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Background: Headache and depression are prevalent among general population. The aim of this study was to determine the associated factors of headache in elderly subjects with emphasis to depression. Methods: All cohort of elderly individuals of the Amirkola Health Study Project were included. Data regarding several clinical and demographic characteristics were provided via fill in quesstionnaire, interview and clinical examination. Presence and duration as well as severity of headache were collected through an interview based on self-reported data. Diagnosis of depression was confirmed according to standard Geriatric Depression Scale (GDS) criteria. In statistical analyses chi-square test with logistic regression analysis was used for association. Results: A total of 832 men and 667 women aged >/= 60 years old were studied. Headache and depression were diagnosed in 42% and 42.4% respectively. In depressed subjects, headache was significantly higher by OR=3.1(95% CI, 2.5-3.83, P=0.001). Proportions of headache increased by severity of depression with a dose-response pattern of relationship from 53.3% in mild depression to 72.6% in severe depression. The magnitude of OR for headache increased from 2.59 (95% CI, 2.03-3.31) in patients with mild depression to 6.04 (95% CI, 3.54-10.3) in patients with severe depression. After adjustment for all covariates, headache was significantly associated with female gender and back pain as well as with depression with a significant dose-response relationship. Conclusion: The findings of this study indicated an independent association between headache and psychological factors in elderly subjects, particularly in women

Abstract Different species of Trichoderma harzianum have been used mainly as an antagonist against a number of plant diseases. There is a direct relationship between the biocontrol potential of these fungi and the rate of enzymatic degradation of the cell walls of pathogens. Following the successful production of chimeric chit 42 via protein engineering in the previous work of authors, the resulting recombinant protein was transferred to T. harzianum. In this study presence and stability of chimeric Chitinase (Chit42-ChBD) was verified then growth stimulating and biocontrol potential of the six improved isolates were evaluated on bean plants under laboratory and greenhouse conditions compared to the wild type. Among engineered isolates, Chit42-ChBD3 with 53.83% growth inhibition on Rhizoctonia showed the highest growth rate and sporulation compared to the wild type in the dual culture test. The results showed that ChBD15 and ChBD13 had better performance in terms of increasing nutrient uptake and root growth thereby enhancing the growth rate and yield performance of the whole plant compared to the plants treated with wild type and non-T. harzianum treated plants.

Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people’s trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional mask weight window on eight directions. Consequently, the fractional differential texture descriptors are extracted using the gray-level co-occurrence matrix for image splicing detection. The support vector machine is used as a classifier that distinguishes between authentic and spliced images. Results prove that the achieved improvements of the proposed algorithm are compatible with other splicing detection methods.

Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.