Safe and effective oocyte cryopreservation will have a considerable impact on clinical practice in assisted reproduction. Great improvements have been made in recent years to oocyte vitrification procedures, although further controlled trials are necessary to ensure safety, and it is necessary to know more about pregnancy and live birth outcomes. This study aims to validate various methods of oocyte vitrification as assessed by comparative target gene analysis, hence contributing to information available to clinicians advising women about fertility preservation options before cancer treatment. Target genes investigated were: the maternal effect genes Deleted in Azoospermia Like (DAZL), Maternal Antigen That Embryos Require (MATER/NLRP5) and Zygote Arrest 1 (ZAR1); three genes involved in cell cycle progression and cell death, tumour suppressor protein 53 (p53), B-cell lymphoma 2 (BCL2), BCL2-Associated X Protein (BAX); three genes known to affect spindle and chromatin structure, oocyte-specific histone 1 (H1FOO), kinesin family member 11 (KIF11) and mitotic arrest deficient 2 (MAD2); together with Factor In the GermLine, Alpha (FIGLα) which regulates zona pellucida proteins, octamer-binding transcription factor 4 (OCT4/POU5F1) which is associated with pluripotency and oocyte developmental competence, and superoxide dismutase 2, mitochondrial (SOD2) which responds to oxidative stress in the mitochondria. These genes may be useful indicators of oocyte quality following vitrification. Lysis, complementary DNA (cDNA) amplification, polyadenylic acid polymerase chain reaction (polyA PCR) and quantitative polymerase chain reaction (QPCR) were used to investigate gene expression patterns in failed-to-fertilize non-vitrified, vitrified and slow frozen human MII oocytes. Comparative gene analyses included oocytes vitrified using closed and open carriers, and two different media. Results indicate that the impact of vitrification varies by gene and oocyte variability, highlighting the importance of studies based on single oocytes and the need for caution in interpretation of generalised findings. OCT4 and also β-actin were significantly affected by all methods investigated, while FIGLα, MAD2, ZAR1 and DAZL were affected by some methods. Oocyte survival rate after thawing and the number of genes expressed by individual oocytes were higher with media incorporating dimethyl sulfoxide (DMSO) and Dextran Serum Supplement (DSS) and first-step warming in a larger volume. All methods led to altered expression of target genes, most noticeably when the second media was used. Further quantitative studies of the impact of OCT4, FIGLα and β-actin should be conducted, together with clinical comparisons between media and a longitudinal multi-centre study regarding outcomes arising from different vitrification methods.

Understanding the molecular mechanisms that govern productivity and growth of recombinant host cells is essential to devise informed approaches to increase commercial viability and availability of biopharmaceuticals. This work has focused on the roles of the mammalian target of rapamycin complex 1 (mTORC1) signalling pathway in CHO cells, the most widely used expression system in the biopharmaceutical industry. mTORC1 is a master regulator of cell growth, protein synthesis and metabolism in response to availability of nutrients, oxygen and growth factors. Therefore, it was hypothesised that increased activity of mTORC1 enhances growth and productivity of recombinant CHO cells. The study of a recombinant GS-CHO cell line in the serum-free suspension batch culture indicated a gradual decrease in the activity of mTORC1, as defined by the decreased extent of site-specific phosphorylation of two widely ascribed downstream target proteins (ribosomal protein S6 kinase 1 (S6K1) and 4E-BP1, an inhibitor of translation initiation). The decline in the activity of mTORC1 paralleled decreased growth rate, recombinant protein specific productivity and global protein translation. To further clarify the role of the mTOR pathway in cell growth and protein production, cells in batch culture were treated with rapamycin, a specific inhibitor of mTORC1. Treatment with rapamycin stalled the growth of the CHO cell line transiently, but recombinant protein specific productivity, longevity of batch culture, and final antibody titre were greater than control. Rapamycin addition produced discriminating effects on downstream signalling targets, implicating distinct roles for these targets in control of growth and protein synthesis. Engineering the mTORC1 pathway by overexpression of specific components of this pathway (S6K1 and Rheb) generated increased growth and extended viability. Greater proliferation was not associated with improved productivity suggesting highly proliferative phenotypes that prioritise cell growth over synthesis and secretion of recombinant antibody in the recombinant GS-CHO cells examined. Therefore, the engineering of mTORC1 pathway may be beneficial to increase robustness or adaptation to stressed conditions (such as serum- free suspension growth, low nutrition availability and hypoxia).

En el presente artículo, el Dr. Antonio de la Haza realiza un análisis de la aplicación de la Ley N° 29733, Ley de Protección de Datos Personales, respecto de los clientes sensibles y los denominados clientes PEP, persona expuesta políticamente, en el marco de la exigencia de la política bancaria de conocimiento del cliente como principio de prevención del lavado de activos.

abstract: Constant false alarm rate is one of the essential algorithms in a RADAR detection system. It allows the RADAR system to dynamically set thresholds based on the data power level to distinguish targets with interfering noise and clutters. To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm. The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting. In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time. The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Lung cancer is the leading cause of cancer related deaths in the United States, with an estimated 158, 040 deaths in 2015, accounting for 27% of all cancer deaths. Recent research has identified several important molecular driver oncogenes, including epidermal growth factor receptor (EGFR). EGFR is encoded by exons 18-21, each of which harbor specific mutations within the tyrosine kinase domain. These mutations can drive cell growth, proliferation, and survival, resulting in the formation of non-small cell lung cancer. The development of EGFR tyrosine kinase inhibitors, allows the targeting of these specific mutations without the toxicity normally associated with standard chemotherapy. Unfortunately, inevitably resistance to therapy manifests, requiring a change in therapy and adding complexity to treatment decision making for clinicians and patients alike. Through a comprehensive examination of current literature, this review will establish a standard for first line, targeted treatment for specific genetic mutations within the EGFR gene, as well as address treatment options once resistance to first-line therapy inevitably develops.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV’s field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. While the ERHC achieved optimal stead-state estimates the DeepRL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV's field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target's entropy. While the ERHC achieved optimal stead-state estimates the Deep-RL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Årets resultat är en av de viktigaste posterna som ett företag förmedlar i sin finansiella rapportering och tidigare internationella studier har funnit tecken på manipulation av resultaten för att precis slå olika resultatmål, så kallad target beating. Syftet med denna studie är att undersöka om ett liknande beteende finns på den svenska marknaden samt om något samband mellan signaler om target beating och aktieavkastning föreligger. Finansiella kvartalsdata från företag noterade på Nasdaq Stockholm från perioden 2011 till 2019 har undersökts och resultatet visar på att ett target beating-beteende för att göra positivt resultat förekommer på den svenska marknaden, medan studiens statistiska test inte kan utföras för tidigare års resultat och analytikers resultatprognoser. Marknadsreaktionen på target beating har undersökts i en eventstudie genom att ställa rapporterat årsresultat i relation till Jacob och Jorgensens (2007) proxy för icke-manipulerat resultat. En signifikant positiv avvikelseavkastning hittas hos företag som precis slagit sitt mål efter att ha manipulerat upp resultatet.

Conventional drug discovery focuses on single protein targets and follows a “sequence, structure, and function” paradigm for selecting best protein targets to screen lead chemical compounds. This established paradigm simply avoids addressing directly the challenge of evaluating chemical toxicity and side effects until a later stage of drug discovery, resulting in inefficiencies and increased time and cost. We developed a new “network biology” perspective to assess proteins as potential drug targets using emerging biomolecular network data sets. To do so, we integrated several types of biological data for current drug targets from DrugBank, protein interaction data from the HAPPI and HPRD databases, literature co-citation data from PubMed, and side effects data from FDA-approved drug usage warnings. We used the Bayes factor and Positive Predictive Values to examine the use of certain network properties, such as network node degrees and essentiality, to predict candidate drug targets. We also developed a metric to evaluate a protein target’s overall side effects by taking into account aggregated side effect scores of all FDA-approved drugs targeting the protein. We discovered that non-essential protein with lower-to-medium network node degree could better serve as drug targets when combined with conventional protein function information. Integrated biomolecular associations, instead of physical interactions, are better sources for predicting drug targets with network biology methods. Our network biology framework presents exciting promises in developing better drug targets that lower the side-effects at later stages of drug development and help establish the field of “network pharmacology.”

Background and aims: Hypertension is a public health problem and a major reason for hospitalisation and death. In Mozambique, low levels of detection, treatment and control have been described. However, data on target-organ damage and associated clinical conditions is lacking. We therefore aimed at characterising the clinical profile of patients with severe hypertension, describing the pattern of target organ damage and determining the outcomes at 6-month follow-up. Methods: We designed a prospective descriptive cohort study to assess adult patients with severe hypertension defined according to the Joint National Committee VII guidelines. The study was conducted from July 2015 to May 2017 at Mavalane General Hospital in Maputo-Mozambique. Patients were characterized through physical examination, laboratory profile, electrocardiography, and echocardiography, and followed for six months to assess occurrence of complications such as hypertensive heart failure, stroke, renal failure, hospital admission and death. Data were analysed using SPSS software version 20.0. The study was approved by the National Bioethics Committee for Health of Mozambique. Results: We studied 116 subjects (111 [95.7%] black; women 81 [70%]). Women were slightly younger than men (mean 57 years vs 59 years); 18 (15.5%) patients were younger than 44 years. The risk profile of the studied population included obesity (46; 42.5%); dyslipidaemia (59; 54.1%); diabetes (10; 8.6%) and smoking (8; 6.9%). At baseline, mean values for systolic and diastolic blood pressure were 192.3 ± 23.6 and 104.2 ± 15.2, respectively. The most frequent target-organ damage were left atrial enlargement in 91 (88.3%) with atrial fibrillation in 9 (7.9%); left ventricular hypertrophy in 57 (50.4%); hypertensive retinopathy in 30 (26. 3%) and renal damage in 29 (25.7%) subjects. Major events during 6-month follow-up were hospitalisations (12; 10.3%) and death (10; 8.6%). Renal damage (4; 4.2%), stroke (4; 3.4%) and heart failure (2; 1.7%) were the most common complications occurring over the follow up period. Conclusion: Severe and complicated hypertension affects young people with higher incidence of obesity, diabetes and smoking than that found in general population. High occurrence of target organ damage is found at baseline, particularly heart damage, renal lesion and stroke. On follow up, severe hypertension is associated with high number of hospitalisations and high case-fatality rate. Moreover, renal damage, stroke and hypertensive heart disease were common complications on follow up. Further research is needed to understand the determinants of these poor outcomes.