Mycorrhizal Symbiosis Can Change the Composition of Secondary Metabolites in Fruits of Solanum nigrum L.

Solanum nigrum is a common weed in arable land, while being used in traditional medicine around the world due to its remarkable levels of valuable secondary metabolites. Agronomic and biological techniques can alter the production of a specific metabolite by influencing plant growth and metabolism. The effects of colonization with three arbuscular mycorrhizal fungi (AMF), including Funneliformis mosseae, Rhizoglomus intraradices, and Rhizoglomus fasciculatum, on the chemical composition of S. nigrum fruits were evaluated by gas chromatography-mass spectrometry (GC-MS) analysis. More than 100 different chemical constituents were evaluated by GC-MS. Our study revealed that the levels of phenols (quinic acid), benzenes (hydroquinone), sulfur-containing compounds, lactone and carboxylic acids were improved by R. intraradices. In contrast, hydroxymethylfurfural increased by 68 % in R. fasciculatum inoculated with uninoculated S. nigrum plants, and this species was also the most efficient in inducing sugar compounds (D-galactose, lactose, and melezitose). Our results suggest that AMF colonization is an effective biological strategy that can alter the chemical composition and improve the medicinal properties of S. nigrum.

The effect of dietary coenzyme Q10 supplementation on egg quality and liver histopathology of layer quails under cadmium challenge.

The demand for quail eggs has been increased over the last decade due to its beneficial nutritional quality characteristics; however, different nutritional and environmental stressors adversely impact the quality of the produced eggs. This study was conducted to investigate whether dietary supplementation of coenzyme Q10 (CoQ10) could mitigate the negative impact of cadmium (Cd) administration on egg quality and liver histopathology. A total of 162 six-week-old laying Japanese quail (Coturnix japonica) were randomly allotted into three experimental groups. Treatments were as follows: (1) negative control (NC): feeding basal diet; (2) positive control (PC): feeding basal diet and Cd administration; and (3) CdQ10: feeding basal diet supplemented with CoQ10 (900 mg/kg diet) and Cd administration. Cadmium (10 mg/kg BW) was subcutaneously administrated at 10 and 11 weeks of age (woa). Feed conversion ratio (FCR), egg production, egg mass, mortality rate, Cd residue in egg, liver histopathology, and some internal and external egg quality indices were evaluated. Administration of Cd increased FCR in the PC group, but supplementation of CoQ10 partially ameliorated the impact of Cd on FCR (p < 0.05). Cadmium administration decreased both egg production and egg mass; however, CoQ10 supplementation partially mitigated these adverse effects of Cd injection in the CdQ10 compared to the PC group (p < 0.05). Cadmium decreased eggshell thickness and Haugh unit in PC quail compared to both NC and CdQ10 quail (p < 0.05). Moreover, egg yolk colour intensity was enhanced by CoQ10, where a* and b* indices were higher in CdQ10 compared to PC (p < 0.05). In conclusion, the current results demonstrate the beneficial effects of dietary CoQ10 supplementation on liver histopathology and some egg quality indices of Cd-challenged quail.

Evaluation of serological diagnostic tests for bovine brucellosis in dairy cattle herds in an endemic area: a multicenter study.

Brucellosis is known as one of the most common zoonotic diseases worldwide affecting both livestock and humans. It causes abortions, reduces milk production, and infertility in infected animals. The disease is routinely diagnosed through three serological techniques, such as rose bengal plate test (RBPT), standard agglutination test (SAT), and indirect enzyme-linked immunosorbent assay (I-ELISA). The aim of this study was to identify and compare the brucellosis seroprevalence among dairy cattle farms through these different serological tests. From 2112 sampled dairy cattle in different parts of Iran, RBPT, SAT, and I-ELISA led to 296 (14.02%), 215 (10.18%), and 297 (14.06%) positive results, respectively. Brucella abortus biovar 3 (62 cases) was identified as the most common cause of brucellosis in tested animals. Our results showed that the specificity and sensitivity of I-ELISA were higher than those obtained by RBPT and SAT. In this study, the overall agreement of RBPT and SAT with I-ELISA reached 95.21% and 94.12% in dairy cattle farms, respectively. Furthermore, Cohen's kappa statistical analysis revealed that the best degree of agreement was seen between RBPT and I-ELISA (0.80), followed by RBPT and SAT (0.78) and finally SAT and I-ELISA (0.72), thereby indicating a strong agreement between RBPT and I-ELISA methods and good agreement between SAT and I-ELISA methods. The McNemar analysis also showed that a significant difference exists between positive and negative results determined by SAT and I-ELISA methods (p < 0.0001). However, the positive and negative results determined by I-ELISA and RBPT did not show a significant difference (p = 0.9207). Therefore, I-ELISA was a more specific and sensitive serological test when compared to RBPT and SAT and could remarkably decrease non-specific reaction by improving the serological screening specificity for an accurate brucellosis diagnosis in endemic areas.

Rural vulnerability to water scarcity in Iran: an integrative methodology for evaluating exposure, sensitivity and adaptive capacity.

The water crisis is the main stress in arid and semi-arid areas, especially in rural areas where agriculture is the main livelihood. This study assessed vulnerability to water scarcity in six rural regions of Isfahan, Iran. These areas have lost their primary water source of agriculture, the Zayandeh Rud River, since 2006. They have confronted many socio-ecological problems which threatened their existence. A mixed methodology was used to assess vulnerability as a function of exposure, sensitivity, and adaptive capacity. Structured questionnaires and in-depth interviews were conducted with key informants and 266 households. The method of Multidimensional Poverty Index was applied to calculate the sensitivity index, which has not been used for sensitivity assessment yet. The results showed that the leading cause of water scarcity is poor water governance. The three districts that had direct access to the Zayandeh Rud river were more vulnerable to water scarcity (scores of 0.35, 0.39, and 0.44) than those that had never had direct access to the river (scores of 0.19, 0.21, and 0.23) due to the more exposure and less adaption to water shortage. Inappropriate financial resilience (from 0.24 to 0.41) and living standards (from 0.19 to 0.36) have made more contributions to creating sensitivity than socioeconomic factors (from 0.14 to 0.28). Different natural capitals have mainly created differences in adaptive capacity across rural areas. Villages located downstream have lost their natural capital due to water-quality degradation caused by river drying up and groundwater overexploitation. Supplementary Information: The online version contains supplementary material available at 10.1007/s10708-022-10726-0.

Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.

Atomic Force Microscopy Stiffness Mapping in Human Aortic Smooth Muscle Cells.

Aortic smooth muscle cells (SMCs) play a vital role in maintaining mechanical homeostasis in the aorta. We recently found that SMCs of aneurysmal aortas apply larger traction forces than SMCs of healthy aortas. This result was explained by the significant increase of hypertrophic SMCs abundance in aneurysms. In this study, we investigate whether the cytoskeleton stiffness of SMCs may also be altered in aneurysmal aortas. For that, we use atomic force microscopy (AFM) nano-indentation with a specific mode that allows subcellular-resolution mapping of the local stiffness across a specified region of interest of the cell. Aortic SMCs from a commercial human lineage (AoSMCs, Lonza) and primary aneurysmal SMCs (AnevSMCs) are cultured in conditions promoting the development of their contractile apparatus, and seeded on hydrogels with stiffness properties of 12 kPa and 25 kPa. Results show that all SMCs exhibit globally a lognormal stiffness distribution, with medians in the range 10-30 kPa. The mean of stiffness distributions is 16 kPa in aneurysmal SMCs and 12 kPa in healthy cells, but the differences are not statistically significant due to the large dispersion of AFM nano-indentation stiffness. We conclude that the possible alterations previously found in aneurysmal SMCs do not affect significantly the AFM nano-indentation stiffness of their cytoskeleton.

Concentrations-dependent effect of exogenous abscisic acid on photosynthesis, growth and phenolic content of Dracocephalum moldavica L. under drought stress.

MAIN CONCLUSION: The drought conditions and the application of ABA reduce the photosynthetic activity, and the processes related to the transpiration of Dracocephalum moldavica L. At the same time, the plant increases the production of phenolic compounds and essential oil as a response to stress conditions. In the semi-arid regions, drought stress is the most important environmental limitations for crop production. Abscisic acid (ABA) plays a crucial role in the reactions of plants towards environmental stress such as drought. Field experiments for two consecutive years in 2016 and 2017 were conducted to evaluate the effect of three watering regimes (well-watered, moderate and severe drought) and five exogenous ABA concentrations (0, 5, 10, 20 and 40 µM) on growth, photosynthesis, total phenolic and essential oil content of Dracocephalum moldavica L. Without ABA application, the highest photosynthetic rate (6.1 µmol CO2 m-2 s-1) was obtained under well-watered condition and, moderate and severe drought stress decreased photosynthesis rate by 26.39% and 34.43%, respectively. Some growth parameters such as stem height, leaf area, leaf dry weight and biological yield were also reduced by drought stress. ABA application showed a decreasing trend in photosynthesis rate and mentioned plant growth parameters under all moisture regimes. The highest seed yield (1243.56 kg ha-1) was obtained under well-watered condition without ABA application. Increasing ABA concentration decreased seed yield in all moisture regimes. The highest total phenolic content (8.9 mg g-1 FW) and essential oil yield (20.58 kg ha-1) were obtained from 20 and 5 µM ABA concentration, respectively, under moderate drought stress.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework.

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

Mycorrhizal impact on competitive relationships and yield parameters in Phaseolus vulgaris L. - weed mixtures.

Arbuscular mycorrhizal fungi (AMF) are known to improve plant growth and nutrition and therefore are likely to affect the competitive relationships between crops and weeds. In this study, we evaluated whether AMF (Funneliformis mosseae, Rhizoglomus fasciculatum, Rhizoglomus intraradices) change plant competition between Phaseolus vulgaris and the weeds Solanum nigrum L., Digitaria sanguinalis L., and Ipomoea purpurea L. Mycorrhizal colonization, aggressivity index, photosynthetic rates, and yield parameters were measured. While the presence of AMF reduced the total biomass of D. sanguinalis and S. nigrum when grown in competition with P. vulgaris, it increased the total biomass of I. purpurea when grown with P. vulgaris. Significantly, elevated mycorrhizal growth responses (38-44%) improved the competitive ability of I. purpurea. In contrast, the competitive ability of S. nigrum was increased only when plants colonized by R. intraradices. The total protein content of P. vulgaris pods when in competition was negatively affected by AMF, thus leading to low nutritional quality. The results suggest that AMF have the potential to affect the outcome of weed-P. vulgaris competition. We demonstrate that not only colonization with AMF but also AMF species can affect the competitive relationships between crops and weeds, and thus, AMF represent key soil organisms to be taken into account in sustainable weed management strategies.

Isolation, identification and chemical inactivation of infectious laryngotracheitis virus for use as a vaccine candidate.

Infectious laryngotracheitis (ILT) is a poultry respiratory disease associated with considerable mortality in chicken and decreasing egg production. Vaccination along with biosecurity measures are considered as the main strategy for ILT control. This study was aimed to evaluate the potency of an inactive ILT vaccine candidate derived from a local ILTV isolate. The isolated virus was characterized and treated with various chemicals and their concentrations. The virus infectivity was entirely abolished by treatment of 3 mM binary ethylene imine following 16 h incubation. The immune response of inactivated ILTV suspension with adjuvans was evaluated in both SPF chickens (experiment-I) and Hyline pullets (experiment-II). Efficacy of the inactivated and live ILT vaccines combination was compared. The results of experiment-I showed that the inactivated antigen induced specific antibody titers against ILTV. In experiment-II, despite the increase in serum antibody level administration of the inactivated antigen alone did not offer sufficient protection. The full protection was found in chickens that received the combination regimen. We conclude that simultaneous administration of the inactivated and live ILT vaccines was efficient for induction of immunity against ILTV. Keywords: infectious laryngotracheitis virus; vaccine; inactivation; immune response.

CLoSES: A platform for closed-loop intracranial stimulation in humans.

Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Implementing closed-loop systems is challenging particularly with regard to applying consistent strategies considering inter-individual variability. In particular, during intracranial epilepsy monitoring, where much of this research is currently progressing, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. The system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features are in place. In other words, a robust, yet flexible platform is required to perform different tests with a single participant and to comply with clinical requirements. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial electroencephalography (iEEG) signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for a certain duration), stimulation is triggered. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. CLoSES has been successfully used with twelve patients implanted with depth electrodes in the epilepsy monitoring unit. During cognitive tasks (N=5), stimulation in closed loop modified a cognitive hidden state on a trial by trial basis. Sleep spindle oscillations (N=6) and sharp transient epileptic activity (N=9) were detected in near real-time, and stimulation was applied during the event or at specified delays (N=3). In addition, we measured the capabilities of the CLoSES system. Total latency was related to the characteristics of the event being detected, with tens of milliseconds for epileptic activity and hundreds of milliseconds for spindle detection. Stepwise latency, the actual duration of each continuous step, was within the specified fixed-step duration and increased linearly with the number of channels and features. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.

Toxoplasma gondii ROP38 protein: Bioinformatics analysis for vaccine design improvement against toxoplasmosis.

Rhoptry proteins (ROPs) play a significant role in various stages of Toxoplasma gondii (T. gondii) life cycle, being critical for both invasion and intracellular survival. ROP38 is a key manipulator of host gene expression and has a function in tachyzoite to bradyzoite conversion. In this study, we've employed various bioinformatics online tools for immunogenicity prediction of ROP38 protein, comprising physico-chemical, antigenic and allergenic profiles, transmembrane domain, subcellular localization, post-translational modification (PTM) sites, secondary and 3D structure, B-cell, MHC-binding and cytotoxic T-lymphocyte (CTL) epitopes. The findings showed 54 PTM sites without a transmembrane domain. Also, ROP38 was proved a non-allergenic and antigenic protein. The protein had Sec signal peptide (Sec/SPI) with 0.8762 likelihood. The secondary structure included 52.68% random coil, 29.57% alpha helix and 17.74% extended strand. Based on Ramachandran plot output for refined model, 95.3%, 3.4%, and 1.4% of amino acid residues were incorporated in the favored, allowed, and outlier regions, respectively. B-cell epitopes TFPGDDIQTSS (67-72) and KAKNKWGRTRYTLQG (207-221) as well as T-cell epitope LSPVGFFTAL (6-15) possessed the highest antigenic index in the protein sequence. This paper is a premise for further researches, and provides insights for the development of a suitable vaccine against toxoplasmosis. More empirical studies are required using the ROP38 alone or in combination with other antigens/epitopes in the future.

At the height of the storm: Healthcare staff's health conditions and job satisfaction and their associated predictors during the epidemic peak of COVID-19.

This study reports the physical health, mental health, anxiety, depression, distress, and job satisfaction of healthcare staff in Iran when the country faced its highest number of total active COVID-19 cases. In a sample of 304 healthcare staff (doctors, nurses, radiologists, technicians, etc.), we found a sizable portion reached the cutoff levels of disorders in anxiety (28.0%), depression (30.6%), and distress (20.1%). Age, gender, education, access to PPE (personal protective equipment), healthcare institutions (public vs. private), and individual status of COVID-19 infection each predicted some but not all the outcome variables of SF-12, PHQ-4, K6, and job satisfaction. The healthcare workers varied greatly in their access to PPE and in their status of COVID-19 infection: negative (69.7%), unsure (28.0%), and positive (2.3%). The predictors were also different from those identified in previous studies of healthcare staff during the COVID-19 crisis in China. This study helps to identify the healthcare staff in need to enable more targeted help as healthcare staff in many countries are facing peaks in their COVID-19 cases.

Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling.

Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked point process models based on time rescaling, which for a correct model produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations in both time and the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multidimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss the properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the appendix.

Filarial worms: a systematic review and meta-analysis of diversity in animals from Iran with emphasis on human cases.

Current systematic review and meta-analysis demonstrate the prevalence reports of filariasis in animals in Iran along with human cases. Studies were screened, relevant papers were selected and the random-effect model was used by forest plot with 95% confidence interval (CI). Of 17 records of human case-reports, particularly from Khuzestan province (5 cases), Dirofilaria repens was the most detected parasite (10 cases) with higher involvement of the right eye (7 cases) than other organs. Eleven animal species were reported to be parasitised by filarioids in Iran. The prevalence of Dirofilaria immitis in canids was 14.69% (95% CI: 10.33-19.67), with highest rates (20.92%; 95% CI: 13.84-29.03) in free-ranging dogs. Male (10.07%; 95% CI: 5.10-16.47) and more than 1-year old (20.77%; 95% CI: 8.66-36.42) dogs were more likely to be found infected. The frequency of other filarioids of zoonotic interest was: Acanthocheilonema reconditum in dogs 2.15% (95% CI: 0.71-4.33), Dipetalonema evansi in camels 10.16% (95% CI: 4.73-17.34), Onchocerca cervicalis in horses 3.63% (95% CI: 1.44-6.75%) and Onchocerca fasciata 16.57% (95% CI: 10.12-24.24%) in camels. Still, our knowledge on parasitic filariae in Iran is limited and more investigation is needed in both human and animal populations.

AntAngioCOOL: computational detection of anti-angiogenic peptides.

BACKGROUND: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. METHODS: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides. RESULTS: Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/ . CONCLUSIONS: Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.

Is there association between Trichomonas vaginalis infection and prostate cancer risk?: A systematic review and meta-analysis.

We performed a systematic review and meta-analysis to reveal the association between Trichomonas vaginalis (TV) infection and the risk of prostate cancer (PCa) development. Systematic searching (PubMed/Medline, Scopus, Embase, Web of Science, Cinhal, Wiley, Cochrane, Psychoinfo, ProQuest and Google Scholar) was done, up to March 2018 for case-control studies. Random effects model was applied to define odds ratios and their 95% confidence intervals. In total, 6 enteries were included in our meta-analysis, comprising 5590 individuals (2677 PCa cases and 2913 control individuals) examined for trichomoniasis, with a total positivity of 469 (17.51%) and 482 (16.54%) individuals, respectively. Totally, such association was documented in three countries, including USA (4 studies), Kuwait (one study) and Taiwan (one study). Based on pooled estimations, however a 1.17-time increase of PCa was observed among individuals with a previous exposure of TV, it was not statistically significant [OR = 1.17 (95% CI: 1.01 to 1.36)]. Egger's regression test demonstrated no publication bias among studies. Also, year of publication for included records was not significantly correlated to the relationship between trichomoniasis and PCa. Any further inferences should be based on future investigations for better understanding this relationship and shedding light on the cryptic pathogenesis of TV in PCa patients.

Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach.

Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable-called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants (N=8) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.

Determination of optimum carcass weight for meat quality and fatty acid composition in fat-tailed male and female Chall lambs.

The aim of this study was to determine the optimum carcass weight for meat quality and fatty acid composition in fat-tailed Chall lambs. Thirty lambs (15 male and 15 female) were allotted to three carcass weight groups: (1) light carcass weight (LCW 10-15 kg), (2) moderate carcass weight (MCW 15-20 kg), and (3) heavy carcass weight (HCW 20-25 kg). Back fat thickness and intramuscular fat (IMF) content were greater (P < 0.05) for HCW and female groups than their counterparts, respectively. Drip loss was lower (P < 0.05) for female and HCW lamb groups than male and LCW group, respectively. Female and LCW lambs had lower (P < 0.05) shear force compared with their corresponding male and HCW groups. Meat from LCW and MCW lambs had higher lightness (L* value; 43.6, 43.5 vs. 39.9), while redness (a* value; 13.6, 13.9 vs. 15.4) was greater for HCW and female (13.7 vs. 14.9) lambs compared with their counterparts (P < 0.05). The MCW lambs produced meat with higher overall acceptability compared with other two groups (P < 0.05). The HCW lambs contained lower polyunsaturated fatty acids (PUFA), polyunsaturated fatty acids to saturated fatty acids (P:S) ratio, and n-3 PUFA compared with LCW group (P < 0.05). Results show that as the animal grow faster and achieved HCW, the IMF content also increased mainly as storage triglyceride, while functional fats consisting long-chain omega-3 did not increase proportionately. In addition, the study also demonstrates that using IMF for predicting or assessing meat quality aspects such as juiciness and flavor or the nutritional value of meat relating to health claimable fatty acids would not be appropriate.