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Abstract The health, growth and fertility of avian species can be negatively affected by parasite infection. This survey assesses the presence, variety and distribution of internal and external parasites among parrots and perching birds in Mashhad, Iran. This study examined 751 caged pet birds from different species and regions in Mashhad for faecal samples and 132 oral swabs for digestive tract parasites. Furthermore, skin scrapings were conducted on 14 canaries displaying dishevelled feathers. During the study, mortalities and moribund birds that had been referred underwent necropsies to examine internal parasites. Following the formol ether faecal examination, only one Malango parrot tested positive for Heterakoidea eggs among 751 faecal samples (0.13%). Further, one cockatiel showed evidence of parasitic infection with Eimeria spp. (0.13%). However, neither Cryptosporidium nor Giardia protozoa were detected in the samples (0%). Oral swabs revealed no evidence of Trichomonas (0%). On the other hand, skin scraping revealed the presence of the mite Dermanyssus in 7 out of 14 canaries with dishevelled feathers (50%). Of 25 moribund and weak budgerigars, 2 were infected with Acuaria in their proventriculus (8%). In addition, 3 out of 14 deceased myna birds carried the nematode Diplotriana in their coelomic cavities (21.42%). In conclusion, the rate of internal parasites has been relatively low in ornamental birds of Mashhad, whereas the prevalence of external parasites has been higher.

Summary: Recent evidence demonstrates that long-term object value association can enhance visual search efficiency, a phenomenon known as value pop-out. However, the neural mechanism underlying this effect is not fully understood. Given the known role of the ventrolateral prefrontal cortex (vlPFC) in visual search and value memory, we recorded its single-unit activity (n = 526) in two macaque monkeys while they engaged in the value-driven search. Monkeys had to determine whether a high-value target was present within a variable number of low-value objects. Differential neural firing, as well as gamma-band power, indicated the presence of a target within ∼150ms of display onset. Notably, this differential activity was negatively correlated with search time and had reduced set-size dependence during efficient search. On the other hand, neural firing and its variability were higher in inefficient search. These findings implicate vlPFC in rapid detection of valuable targets which would be a crucial skill in competitive environments.

Background: Musculoskeletal disorder (MSD) is one of the important problems concerning the staffschr('39') health and productivity in the workplace. Nutritional status and consumption of some foods are also among the determining factors of MSD. So, this study aimed to evaluate the correlation of diet and consumed food groups with MSD. Methods: This cross-sectional study was conducted on 100 office workers. The participantschr('39') anthropometric parameters and their dietary information were collected using a semi-quantitative food frequency questionnaire. The findings were categorized into nine levels. The total scores were calculated for all the items per food group and per person. Nordic musculoskeletal questionnaire was also administered to evaluate the MSD symptoms in nine parts of body. Results: The score of consumed food groups was compared between individuals “with pain” and “without pain” in nine parts of the body. The scores of fruit intake in individuals “with pain” and “without pain” were 2.94 ± 1.27 vs. 3.29 ± 1.16 and 2.81 ± 1.10 vs. 3.49 ± 1.38 in terms of neck and wrists, respectively. The difference between the two groups were significant (P < 0.05). Furthermore, the participants with pain in the neck consumed significantly lower amounts of cereals and nuts (P = 0.03, 0.04). In the case of the shoulder pain, consuming legumes and nuts in the “without pain” group was higher than the group of participants who had pain (P = 0.01, P = 0.03). Fat intake was higher in the patients who had pain in their hips (P = 0.02). Conclusion: Less pain was reported in the musculoskeletal system by higher consumption of fruits, nuts, and legumes. It seems that plant-based dietary pattern is more effective in musculoskeletal health.

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of ‘weak classifiers’ (i.e., single voxels) in higher dimensions. We propose instead that ‘real’ multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.

Introduction and aim: Electrocardiography is one of the most useful and common tools for examining heart function. In studies in the 1960s, heart rate variability (HRV) was introduced and used as a non-invasive tool to evaluate the functions of the autonomic nervous system and other functional disorders of the heart under various conditions. In addition to the clinical applications of heart rate variability, this physiological parameter is also widely used in ergonomics and occupational health. In the study of mental workload, exposure to industrial noise, shift work, stress, and other parameters related to the workplace benefit heart rate variability. Heart rate variability is regulated by the autonomic nervous system and by the sinoatrial (SA) node. The autonomic nervous system is divided into sympathetic and parasympathetic branches, the autonomic nervous system is divided into sympathetic and parasympathetic branches, thereby affecting the heart rate and heart rate variability. Sympathetic activity tends to increase heart rate and decrease heart rate variability, while parasympathetic tend to reduce heart rate and increase heart rate variability. The most prominent component of the variable heart rate period is a respiratory arrhythmia (RSA), which is considered to be 0.15 to 0.4 Hz. The high-frequency component is only affected by parasympathetic neural activity. Another component of heart rate variability is the low-frequency (LF) component in the frequency range, 0.04 to 0.15 Hz. This component is affected by sympathetic and parasympathetic neural activity. Many computational methods have been developed for HRV analysis, each of which is associated with strengths and weaknesses. Although there may still be difficulties in interpreting this index, it provides researchers and health experts with reliable non-invasive physiological information. HRV parameters can be classified in terms of time, frequency, and nonlinear methods. The commercially available ECG equipment does not usually include HRV features due to the lack of standard diagnostic protocols. As an alternative to this commercial software, many simple, online and free, device-independent, and portable software has been developed for HRV analysis and cardiovascular research. Heart Rate Variability Quantification: Heart rate variability can be measured in long-term (24 hours), short-term (5 minutes), and very short-term periods (less than 5 minutes) and can be analyzed as time- -domain, frequency-domain, and non-linear. 1. Time-domain measures The simplest method to analyze heart rate variability is time-domain. Analysis of heart rate variability in the time-domain is performed through both statistical and geometric analysis, both of which are based on heart rate or RR intervals between successive QRS series. Using these methods, the heart rate can be assessed at any point in time or between normal consecutive sets. Statistical parameters recommended by the European Society of Cardiology and the American Society of Pacing Electrophysiology include: 1) SDNN 2) NN50 3) SDSD 4) SDANN 5) RMSSD 6) SDSD 7) NN50 count (Table 1). Table 1: time-domain measures of HRV Variable Units Description [1]SDNN ms Standard deviation of all NN intervals SDRR[2] ms standard deviation of RR interval series SDANN ms Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording SDNN index ms Mean of the standard deviations of all NN intervals for all 5 min segments of the entire recording pNN50 % NN50 count divided by the total number of all NN intervals HR Max –HR Min bmp The average difference between the highest and lowest HRs during each respiratory cycle rMSSD ms Root mean square of successive RR interval difference HRV triangular index Integral of the density of the RR interval histogram divided by its height TINN ms Baseline width of the RR interval histogram 2. Frequency-domain measures: Time-domain measurements information about the overall change in time series or maximum variable amplitude, but no information about periodic heart rate fluctuations. Data frequency domain analysis provides information on how power distribution is a function of frequency. According to the European Society of Cardiology, the power range of a healthy person is usually divided into four main frequency bands. The range of components used is usually: high-frequency (0.4 - 0.15 Hz), low-frequency (0.15-0.04 Hz), very low frequency (0.04 - 0.003 Hz) and infinitely low frequency (

Aims: The present study aimed to investigate the relationship between traffic noise and drivers′ reaction time (RT) and their error in estimates of movement time. Materials and Methods: In over all, 80 university students with at least 3 years postlicense experience of driving were divided into two groups including traffic noise exposure and control group. S5 test of Vienna test system package was used for assessment of RT. In addition, time-movement anticipation was employed for measuring the rate of error in estimation of movement time before exposing to the traffic noise. After a 1-month interval, participants in the experimental group were exposed to traffic noise about 2 h and the tests were performed again for both groups. Results: Accordingly, RT was statistically higher after traffic noise exposure for male and female. However, there was no significant difference between time of movement before and after exposure to traffic noise both for male and female (P > 0.05). In addition, no difference was shown between movement time, number of accurate estimates, and number of estimates with gross errors before and after of traffic noise exposure in experimental and control group. Conclusion: The results suggest that traffic noise may be associated with poorer mental processing, which can result in longer RT during driving. Therefore, traffic noise would expose drivers to consequent accidents and incidents.