DEVELOPMENT OF INTEGRATED BIOPROCESSING TECHNOLOGIES FOR THE PRODUCTION OF PECTIC OLIGOSACCHARIDES (POS) FROM AGRO-PROCESSING RESIDUES

This research deals with the production of pectic oligosaccharides (POS) from agro-industrial residues, with specific focus on development of continuous cross flow enzyme membrane reactor. Pectic oligosaccharides have recently gained attention due to their prebiotic activity. Lack of information on the continuous production of POS from agro-industrial residues formed the basis for the present study. Four residues i.e sugar beet pulp, onion hulls, pressed pumpkin cake and berry pomace were taken to study their pectin content. Based on the presence of higher galacturonic acid and arabinose (both homogalacturonan and rhamnogalacturonan) in sugar beet pulp and galacturonic acid (only homogalacturonan) in onion hulls, further optimization of different extraction methods of pectin (causing minimum damage to pectic chain) from these residues were done. The most suitable extractant for sugar beet pulp and onion hulls were nitric acid and sodium hexametaphosphate respectively. Further the experiments on the continuous production of POS from sugar beet pulp in an enzyme membrane reactor was initiated. Several optimization experiments indicated the optimum enzyme (Viscozyme) as well as feed concentration (25 g/L) to be used for producing POS from sugar beet pulp in an enzyme membrane reactor. The results highlighted that steady state POS production with volumetric and specific productivity of 22g/L/h and 11 g/gE/h respectively could be achieved by continuous cross flow filtration of sugar beet pulp pectic extract over 10 kDa membrane at residence time of 20 min. The POS yield of about 80% could be achieved using above conditions. Also, in this thesis preliminary experiments on the production and characterization of POS from onion hulls were conducted. The results revelaed that the most suitable enzyme for POS production from onion hulls is endo-polygalacturonase M2. The POS produced from onion hulls were present in the form of DP1 -DP10 in substituted as well as unsubstituted forms. This study clearly demonstrates that continuous production of POS from pectin rich sources can be achieved by using cross flow continuous enzyme membrane reactor.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.