Overview of knowledge, attitudes and barriers associated with HPV vaccination in Beirut, Lebanon.

BACKGROUND: Human Papillomavirus (HPV), a prevalent sexually transmitted infection carrying significant risks ranging from benign lesions to various types of malignancies, represents a matter of great public health concern. Notably, most Arab countries lack public awareness campaigns or national immunization programs. This study aims at assessing the overall knowledge on HPV and HPV vaccination among the Lebanese population, exploring the prevalent attitude on the matter, and identifying barriers and misconceptions that prevent individuals from receiving the HPV vaccine. METHODS: A cross-sectional study was conducted in Beirut, on 201 participants aged between 18 and 36 years old. We performed ordinal analysis to assess the trend between Knowledge levels, attitude levels and hesitancy Levels. RESULTS: Majority of participants (77%) demonstrated a low level of knowledge on HPV vaccination, 50% held a positive attitude, with only 18.4% being already vaccinated. Negative trend was identified between levels of knowledge, attitude and hesitancy (gamma = -0.7415, p-value < 0.01; gamma= -0.58, p-value < 0.01 respectively). Unavailability or limited access to the vaccine, and misconceptions about HPV immunization were shown to be impeding vaccination. CONCLUSION: Analysis of our results strongly suggests that improving knowledge and attitudes is likely to foster trust and reduce hesitancy, thereby promoting higher vaccine uptake.

Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks.

A key aspect of how the brain learns and enables decision-making processes is through synaptic interactions. Electrical transmission and communication in a network of synapses are modulated by extracellular fields generated by ionic chemical gradients. Emulating such spatial interactions in synthetic networks can be of potential use for neuromorphic learning and the hardware implementation of artificial intelligence. Here, we demonstrate that in a network of hydrogen-doped perovskite nickelate devices, electric bias across a single junction can tune the coupling strength between the neighboring cells. Electrical transport measurements and spatially resolved diffraction and nanoprobe X-ray and scanning microwave impedance spectroscopic studies suggest that graded proton distribution in the inhomogeneous medium of hydrogen-doped nickelate film enables this behavior. We further demonstrate signal integration through the coupling of various junctions.

Intrinsic (Trap-Free) Transistors Based on Epitaxial Single-Crystal Perovskites.

The first experimental realization of the intrinsic (not dominated by defects) charge conduction regime in lead-halide perovskite field-effect transistors (FETs) is reported. The advance is enabled by: i) a new vapor-phase epitaxy technique that results in large-area single-crystalline cesium lead bromide (CsPbBr3 ) films with excellent structural and surface properties, including atomically flat surface morphology, essentially free from defects and traps at the level relevant to device operation; ii) an extensive materials analysis of these films using a variety of thin-film and surface probes certifying the chemical and structural quality of the material; and iii) the fabrication of nearly ideal (trap-free) FETs with characteristics superior to any reported to date. These devices allow the investigation of the intrinsic FET and (gated) Hall-effect carrier mobilities as functions of temperature. The intrinsic mobility is found to increase on cooling from ≈30 cm2 V-1 s-1 at room temperature to ≈250 cm2 V-1 s-1 at 50 K, revealing a band transport limited by phonon scattering. Establishing the intrinsic (phonon-limited) mobility provides a solid test for theoretical descriptions of carrier transport in perovskites, reveals basic limits to the technology, and points to a path for future high-performance perovskite electronic devices.

Dual Light and Redox Control of NIR Luminescence with Complementary Photochromic and Organometallic Antennae.

With the help of a judicious association between dithienylethene (DTE) units, an ytterbium ion, and a ruthenium carbon-rich complex, we describe (i) the efficient (on/off) switching of pure NIR luminescence with a photochromic unit absorbing in the UV range and (ii) the association of electrochemical and photochemical control of this NIR emission in a single system with nondestructive readout.

Electrochemically driven interfacial halogen bonding on self-assembled monolayers for anion detection.

Electrochemically driven interfacial halogen bonding between redox-active SAMs and halide anions was quantitatively studied for the first time. The halogen bond donor properties were switched on by electrochemically controlling the oxidation state of the adsorbates. Experimental data and simulation show high binding enhancement towards halide anions compared to homogeneous systems.

New insights into root gravitropic signalling.

An important feature of plants is the ability to adapt their growth towards or away from external stimuli such as light, water, temperature, and gravity. These responsive plant growth movements are called tropisms and they contribute to the plant's survival and reproduction. Roots modulate their growth towards gravity to exploit the soil for water and nutrient uptake, and to provide anchorage. The physiological process of root gravitropism comprises gravity perception, signal transmission, growth response, and the re-establishment of normal growth. Gravity perception is best explained by the starch-statolith hypothesis that states that dense starch-filled amyloplasts or statoliths within columella cells sediment in the direction of gravity, resulting in the generation of a signal that causes asymmetric growth. Though little is known about the gravity receptor(s), the role of auxin linking gravity sensing to the response is well established. Auxin influx and efflux carriers facilitate creation of a differential auxin gradient between the upper and lower side of gravistimulated roots. This asymmetric auxin gradient causes differential growth responses in the graviresponding tissue of the elongation zone, leading to root curvature. Cell biological and mathematical modelling approaches suggest that the root gravitropic response begins within minutes of a gravity stimulus, triggering genomic and non-genomic responses. This review discusses recent advances in our understanding of root gravitropism in Arabidopsis thaliana and identifies current challenges and future perspectives.

Systems analysis of auxin transport in the Arabidopsis root apex.

Auxin is a key regulator of plant growth and development. Within the root tip, auxin distribution plays a crucial role specifying developmental zones and coordinating tropic responses. Determining how the organ-scale auxin pattern is regulated at the cellular scale is essential to understanding how these processes are controlled. In this study, we developed an auxin transport model based on actual root cell geometries and carrier subcellular localizations. We tested model predictions using the DII-VENUS auxin sensor in conjunction with state-of-the-art segmentation tools. Our study revealed that auxin efflux carriers alone cannot create the pattern of auxin distribution at the root tip and that AUX1/LAX influx carriers are also required. We observed that AUX1 in lateral root cap (LRC) and elongating epidermal cells greatly enhance auxin's shootward flux, with this flux being predominantly through the LRC, entering the epidermal cells only as they enter the elongation zone. We conclude that the nonpolar AUX1/LAX influx carriers control which tissues have high auxin levels, whereas the polar PIN carriers control the direction of auxin transport within these tissues.

A classification framework applied to cancer gene expression profiles.

Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF) and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression) increase the prediction accuracy as compared to using gene expression alone.

A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

UNLABELLED: An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically accompanied by numerous collateral effects. In this study, we design a multi-layer approach that is able to reconstruct condition-specific genes and their regulation through an integrative analysis of large-scale information of gene expression, protein interaction and transcriptional regulation (transcription factor-target gene relationships). We establish the accuracy of our methodology against synthetic datasets, as well as a yeast dataset. We then extend the framework to the application of higher eukaryotic systems, including human breast cancer and Arabidopsis thaliana cold acclimation. Our study identified TACSTD2 (TROP2) as a target gene for human breast cancer and discovered its regulation by transcription factors CREB, as well as NFkB. We also predict KIF2C is a target gene for ER-/HER2- breast cancer and is positively regulated by E2F1. The predictions were further confirmed through experimental studies. AVAILABILITY: The implementation and detailed protocol of the layer approach is available at http://www.egr.msu.edu/changroup/Protocols/Three-layer%20approach%20 to % 20reconstruct%20condition.html.

Recovering the dynamics of root growth and development using novel image acquisition and analysis methods.

Roots are highly responsive to environmental signals encountered in the rhizosphere, such as nutrients, mechanical resistance and gravity. As a result, root growth and development is very plastic. If this complex and vital process is to be understood, methods and tools are required to capture the dynamics of root responses. Tools are needed which are high-throughput, supporting large-scale experimental work, and provide accurate, high-resolution, quantitative data. We describe and demonstrate the efficacy of the high-throughput and high-resolution root imaging systems recently developed within the Centre for Plant Integrative Biology (CPIB). This toolset includes (i) robotic imaging hardware to generate time-lapse datasets from standard cameras under infrared illumination and (ii) automated image analysis methods and software to extract quantitative information about root growth and development both from these images and via high-resolution light microscopy. These methods are demonstrated using data gathered during an experimental study of the gravitropic response of Arabidopsis thaliana.

Ensemble Classification of Cancer Types and Biomarker Identification.

Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.