Uncontrolled Sources Research Articles

The Impact of Pre-Breeding on Vegetable Crop Improvement: A Review

Pre-breeding begins with the finding of beneficial genes from wild relatives, native species, and various other unadapted materials. These advantageous qualities are subsequently transferred to a moderate pool of resources so that breeders can create new varieties for farmers. With the process of discovering useful characteristics, preserving their genetic diversity, and incorporating these genes into a form that can be used, crop improvement innovation is generated. Connecting genetic diversity from wild relatives along with other uncontrolled sources is the main goal. Pre-breeding strategies aim to introduce genes for tolerance to environmental difficulties and resistance to major diseases and pests from wild relatives into cultivated crops using the application of introgression and integration procedures. Pre-breeding provides the base for commercially important techniques for plant breeding by broadening the diversity of germplasm and providing breeders with easily accessible resources to breeding beneficial characteristics, while adhering to the basic concepts of the field. The comprehensive discussion of pre-breeding is a valuable resource for scientists and researchers alike, addressing all aspects of this crucial phase in improving vegetable crops.

Моделирование системы очистки сточных вод животноводческих ферм

Among the sources of pollution of surface water bodies from agricultural activities, livestock, pig and poultry complexes can be prioritized, which are uncontrolled and diffuse sources of suspended solids, organic compounds, biogenic elements, heavy metals and pathogenic microflora, which causes biochemical pollution and has a particularly detrimental effect on the state of inland reservoirs. Wastewater from agricultural processing enterprises and household water from rural settlements with unpredictable pollutants play a significant role in pollution. The livestock sector is one of the largest consumers of water. The large number of animals in a confined space, the use of hydraulic harvesting systems when removing their waste products, leads to the formation of manure in significant quantities. Sanitary and hygienic standards in industrial premises are supported with the help of water – animals are washed with it, rooms are cleaned, and they are disinfected, food is prepared, dishes and equipment are washed, manure is washed off with water, and so on. Accordingly, as the demand for water in animal husbandry increases, the volume of wastewater discharge that contains manure into nearby water bodies increases. The effluents that are formed as a result of the activities of the livestock sector are classified as containing a large amount of harmful substances with a high concentration. Depending on the sources of pollution and the main pollutants, technical solutions should be developed to reduce the volume of wastewater and its treatment. The effectiveness of their functioning can be ensured with the help of modern information processing methods, using methods of system analysis of complex objects based on a mathematical description of the technological process. To analyze the state of the treatment system as a whole and predict the development of emergency situations, a software implementation of a wastewater treatment system for livestock farms was created.

Optical control and coherent coupling of spin diffusive modes in thermal gases

Quantum science and technology devices exploiting collective spins in thermal gases are extremely appealing due to their simplicity and robustness. This comes at the cost of dealing with the random thermal motion of the atoms which is usually an uncontrolled source of decoherence and noise. There are however conditions, for example, when diffusing in a buffer gas, where thermal atoms can occupy a discrete set of stable spatial modes. Diffusive modes can be extended or localized, have different magnetic properties depending on boundary conditions, and can react differently to external perturbations. Here, we selectively excite, manipulate, and interrogate the longest-lived of these modes by using laser light. In particular, we identify the conditions for the generation of modes that are exceptionally resilient to detrimental effects such as light induced frequency shifts and power-broadening, which are often the dominant sources of systematic errors in atomic magnetometers and comagnetometers. Moreover, we show that the presence of spatial inhomogeneities in the pump introduces a coupling that leads to a coherent exchange of excitation between the two longest-lived modes. Our results demonstrate that systematic engineering of the multi-mode nature of diffusive gases has great potential for improving the performance of quantum sensors based on alkali-metal thermal vapors, and opens new perspectives for quantum information applications. Published by the American Physical Society 2024

Diving dinosaurs? Caveats on the use of bone compactness and pFDA for inferring lifestyle.

The lifestyle of spinosaurid dinosaurs has been a topic of lively debate ever since the unveiling of important new skeletal parts for Spinosaurus aegyptiacus in 2014 and 2020. Disparate lifestyles for this taxon have been proposed in the literature; some have argued that it was semiaquatic to varying degrees, hunting fish from the margins of water bodies, or perhaps while wading or swimming on the surface; others suggest that it was a fully aquatic underwater pursuit predator. The various proposals are based on equally disparate lines of evidence. A recent study by Fabbri and coworkers sought to resolve this matter by applying the statistical method of phylogenetic flexible discriminant analysis to femur and rib bone diameters and a bone microanatomy metric called global bone compactness. From their statistical analyses of datasets based on a wide range of extant and extinct taxa, they concluded that two spinosaurid dinosaurs (S. aegyptiacus, Baryonyx walkeri) were fully submerged "subaqueous foragers," whereas a third spinosaurid (Suchomimus tenerensis) remained a terrestrial predator. We performed a thorough reexamination of the datasets, analyses, and methodological assumptions on which those conclusions were based, which reveals substantial problems in each of these areas. In the datasets of exemplar taxa, we found unsupported categorization of taxon lifestyle, inconsistent inclusion and exclusion of taxa, and inappropriate choice of taxa and independent variables. We also explored the effects of uncontrolled sources of variation in estimates of bone compactness that arise from biological factors and measurement error. We found that the ability to draw quantitative conclusions is limited when taxa are represented by single data points with potentially large intrinsic variability. The results of our analysis of the statistical method show that it has low accuracy when applied to these datasets and that the data distributions do not meet fundamental assumptions of the method. These findings not only invalidate the conclusions of the particular analysis of Fabbri et al. but also have important implications for future quantitative uses of bone compactness and discriminant analysis in paleontology.

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

AbstractHigh‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer

Abstract. The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17–20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100–1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efficiently determine fluxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: the DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N = 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R = 0.83 and N = 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h−1 for the mIME and 500 kg h−1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Archetype Building Energy Models for Residential Sector: Importance of Uncontrolled Inputs in Predictive Control and Energy Flexibility

A 1st-order gray-box archetype is developed using high-quality measurements from an unoccupied experimental house to investigate the sensitivity of economic model-based predictive control performance under Québec’s dynamic electricity tariff for the residential sector, rate Flex-D, to uncertainty in the weather forecast. The archetype incorporates the outside air temperature and the solar gain as uncontrolled heat sources and has three parameters: the overall thermal capacitance, the thermal resistance to the outside air temperature, and the solar aperture. The performance of economic model-based predictive control is compared under the uncertain day-ahead forecast and the historical measurements of the same day. The results show that the most influential uncontrolled input directing the performance of economic model-based predictive control under rate Flex-D is the price signal, which makes the impact of uncertainty in the weather forecast almost negligible.

Switch to oral antibiotics in Gram-negative bacteraemia: a randomized, open-label, clinical trial

ObjectivesTo evaluate the safety and efficacy of switching from intravenous (IV) to oral antimicrobial therapy in patients with Enterobacterales bacteraemia, after completion of 3–5 days of microbiologically active IV therapy. MethodsA multicentre, open-label, randomized trial of adults with monomicrobial Enterobacterales bacteraemia caused by a strain susceptible to ≥1 oral beta-lactam, quinolone, or trimethoprim/sulfamethoxazole. Inclusion criteria included completion of 3–5 days of microbiologically active IV therapy, being afebrile and haemodynamically stable for ≥48 hours, and absence of an uncontrolled source of infection. Pregnancy, endocarditis, and neurological infections were exclusion criteria. Randomization, stratified by urinary source of bacteraemia, was to continue IV (IV Group) or to switch to oral therapy (Oral Group). Agents and duration of therapy were determined by the treating physicians. The primary endpoint was treatment failure, defined as death, need for additional antimicrobial therapy, microbiological relapse, or infection-related re-admission within 90 days. Non-inferiority threshold was set at 10% in the 95% CI for the difference in the proportion with treatment failure between the Oral and IV Groups in the modified intention-to-treat population. The protocol was registered at ClinicalTrials.gov (NCT04146922). ResultsIn the modified intention-to-treat population, treatment failure occurred in 21 of 82 (25.6%) in the IV Group, and 18 of 83 (21.7%) in the Oral Group (risk difference –3.7%, 95% CI –16.6% to 9.2%). The proportions of subjects with any adverse events (AE), serious AE, or AE leading to treatment discontinuation were comparable. DiscussionIn patients with Enterobacterales bacteraemia, oral switch, after initial IV antimicrobial therapy, clinical stability, and source control, is non-inferior to continuing IV therapy.

Radiation and Environmental Safety in the Exploration and Development of Metal Deposits in South Yakutia

Radiometric surveys of mining dumps on spent placers of gold, monazite and the exploited gold-uranium ore deposit in South Yakutia were carried out. It is established that the dumps of alluvial, overburden and waste rocks in terms of the effective specific activity of radionuclides when used in construction work are characterized by different degrees of tolerance. It is shown that stacked radioactive ore dumps at the “Lunnoye” deposit after heap leaching of gold become powerful uncontrolled sources of radioactive pollution of the environment. It is recommended to conduct periodic monitoring of their radioactivity levels by express methods of field radiometry for the safe use of mining dumps as building materials.

ACA: Automatic search strategy for radioactive source

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

Including measurement effects and temporal variations in VIS-NIRS models to improve early detection of plant disease: Application to Alternaria solani in potatoes

Early detection of plant diseases with automated, non destructive and high-throughput techniques is a major objective in plant breeding and crop protection. Near infrared spectroscopy and hyperspectral imaging are proven to be particularly relevant technologies. However, robust discriminant models remains a challenge because of the many uncontrolled sources of variability during the experiment. Indeed, at early stages of most diseases, the temporal variations due to environment and measurement effects can induce signal shifts of greater magnitude than the infection itself, masking the information of interest. Excluding the variations of the measurement environment and the temporal fluctuation of the plant-pathogen interaction can depreciate the model robustness. Here, the problem is addressed in a study of the seven potato cultivars monitored for the presence of early blight disease at 0, 18, 36, and 96 h after inoculation. Three practical corrections are proposed regarding the effect of temporal fluctuations. (i) subclass effect, (ii) kinetic effect of the disease, and (iii) measurement effect. Eventually, the application of EPO-PLSDA to orthogonalise the model regarding temporal variation to produce invariant models proved to be the only suitable and well-performing of the tested solutions. With this approach the disease can be detected from 36 h after inoculation for 6 of the 7 tested cultivars. Classification errors differ among the cultivars but on average are below 25% of errors.

Investigating Drought in Herat Province and Ways to Combat It

From ancient times, drought has been a part of environmental climate changes. Water scarcity, mismanagement, and improper use of groundwater resources, along with uncontrolled surface water sources, have posed serious and noticeable threats in Afghanistan, particularly in Herat province. Herat is one of the provinces in Afghanistan severely affected by this issue. Improper management of surface waters has led farmers to excessively utilize groundwater resources in recent years, significantly reducing the water tables in this province. Severe reductions in rainfall, excessive agricultural water usage, and uncontrolled digging of deep wells are among the major reasons for the drastic decline in groundwater levels, exacerbating drought conditions in this province. The concerns of Herat citizens are rising as the consumption of this province solely relies on groundwater sources. This situation could potentially lead to severe water shortages and increased drought in the future. On the other hand, a country like Afghanistan, which is among the world's dry regions, faces a serious threat to its environment and water resources. At present, even the northern regions of the country are under the influence of this phenomenon. The current precipitation status, limited water resources, and the country's climatic conditions underscore the need to plan for and seriously cope with the impacts and consequences of drought. Drought indicates various environmental effects on living organisms, animals, and humans. However, aridity is a general concept and is not used for environmental elements. Proper management practices and reducing the consequences of drought in the development of regions necessitate planning and implementing preventive measures to combat this phenomenon. This, in turn, requires sufficient knowledge in drought prediction.

ID: 220996 Use of Remote Physiological Monitoring to Track Patient Well-Being During the SCS Trial

Spinal cord stimulation (SCS) for chronic intractable back pain is often prescribed after a successful trial period in which a patient demonstrates a sufficient reduction in pain intensity. Currently, the decision of whether to recommend a patient for permanent implant relies upon one or two snapshots from a subjective rating scale (e.g., Numerical Rating Scale, Percent Pain Reduction, Visual Analog Scale). However, subjective rating scales are subject to variability from un-controlled external sources [1]. Additionally, chronic pain is a complex multi-dimensional struggle, and resolution of symptoms may also be manifest via improvements to mental, physical, and social well-being [2]. This research demonstrates proof-of-concept for a remote monitoring system to track and report patient well-being using objective physiologic data collected before, during, and after the SCS trial period.

Container substrate components are a potential source of micronutrients for plant growth

The objective was to quantify the extractable micronutrient levels from container substrate components and limestone and their potential contribution as fertilizer sources for crop growth. Samples of horticultural limestone (29 samples) and substrate components [peat (48 samples), perlite (10), vermiculite (14), bark (15), and coconut coir (6)] were analyzed for micronutrient content with the SME (Saturated Medium Extract) method using either water (SMEW) or diethylenetriamine pentaacetate (SMED) as extractants for the substrate components, or following acid digestion of limestone samples. Samples were obtained from commercial substrate and greenhouse companies throughout the U.S. and Canada. Peat included 36 peat samples from two peat bogs in Alberta Canada to evaluate whether species composition, decomposition, and bog location affected micronutrient content. Increasing von Post decomposition increased SMEW and SMED Mn level and SMED Zn. As percent Sphagnum fuscum increased, extractable Fe and Mn levels increased for both extractant types, and SMEW extractable Zn increased. Based on established SME guidelines for acceptable to optimum nutrient levels for soilless substrates, peat and bark could potentially provide sufficient Fe and Mn to contribute to plant requirements. Average Fe content in limestone was 0.11%, and Mn content was 0.012%. Based on SMED extractions and lime analysis, Fe supplied by 100% peat and lime could potentially provide Fe at levels comparable with commercial pre-plant fertilizers if these Fe sources are plant-available. Substrate-pH affected solubility of Fe, Mn and Zn in peat. As substrate-pH increased, Fe and Zn solubility in peat decreased with both SMEW and SMED. The SMEW-extractable Mn decreased with increasing pH, in contrast to SMED-extractable Mn which increased as pH increased. Analysis of raw materials using SMED (substrate components) or complete digestion (limestone) is advised for substrate companies to proactively identify batches of components that may affect crop nutrition. Given this high level of potential contribution of Fe by peat and lime, it is likely that these uncontrolled sources contribute to nutrient uptake in horticultural production. However, further research is required to compare extractable micronutrients with actual plant uptake.

Design and Implementation of an Intelligent Searching Robot for Radioactive Sources Based on Edge Computing

In recent years, the worldwide nuclear industry has developed vigorously. At the same time, incidents of radioactive source loss have also occurred frequently. At present, most of the search for radioactive sources uses manual search, which is inefficient and the search personnel are vulnerable to radiation damage. Sending the robot into the area where the uncontrolled radioactive source may be located for search is different. Not only the efficiency can be improved, but the personnel are also prevented from being exposed to radiation. Therefore, designing a radioactive source search robot has great practical significance. This paper mainly introduces the design and implementation of an intelligent search robot for radioactive sources based on edge computing, and intends to provide some ideas and directions for the research of intelligent robots for searching for radioactive sources. This paper proposes a research method for the design and implementation of a radioactive source intelligent search robot based on edge computing, including intelligent edge computing and gamma ray imaging algorithms, which are used to carry out related experiments on the design and implementation of a radioactive source intelligent search robot based on edge computing. The experimental results in this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.

CRC-Based Reliable WiFi Backscatter Communiation for Supply Chain Management

Supply chain management aims to achieve both efficiency and low cost. Backscatter technology provides a low-energy consumption approach for critical links in the supply chain, such as warehouse management and cargo identification. Traditional backscatter systems achieve tag data transmission through dedicated hardware or controlled transmission sources. An additional access point (AP) can be used to ensure that the original data are always known in tag data decoding. These requirements increase the deployment costs and are not suitable for large-scale applications. To address these challenges, we introduce CRCScatter, a backscatter system based on a cyclic redundancy check (CRC) reverse algorithm, with an uncontrolled source and a single-AP receiver. The CRCScatter decoder at the receiver uses the constraints within 802.11b WiFi packets to recover the original packet and decode tag data from the backscatter packet. Our Matlab simulation results show that CRCScatter is effective in the low signal-to-noise ratio (SNR) regime, and its average decoding time is independent of the length of tag data. By appending redundant bits in tag data, the decoding accuracy of CRCScatter can be improved. In summary, CRCScatter presents a backscatter communication mode based on ambient WiFi signals with fewer hardware requirements and low deployment costs. Furthermore, the decoding idea of calculating unknown data based on the packet constraints has the potential to expand to different types of excitation packages.

A Path to a Reduction in Micro and Nanoplastics Pollution

Microplastics (MP) are plastic particles less than 5 mm in size. There are two categories of MP: primary and secondary. Primary or microscopic-sized MP are intentionally produced material. Fragmentation of large plastic debris through physical, chemical, and oxidative processes creates secondary MP, the most abundant type in the environment. Microplastic pollution has become a global environmental problem due to their abundance, poor biodegradability, toxicological properties, and negative impact on aquatic and terrestrial organisms including humans. Plastic debris enters the aquatic environment via direct dumping or uncontrolled land-based sources. While plastic debris slowly degrades into MP, wastewater and stormwater outlets discharge a large amount of MP directly into water bodies. Additionally, stormwater carries MP from sources such as tire wear, artificial turf, fertilizers, and land-applied biosolids. To protect the environment and human health, the entry of MP into the environment must be reduced or eliminated. Source control is one of the best methods available. The existing and growing abundance of MP in the environment requires the use of multiple strategies to combat pollution. These strategies include reducing the usage, public outreach to eliminate littering, reevaluation and use of new wastewater treatment and sludge disposal methods, regulations on macro and MP sources, and a wide implementation of appropriate stormwater management practices such as filtration, bioretention, and wetlands.

Towards passive non-line-of-sight acoustic localization around corners using uncontrolled random noise sources

Non-line-of-sight (NLoS) imaging is an important challenge in many fields ranging from autonomous vehicles and smart cities to defense applications. Several recent works in optics and acoustics tackle the challenge of imaging targets hidden from view (e.g. placed around a corner) by measuring time-of-flight information using active SONAR/LiDAR techniques, effectively mapping the Green functions (impulse responses) from several controlled sources to an array of detectors. Here, leveraging passive correlations-based imaging techniques (also termed ’acoustic daylight imaging’), we study the possibility of acoustic NLoS target localization around a corner without the use of controlled active sources. We demonstrate localization and tracking of a human subject hidden around a corner in a reverberating room using Green functions retrieved from correlations of broadband uncontrolled noise sources recorded by multiple detectors. Our results demonstrate that for NLoS localization controlled active sources can be replaced by passive detectors as long as a sufficiently broadband noise is present in the scene.

A quality analysis of robotic-assisted knee replacement surgery videos on Youtube

Aim: Robotic technology has been used to decrease soft tissue dissection and improve postoperative rehabilitation in several areas and robotic-assisted knee replacement has gained popularity last decades. Youtube has an uncontrolled source, making it difficult to verify the correctness of its process. It is critical to assess what they include. This study aimed to assess the quality of the YouTube video content related to robotic-assisted knee replacement surgeries
 Material and Method: We conducted a search on YouTube using the keywords “robotic-assisted knee replacement”. The headings of the first 50 videos on YouTube were obtained and simultaneously evaluated by two orthopedic surgeons.. We analyzed the general features and categorized videos according to content. The videos were evaluated by using the DISCERN and JAMA scores.
 Results: 37 videos were included. Twenty-three videos contained total knee replacement surgery while fourteen consisted of unicondylar prosthesis system. The content of the videos included 51.4% (n=19) interviews, 16.2% (n=6) live surgery, 13.5% (n=5) patient testimonials, 8.1% (n=3) animation and presentation-lesson, and 2.7% (n=1) model81.1% of the videos were uploaded by hospital accounts. These were followed by health channels (8.1%), firms (5.4%), doctors (2.7%), and patients (2.7%) the average DISCERN score was 40.1±9.4 and the average JAMA score was 2.2±1
 Conclusion: The quality of the information in videos on robotic-assisted knee replacement surgery is poor, YouTube is not currently an appropriate source of such information for patients and there appears to be a disproportionate amount of information focusing on robotic-assisted knee replacement surgery.

How Functional Connectivity Measures Affect the Outcomes of Global Neuronal Network Characteristics in Patients with Schizophrenia Compared to Healthy Controls.

Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia.