Ferdowsi University of Mashhad

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

Experiments suggest that biodiversity enhances the ability of ecosystems to maintain multiple functions, such as carbon storage, productivity, and the buildup of nutrient pools (multifunctionality). However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth's land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.

Soil bacteria and fungi play key roles in the functioning of terrestrial ecosystems, yet our understanding of their responses to climate change lags significantly behind that of other organisms. This gap in our understanding is particularly true for drylands, which occupy ∼41% of Earth´s surface, because no global, systematic assessments of the joint diversity of soil bacteria and fungi have been conducted in these environments to date. Here we present results from a study conducted across 80 dryland sites from all continents, except Antarctica, to assess how changes in aridity affect the composition, abundance, and diversity of soil bacteria and fungi. The diversity and abundance of soil bacteria and fungi was reduced as aridity increased. These results were largely driven by the negative impacts of aridity on soil organic carbon content, which positively affected the abundance and diversity of both bacteria and fungi. Aridity promoted shifts in the composition of soil bacteria, with increases in the relative abundance of Chloroflexi and α-Proteobacteria and decreases in Acidobacteria and Verrucomicrobia. Contrary to what has been reported by previous continental and global-scale studies, soil pH was not a major driver of bacterial diversity, and fungal communities were dominated by Ascomycota. Our results fill a critical gap in our understanding of soil microbial communities in terrestrial ecosystems. They suggest that changes in aridity, such as those predicted by climate-change models, may reduce microbial abundance and diversity, a response that will likely impact the provision of key ecosystem services by global drylands.

The phase-locked loops (PLLs) are probably the most widely used synchronization technique in grid-connected applications. The main challenge that is associated with the PLLs is how to precisely and fast estimate the phase and frequency, when the grid voltage is unbalanced and/or distorted. To overcome this challenge, incorporating moving average filter(s) (MAF) into the PLL structure has been proposed in some recent literature. An MAF is a linear-phase finite-impulse-response filter, which can act as an ideal low-pass filter, if certain conditions hold. The main aim of this paper is to present the control design guidelines for a typical MAF-based PLL. The paper starts with the general description of MAFs. The main challenge associated with using the MAFs is then explained, and its possible solutions are discussed. The paper then proceeds with a brief overview of the different MAF-based PLLs. In each case, the PLL block diagram description is shown, the advantages and limitations are briefly discussed, and the tuning approach (if available) is evaluated. The paper then presents two systematic methods to design the control parameters of a typical MAF-based PLL: one for the case of using a proportional-integral (PI) type loop filter (LF) in the PLL, and the other for the case of using a proportional-integral-derivative (PID) type LF. Finally, the paper compares the performance of a well-tuned MAF-based PLL when using the PI-type LF with the results of using the PID-type LF, which provides useful insights into their capabilities and limitations.

Heat as a stressor of poultry has been studied extensively for many decades; it affects poultry production on a worldwide basis and has significant impact on well-being and production. More recently, the involvement of heat stress in inducing oxidative stress has received much interest. Oxidative stress is defined as the presence of reactive species in excess of the available antioxidant capacity of animal cells. Reactive species can modify several biologically cellular macromolecules and can interfere with cell signaling pathways. Furthermore, during the last decade, there has been an ever-increasing interest in the use of a wide array of natural feed-delivered phytochemicals that have potential antioxidant properties for poultry. In light of this, the current review aims to (1) summarize the mechanisms through which heat stress triggers excessive superoxide radical production in the mitochondrion and progresses into oxidative stress, (2) illustrate that this pathophysiology is dependent on the intensity and duration of heat stress, (3) present different nutritional strategies for mitigation of mitochondrial dysfunction, with particular focus on antioxidant phytochemicals. Oxidative stress that occurs with heat exposure can be manifest in all parts of the body; however, mitochondrial dysfunction underlies oxidative stress. In the initial phase of acute heat stress, mitochondrial substrate oxidation and electron transport chain activity are increased resulting in excessive superoxide production. During the later stage of acute heat stress, down-regulation of avian uncoupling protein worsens the oxidative stress situation causing mitochondrial dysfunction and tissue damage. Typically, antioxidant enzyme activities are upregulated. Chronic heat stress, however, leads to downsizing of mitochondrial metabolic oxidative capacity, up-regulation of avian uncoupling protein, a clear alteration in the pattern of antioxidant enzyme activities, and depletion of antioxidant reserves. Some phytochemicals, such as various types of flavonoids and related compounds, were shown to be beneficial in chronic heat-stressed poultry, but were less or not effective in non-heat-stressed counterparts. This supports the contention that antioxidant phytochemicals have potential under challenging conditions. Though substantial progress has been made in our understanding of the association between heat stress and oxidative stress, the means by which phytochemicals can alleviate oxidative stress have been sparsely explored.

The search for predictions of species diversity across environmental gradients has challenged ecologists for decades. The humped-back model (HBM) suggests that plant diversity peaks at intermediate productivity; at low productivity few species can tolerate the environmental stresses, and at high productivity a few highly competitive species dominate. Over time the HBM has become increasingly controversial, and recent studies claim to have refuted it. Here, by using data from coordinated surveys conducted throughout grasslands worldwide and comprising a wide range of site productivities, we provide evidence in support of the HBM pattern at both global and regional extents. The relationships described here provide a foundation for further research into the local, landscape, and historical factors that maintain biodiversity.

Zinc oxide nanoparticles (ZnO-NPs) were synthesized via a solvothermal method in triethanolamine (TEA) media. TEA was utilized as a polymer agent to terminate the growth of ZnO-NPs. The ZnO-NPs were characterized by a number of techniques, including X-ray diffraction analysis, transition electron microscopy, and field emission electron microscopy. The ZnO-NPs prepared by the solvothermal process at 150°C for 18 hours exhibited a hexagonal (wurtzite) structure, with a crystalline size of 33 ± 2 nm, and particle size of 48 ± 7 nm. The results confirm that TEA is a suitable polymer agent to prepare homogenous ZnO-NPs.

The need for ultra low-power, area efficient, and high speed analog-to-digital converters is pushing toward the use of dynamic regenerative comparators to maximize speed and power efficiency. In this paper, an analysis on the delay of the dynamic comparators will be presented and analytical expressions are derived. From the analytical expressions, designers can obtain an intuition about the main contributors to the comparator delay and fully explore the tradeoffs in dynamic comparator design. Based on the presented analysis, a new dynamic comparator is proposed, where the circuit of a conventional double-tail comparator is modified for low-power and fast operation even in small supply voltages. Without complicating the design and by adding few transistors, the positive feedback during the regeneration is strengthened, which results in remarkably reduced delay time. Post-layout simulation results in a 0.18- μm CMOS technology confirm the analysis results. It is shown that in the proposed dynamic comparator both the power consumption and delay time are significantly reduced. The maximum clock frequency of the proposed comparator can be increased to 2.5 and 1.1 GHz at supply voltages of 1.2 and 0.6 V, while consuming 1.4 mW and 153 μW, respectively. The standard deviation of the input-referred offset is 7.8 mV at 1.2 V supply.

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

This trial was conducted to evaluate the effects of dietary supplementation of phytogenic product containing an equal mixture of thymol and carvacrol at 4 levels (0, 60, 100, and 200 mg/kg of diet) on performance, antioxidant enzyme activities, fatty acid composition, digestive enzyme activities, and immune response in broiler chickens. Each of the 4 diets was fed to 5 replicates of 12 chicks each from d 0 to 42. The inclusion of thymol + carvacrol linearly decreased (P < 0.05) feed intake, but the highest (P < 0.05) BW gain (ADG) and feed efficiency was observed in broilers offered 200 mg/kg of phytogenic product. The phytogenic product linearly increased (P < 0.05) superoxide dismutase and glutathione peroxidase activities and decreased (P < 0.05) malondialdehyde level in thigh muscle at d 42 and serum and liver at d 24 and 42. Total saturated fatty acids were depressed (P < 0.05) and total polyunsaturated fatty acid and n-6 were linearly increased (P < 0.05) in serum and thigh by the inclusion of phytogenic product compared with the control diet. Supplementation with thymol + carvacrol also increased intestinal and pancreatic trypsin, lipase, and protease activities in 24-d-old (linear, P < 0.05) but not in 42-d-old birds. Thymol + carvacrol modified (linear, P < 0.05) immune response by increasing hypersensitivity response, total and IgG anti-sheep red blood cell titers, and decreasing heterophil to lymphocyte ratio compared with the control group. However, hematological parameters and lymphoid organ weight were not affected by thymol + carvacrol. Thus, feed supplementation with thymol + carvacrol enhanced performance, increased antioxidant enzyme activities, retarded lipid oxidation, enhanced digestive enzyme activities, and improved immune response of broilers.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

In grid-connected applications, the synchronous reference frame phase-locked loop (SRF-PLL) is a commonly used synchronization technique due to the advantages it offers such as ease of implementation and robust performance. Under ideal grid conditions, the SRF-PLL enables a fast and accurate phase/frequency detection; however, unbalanced and distorted grid conditions highly degrade its performance. To overcome this drawback, several advanced PLLs have been proposed, such as the multiple reference frame-based PLL, the dual second-order generalized integrator-based PLL, and the multiple complex coefficient filter-based PLL. In this paper, a comprehensive design-oriented study of these advanced PLLs is presented. The starting point of this study is to derive the small-signal model of the aforementioned PLLs, which simplifies the parameter design and the stability analysis. Then, a systematic design procedure to fine tune the PLLs parameters is presented. The stability margin, the transient response, and the disturbance rejection capability are the key factors that are considered in the design procedure. Finally, the experimental results are presented to support the theoretical analysis.

We present SPMF, an open-source data mining library offering implementations of more than 55 data mining algorithms. SPMF is a cross-platform library implemented in Java, specialized for discovering patterns in transaction and sequence databases such as frequent itemsets, association rules and sequential patterns. The source code can be integrated in other Java programs. Moreover, SPMF offers a command line interface and a simple graphical interface for quick testing. The source code is available under the GNU General Public License, version 3. The website of the project offers several resources such as documentation with examples of how to run each algorithm, a developer's guide, performance comparisons of algorithms, data sets, an active forum, a FAQ and a mailing list.

This study employed panel spatial simultaneous equations models with a generalized spatial two-stage least squares (GS2SLS) method to explore the three-way linkages among economic growth, carbon emissions, and renewable energy consumption for European Union (EU) countries from 1995 to 2014. Findings indicate that positive spatial correlations of the variables of concern are confirmed across countries. Specifically, the evidence shows that economic growth is stronger spatially correlated than carbon dioxide (CO2) emissions or renewable energy consumption. Our findings demonstrate that the link between economic growth and carbon emissions, and between economic growth and renewable energy consumption is bidirectional. Moreover, the evidence also confirms that the nexus between economic growth and renewable energy consumption is unidirectional. These novel empirical results would help policy makers in designing appropriate environmental and energy policies to fulfill EU targets for economic development and sustainability.

Lignin is a major component of soil organic matter and also a rich source of carbon dioxide in soils. However, because of its complex structure and recalcitrant nature, lignin degradation is a major challenge. Efforts have been made from time to time to understand the lignin polymeric structure better and develop simpler, economical, and bio-friendly methods of degradation. Certain enzymes from specialized bacteria and fungi have been identified by researchers that can metabolize lignin and enable utilization of lignin-derived carbon sources. In this review, we attempt to provide an overview of the complexity of lignin’s polymeric structure, its distribution in forest soils, and its chemical nature. Herein, we focus on lignin biodegradation by various microorganism, fungi and bacteria present in plant biomass and soils that are capable of producing ligninolytic enzymes such as lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and dye-decolorizing peroxidase (DyP). The relevant and recent reports have been included in this review.

The embedding of molecular metal oxides, or polyoxometalates (POMs), in metal–organic frameworks (MOFs) opens new research avenues in catalysis and beyond. This review explores the host–guest chemistry of POMs embedded in MOF hosts and discusses the synergism of the resulting composites for heterogeneous catalysis. The review focuses on well-established and well-studied classes of POMs, such as Keggin and Wells–Dawson anions, and well-researched MOFs, including the MIL, UiO, and NENU families. Outstanding examples of synergistic catalytic activity between the POM and MOF are described, and key performance parameters, including POM localization, pore size and pore structure, as well as particle size effects are described for technologically important catalytic processes. In addition to thermal catalysis, we discuss the use of POM@MOF composites for electro- and photocatalysis with an emphasis on energy conversion systems. Finally, we provide an outlook on emerging areas where POM@MOF composites could lead to new catalytic reactivity.

Polyethylene (PE) is the most abundant synthetic, petroleum-based plastic materials produced globally, and one of the most resistant to biodegradation, resulting in massive accumulation in the environment. Although the microbial degradation of polyethylene has been reported, complete biodegradation of polyethylene has not been achieved, and rapid degradation of polyethylene under ambient conditions in the environment is still not feasible. Experiments reported in the literature suffer from a number of limitations, and conclusive evidence for the complete biodegradation of polyethylene by microorganisms has been elusive. These limitations include the lack of a working definition for the biodegradation of polyethylene that can lead to testable hypotheses, a non-uniform description of experimental conditions used, and variations in the type(s) of polyethylene used, leading to a profound limitation in our understanding of the processes and mechanisms involved in the microbial degradation of polyethylene. The objective of this review is to outline the challenges in polyethylene degradation experiments and clarify the parameters required to achieve polyethylene biodegradation. This review emphasizes the necessity of developing a biochemically-based definition for the biodegradation of polyethylene (and other synthetic plastics) to simplify the comparison of results of experiments focused for the microbial degradation of polyethylene.

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Grazing represents the most extensive use of land worldwide. Yet its impacts on ecosystem services remain uncertain because pervasive interactions between grazing pressure, climate, soil properties, and biodiversity may occur but have never been addressed simultaneously. Using a standardized survey at 98 sites across six continents, we show that interactions between grazing pressure, climate, soil, and biodiversity are critical to explain the delivery of fundamental ecosystem services across drylands worldwide. Increasing grazing pressure reduced ecosystem service delivery in warmer and species-poor drylands, whereas positive effects of grazing were observed in colder and species-rich areas. Considering interactions between grazing and local abiotic and biotic factors is key for understanding the fate of dryland ecosystems under climate change and increasing human pressure.