Drug Resistance and Biofilm Formation in Candida Species of Vaginal Origin.

Background: Candida species are normal vaginal flora in healthy women, which can cause vulvovaginal candid-iasis (VVC). The formation of biofilm is a cause of drug resistance in Candida species of vaginal origin. We aimed to specify Candida species cause VVC, detect their biofilm-forming ability, and antifungal susceptibility pattern. Methods: Overall 150 vaginal samples were collected from suspected cases of referring to Bahar Hospital of Shahroud, Iran between Jan 2018 and Jan 2019. Samples were cultured on Sabouraud dextrose agar (SDA), Chrome gar Candida and Corn meal agar (CMA). PCR-RFLP was performed to confirm the identification. Bio-film formation of the identified species was measured by the Crystal Violet method. The susceptibility to fluconazole, clotrimazole, and miconazole was determined based on the CLSI document M27-A3. Results: Of 50 women (33.3%) were suffering from VVC. C.albicans was the predominant species isolated in this study (n=39, 78%) followed by C. glabratia (n=11, 22%). In addition, in 25 (50%) of positive samples, bio-film formation was determined. The mean MIC of fluconazole and clotrimazole for C. albicans was 5.02 µg/mL and 3.92 µg/mL, respectively. Furthermore, the mean MIC related to these drugs for C. glabrata was 12.45 µg / mL and 4.1µg / mL, respectively. The mean diameter of miconazole inhibition zone for C. albicans and C. glabra isolates was 25.13 mm and 24.5mm, respectively and all of them were susceptible to this drug. Conclusion: C.albicans was the predominant Candida species isolated from patients with VVC and also was the predominant biofilm producer species.

Fault Tolerant DHT-Based Routing in MANET.

In Distributed Hash Table (DHT)-based Mobile Ad Hoc Networks (MANETs), a logical structured network (i.e., follows a tree, ring, chord, 3D, etc., structure) is built over the ad hoc physical topology in a distributed manner. The logical structures guide routing processes and eliminate flooding at the control and the data plans, thus making the system scalable. However, limited radio range, mobility, and lack of infrastructure introduce frequent and unpredictable changes to network topology, i.e., connectivity/dis-connectivity, node/link failure, network partition, and frequent merging. Moreover, every single change in the physical topology has an associated impact on the logical structured network and results in unevenly distributed and disrupted logical structures. This completely halts communication in the logical network, even physically connected nodes would not remain reachable due to disrupted logical structure, and unavailability of index information maintained at anchor nodes (ANs) in DHT networks. Therefore, distributed solutions are needed to tolerate faults in the logical network and provide end-to-end connectivity in such an adversarial environment. This paper defines the scope of the problem in the context of DHT networks and contributes a Fault-Tolerant DHT-based routing protocol (FTDN). FTDN, using a cross-layer design approach, investigates network dynamics in the physical network and adaptively makes arrangements to tolerate faults in the logically structured DHT network. In particular, FTDN ensures network availability (i.e., maintains connected and evenly distributed logical structures and ensures access to index information) in the face of failures and significantly improves performance. Analysis and simulation results show the effectiveness of the proposed solutions.

Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico.

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population's mental health and health programs. It is complex for public organizations to measure the population's mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population's mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter's usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

Job burnout among nurses during COVID-19 pandemic: A systematic review.

BACKGROUND: Nurses are in close contact with COVID-19 patients and due to the high risk of infection, they experience fear and anxiety that can result in burnout. This study aimed to review the studies on burnout among nurses during the COVID-19 epidemic. MATERIALS AND METHODS: The study followed the guideline for Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). Using the keywords: "burnout," "nurse," and "COVID-19" and with the help of Boolean operators, "AND" and "OR" the online databases, namely PubMed, Scopus, Google Scholar, Web of Science, and Science Direct were searched. Articles published from the first of February 2020 to 30 October, 2020 were retrieved. After the quality appraisal, the required data were extracted and analyzed. RESULTS: Out of 85 articles identified in the initial search, and after removing duplicates and those that did not have the required data, seven articles entered the analysis. Among these articles, four (57.14%) reported moderate burnout and three articles (42.86) reported high level of burnout among nurses during the COVID-19 pandemic. CONCLUSIONS: A majority of the studies reported that nurses experienced a moderate level of burnout during the COVID-19 pandemic. Given the prevalence of burnout in nurses and because nurses are the largest portion of the healthcare providers who are in close contact with patients infected by COVID-19, it is necessary for health care policymakers to adopt strategies for preventing or reducing burnout among nurses.

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN.

The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme's effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society.

The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth's tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie-Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic's evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing.

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

A Mycological and Molecular Epidemiologic Study on Onychomycosis and Determination In Vitro Susceptibilities of Isolated Fungal Strains to Conventional and New Antifungals.

Background: Onychomycosis is one of the most common and recurrent dermatological diseases worldwide. The antimycotic activity of prescribed medications varies according to the causative agents, and treatment failure rates exceeding 30%. This study aimed to assess the epidemiological profile of onychomycosis in Iran. Also, the susceptibilities to conventional and new antifungals were investigated. Methods: In this descriptive cross-sectional study, during the period of 18 months starting from September 2019 until March 2020, 594 nail specimens were obtained from patients who presented nail changes compatible with a clinical diagnosis of onychomycosis. The patients were referred from different cities, including Tehran, Kermanshah, Arak, Kashan, Rasht, Qom, Urmia, Zahedan, Hamadan, Zanjan, Borujerd, Bushehr, and Yazd. All the samples were subjected to microscopic examination and fungal culture. Fungi identified were confirmed through the PCR-sequencing method. The susceptibility to itraconazole, fluconazole, terbinafine, griseofulvin, posaconazole, ravuconazole, efinaconazole, luliconazole, and tavaborole was evaluated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines, document M38-A2 for filamentous fungi, and document M27-A3 for yeasts. Results: 594 patients were included. Of these, in 179 cases (30.1%) (95% CI:0.3 ± 0.037) onychomycosis was confirmed. The majority of patients were ≥ 60 years of age (n=58, 32.6%) and female (n=113, 63.1%). Saprophytic fungi accounted for the vast majority of the nail isolates (n=92, 51.4%) (95% CI:0.051 ± 0.0.073), followed by dermatophytes (n=45, 25.1%) (95% CI:0.25 ± 0.063), and yeasts (n=42, 23.5%) (95% CI:0.23 ± 0.061). Diabetes mellitus (77.3%), hypothyroidism (18.2%), and solid tumors (4.5%) were documented as the most prevalent underlying conditions. Antifungal susceptibility testing was performed against 60 fungal isolates (20 each of Candida species, saprophytic fungi, and dermatophytes). Efinaconazole, ravuconazole, and luliconazole were the most active agents against Candida species. Also, luliconazole, posaconazole, and efinaconazole were most potent against dermatophytes. Luliconazole had the greatest antifungal activity against saprophytic fungi. Conclusions: The prevalence of onychomycosis in Iranian patients was relatively high. LUL exhibited potent antifungal activity against the three groups of fungi tested, determining its broad-spectrum antimycotic activity and its probable use as the first-line therapy for onychomycosis.

Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks.

Recently, by the rapid development of Vehicular Ad Hoc Networks (VANETs) and the advancement of Software Defined Networking (SDN) as an emerging technology, the Software-Defined Vehicular Network (SDVN) has a tremendous attraction in the academia and research community. SDN's unique properties and features, such as its flexibility, programmability, and centralized control, make the network scalable and straightforward. In VANETs, traffic management and secure communication of vehicle information using the public network are the main research dimensions in the current era for the researchers to be considered while designing an efficient and secure VANETs architecture. This paper highlights the possible identified threat vectors and efficiently resolves the network vulnerabilities to design a novel and secure hierarchic architecture for SDVN. To solve the above problem, we proposed a Public Key Infrastructure-based digital signature model for efficient and secure communication from Vehicle to Vehicle. We also used the public key authority infrastructure for Vehicle to Infrastructure and the three-way handshake method for secure session creation and secure data communication in the SDN controller. The proposed security is validated through the well-known simulation tool AVISPA. Additionally, a formal security model is applied to validate the design hierarchic architecture's fundamental security properties for SDVN in an efficient and desirable way. In a comparative analysis, we prove that our proposed scheme fulfills all the essential security properties compared to other states of the art schemes.

On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks.

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Distribution of invasive fungal infections: Molecular epidemiology, etiology, clinical conditions, diagnosis and risk factors: A 3-year experience with 490 patients under intensive care.

Recently, the prevalence of invasive fungal infections (IFIs) is rising. The global mortality rate of IFIs is 10-49%. This study aimed to determine the prevalence, the causative agents, and the risk factors associated with the invasive fungal infections in a tertiary health center to provide valid decision-grounds for healthcare professionals to effectively prevent, control, and treat fungal infections. The current study was conducted on 1477 patients suspected to have systemic fungal infections from different units of the hospital. After screening using routine mycological examination, the patients were confirmed with complementary mycological and molecular methods. Patients were included based on the confirmed diagnosis of IFI and excluded based on lack of a microbiologically and histologically proven diagnosis of IFI. Of the 1477 patients recruited in this study, confirmed cases of fungal infection were 490 (169 proven; 321 cases probable). Among the fungi recovered, Candida species had the highest frequency 337 (68.8%) followed by Aspergillus species 108 (22.1%), Zygomycetes species 21 (4.3%), non-Candida yeast 9 (1.8%). Others were black fungi 5 (1%), mycetoma agents 5 (1%), Fusarium 4 (0.8%), and Trichoderma (0.2%). Hematologic malignancies and diabetes mellitus were the most common underlying diseases among IFI-confirmed patients. This study observed an increased frequency of invasive candidiasis with non-albicans Candida and other invasive saprophytic fungal infections. The increased rate of invasive candidiasis with non-albicans agents highlights a new perspective in the epidemiology and treatment of invasive fungal infections.

Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization.

Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.

Study on invasive aspergillosis using galactomannan enzyme immunoassay and determining antifungal drug susceptibility among hospitalized patients with hematologic malignancies or candidates for organ transplantation.

The incidence of invasive aspergillosis (IA) has dramatically increased during the last decade. This infection is associated with high morbidity and mortality, ranging from 30% to 70%, especially in immunocompromised patients. Delay in diagnosis and treatment is usually associated with high mortality rates. This study was aimed to assess the diagnostic value of Galactomannan EIA (GM) for early diagnosis of aspergillosis in hospitalized patients with underlying conditions. Also, the antifungal drug susceptibility profiles of causative agents were investigated. In this descriptive cross-sectional study, during the period of 18 months starting from September 2017 until February 2019, 22 bronchoalveolar lavage (BAL) and 13 biopsies from infected sinuses were obtained from a total of 150 patients suffering from different types of hematologic malignancies. All the samples were subjected to microscopic examination and fungal culture. Also, serum specimens were obtained from all patients (n = 135). 22 serum and 17 BAL specimens were tested for the GM level. Fungal identified were confirmed through the PCR-sequencing of the ß-tubulin gene. The susceptibility to amphotericin B, itraconazole, voriconazole, posaconazole, ravuconazole, and caspofungin was evaluated according to the Clinical and Laboratory Standards Institute document M38-A2 (CLSI M38-A2) broth microdilution protocol. The results showed that the incident rate of IA was 23.33% and 35 patients with IA (12 proven cases and 23 probable cases) were diagnosed according to the European Organization for Research and Treatment of Cancer and Mycoses Study Group criteria. The 35 patients with IA in the current study comprised 19 men (54.29%) and 16 women (45.71%) with the median age of 42 years. AML (31.5%) was documented as the most prevalent risk factor among our subjects with IA and Aspergillus flavus (65.7%) was the most prevailing causal agent in this study. Among patients with IA, ague (71%) and cough (60%) were the most common symptoms. In the present study, a sensitivity of 94% and a specificity of 98% was reported for GM ELISA in BAL specimens. Also, a sensitivity of 58% and a specificity of 98% was reported for GM ELISA in serum samples. Among 6 tested antifungal drugs, the lowest minimum inhibitory concentration (MIC) values were observed for posaconazole and ravuconazole which showed the range of 0.008-0.0062 µgml and 0.031-0.125 µgml, respectively. The current study has demonstrated that determining the value of GM investigation in BAL and serum specimens can be promising in early diagnosis of IA, also molecular identification of the agents causing IA and their antifungal susceptibility patterns are essential issues for the targeted antifungal therapy and outcome improvement of patients with this life-threatening disease.

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm.

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

Recurrent eumycetoma caused by novel species Madurella pseudomycetomatis: A case report.

Mycetoma is a chronic-granulomatous disease characterized by the inflammation, swollen organ, draining sinuses containing blood, pus, and grains. We present a case of madura foot with novel etiologic agent Madurella pseudomycetomatis. Diagnosis was based on morphologic, physiologic, histipathologic and molecular methods. In vitro antifungal susceptibility tests revealed that MIC values for itraconazole, amphotericin B, and posaconazole were 0.0313 µg/ml, 0.0313 µg/ml, and 0.004 µg/ml, respectively. The patient was treated and recovered by itraconazole(400 mg/day) after prolonged course.

Game Theory-Based Cooperation for Underwater Acoustic Sensor Networks: Taxonomy, Review, Research Challenges and Directions.

Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted.

Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey.

New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective.

Fungal rhino sinusitisin in tehran, iran.

BACKGROUND: Fungal rhino sinusitis (FRS) is an important infection of para nasal sinuses, which encompasses two main categories; invasive and noninvasive forms according to histopathological findings. Aspergillus spp are the most common species isolated from noninvasive form, while Mucorales are more frequently isolates from acute infections. METHODS: Four hundred fifty patients suspected to fungal rhino sinusitis were investigated in a cross-sectional prospective study from June 2009 to Sep 2013. All patients under went endoscopic sinus surgery of the middle meatus. Tissue biopsies were investigated for culture, histopathology and molecular examination. RESULTS: Totally, 87 patients were diagnosed with fungal rhinosinusitis. A. flavus was the most common etiological agent of chronic invasive form (CIFRS), allergic fungal rhino sinusitis (AFRS) and fungus ball (FB), while Rhizopus oryze (26.7%) was the most common cause of infection in acute invasive fungal rhino sinusitis (AIFR). However, a few rare species such as Shyzophyllum commune and Fusarium proliferatum were also isolated. CONCLUSION: Diabetes is the most important predisposing factor for patients with acute invasive form of sinusitis and the most involved sinuses were unilateral multiple sinuses and maxillary sinus.