve duyguyu belirleyebilmek son dönemdeki gözde konulardan biridir. Bir ürün ya da hizmet hakkında yapılan binlerce yorumun tek tek okunup anlamlandırılması ve yorumlayanların fikirlerinin sınıflandırılması geleneksel yöntemlerde oldukça zaman ve emek alan bir alandır. Gerek makine öğrenmesi ve derin öğrenme algoritmalarındaki gelişmeler gerekse de bunları işleyip yorumlayacak bilgisayar sistemlerinin gelişimine parallel olarak milyonlarca veri üzerinde duygu sınıflandırılması mümkün hale gelmiştir. Gerçekleştirdiğimiz çalışmada Türkçe ve İngilizce tivitler üzerinde duygusal sınıflandırma çalışması gerçekleştirilmiştir. Döküman vektörleri (Doc2Vec) kullanılarak yapılan çalışmada hem DBoW ve DM gibi iki farklı döküman vektörü yönteminin çalışması hemde Yarı Danışmanlı ve Danışmanlı öğrenmenin etkileri araştırılmıştır. Çalışma sonuçları doğruluk, kesinlik, anma, özgünlük ve F-ölçütü metrikleri ile raporlanmıştır. Gerçekleştirilen çalışma sonucunda Yarı Danışmanlı öğrenme yöntemi hem Türkçe hemde İngilizce veri kümesinde Danışmanlı öğrenmeye göre daha başarılı sonuçlar elde etmiştir.

Bu çalışmada duygu analizi için oluşturulan iki farklı veri kümesi, kelime vektörü algoritması olan Word2Vec ile modellenmiştir. Model oluşturulurken Word2Vec’in iki farklı yöntemi olan CBoW (Continous Bag of Words) ve Skip-Gram kullanılmıştır. Word2Vec ile bir metnin modelini oluşturmak için genellikle Ortalama yöntemi kullanılmaktadır. Bu çalışmada hem CBoW hem de Skip-Gram yöntemleriyle bir metni modellemek için üç farklı yöntem önerilmiştir. Model oluşturma (eğitim zamanı) süreleri her ikisi içinde ölçülmüştür. Sonuç olarak modelleme süresi açısından CBoW’un Skip-Gram’dan daha başarılı olduğu deneysel olarak gösterilmiştir.

Bu çalışma, yazarı bilinmeyen bir dokümanının yazarını tahmin etmeyi amaçlamaktadır. Bunun için 6 farklı köşe yazarına ait 6 köşe yazısı öncelikle ön-işlem aşamasına sokulmuştur. Ardından bu metinler üzerinden n-gram (2-3) ile özellikler çıkarılmıştır. Çıkarılan özellikler üzerinden sistem 6 farklı makine öğrenmesi üzerinde çapraz geçerleme (10) ile test edilmiştir. Buraya kadar olan kısım literatürde şimdiye kadar uygulanmış olan yöntemdir. Bizim önerimiz ön işlem aşamasının ardından eldeki metinleri LZW algoritması ile kayıpsız sıkıştırarak özellik sayısını azaltmak ve bunun sistemin başarısı üzerindeki etkileri araştırmak üzerinedir. Ön-işlemden geçmiş olan metinler LZW algoritması ile binary (ikili) ve decimal (onlu) olarak sıkıştırılır. Sıkıştırmanın ardından n-gram (2-3) ile çıkarılan özellikler ile sistem 6 farklı makine öğrenmesi yönteminde test edilmiş ve çalışma sonuçları 5 farklı metrik için incelenmiştir. Yapılan çalışma sonucunda ikili olarak sıkıştırılmış metinler hem 2-gram hem de 3-gramda, 6 farklı makine öğrenmesi algoritmasında da daha iyi sonuçlar elde etmiştir. Random Tree ve Naïve bayes algoritmasında onlu sıkıştırma, ham verinin gerisinde kalsa da diğer 4 algoritmada daha iyi elde sonuçlar elde etmiş ama ortalama başarı değerlerinde geride kalmıştır. Yapılan çalışma sonucunda ikili sıkıştırma tüm metriklerinde diğer iki yönteme göre daha başarılıdır. Yapılan çalışmada yazar tanıma işlemi yapılmış olsa da önerilen bu yöntemin tüm metin sınıflandırma işlemlerinde kullanılabileceği düşünülmektedir.

Dependency Parsing (DP) is the existence of sub-term/upper-term relations between the words that make up that sentence for each sentence in the text. DP serves to produce meaningful information for high-level applications. Correct labeling of the text corpus used in DP studies is very important. There will be mistakes in the results of the studies that will be performed with the wrongly-labeled text corpus. If text corpus is labeled manually or automatically by human beings, then faulty cases will occur. As a result of the cases that may arise from human factors or annotations used for labeling, faulty labels will be on treebanks. In order to prevent these errors, detection, and correction of possible faulty labeling is very important in terms of increasing the accuracy of the studies to be carried out. Manual correction of possible faulty labels requires great effort and time. The purpose of this study is to create a model that automatically finds possible faulty labels and offers new label suggestions for faulty labels. With the help of the proposed model, it is aimed to detect and correct possible faulty labels that are included in a text corpus, and to increase consistency among the text corpus of the same language. With the help of the developed model, suggesting new labels for faulty labels by a language expert will be a great convenient for the specialist. Another advantage of the model is that the developed model provides a language-independent structure. It has succeeded in obtaining successful results in finding and correcting potentially faulty labels in experimental studies for Turkish. An increase in accuracy has been detected in studies carried out for languages other than Turkish. In investigating the accuracy of the results obtained by the system, the results were analyzed with the help of 10 different language experts.

The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.

In this study, the problem of vehicle routing, which arises from supplying a heterogeneous fleet of vehicles with different capacities and features, is discussed. First, a two-level method has been developed to address the transport-carrying assignment and then the route approach. In the first stage of the proposed method, the transported persons are matched with the transported vehicles considering their suitability. In the second stage, the routing process is completed by using the simulated annealing algorithm, which is a metaheuristic method. The novelty aspect of this study is the creation of a solution model by simulating the scheduling model of parallel machines. The implementation of the study was carried out on the distribution plans obtained from a large-scale logistics company performing cost analysis over the total distance. The proposed solution model is compared with the solution method used by the firm and it is concluded that the total distance and the resulting costs are reduced.

Sign language is an argument that has vital importance in the life of hard of hearing people for communication with others. With sign language, a person with disabilities can convey his/her opinions and thoughts by using gestures and mimics to another person. Today, understanding and using sign language of people without disabilities along with the people with disabilities is really important to socialize them. With the development of technology, software tools become usable for identification of sign language and various software products are produced.In this study, recognition of sign language characters via a system, which was trained using images of letters in American sign language, is aimed. Capsule networks, which is proposed in the recent period, are used for training and test processes and compared with LeNet which is one of the first successful and currently using a model of deep learning. As a result of the study, it has been seen that capsule networks are useful for sign language character recognition and produced more successful results than LeNet.

One of the main goals of computerized linguistic studies is automatically finding the elements of a sentence. Separation of sentences with too many judgements into their elements is a more complicated process when compared with simple sentences. In this study, instead of separating the whole sentence into its elements, the separation of sub clauses into their own elements is suggested. This approach can be considered as dividing a hard problem into sub parts; and this had higher rate of achievement when compared with dividing the whole sentence into its elements. Condition Random Fields (CRF) algorithm was used for dividing the sentences automatically into subclauses and finding its elements.

In this paper, a novel translinear loop based, high performance Complementary Metal-Oxide-Semiconductor (CMOS) second-generation differential current conveyor (DCCII) is introduced. By using super source follower transistors, very low equivalent impedances are obtained at input terminals xn and xp. In addition, new voltage-mode (VM) and current-mode (CM) first-order all-pass filters (APFs) are proposed to highlight the performance of the designed CMOS DCCII. The designed CMOS implementation is simulated with HSpice using AMS 0.35 μm real process parameters. It consumes only 1.3 mW power with using ±1.25 V power supply voltages. The simulation results of the proposed CMOS DCII circuit and the experimental results for designed VM APF are in very good agreement with the theoretical ones.

Twitter is one of the most popular microblog sites developed in recent years. Feelings are analysed on the messages shared on Twitter so that users ideas on the products and companies can be determined. Sentiment analysis helps companies to improve their products and services based on the feedback obtained from the users through Twitter. In this study, it was aimed to perform sentiment analysis on Turkish and English Twitter messages using Doc2Vec. The Doc2Vec algorithm was run on Positive, Negative and Neutral tagged data using the Semi-Supervised learning method and the results were recorded.

Sekans etiketleme bir giris dizisine karsilik bir cikis dizisinin uretimidir. Giris ve cikis dizisinin iceriklerine gore dogal dil islemenin bircok konusu (varlik isim tanima, makine cevirisi, morfolojik analiz, cumleleri ogelerine ayirma vb.) sekans etiketleme olarak tanimlanabilir. Baglilik ayristirmasi, bir cumle icerisindeki sozcukler arasindaki iliskilerin ve iliski turlerinin belirlenmesidir ve bir cumlenin anlamsal analizinin yapilabilmesi icin sarttir. Baglilik ayristirmasi sekans etiketleme problemi olarak tanimlandiginda iki cikis dizisinin (iliski turu, iliskili kelime) birden uretilmesi gerekmektedir. Bizim onerimiz, ozellikle Sekans etiketleme problemlerinin cozumunde siklikla kullanilan Sartli Rastgele Alanlarin baglilik ayristirmasi problemi icinde kullanilabilir oldugudur. Ancak Sartli Rastgele Alanlar tek cikis ureten bir yontemdir. Bu zorlugu asabilmek icin iki cikisli (Baglilik Turu ve Baglanilan Kelime) bir problem olan Baglilik Ayristirmasi iki parcaya bolunerek cozulmustur. Ardindan elde edilen sonuclar birlestirilerek sistemin ciktisi olarak verilmistir. Gerceklestirilen bu calisma ile Turkce icin en yuksek baglilik ayristirmasi sonuclarina ulasilmistir.

AIM: Our aims were to assess the phylogeographic patterns of genetic diversity in eastern Mediterranean water frogs and to estimate divergence times using different geological scenarios. We related divergence times to past geological events and discuss the relevance of our data for the systematics of eastern Mediterranean water frogs. LOCATION: The eastern Mediterranean region. METHODS: Genetic diversity and divergence were calculated using sequences of two protein-coding mitochondrial (mt) genes: ND2 (1038 bp, 119 sequences) and ND3 (340 bp, 612 sequences). Divergence times were estimated in a Bayesian framework under four geological scenarios representing alternative possible geological histories for the eastern Mediterranean. We then compared the different scenarios using Bayes factors and additional geological data. RESULTS: Extensive genetic diversity in mtDNA divides eastern Mediterranean water frogs into six main haplogroups (MHG). Three MHGs were identified on the Anatolian mainland; the most widespread MHG with the highest diversity is distributed from western Anatolia to the northern shore of the Caspian Sea, including the type locality of Pelophylax ridibundus. The other two Anatolian MHGs are restricted to south-eastern Turkey, occupying localities west and east of the Amanos mountain range. One of the remaining three MHGs is restricted to Cyprus; a second to the Levant; the third was found in the distribution area of European lake frogs (P. ridibundus group), including the Balkans. MAIN CONCLUSIONS: Based on geological evidence and estimates of genetic divergence we hypothesize that the water frogs of Cyprus have been isolated from the Anatolian mainland populations since the end of the Messinian salinity crisis (MSC), i.e. since c. 5.5-5.3 Ma, while our divergence time estimates indicate that the isolation of Crete from the mainland populations (Peloponnese, Anatolia) most likely pre-dates the MSC. The observed rates of divergence imply a time window of c. 1.6-1.1 million years for diversification of the largest Anatolian MHG; divergence between the two other Anatolian MHGs may have begun about 3.0 Ma, apparently as a result of uplift of the Amanos Mountains. Our mtDNA data suggest that the Anatolian water frogs and frogs from Cyprus represent several undescribed species.

In this study, on artificial data sets, it was compared condition random fields(CRF) and classical machine learning(CML) types. First part of this study, the performances of CRF and CML types were measured on artificial data sets. As the result of studies, CML types, except Naive Bayes, performanced higher than CRF. The success of NR and CRF is high when the outputs consist of one distribution, in other case it stays low. Besides in this study, it was evaluated the effect of education set size on success. The second study was made to test this situation.

Annotation of the genome sequence of Arabidopsis thaliana has identified a diverse array of 245 full-length cytochrome P450 monooxygenase (P450) genes whose known functions span the synthetic gamut from critical structural components (phenylpropanoids, fatty acids, sterols) to signaling molecules (oxylipins, brassinosteroids, abscisic acid, gibberellic acid) and defense compounds (alkaloids, terpenes, coumarins). Numerous others in this collection mediate functions that are now being addressed using microarray and oligoarray technologies, molecular modeling, heterologous expression and insertional mutageneses. Profilings of their constitutive and inducible transcript levels have begun to cluster P450s that are likely to mediate tissue-specific and stress-specific monooxygenations. With proper appreciation of the high identities that exist among some of the most recently duplicated P450 sequences, these studies have begun to differentiate P450s with early response functions leading to production of stress signaling molecules and late response functions leading to the synthesis of protective compounds. Further functional analyses of these P450 sequences with perspectives on their response profiles rely on a variety of theoretical modeling and experimental approaches that can ultimately be tied to the transcriptional profiles and genetic mutants. This review surveys historical and evolutionary aspects of P450 studies, expression variations among Arabidopsis P450 loci, catalytic site regions critical for substrate recognition and, finally, genetic mutations/disruptions that can ultimately tie biochemical reactions to physiological functions in a manner not yet possible in most other organisms.

▪ Abstract Fueled by ever-growing DNA sequence information, proteomics-the large scale analysis of proteins-has become one of the most important disciplines for characterizing gene function, for building functional linkages between protein molecules, and for providing insight into the mechanisms of biological processes in a high-throughput mode. It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods. High-throughput yeast two-hybrid approaches and analysis of protein complexes using affinity tag purification have yielded valuable protein-protein interaction maps. Large-scale protein tagging and subcellular localization projects have provided considerable information about protein function. Finally, recent developments in protein microarray technology provide a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate, and protein-drug interactions. Other types of microarrays, though not fully developed, also show great potential in diagnostics, protein profiling, and drug identification and validation. This review discusses high-throughput technologies for proteome analysis and their applications. Also discussed are the approaches used for the integrated analysis of the voluminous sets of data generated by proteome analysis conducted on a global scale.

To facilitate studies of the yeast proteome, we cloned 5800 open reading frames and overexpressed and purified their corresponding proteins. The proteins were printed onto slides at high spatial density to form a yeast proteome microarray and screened for their ability to interact with proteins and phospholipids. We identified many new calmodulin- and phospholipid-interacting proteins; a common potential binding motif was identified for many of the calmodulin-binding proteins. Thus, microarrays of an entire eukaryotic proteome can be prepared and screened for diverse biochemical activities. The microarrays can also be used to screen protein-drug interactions and to detect posttranslational modifications.

We have studied the activity of a wheat histone H4 promoter in transgenic maize plants produced by direct DNA uptake into embryogenic cell suspension protoplasts. Expression patterns revealed by fluorimetric as well as histochemical GUS reporter enzyme assays and Northern hybridisation indicated the cell division-dependent expression of the GUS gene driven by the histone H4 promoter in proliferating cells and meristems. Changes in gene expression associated with the progression through the cell cycle in maize cell-suspension cultures were also investigated after partial synchronisation of cell division by aphidicolin or hydroxyurea. The transgene expression regulated by the wheat histone H4 promoter coincided with elevated levels of mRNAs from other S phase-associated genes such as maize histone H4, histone H2B and proliferating cell nuclear antigen (PCNA). The reporter gene expression was low during mitosis when accumulation of the transcripts of a B-type mitotic cyclin CycB1;zm;1 could be detected. The presented data indicate that the used 720-bp-long promoter region can provide replication-dependent expression for the GUS reporter gene in transgenic maize. In these maize plants, external hormone stimuli generated by a synthetic auxin 2,4-dichlorophenoxy acetic acid (2,4-D) or abscisic acid (ABA) have modified the histone H4 promoter-driven GUS gene transcription. These findings support the usefulness of the H4::GUS transgenic plants for further studies on cell cycle activation/inactivation by mitogenic or stress related stimuli in maize.

The replication of the geminiviruses depends on the viral encoded early (complementary-sense) proteins and on host genome encoded factors. Additionally, some of the early proteins (the AL2 protein of subgroup III, and the RepA (formerly known as C1) or Rep (C1:C2) proteins of subgroup I geminiviruses) can function as transcriptional activators of virion- (V-)sense gene expression. The yeast two-hybrid system has allowed us to predict some of the functionally important regions of the maize streak virus (MSV) early proteins RepA and Rep. Defined domains of these proteins were shown to act as transactivators in yeast cells. We detected the association of the RepA and Rep proteins, and their subfragments, with the maize retinoblastoma protein (ZmRb1) which is likely to be one of the interacting host proteins. We showed the self-association capability of the MSV proteins and suggest that homo- or hetero-oligomerization may play an important role in virus replication. These results provide new insights into the role of different regions of the MSV proteins in relation to transcriptional activation and regulation of viral DNA replication.

The eukaryotic cell division cycle is coordinated by cyclin-dependent kinases (CDKs), represented by a single major serine/threonine kinase in yeasts (Cdc2/CDC28) and a family of kinases (CDK1 to CDK8) in human cells. Previously, two cdc2 homologs, cdc2MsA and cdc2MsB, have been identified in alfalfa (Medicago sativa). By isolating cDNAs using a cdc2MsA probe, we demonstrate here that at least four additional cdc2 homologous genes are expressed in the tetraploid alfalfa. Proteins encoded by the new cdc2MsC to cdc2MsF cDNAs share the characteristic functional domains of CDKs with the conserved and plant-specific sequence elements. Transcripts from cdc2MsA, cdc2MsB, cdc2MsC, and cdc2MsE genes are synthesized throughout the cell cycle, whereas the amounts of cdc2MsD and cdc2MsF mRNAs peak during G2-to-M phases. The translation of Cdc2MsA/B, Cdc2MsD, and Cdc2MsF proteins follows the pattern of transcript accumulation. The multiplicity of kinase complexes with cell cycle phase-dependent activities was revealed by in vitro phosphorylation experiments. Proteins bound to p13suc1-Sepharose or immunoprecipitated with Cdc2MsA/B antibodies from cells at G1-to-S and G2-to-M phase boundaries showed elevated kinase activities. the Cdc2MsF antibodies separated a G2-to-M phase-related kinase complex. Detection of histone H1 phosphorylation activities in fractions immunoprecipitated with antimitotic cyclin (CyclinMs2) antibodies from G2-to-M phase cells indicates the complex formation between this cyclin and a kinase partner in alfalfa. The observed fluctuation of transcript levels, amounts, and activities of kinases in different cell cycle phases reflects a multilevel regulatory system during cell cycle progression in plants.

AIM: Our aims were to assess the phylogeographic patterns of genetic diversity in eastern Mediterranean water frogs and to estimate divergence times using different geological scenarios. We related divergence times to past geological events and discuss the relevance of our data for the systematics of eastern Mediterranean water frogs. LOCATION: The eastern Mediterranean region. METHODS: Genetic diversity and divergence were calculated using sequences of two protein-coding mitochondrial (mt) genes: ND2 (1038 bp, 119 sequences) and ND3 (340 bp, 612 sequences). Divergence times were estimated in a Bayesian framework under four geological scenarios representing alternative possible geological histories for the eastern Mediterranean. We then compared the different scenarios using Bayes factors and additional geological data. RESULTS: Extensive genetic diversity in mtDNA divides eastern Mediterranean water frogs into six main haplogroups (MHG). Three MHGs were identified on the Anatolian mainland; the most widespread MHG with the highest diversity is distributed from western Anatolia to the northern shore of the Caspian Sea, including the type locality of Pelophylax ridibundus. The other two Anatolian MHGs are restricted to south-eastern Turkey, occupying localities west and east of the Amanos mountain range. One of the remaining three MHGs is restricted to Cyprus; a second to the Levant; the third was found in the distribution area of European lake frogs (P. ridibundus group), including the Balkans. MAIN CONCLUSIONS: Based on geological evidence and estimates of genetic divergence we hypothesize that the water frogs of Cyprus have been isolated from the Anatolian mainland populations since the end of the Messinian salinity crisis (MSC), i.e. since c. 5.5-5.3 Ma, while our divergence time estimates indicate that the isolation of Crete from the mainland populations (Peloponnese, Anatolia) most likely pre-dates the MSC. The observed rates of divergence imply a time window of c. 1.6-1.1 million years for diversification of the largest Anatolian MHG; divergence between the two other Anatolian MHGs may have begun about 3.0 Ma, apparently as a result of uplift of the Amanos Mountains. Our mtDNA data suggest that the Anatolian water frogs and frogs from Cyprus represent several undescribed species.

A species-specific repetitive DNA fragment has been isolated from a genomic library of Solanum brevidens. Sequence analysis revealed a regular organization of three non-homologous subrepeats forming tandemly-arranged composite repetitive units. Interpretation of Southern hybridization patterns based on the known sequence data suggests that the isolated sequence element represents an abundant organization type, although the presence of simple tandem arrays of the subrepeats is also indicated. Seventy-four percent sequence similarity was found between one of the S. brevidens subrepeats (Sb4AX) and a satellite DNA (TGRI) localized as a subtelomeric repeat on almost all Lycopersicon esculentum chromosomes. Insitu hybridization indicated that, similarly to TGRI, the S. brevidens-specific repeats are located at the ends of the arms of several chromosomes. On the basis of the data obtained, a common ancestral sequence can be proposed for the tomato (TGRI) and the S. brevidens (Sb4AX) repeat however, the molecular organization of this element in these two species evolved in a basically different manner.

The genomes of a wide variety of organisms have now been sequenced; a major challenge ahead is to understand the function, regulation and modification of the many encoded gene products. We have been carrying out proteomics approaches to the identification and analysis of signalling pathways in yeast. 121 of 122 protein kinases were cloned and purifed from yeast as GST fusions and analyzed for their ability to phosphorylate 60 different yeast substrates. More than 93% of the kinases exhibited activities that are 5 fold or higher, relative to controls, including 18 of 24 previously uncharacterized kinases. Many protein kinases had novel activities; for example 27 yeast kinases were found to phosphorylate Tyr. In addition, we have now cloned 6000 open reading frames and overexpressed their corresponding proteins. The proteins were printed onto slides at high spatial density to form a yeast proteome microarray and screened for their ability to interact with a variety of different proteins, nucleic acids and phospholipids. As examples, we have probed yeast proteome chips with calmodulin and six different phospholipids. These studies revealed many new calmodulin and phospholipid-interacting proteins; a common potential binding motif was identified for many of the calmodulin-binding proteins. Thus, microarrays of an entire eukaryotic proteome can be prepared and screened for diverse biochemical activities. They can also be used to screen protein-drug interactions and to detect posttranslational modifications.

The recent methodology of introducing foreign genes into the genome of monocot cereal plants, including maize, is based on a variety of experimental approaches. Several laboratories have demonstrated the potentials in the use of microprojectile bombardment to transform embryogenic maize tissue cultures (Fromm et al. 1990; Gordon-Kamm et al. 1990; Walters et al. 1992). Furthermore, the production of transgenic maize plants expressing agronomically significant traits was also achieved by this method (Koziel et al. 1993). Regeneration of fertile transgenic maize plants was recently reported by D’Halluin et al. (1992) too, who electroporated plasmid molecules into wounded tissues of immature zygotic embryos or of embryogenic callus cultures. Both of these methods are proposed to be widely applicable for transformation of different breeding stocks. However, the regeneration capability was shown to be genotype-dependent by several tissue culture studies (Tomes and Smith 1985; Lupotto 1986; Petersen et al. 1992).