Oral White Lesions Associated with Chewing Khat

<p>Abstract</p> <p>Introduction</p> <p>Khat is a cultivated plant whose leaves when chewed elevate mood. Unlike the chewing of betel nut, no association between the white oral mucosal lesions in khat users and oral malignancies has been reported. Chewing of khat has been documented in many countries and has increased with worldwide migration. The impact of chewing khat upon the oral mucosa is essentially unknown.</p> <p>Purpose</p> <p>The purpose of this study was to assess the occurrence of oral white changes in chronic khat chewers. Oral mucosal changes in a group of 47 Yemenite Israeli men over 30 years of age, who had chewed khat more than 3 years, were compared to those of 55 Yemenite men who did not chew.</p> <p>Results</p> <p>White lesions were significantly more prevalent in the khat chewers (83%) compared to the non chewing individuals (16%) (P < 0.001). White oral lesions were identified primarily on the lower buccal attached gingival mucosa, the alveolar mucosa and the lower mucobuccal fold on the chewing side (p < 0.001). There was no significant association between the occurrence of the white lesions and smoking. Even though the majority of the white lesions (85.4%) were homogenous, 71.4% of the non homogenous lesions were identified in khat chewers. Vital staining with toluidine blue and exfoliative cytology was conducted on a subset of patients with homogenous and non-homogenous oral lesions, and there were no findings suspicious for pre-malignant or malignant changes.</p> <p>Discussion</p> <p>This study demonstrated a relationship between khat chewing and oral white lesions, which we attribute to chronic local mechanical and chemical irritation of the mucosa. Our findings also suggest that mucosal changes associated with khat are benign, however, this initial study requires further studies including follow-up of khat users to confirm the current findings, including the likely benign changes associated with chronic use and histologic findings of clinical lesions.</p

Coherent, super resolved radar beamforming using self-supervised learning

High resolution automotive radar sensors are required in order to meet the high bar of autonomous vehicles needs and regulations. However, current radar systems are limited in their angular resolution causing a technological gap. An industry and academic trend to improve angular resolution by increasing the number of physical channels, also increases system complexity, requires sensitive calibration processes, lowers robustness to hardware malfunctions and drives higher costs. We offer an alternative approach, named Radar signal Reconstruction using Self Supervision (R2-S2), which significantly improves the angular resolution of a given radar array without increasing the number of physical channels. R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple data representation spaces. Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions.Comment: 28 pages 10 figure

Proteomics of Heat-Stress and Ethylene-Mediated Thermotolerance Mechanisms in Tomato Pollen Grains

Heat stress is a major cause for yield loss in many crops, including vegetable crops. Even short waves of high temperature, becoming more frequent during recent years, can be detrimental. Pollen development is most heat-sensitive, being the main cause for reduced productivity under heat-stress across a wide range of crops. The molecular mechanisms involved in pollen heat-stress response and thermotolerance are however, not fully understood. Recently, we have demonstrated that ethylene, a gaseous plant hormone, plays a role in tomato (Solanum lycopersicum) pollen thermotolerance. These results were substantiated in the current work showing that increasing ethylene levels by using an ethylene-releasing substance, ethephon, prior to heat-stress exposure, increased pollen quality. A proteomic approach was undertaken, to unravel the mechanisms underlying pollen heat-stress response and ethylene-mediated pollen thermotolerance in developing pollen grains. Proteins were extracted and analyzed by means of a gel LC-MS fractionation protocol, and a total of 1,355 proteins were identified. A dataset of 721 proteins, detected in three biological replicates of at least one of the applied treatments, was used for all analyses. Quantitative analysis was performed based on peptide count. The analysis revealed that heat-stress affected the developmental program of pollen, including protein homeostasis (components of the translational and degradation machinery), carbohydrate, and energy metabolism. Ethephon-pre-treatment shifted the heat-stressed pollen proteome closer to the proteome under non-stressful conditions, namely, by showing higher abundance of proteins involved in protein synthesis, degradation, tricarboxylic acid cycle, and RNA regulation. Furthermore, up-regulation of protective mechanisms against oxidative stress was observed following ethephon-treatment (including higher abundance of glutathione-disulfide reductase, glutaredoxin, and protein disulfide isomerase). Taken together, the findings identified systemic and fundamental components of pollen thermotolerance, and serve as a valuable quantitative protein database for further research

Transcriptomic Events Involved in Melon Mature-Fruit Abscission Comprise the Sequential Induction of Cell-Wall Degrading Genes Coupled to a Stimulation of Endo and Exocytosis

Background: Mature-fruit abscission (MFA) in fleshy-fruit is a genetically controlled process with mechanisms that, contrary to immature-fruit abscission, has not been fully characterized. Here, we use pyrosequencing to characterize the transcriptomes of melon abscission zone (AZ) at three stages during AZ-cell separation in order to understand MFA control at an early stage of AZ-activation. Principal Findings: The results show that by early induction of MFA, the melon AZ exhibits major gene induction, while by late induction of MFA, melon AZ shows major gene repression. Although some genes displayed similar regulation in both early and late induction of abscission, such as EXT1-EXT4, EGase1, IAA2, ERF1, AP2D15, FLC, MADS2, ERAF17, SAP5 and SCL13 genes, the majority had different expression patterns. This implies that time-specific events occur during MFA, and emphasizes the value of characterizing multiple time-specific abscission transcriptomes. Analysis of gene-expression from these AZs reveal that a sequential induction of cell-wall-degrading genes is associated with the upregulation of genes involved in endo and exocytosis, and a shift in plant-hormone metabolism and signaling genes during MFA. This is accompanied by transcriptional activity of small-GTPases and synthaxins together with tubulins, dynamins, V-type ATPases and kinesin-like proteins potentially involved in MFA signaling. Early events are potentially controlled by down-regulation of MADS-box, AP2/ERF and Aux/IAA transcription-factors, and up-regulation of homeobox, zinc finger, bZIP, and WRKY transcription-factors, while late events may be controlled by up-regulation of MYB transcription-factors. Significance: Overall, the data provide a comprehensive view on MFA in fleshy-fruit, identifying candidate genes and pathways associated with early induction of MFA. Our comprehensive gene-expression profile will be very useful for elucidating gene regulatory networks of the MFA in fleshy-fruit

An Incremental Drawing Algorithm for Planar Graphs

We present a new algorithm for drawing planar graphs on the plane. It can be viewed as a generalization of the algorithm of Chrobak and Payne, which in turn, is based on an algorithm by de Fraysseix, Pach and Pollack. Our algorithm improves the previous ones in that it does not require a preliminary triangulation step; triangulation proves problematic in drawing graphs ``nicely", as it has the tendency to ruin the structure of the input graph. The new algorithm retains the positive features of the previous algorithms: It embeds a graph of $n$ vertices on a grid of size $(2n-4)\times (n-2)$ in linear time. We have implemented the algorithm as part of a software system for drawing graphs nicely