Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy.

AIMS: This study explores the possibility of using routinely taken blood tests in the diagnosis and triage of patients with suspected musculoskeletal malignancy. METHODS: A retrospective study was performed on results of patients who had presented for assessment to a regional musculoskeletal tumour unit. Blood results of patients with a histologically confirmed diagnosis between 2010 and 2020 were retrieved. 33 distinct blood tests were available for model forming. Results were standardised by calculating z-scores. Data were split into a training set (70%) and a test set (30%). The training set was balanced by resampling underrepresented classes. The random forest algorithm performed best and was selected for model forming. Receiver operating characteristic curves were used to find the optimum threshold. Models were calibrated and performance metrics evaluated with confusion tables. RESULTS: 2371 patients formed the study population. 1080 had a malignant diagnosis in one of three categories: sarcoma, metastasis, or haematological malignancy. 1291 had a benign condition. Metastasis could be predicted with an accuracy of 79% (AUC 87%, sensitivity 79%, specificity 80% NPV 91%). Haematological malignancy accuracy 79% (AUC 81%, sensitivity 77%, specificity 79%, NPV 97%). Sarcoma accuracy 64% (AUC 73%, sensitivity 76%, specificity 61%, NPV 88%) and all malignancy accuracy 74% (AUC 80%, sensitivity 72%, specificity 75%, NPV 76%). CONCLUSION: Routinely performed blood tests can be useful in triage of musculoskeletal tumours and can be used to predict presence of musculoskeletal malignancy.

Clematis vitalba Is a Natural Host of the Novel Ilarvirus, Prunus Virus I.

Clematis vitalba L. is a climbing shrub and a pioneer plant in abandoned orchards or vineyards that are widespread in temperate climate zones. In past years, several viruses infecting the Clematis species have been identified, including different ilarviruses. Prunus virus I (PrVI) is a recently described ilarvirus, which has been shown to infect sweet cherries and peaches in Greece. Moreover, its presence has been detected in ornamental Clematis in Russia. In the present work, we analyzed the virome of wildly growing C. vitalba plants from Hungary, Slovakia and Croatia showing different kinds of symptoms using high-throughput sequencing (HTS) of small RNAs or ribodepleted RNAs. Applying HTS enabled us to identify the presence of PrVI in C. vitalba, and the bioinformatic analyses were further validated with RT-PCR using PrVI-specific primers and Sanger dideoxy sequencing. Nearly full genome sequences of all three viral RNAs of one Hungarian, two Slovak and one Croatian isolate were determined. Their phylogenetic analysis showed high similarity to each other and to other PrVI isolates described from Central Europe. As the sampled plants were co-infected with other viruses, it is not possible to determine a direct correlation between the infection with PrVI and the observed symptoms. Analyses of different Prunus species in stock collection showed infection of several peach and sweet cherry varieties in Hungary. Our results expand the knowledge on the natural host range of PrVI and highlight the necessity to evaluate alternative plant hosts (even non-Prunus) of PrVI and the role of the virus in the etiology of the potential diseases.

Predicting sediment yield on different landuse surfaces in Calabar River Catchment, Nigeria.

This study predicts sediment yield on various landuse surfaces within the Calabar River Catchment, Nigeria. Five experimental plots of 31 by 23 cm (representing urban, farm, grass, bare, and forest surfaces) were established on a convex slope series with a 20% gradient, oriented along the slope strike. Rainfall, morphological, and hydraulic stations were derived for each plot. Multiple regressions and Factor analysis were employed to analyse the collected data. The research identifies critical factors influencing sediment yield, such as rainfall amount, rainfall intensity, slope gradient, slope length, sand, silt, clay, vegetation cover, and infiltration capacity. The results (p < 0.05) indicate that slope length, sand, silt, clay, infiltration capacity, and vegetation cover significantly influence sediment yield for urban, farmland, grassland, and bare surfaces, respectively. Factor analysis revealed strong correlations between sediment yield, silt, rainfall amount, rainfall intensity, and slope gradient. Case-wise diagnostics predictions indicate sediment yields for urban, bare, farm, grass, and vegetation-covered surfaces as 14.95 kg, 33.91 kg, 28.78 kg, 33.50 kg, and 5.66 kg, respectively. The regression model, with case-wise diagnostic residual statistics and standard prediction coefficients, provides valuable insights. For example, the forest surface exhibited a minimum sediment yield of -1.413 kg/m2 with each unit decrease in forest area, emphasising the significance of vegetation cover in sediment retention. Conversely, bare surfaces showed a maximum sediment yield of 0.843 kg/m2 with each unit increase in bare surface area, highlighting their heightened vulnerability to sediment erosion. Considering the implications of these findings, the development of urban master plans that incorporate well-designed landscaping and drainage systems is crucial, particularly in high rainfall catchments like the study area. Such measures can effectively mitigate sediment yield and address the adverse effects of land use changes on different surfaces.

A preoperative ultrasound-based protocol for optimisation of fluid therapy to prevent early intraoperative hypotension: a randomised controlled study.

BACKGROUND: Intraoperative hypotension is a risk factor for postoperative complications. Preoperative dehydration is a major contributor, although it is difficult to estimate its severity. Point-of-care ultrasound offers several potential methods, including measurements of the inferior vena cava. The addition of lung ultrasound may offer a safety limit. We aimed to evaluate whether the implication of an ultrasound-based preoperative fluid therapy protocol can decrease the incidence of early intraoperative hypotension. METHODS: Randomised controlled study in a tertiary university department involves elective surgical patients of ASA 2-3 class, scheduled for elective major abdominal surgery under general anaesthesia with intubation. We randomised 40-40 patients; 38-38 were available for analysis. Conventional fluid therapy was ordered on routine preoperative visits. Ultrasound-based protocol evaluated the collapsibility index of inferior vena cava and lung ultrasound profiles. Scans were performed twice: 2 h and 30 min before surgery. A high collapsibility index (≥ 40%) indicated a standardised fluid bolus, while the anterior B-profile of the lung ultrasound contraindicated further fluid. The primary outcome was the incidence of postinduction and early intraoperative (0-10 min) hypotension (MAP < 65 mmHg and/or ≥ 30% of decrease from baseline). Secondary endpoints were postoperative lactate level, urine output and lung ultrasound score at 24 h. RESULTS: The absolute criterion of postinduction hypotension was fulfilled in 12 patients in the conventional group (31.6%) and 3 in the ultrasound-based group (7.9%) (p = 0.0246). Based on composite criteria of absolute and/or relative hypotension, we observed 17 (44.7%) and 7 (18.4%) cases, respectively (p = 0.0136). The incidence of early intraoperative hypotension was also lower: HR for absolute hypotension was 2.10 (95% CI 1.00-4.42) in the conventional group (p = 0.0387). Secondary outcome measures were similar in the study groups. CONCLUSION: We implemented a safe and effective point-of-care ultrasound-based preoperative fluid replacement protocol into perioperative care. TRIAL REGISTRATION: The study was registered to ClinicalTrials.gov on 10/12/2021, registration number: NCT05171608 (registered prospectively on 10/12/2021).

The Importance of Environmental Food Quality Labels for Regional Producers: A Slovak Case Study.

Regional products are considered an important element of European cultural identity, contributing to the development and sustainability of rural areas. The article presents the research of regional labels from the territory of the Slovak Republic. Our research was aimed at determining the views of producers towards the regional product labeling scheme. The survey took place from January 2020 to April 2020 using an online questionnaire via Google Docs. The results obtained from the questionnaire survey were statistically processed: t-test, Fisher's exact test, and Pearson correlation coefficient. We used Microsoft Excel and IBM SPSS Statistics 23 software. Based on the research results, we can state that two-thirds of producers (71.8%) are aware of the concept of a regional product. Most producers (82.0%) associate this concept with tradition and a specific region. They consider regional product labeling to be a tool to support the development of tourism (36.0%). A quarter of producers (25.7%) joined this scheme in order to add value to their products and help make consumers perceive them as safer products of higher quality. Based on the research results, we would recommend increasing the promotion of regional products on the part of the label coordinator.

Study of Potentially Toxic Elements Uptake into Organs of Quercus spp. from Copper Deposits in Slovakia, Italy and Portugal.

The article is focused on the application of Energy dispersive micro X-ray fluorescence spectroscopy as a specific method to determine the contents of potentially toxic elements and its spread in plant tissues. As a model species, Quercus spp. were selected. In order to compare the obtained results with previous research, four well-described abandoned Cu-deposits were selected for sampling: Lubietová (Slovakia), Libiola and Caporciano (Italy), and São Domingos (Portugal). The results of micro X-ray fluorescence spectrometry confirm the irregular contamination of Quercus spp. by potentially toxic elements. The level of contamination is the highest predominantly in the root cortex, where is also the highest Ca contents (with exception of São Domingos). At Lubietová and Caporciano, high Ni content was described in branches cortex, in branches mesoderm also Fe, Cu and Zn. At the same time, the inhibition influence of Ca was also confirmed regarding the input of these elements into plants.

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things.

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT - as standalone technologies - have already proved to be highly disruptive but there is a need for further research to apply federated learning to the IoMT.

Where do successful populations originate from?

In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these populations. Here we focus on the role of geographical factors, such as land fertility and mountains in the context of human population evolution and distribution dynamics. We use a constrained diffusion-based computational model, computer simulations and the analysis of geographical and land-quality data. Our analysis shows that successful human populations, i.e. those which become dominant in their socio - geographical environment, originate from lands of many valleys with relatively low land fertility, which are close to areas of high land fertility. Many of the homelands predicted by our analysis match the assumed homelands of known successful populations (e.g. Bantus, Turkic, Maya). We also predict other likely homelands as well, where further archaeological, linguistic or genetic exploration may confirm the place of origin for populations with no currently identified urheimat. Our work is significant because it advances the understanding of human population dynamics by guiding the identification of the origin locations of successful populations.

Mercury in fish tissues in the area of Malachov Hg-ore deposit (Slovakia).

The abandoned Malachov deposit belongs among the most important historic Hg deposits in the world. The soil, groundwater, surface water, plants, and animals are still heavily contaminated by potentially toxic elements such as As and Cd, but mainly by Hg. This article is focused on the Hg contamination of aquatic plants and animals in the Malachov creek. Mercury concentrations were measured in fresh water (on average 3 µg L-1), in the zoobenthos (on average 362.47 µg kg-1), and in the phytobenthos (on average 578.36 µg kg-1). Higher Hg concentrations were determined in the muscles of Salmo trutta morpha fario (on average 362.47 µg kg-1) and lower in Cottus poecilopus (on average 352.75 µg kg-1). The Hg concentrations were higher in the internal tissues (578.36-1185.75 µg kg-1) than in the muscles of the fish. The Hg content in fresh water exceeded Regulation of the Slovak Government No. 269/2010, which stipulates the criteria for achieving a proper water balance, but the Hg content in the fish muscles of both fish species was below the specified limit of the Food Code of Governmental Regulation of the Slovak Republic No. 608/3/2004-100. The Hg contamination in fishes is controlled by their weight and age. The presented data may be used as the base information for future studies in order to be able to estimate consumption recommendations and warnings.

The use of bibliography enriched features for automatic citation screening.

CONTEXT: Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalanced between the relevant and the irrelevant classes of returned citations. Class imbalance among other factors has been a persistent problem that impairs the performance of TM models, particularly in the context of automatic citation screening for systematic reviews. This has often caused the performance of classification models using the basic title and abstract data to ordinarily fall short of expectations. OBJECTIVE: In this study, we explore the effects of using full bibliography data in addition to title and abstract on text classification performance for automatic citation screening. METHODS: We experiment with binary and Word2vec feature representations and SVM models using 4 software engineering (SE) and 15 medical review datasets. We build and compare 3 types of models (binary-non-linear, Word2vec-linear and Word2vec-non-linear kernels) with each dataset using the two feature sets. RESULTS: The bibliography enriched data exhibited consistent improved performance in terms of recall, work saved over sampling (WSS) and Matthews correlation coefficient (MCC) in 3 of the 4 SE datasets that are fairly large in size. For the medical datasets, the results vary, however in the majority of cases the performance is the same or better. CONCLUSION: Inclusion of the bibliography data provides the potential of improving the performance of the models but to date results are inconclusive.

The interaction of heavy metals and metalloids in the soil-plant system in the São Domingos mining area (Iberian Pyrite Belt, Portugal).

São Domingos belongs among the most important historic Iberian Pyrite Belt Cu mines. The anthrosoil is contaminated by a very high content of heavy metals and metalloids. The study was focused on evaluating the interaction of some chemical elements (Ca, Mg, Fe, Mn, Cu, Pb, Zn, Ag, Cd, Ni, Co, As, Sb) in the system soil vs. five autochthonous dominant plant species: Pinus pinaster Aiton, Quercus rotundifolia Lam., Agrostis sp., Juncus conglomeratus L. and Juncus effusus L. The plants are heavily contaminated by Cu, Pb, As and Zn. The bioconcentration factor proved that they exhibit features of metal tolerant excluders. The trees are accumulators of Ag, whereas the graminoids are hyper-accumulators of Ag and Juncus effusus of Co. The translocation factor confirmed that the selected elements are immobilised in the roots except for Mn and Zn in Pinus pinaster and Mn in Quercus rotundifolia and Juncus conglomeratus. The bioaccumulation of Mn, Zn and Cu at low pH increases. The increased content of Ca and Mg in the soil inhibits, in the case of some metals and metalloids, their intake to plants. Although the studied plants, despite their fitness and vitality at the contaminated sites, are not suitable for phytoextraction (except Co and Ag), they can be used for phytostabilisation at the mining habitats.

Social Learning and Cultural Evolution in Artificial Life.

We describe the questions and discussions raised at the First Workshop on Social Learning and Cultural Evolution held at theArtificial Life Conference 2016 in Cancún, Mexico in July 2016. The purpose of the workshop was to assemble artificial life researchers interested in social learning and cultural evolution into one group so that we could focus on recent work and interesting open questions. Our discussion related to both the mechanisms of social learning and cultural evolution and the consequences and influence of social learning and cultural evolution on living systems. We present the contributions of our workshop presenters and conclude with a discussion of the more important open questions in this area.

High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

The role of organo-zeolitic material in supporting phytoremediation of a copper mining waste dump.

The effect of applying organo-zeolitic (OZ) amendment in supporting phytoremediation of a copper mine waste dump, using a substrate obtained from Lubietová, Central Slovakia, was studied. Three plant growth substrates were prepared: (i) the untreated substrate sample from the contaminated site (D-O), (ii) the contaminated sample treated with OZ material, with 82% D-O and 18% OZ (DO + OZ), (iii) the soil from a reference site (R). The largest amount of dry biomass was obtained from the DO + OZ sample. Whole plants grown on the original substrate (D-O) were accumulating a high concentration of Cu (3057 mg kg-1). The plants root grown on DO + OZ sample accumulated highest Cu concentrations (3127 mg kg-1), but in their aerial part, only 83.54 mg kg-1 was accumulated. The translocation factor's lower than the unit values prove that these plant species do not transfer heavy metals from root to shoot, this is why their heavy metal excluder role is evident. The value of the bioconcentration factor of the DO + OZ root/substrate obtained to be greater than the unit for Cu, Pb, Zn, Cd, Co, Ni, and Mn, suggests the OZ material's positive contribution in the phytoremediation process that can be applied for these waste dumps.

Locally Excited State-Charge Transfer State Coupled Dyes as Optically Responsive Neuron Firing Probes.

A selection of NIR-optically responsive neuron probes was produced comprising of a donor julolidyl group connected to a BODIPY core and several different styryl and vinylpyridinyl derived acceptor moieties. The strength of the donor-acceptor interaction was systematically modulated by altering the electron withdrawing nature of the aryl unit. The fluorescence quantum yield was observed to decrease as the electron withdrawing effect of the aryl subunit increased in line with changes of the Hammett parameter. The effectiveness of these fluorophores as optically responsive dyes for neuronal imaging was assessed by measuring the toxicity and signal-to-noise ratio (SNR) of each dye. A great improvement of SNR was obtained when compared to the first-generation BODIPY-based voltage sensitive dyes with concomitant toxicity decrease. The mechanism for the optical response is disparate from conventional cyanine-based dyes, opening up a new way to produce effective voltage sensitive dyes that respond well into the NIR region.

Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist.

CONTEXT: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field. OBJECTIVE: In this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility. METHODS: The study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach. RESULTS: Our work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available. CONCLUSIONS: The reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and how any randomization was controlled. We introduce a checklist of information that needs to be provided in order to ensure that a published study can be reproduced.

Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

Analysis of the dynamics of temporal relationships of neural activities using optical imaging data.

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology.

Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers.

INTRODUCTION: Current PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation. This work describes the development of a novel PD disease state detection system informed by algorithms based on data collected in an unsupervised, home environment. We evaluated whether this approach can reproduce patient-completed symptom diaries and clinical assessment of disease state. METHODS: 34 participants with PD wore bilateral wrist-worn accelerometers for 4 h in a research facility (phase 1) and for 7 days at home whilst completing symptom diaries (phase 2). An ANN to predict disease state was developed based on home-derived accelerometer data. Using a leave-one-out approach, ANN performance was evaluated against patient-completed symptom diaries and against clinician rating of disease state. RESULTS: In the clinical setting, specificity for dyskinesia detection was extremely high (0.99); high specificity was also demonstrated for home-derived data (0.93), but with low sensitivity (0.38). In both settings, sensitivity for on/off detection was sub-optimal. ANN-derived values of the proportions of time in each disease state showed strong, significant correlations with patient-completed symptom diaries. CONCLUSION: Accurate, real-time evaluation of symptoms in an unsupervised, home environment, with this sensor system, is not yet achievable. In terms of the amounts of time spent in each disease state, ANN-derived results were comparable to those of symptom diaries, suggesting this method may provide a valuable outcome measure for medication trials.