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.

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.

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.

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.

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.

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.

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.

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 Ľubietová, 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.

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.

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.

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.

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.

Familiarity with images affects how dogs ( Canis familiaris) process life-size video projections of humans.

A central problem of behavioural studies providing artificial visual stimuli for non-human animals is to determine how subjects perceive and process these stimuli. Especially in the case of videos, it is important to ascertain that animals perceive the actual content of the images and are not just reacting to the motion cues in the presentation. In this study, we set out to investigate how dogs process life-sized videos.

Do you see what I see? The difference between dog and human visual perception may affect the outcome of experiments.

The visual sense of dogs is in many aspects different than that of humans. Unfortunately, authors do not explicitly take into consideration dog-human differences in visual perception when designing their experiments. With an image manipulation program we altered stationary images, according to the present knowledge about dog-vision.

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.

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.

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.

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.

I saw where you have been--The topography of human demonstration affects dogs' search patterns and perseverative errors.

Performance in object search tasks is not only influenced by the subjects' object permanence ability. For example, ostensive cues of the human manipulating the target markedly affect dogs' choices. However, the interference between the target's location and the spatial cues of the human hiding the object is still unknown.

Mercury contamination from historical mining territory at Malachov Hg-deposit (Central Slovakia).

Environmental contamination caused by mercury is a serious problem worldwide. The study was conducted in order to identify Hg contamination in soil, technosoil from dumps, groundwater, and surface water in the surroundings of the abandoned Hg deposit of Malachov in Central Slovakia. Soil from the Malachovský brook valley was classified as cambi-soil (rendzina).

Body-Worn Sensors in Parkinson's Disease: Evaluating Their Acceptability to Patients.

Background: Remote monitoring of symptoms in Parkinson's disease (PD) using body-worn sensors would assist treatment decisions and evaluation of new treatments. To date, a rigorous, systematic evaluation of the acceptability of body-worn sensors in PD has not been undertaken.

Materials And Methods: Thirty-four participants wore bilateral wrist-worn sensors for 4 h in a research facility and then for 1 week at home.

A simple but powerful test of perseverative search in dogs and toddlers.

Perseverative (A-not-B) errors during the search of a hidden object were recently described in both dogs and 10-month-old infants. It was found that ostensive cues indicating a communicative intent of the person who hides the object played a major role in eliciting perseverative errors in both species. However, the employed experimental set-up gave rise to several alternative explanations regarding the source of these errors.

Function approximation using combined unsupervised and supervised learning.

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold.