BACKGROUND Boron and boron containing compounds are known for their biological and protective roles being non-toxic and non-mutagenic in low concentrations. Male rats were exposed to halogenated boroxine (HB), dipotassium-trioxohydroxytetrafluorotriborate K2[B3O3F4OH], a potential new boron-containing therapeutic, aiming to determine concentrations with no adverse effects on selected serum biochemical parameters and histomorphological features. METHODS HB was prepared by reacting potassium hydrofluoride (KHF2) with boric acid in molar ratios 2:3 at room temperature and its primary structure contains 4 fluorine atoms substituted in 6-membered ring. In concentrations of 10, 25, 35 and 45 mg/kg, HB was administered intraperitoneally as a single dose. Biochemical parameters were observed 24 and 96 h following the treatment. Effects of HB on biochemical blood parameters were also observed 24 h following continuous nine days application in concentrations of 10 mg/kg intraperitoneally and 50 mg/kg per os. Histomorphological observation of kidneys, liver, spleen, lungs and heart was performed for all treated animals. RESULTS Administration of single high dose of HB (35 mg/kg-45 mg/kg) effected high levels of urea and creatinine, which indicated renal injury that appeared to be temporary. Possible cause of concern is pancreatic injury indicated by elevated levels of serum amylase in the groups of animals that received the highest dosages of the substance. Histopathological examination of selected tissues revealed mild to moderate lesions in the kidneys and livers associated with administration of HB. CONCLUSION Observation of biochemical serum parameters or histopathology of examined tissues revealed no adverse effects of HB either after the administration of single dose lower than 35 mg/kg or following repeated administration at 10 mg/kg. These dosages should be further considered for potential therapeutic applications.

While metasurface-based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require such widespread deployment. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the presence of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The article presents simulations to demonstrate the encoding process that represents the number of virus particles that have bound to the IR-VD.

A practical compound of education in computer science and electrical engineering, driven by increased availability and maturity of many emerging technologies, should be enriched by various laboratory resources in order to synchronize the paces between technology advancements and education. In particular, advancements in containerization as a virtualization technique pave the way towards allowing students to deploy their project applications with a lightweight resource footprint on top of the cloud. Being backed by a valuable feedback from 45 Bachelor students, in this paper we present the best practices on how virtualization can be leveraged to create a scalable environment for on-demand remote experimentation with distributed systems.

Cellular technologies are widely used in the ICT domain, that paving the way towards new opportunities for education and practical experimentation. It also tackles software-related engineering areas, from Digital Processing (DSP) to Software Defined Networks and Network Virtualization applications. Due to the various constraints such as limited access to hardware components, high costs of such equipment, and spectrum regulations that must not be violated, practical education in telecommunications, electrical engineering, and electronics usually lacks the opportunity to pursue experimentation on the realistic cellular deployments. Therefore, in this paper we present a network testbed that allows students to experiment with a fully functional 4G LTE system with no radio. This testbed system mimics the realistic 4G LTE deployment, supporting students towards acquiring valuable knowledge in the field of cellular networks. It is low-cost due the fact there is no need for radio components or Software Defined Radio (SDR) devices, with no limitation on frequency utilization and regulations. The testbed provides seamless scalability for education classes as it can be deployed on top of any machine with general-purpose processor, installing the whole system within two networked PCs or in a fully virtualized environment. We present the testbed framework, as well as the hands-on practices on incorporating such low-cost realistic testbed into the education within various engineering fields.

Storage of large quantities of industrial by-products can pose a serious environmental problem. There is a growing need to recycle these industrial by-products, including red mud, in the construction industry, which is one of the largest consumers of raw materials. A preliminary study of the potential of red mud as a raw material for concrete is presented in this paper. Chemical composition of red mud, determined by X-ray fluorescence and particle size distribution by laser diffraction, is tested as part of its initial evaluation. The reactivity of red mud is evaluated by the R3 test method with isothermal calorimetry. The compressive strength test is carried out on a mortar sample in which 30% by weight of cement is replaced by red mud. Preliminary tests indicate that red mud can be used as raw material in alternative binders.

Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning. Here, we show that an embodied agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with conventional reinforcement learning algorithms. After a single introduction to a novel object via continuous visual perception and a language prompt ("This is a dax"), the agent can re-identify the object and manipulate it as instructed ("Put the dax on the bed"). In doing so, it seamlessly integrates short-term, within-episode knowledge of the appropriate referent for the word "dax" with long-term lexical and motor knowledge acquired across episodes (i.e. "bed" and "putting"). We find that, under certain training conditions and with a particular memory writing mechanism, the agent's one-shot word-object binding generalizes to novel exemplars within the same ShapeNet category, and is effective in settings with unfamiliar numbers of objects. We further show how dual-coding memory can be exploited as a signal for intrinsic motivation, stimulating the agent to seek names for objects that may be useful for later executing instructions. Together, the results demonstrate that deep neural networks can exploit meta-learning, episodic memory and an explicitly multi-modal environment to account for 'fast-mapping', a fundamental pillar of human cognitive development and a potentially transformative capacity for agents that interact with human users.

The SARS-CoV-2 pandemic has posed a significant challenge for risk evaluation and mitigation among cancer patients. Susceptibility to and severity of COVID-19 in cancer patients has not been studied in a prospective and broadly applicable manner. CAPTURE is a pan-cancer, longitudinal immune profiling study designed to address this knowledge gap.

Chromosomal instability in cancer consists of dynamic changes to the number and structure of chromosomes1,2. The resulting diversity in somatic copy number alterations (SCNAs) may provide the variation necessary for tumour evolution1,3,4. Here we use multi-sample phasing and SCNA analysis of 1,421 samples from 394 tumours across 22 tumour types to show that continuous chromosomal instability results in pervasive SCNA heterogeneity. Parallel evolutionary events, which cause disruption in the same genes (such as BCL9, MCL1, ARNT (also known as HIF1B), TERT and MYC) within separate subclones, were present in 37% of tumours. Most recurrent losses probably occurred before whole-genome doubling, that was found as a clonal event in 49% of tumours. However, loss of heterozygosity at the human leukocyte antigen (HLA) locus and loss of chromosome 8p to a single haploid copy recurred at substantial subclonal frequencies, even in tumours with whole-genome doubling, indicating ongoing karyotype remodelling. Focal amplifications that affected chromosomes 1q21 (which encompasses BCL9, MCL1 and ARNT), 5p15.33 (TERT), 11q13.3 (CCND1), 19q12 (CCNE1) and 8q24.1 (MYC) were frequently subclonal yet appeared to be clonal within single samples. Analysis of an independent series of 1,024 metastatic samples revealed that 13 focal SCNAs were enriched in metastatic samples, including gains in chromosome 8q24.1 (encompassing MYC) in clear cell renal cell carcinoma and chromosome 11q13.3 (encompassing CCND1) in HER2+ breast cancer. Chromosomal instability may enable the continuous selection of SCNAs, which are established as ordered events that often occur in parallel, throughout tumour evolution. Chromosomal instability enables the continuous selection of somatic copy number alterations, which are established as ordered events that often occur in parallel, throughout tumour evolution and metastasis.

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

U ovome ćemo radu nastojati opisati umetnute nerestriktivne relativne rečenice kao tipične parentetičke strukture. Opis će podrazumijevati njihovu sintaksičku i semantičku interpretaciju, ali i naznake o njihovoj pragmatičkoj ulozi i zasnivat će se na analizi primjera pronađenih u korpusu tekstova koji pripadaju različitim stilovima bosanskoga jezika. Rad je podijeljen u tri dijela. U prvom ćemo dijelu definirati parentetike i dati pregled dosadašnjih pristupa u analiziranju te pojave. U drugome dijelu rada fokusirat ćemo se na definiranje i analizu umetnutih nerestriktivnih relativnih rečenica, a u trećem će se dijelu izdvojiti najznačajnija zapažanja.

Abstract Objectives The aim of the study was to determine carotid intima-media thickness (CIMT) values in patients who developed and did not develop preeclampsia (PE), and to determine whether CIMT values could be predictors of PE development. Methods The study included pregnant women who were examined by regular ultrasound examination at the Materno-Infantil Presidente Vargas Hospital (HMIPV) in Porto Alegre, Brazil, from April 2016 to September 2017. The examinations were performed every three months. Patients were divided into two groups. The first group included patients diagnosed with PE (n=21) and second group included patients who did not have PE (n=199). A high frequency ultrasound device (12 MHz) with a semi-automatic method was used to estimate CIMT. Results CIMT was significantly higher in pregnant women with PE than in women without PE (55±0.11 vs. 0.44±0.06, respectively; p<0.001). Using a cut-off value of 0.51 mm, CIMT had a specificity of 77.9% and sensitivity of 81% in the diagnosis of PE. With CIMT ≥0.6 mm, the probability of a patient developing PE was 44.4%; with CIMT >0.42 mm, the probability was only 4.2%. Conclusions An increase in CIMT was associated with the onset of PE. CIMT values were significantly higher in patients who develop PE.