Upregulation of long noncoding RNA MIAT in aggressive form of chronic lymphocytic leukemias.

Long noncoding RNAs (lncRNAs) are non-proten-coding transcripts of more than 200 nucleotides generated by RNA polymerase II and their expressions are tightly regulated in cell type specific- and/or cellular differential stage specific- manner. MIAT, originally isolated as a candidate gene for myocardial infarction, encodes lncRNA (termed MIAT). Here, we determined the expression level of MIAT in established leukemia/lymphoma cell lines and found its upregulation in lymphoid but not in myeloid cell lineage with mature B cell phenotype. MIAT expression level was further determined in chronic lymphocytic leukemias (CLL), characterized by expansion of leukemic cells with mature B phenotype, to demonstrate relatively high occurrence of MIAT upregulation in aggressive form of CLL carrying either 17p-deletion, 11q-deletion, or Trisomy 12 over indolent form carrying 13p-deletion. Furthermore, we show that MIAT constitutes a regulatory loop with OCT4 in malignant mature B cell, as was previously reported in mouse pulripotent stem cell, and that both molecules are essential for cell survival

CSI-Based Human Activity Recognition using Convolutional Neural Networks

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as “walk”, “run”, and “sleep”. These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks

A CSI-Based Human Activity Recognition Using Deep Learning

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities

Genome-wide screening of mouse knockouts reveals novel genes required for normal integumentary and oculocutaneous structure and function.

Oculocutaneous syndromes are often due to mutations in single genes. In some cases, mouse models for these diseases exist in spontaneously occurring mutations, or in mice resulting from forward mutatagenesis screens. Here we present novel genes that may be causative for oculocutaneous disease in humans, discovered as part of a genome-wide screen of knockout-mice in a targeted single-gene deletion project. The International Mouse Phenotyping Consortium (IMPC) database (data release 10.0) was interrogated for all mouse strains with integument abnormalities, which were then cross-referenced individually to identify knockouts with concomitant ocular abnormalities attributed to the same targeted gene deletion. The search yielded 307 knockout strains from unique genes with integument abnormalities, 226 of which have not been previously associated with oculocutaneous conditions. Of the 307 knockout strains with integument abnormalities, 52 were determined to have ocular changes attributed to the targeted deletion, 35 of which represent novel oculocutaneous genes. Some examples of various integument abnormalities are shown, as well as two examples of knockout strains with oculocutaneous phenotypes. Each of the novel genes provided here are potentially relevant to the pathophysiology of human integumentary, or oculocutaneous conditions, such as albinism, phakomatoses, or other multi-system syndromes. The novel genes reported here may implicate molecular pathways relevant to these human diseases and may contribute to the discovery of novel therapeutic targets

Experimental tests and numerical simulations on the mechanical response of RC slabs externally strengthened by passive and prestressed FRP strips

Externally Bonded Reinforcement on Groove (EBROG) method has been introduced to enhance the bond resistance of FRP strips to concrete. It has demonstrated that EBROG generally outperforms EBR in terms of loadtransfer capacity between FRP strips and concrete. The present study aims to further demonstrate the potential of EBROG applied for flexural strengthening. A specimen reinforced according to the EBR solution and a nominally equal one reinforced through the EBROG system are first presented. Then, the performance of a newly fully-composite mechanical end anchorage for prestressed FRP strip to be used in conjunction with the EBROG method is investigated. The experimental results show that the premature debonding observed in EBR is avoided by EBROG in the case of "passive" FRP strips. Moreover, the combination of EBROG and end anchorage demonstrates their effectiveness, as the pre-stressed slab exhibits the full exploitation of the FRP up to rupture. Numerical analyses, carried out by means of a model already presented by the authors, show that the structural response of the tested slabs can be simulated in a very accurate manner if consistent assumptions are made in terms of bond-slip laws adopted to describe the interaction between FRP and concrete in EBR and EBROG

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers

On the presence of humpback whales in the Persian Gulf: rare or rarely documented? Report of the IWC Scientific Committee Meeting SC/67A/CMP/14, Bled, Slovenia, May 2017

We critically review the evidence for humpback whale presence in the Persian Gulf. Five specimen records, assumed to belong to the endangered Arabian Sea population, are confirmed in the period 1883- 2017: Bassore Bay, Iraq; Doha, Qatar; Kuwait Inner harbour, Kuwait; Qeshm Island, Iran; and Akhtar, Bushehr Province, Iran. The two Iranian cases, both juveniles, are newly recorded. With accumulating reports, an alternate hypothesis to 'rare stragglers' deserves consideration, one in which Arabian Sea humpback whales may enter the Persian Gulf with some regularity, perhaps as normal visitors, if not permanent residents. Deficiency of records may reflect a general sparsity of whale research effort in the Persian Gulf. The historical description of Megaptera indica Gervais, 1883 is translated from French

Preparation and Evaluation of a New Lipopolysaccharide-based Conjugate as a Vaccine Candidate for Brucellosis

Objectives: Development of an efficacious vaccine against brucellosis has been a challenge for scientists for many years. At present, there is no licensed vaccine against human brucellosis. To overcome this problem, currently, antigenic determinants of Brucella cell wall such as Lipopolysaccharide (LPS) are considered as potential candidates to develop subunit vaccines. Methods: In this study, Brucella abortus LPS was used for conjugation to Neisseria meningitidis serogroup B outer membrane vesicle (OMV) as carrier protein using carbodiimide and adipic acid-mediated coupling and linking, respectively. Groups of eight BALB/c mice were injected subcutaneously with 10μg LPS alone, combined LPS+OMV and conjugated LPS-OMV on 0 days, 14 days, 28 days and 42 days. Anti-LPS IgG was measured in serum. Results: The yield of LPS to OMV in LPS-OMV conjugate was 46.55, on the basis of carbohydrate content. The ratio for LPS to OMV was 4.07. The LPS-OMV conjugate was the most immunogenic compound that stimulated following the first injection with increased IgG titer of ~5-fold and ~1.3-fold higher than that produced against LPS and LPS in noncovalent complex to OMV (LPS+OMV), respectively. The highest anti-LPS IgG titer was detected 2 weeks after the third injection (Day 42) of LPS-OMV conjugate. The conjugated compound elicited higher titers of IgG than LPS+OMV, that showed a 100-120-fold rise of anti-LPS IgG in mice. Conclusion: These results indicate that our conjugated LPS-OMV can be used as a brucellosis vaccine, but further investigation is required. © 2014