Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Antioxidant and cytotoxic potential of selected plant species of the boraginaceae family

Antioxidant activity is one of the most important properties of plant extracts. Antioxidants from natural sources have been intensively studied in the last few decades. The antioxidant contents of medicinal plants may contribute to the protection of diseases. Bioactive components of plants have a potential role in chemoprevention and inhibition of different phases of the malignant transformation process. Therefore, plant extracts and essential oils are in the focus of research, and in recent decades have been tested on a large number of malignant cell lines. The aim of this study was to examine antioxidant and cytotoxic potential of selected plant species from the Boraginaceae family. Determination of antioxidant activity was performed by ammonium-thiocyanate method. Testing citotoxic activity was performed by MTT test on cancer cell lines: HEP 2c (human larynx carcinoma), RD (human cell line-rhabdomyosarcoma) and L2OB (mouse tumor fibroblast line). The best antioxidant activity showed ethanol, acetone and chloroform extracts of Anchusa officinalis, Echium vulgare and Echium italicum. The tested extracts showed an inhibitory effect on cancer cells, but chloroform and acetone extracts of all three plant had the most effective effect on L2OB cells. Isolation of individual active components from this plants and their testing for cancer cells would be of great importance for this field of research

Wearable in-ear PPG: detailed respiratory variations enable classification of COPD

An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables

Effect of different fertilizers on the microbial activity and productivity of soil under potato cultivation

This study was conducted to evaluate the effect of the application of different rates of mineral nitrogen, well rotten farmyard manure and Klebsiella planticola SL09- based microbial biofertilizer (enteroplantin) on the count of soil microorganisms (total microbial count, counts of Azotobacter, oligonitrophilic bacteria, fungi and actinomycetes), stem height and yield of potato. The experiment was set up as a randomized block design in four replications at the experimental field of the Biotechnical Faculty, Podgorica in 2008. Potato cultivar Kennebec was used as the test plant. The trial involved six treatments: non-fertilized control; N1 treatment with 100 kg/ha CAN (calcium ammonium nitrate, 27% N); N2 treatment with 200 kg/ha CAN; N3 treatment with 300 kg/ha CAN; treatment with Enteroplantin– K. planticola SL09-based biofertilizer; and treatment with 30 t/ha solid well rotten farmyard manure. The results obtained suggested that well rotten farmyard manure induced the highest increase in microbial counts, potato yield and stem height. A similar effect on all microorganisms, except actinomycetes and fungi was seen with the use of K. planticola SL09-based biofertilizer. The potato yield and stem height obtained with the use of 300 kg/ha CAN was non-significantly higher than that of 200 kg/ha CAN treatment, with the count of the soil microorganisms tested been significantly reduced.Key words: Biofertilization, microorganisms, soil, manure, mineral nitrogen, potato, yield

In-ear SpO2 for classification of cognitive workload

The brain is the most metabolically active organ in the body, which increases its metabolic activity, and thus oxygen consumption, with increasing cognitive demand. This motivates us to question whether increased cognitive workload may be measurable through changes in blood oxygen saturation. To this end, we explore the feasibility of cognitive workload tracking based on in-ear SpO2 measurements, which are known to be both robust and exhibit minimal delay. We consider cognitive workload assessment based on an N-back task with randomised order. It is shown that the 2-back and 3-back tasks (high cognitive workload) yield either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a measurable and statistically significant decrease in blood oxygen in response to increased cognitive workload. This makes it possible to classify the four N-back task categories, over 5 second epochs, with a mean accuracy of 90.6%, using features derived from in-ear pulse oximetry, including SpO2, pulse rate and respiration rate. These findings suggest that in-ear SpO2 measurements provide sufficient information for the reliable classification of cognitive workload over short time windows, which promises a new avenue for real time cognitive workload tracking

Age dependence of serum beta-N-acetylhexosaminidase (NAG) activity

Serum Nacetyl-beta-Dglucosaminidase (NAG; EC 3.2.1.30) is a hexosaminidase and may be a predictor of vascular injury, e.g., in infant respiratory distress syndrome, pneumonia, bronchopulmonary dysplasia and necrotizing enterocolitis. To estimate the new diagnostic prospects we have modified our urinary NAG assay. In this sensitive colorimetric microassay, VRAGlcNAc was used as a substrate. In the present study the age dependence of serum NAG activity was investigated in newborn babies, infants (124 months), children (218 years) and adults (1980 years). Serum NAG activity was found to be agedependent; it is higher in early childhood (1159 U/l) but decreases to a constant value at the age of 12 years. After the age of 2 years it is similar to adults NAG (1030 U/l). In pediatrics agematched reference ranges must be taken into consideration

Developing approaches to control SARS-CoV-2 in a public hospital

The Territorial Public Health Care Company (in Italian, ASST) of the Saints Paolo e Carlo of Milano includes two large public hospitals, and several outpatients and territorial healthcare services. It employs 5642 workers. The outbreak of novel coronavirus disease 2019 (COVID-19) reached our ASST in the last week of February when a doctor in the Intensive Care Unit of the San Paolo Hospital was diagnosed with COVID-19. Our Occupational Health Unit immediately introduced measures to control the epidemic. Our approach was based on contact tracing and isolation of asymptomatic infected workers. A \u2018close contact\u2019 was defined as a person who had face-to-face contact or spent at least 15 min in an indoor environment with a positive subject (patient, colleague or relative) without any protective equipment (surgical mask). From 27 February to 23 April we tested 2907 workers (51% of the total workforce) with nasopharyngeal swabs (NPS) using rtPCR for SARS-CoV-2 detection [1,2], with positive results in 152 hospital and 33 territorial workers (3% of the total workforce). All the infected workers were asked to fill in a daily electronic data collection form for the duration of the infection. About 50% remained substantially asymptomatic for the quarantine period, which ended when the workers underwent two NPS on two consecutive days with a negative result. The time to recovery took from 12\u201347 days, with a median duration of about 30 days, which is longer than normally expected. Symptomatic workers showed only very mild symptoms; mainly loss/change of smell and taste. Four were hospitalized but none had severe or life-threatening infection. The data suggest that the \u2018active search approach\u2019 is more effective in closed communities such as groups of healthcare workers than generalized testing. We have started a retrospective survey of 100 positive workers studying symptoms, source of exposure and co-morbidities using a modified version of the \u2018WHO novel coronavirus acute respiratory infection clinical characterization data tool\u2019, administered by telephone interview. Finally, in order to prepare for future outbreaks, we are testing a novel telemedicine approach enabling us to follow quarantined workers with a digital platform with a mobile phone app that provides remote video examinations and online symptoms and health parameter checking (body temperature, oxygen saturation, etc.). The platform facilitates rapid intervention. Using this approach, we can follow a large cohort of workers with continuous monitoring. The tool may also be able to reduce the rate of patients\u2019 hospitalization. We are also comparing those with positive and negative swabs using a rapid immunochromatographic assay for the detection of IgG and IgM antibodies to SARS-CoV-2 virus in whole blood to assess potential immunity. Preliminary results are promising for IgG, even though the protective capacity of this immunoglobulin is still unknown