Is facility based neonatal care in low resource setting keeping pace? A glance at Uganda’s National Referral Hospital.

Objectives: To identify reasons for neonatal admission and death with the aim of determining areas needing improvement. Method: A retrospective chart review was conducted on records for neonates admitted to Mulago National Referral Hospital Special Care Baby Unit (SCBU) from 1st November 2013 to 31st January 2014. Final diagnosis was generated after analyzing sequence of clinical course by 2 paediatricians. Results: A total of 1192 neonates were admitted. Majority 83.3% were in-born. Main reasons for admissions were prematurity (37.7%) and low APGAR (27.9%).Overall mortality was 22.1% (Out-born 33.6%; in born 19.8%). Half (52%) of these deaths occurred in the first 24 hours of admission. Major contributors to mortality were prematurity with hypothermia and respiratory distress (33.7%) followed by birth asphyxia with HIE grade III (24.6%) and presumed sepsis (8.7%). Majority of stable at risk neonates 318/330 (i.e. low APGAR or prematurity without comorbidity) survived. Factors independently associated with death included gestational age <30 weeks (p 0.002), birth weight <1500g (p 0.007) and a 5 minute APGAR score of < 7 (p 0.001). Neither place of birth nor delayed and after hour admissions were independently associated with mortality. Conclusion and recommendations: Mortality rate in SCBU is high. Prematurity and its complications were major contributors to mortality. The management of hypothermia and respiratory distress needs scaling up. A step down unit for monitoring stable at risk neonates is needed in order to decongest SCBU.

Adaptive Mechanisms of an Estuarine Synechococcus based on Genomics, Transcriptomics, and Proteomics

Picocyanobacteria are important phytoplankton and primary producers in the ocean. Although extensive work has been conducted for picocyanobacteria (i.e. Synechococcus and Prochlorococcus) in coastal and oceanic waters, little is known about those found in estuaries like the Chesapeake Bay. Synechococcus CB0101, an estuarine isolate, is more tolerant to shifts in temperature, salinity, and metal toxicity than coastal and oceanic Synechococcus strains, WH7803 and WH7805. Further, CB0101 has a greater sensitivity to high light intensity, likely due to its adaptation to low light environments. A complete and annotated genome sequence of CB0101 was completed to explore its genetic capacity and to serve as a basis for further molecular analysis. Comparative genomics between CB0101, WH7803, and WH7805 show that CB0101 contains more genes involved in regulation, sensing, and stress response. At the transcript and protein level, CB0101 regulates its metabolic pathways, transport systems, and sensing mechanisms when nitrate and phosphate are limited. Zinc toxicity led to oxidative stress and a global down regulation of photosystems and the translation machinery. From the stress response studies seven chromosomal toxin-antitoxin (TA) genes, were identified in CB0101, which led to the discovery of TA genes in several marine Synechococcus strains. The activation of the relB2/relE1 TA system allows CB0101 to arrest its growth under stressful conditions, but the growth arrest is reversible, once the stressful environment dissipates. The genome of CB0101 contains a relatively large number of genomic island (GI) genes compared to known marine Synechococcus genomes. Interestingly, a massive shutdown (255 out of 343) of GI genes occurred after CB0101 was infected by a lytic phage. On the other hand, phage-encoded host-like proteins (hli, psbA, ThyX) were highly expressed upon phage infection. This research provides new evidence that estuarine Synechococcus like CB0101 have inherited unique genetic machinery, which allows them to be versatile in the estuarine environment.

Graph-based structural analysis of planar mechanisms.

Kinematic structure of planar mechanisms addresses the study of attributes determined exclusively by the joining pattern among the links forming a mechanism. The system group classification is central to the kinematic structure and consists of determining a sequence of kinematically and statically independent-simple chains which represent a modular basis for the kinematics and force analysis of the mechanism. This article presents a novel graph-based algorithm for structural analysis of planar mechanisms with closed-loop kinematic structure which determines a sequence of modules (Assur groups) representing the topology of the mechanism. The computational complexity analysis and proof of correctness of the implemented algorithm are provided. A case study is presented to illustrate the results of the devised method.

Insights into the Specificity of Thioredoxin Reductase-Thioredoxin Interactions. A Structural and Functional Investigation of the Yeast Thioredoxin System

The enzymatic activity of thioredoxin reductase enzymes is endowed by at least two redox centers: a flavin and a dithiol/disulfide CXXC motif. The interaction between thioredoxin reductase and thioredoxin is generally species-specific, but the molecular aspects related to this phenomenon remain elusive. Here, we investigated the yeast cytosolic thioredoxin system, which is composed of NADPH, thioredoxin reductase (ScTrxR1), and thioredoxin 1 (ScTrx1) or thioredoxin 2 (ScTrx2). We showed that ScTrxR1 was able to efficiently reduce yeast thioredoxins (mitochondrial and cytosolic) but failed to reduce the human and Escherichia coli thioredoxin counterparts. To gain insights into this specificity, the crystallographic structure of oxidized ScTrxR1 was solved at 2.4 angstrom resolution. The protein topology of the redox centers indicated the necessity of a large structural rearrangement for FAD and thioredoxin reduction using NADPH. Therefore, we modeled a large structural rotation between the two ScTrxR1 domains (based on the previously described crystal structure, PDB code 1F6M). Employing diverse approaches including enzymatic assays, site-directed mutagenesis, amino acid sequence alignment, and structure comparisons, insights were obtained about the features involved in the species-specificity phenomenon, such as complementary electronic parameters between the surfaces of ScTrxR1 and yeast thioredoxin enzymes and loops and residues (such as Ser(72) in ScTrx2). Finally, structural comparisons and amino acid alignments led us to propose a new classification that includes a larger number of enzymes with thioredoxin reductase activity, neglected in the low/high molecular weight classification.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Chemiluminescence DNA-based Nanotechnologies for Bioanalytical Applications

DNA as powerful building molecule, is widely used for the assembly of molecular structures and dynamic molecular devices with different potential applications, ranging from synthetic biology to diagnostics. The feature of sequence programmability, which makes it possible to predict how single stranded DNA molecules fold and interact with one another, allowed the development of spatiotemporally controlled nanostructures and the engineering of supramolecular devices. The first part of this thesis addresses the development of an integrated chemiluminescence (CL)-based lab-on-chip sensor for detection of Adenosine-5-triphosphate (ATP) life biomarker in extra-terrestrial environments.Subsequently, we investigated whether it is possible to study the interaction and the recognition between biomolecules and their targets, mimicking the intracellular environment in terms of crowding, confinement and compartmentalization. To this purpose, we developed a split G-quadruplex DNAzyme platform for the chemiluminescent and quantitative detection of antibodies based on antibody-induced co-localization proximity mechanism in which a split G-quadruplex DNAzyme is led to reassemble into the functional native G-quadruplex conformation as the effect of a guided spatial nanoconfinement.The following part of this thesis aims at developing chemiluminescent nanoparticles for bioimaging and photodynamic therapy applications.In chapter5 a realistic and accurate evaluation of the potentiality of electrochemistry and chemiluminescence (CL) for biosensors development (i.e., is it better to “measure an electron or a photon”?), has been achieved.In chapter 6 the emission anisotropy phenomenon for an emitting dipole bound to the interface between two media with different refractive index has been investigated for chemiluminescence detection.

Characterization of DNA sequence properties through network and statistical approaches

In this thesis we will see that the DNA sequence is constantly shaped by the interactions with its environment at multiple levels, showing footprints of DNA methylation, of its 3D organization and, in the case of bacteria, of the interaction with the host organisms. In the first chapter, we will see that analyzing the distribution of distances between consecutive dinucleotides of the same type along the sequence, we can detect epigenetic and structural footprints. In particular, we will see that CG distance distribution allows to distinguish among organisms of different biological complexity, depending on how much CG sites are involved in DNA methylation. Moreover, we will see that CG and TA can be described by the same fitting function, suggesting a relationship between the two. We will also provide an interpretation of the observed trend, simulating a positioning process guided by the presence and absence of memory. In the end, we will focus on TA distance distribution, characterizing deviations from the trend predicted by the best fitting function, and identifying specific patterns that might be related to peculiar mechanical properties of the DNA and also to epigenetic and structural processes. In the second chapter, we will see how we can map the 3D structure of the DNA onto its sequence. In particular, we devised a network-based algorithm that produces a genome assembly starting from its 3D configuration, using as inputs Hi-C contact maps. Specifically, we will see how we can identify the different chromosomes and reconstruct their sequences by exploiting the spectral properties of the Laplacian operator of a network. In the third chapter, we will see a novel method for source clustering and source attribution, based on a network approach, that allows to identify host-bacteria interaction starting from the detection of Single-Nucleotide Polymorphisms along the sequence of bacterial genomes.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

A multidisciplinary approach to the study of protein dynamics and signal transmission

Allostery is a phenomenon of fundamental importance in biology, allowing regulation of function and dynamic adaptability of enzymes and proteins. Despite the allosteric effect was first observed more than a century ago allostery remains a biophysical enigma, defined as the “second secret of life”. The challenge is mainly associated to the rather complex nature of the allosteric mechanisms, which manifests itself as the alteration of the biological function of a protein/enzyme (e.g. ligand/substrate binding at the active site) by binding of “other object” (“allos stereos” in Greek) at a site distant (> 1 nanometer) from the active site, namely the effector site. Thus, at the heart of allostery there is signal propagation from the effector to the active site through a dense protein matrix, with a fundamental challenge being represented by the elucidation of the physico-chemical interactions between amino acid residues allowing communicatio n between the two binding sites, i.e. the “allosteric pathways”. Here, we propose a multidisciplinary approach based on a combination of computational chemistry, involving molecular dynamics simulations of protein motions, (bio)physical analysis of allosteric systems, including multiple sequence alignments of known allosteric systems, and mathematical tools based on graph theory and machine learning that can greatly help understanding the complexity of dynamical interactions involved in the different allosteric systems. The project aims at developing robust and fast tools to identify unknown allosteric pathways. The characterization and predictions of such allosteric spots could elucidate and fully exploit the power of allosteric modulation in enzymes and DNA-protein complexes, with great potential applications in enzyme engineering and drug discovery.

[almost An Autobiography: A Study Of Social Scientists In Health Based On The Lattes Curriculum].

Among the various ways of adopting the biographical approach, we used the curriculum vitaes (CVs) of Brazilian researchers who work as social scientists in health as our research material. These CVs are part of the Lattes Platform of CNPq - the National Council for Scientific and Technological Development, which includes Research and Institutional Directories. We analyzed 238 CVs for this study. The CVs contain, among other things, the following information: professional qualifications, activities and projects, academic production, participation in panels for the evaluation of theses and dissertations, research centers and laboratories and a summarized autobiography. In this work there is a brief review of the importance of autobiography for the social sciences, emphasizing the CV as a form of autobiographical practice. We highlight some results, such as it being a group consisting predominantly of women, graduates in social sciences, anthropology, sociology or political science, with postgraduate degrees. The highest concentration of social scientists is located in Brazil's southern and southeastern regions. In some institutions the main activities of social scientists are as teachers and researchers with great thematic diversity in research.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Can We Predict The Clinical Outcomes Of Genitourinary Trauma Patients And Offer Them An Evidence-based Support? The Case For Telemedicine Network.

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Genome Sequence Of Streptomyces Wadayamensis Strain A23, An Endophytic Actinobacterium From Citrus Reticulata.

The actinobacterium Streptomyces wadayamensis A23 is an endophyte of Citrus reticulata that produces the antimycin and mannopeptimycin antibiotics, among others. The strain has the capability to inhibit Xylella fastidiosa growth. The draft genome of S. wadayamensis A23 has ~7.0 Mb and 6,006 protein-coding sequences, with a 73.5% G+C content.