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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Re-prioritizing climate services for agriculture: Insights from Bangladesh
Considerable progress has been made in establishing climate service capabilities over the last few decades, but the gap between the resulting services and national needs remains large. Using climate services for agriculture in Bangladesh as a case study example, we highlight mismatches between local needs on the one hand, and international initiatives that have focused largely on prediction on the other, and we make suggestions for addressing such mismatches in similar settings. To achieve greater benefit at the national level, there should be a stronger focus on addressing important preliminaries for building services. These preliminaries include the identification of priorities, the definition of responsibilities and expectations, the development of climate services skills, and the construction of a high-quality and easily usable national climate record. Once appropriate institutional, human resources and data infrastructure are in place, the implementation of a climate monitoring and watch system would form a more logical basis for initial climate service implementation than attempting to promote sub-seasonal to seasonal climate forecasting, especially when and where the inherent predictability is limited at best. When and where forecasting at these scales is viable, efforts should focus on defining and predicting high-impact events important for decision making, rather than on simple seasonal aggregates that often correlate poorly with outcomes. Some such forecasts may be more skillful than the 3- to 4-month seasonal aggregates that have become the internationally adopted standard. By establishing a firm foundation for climate services within National Meteorological Services, there is a greater chance that individual climate service development initiatives will be sustainable after their respective project lifetimes
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
This paper focuses on the comparison of networks on the basis of statistical
inference. For that purpose, we rely on smooth graphon models as a
nonparametric modeling strategy that is able to capture complex structural
patterns. The graphon itself can be viewed more broadly as density or intensity
function on networks, making the model a natural choice for comparison
purposes. Extending graphon estimation towards modeling multiple networks
simultaneously consequently provides substantial information about the
(dis-)similarity between networks. Fitting such a joint model - which can be
accomplished by applying an EM-type algorithm - provides a joint graphon
estimate plus a corresponding prediction of the node positions for each
network. In particular, it entails a generalized network alignment, where
nearby nodes play similar structural roles in their respective domains. Given
that, we construct a chi-squared test on equivalence of network structures.
Simulation studies and real-world examples support the applicability of our
network comparison strategy.Comment: 25 pages, 6 figure
Computertomographie-basierte Bestimmung von Aortenklappenkalk und seine Assoziation mit Komplikationen nach interventioneller Aortenklappenimplantation (TAVI)
Background: Severe aortic valve calcification (AVC) has generally been recognized as a key factor in the occurrence of adverse events after transcatheter aortic valve implantation (TAVI). To date, however, a consensus on a standardized calcium detection threshold for aortic valve calcium quantification in contrast-enhanced computed tomography angiography (CTA) is still lacking. The present thesis aimed at comparing two different approaches for quantifying AVC in CTA scans based on their predictive power for adverse events and survival after a TAVI procedure.
Methods: The extensive dataset of this study included 198 characteristics for each of the 965 prospectively included patients who had undergone TAVI between November 2012 and December 2019 at the German Heart Center Berlin (DHZB). AVC quantification in CTA scans was performed at a fixed Hounsfield Unit (HU) threshold of 850 HU (HU 850 approach) and at a patient-specific threshold, where the HU threshold was set by multiplying the mean luminal attenuation of the ascending aorta by 2 (+100 % HUAorta approach). The primary endpoint of this study consisted of a combination of post-TAVI outcomes (paravalvular leak ≥ mild, implant-related conduction disturbances, 30-day mortality, post-procedural stroke, annulus rupture, and device migration). The Akaike information criterion was used to select variables for the multivariable regression model. Multivariable analysis was carried out to determine the predictive power of the investigated approaches.
Results: Multivariable analyses showed that a fixed threshold of 850 HU (calcium volume cut-off 146 mm3) was unable to predict the composite clinical endpoint post-TAVI (OR=1.13, 95 % CI 0.87 to 1.48, p=0.35). In contrast, the +100 % HUAorta approach (calcium volume cut-off 1421 mm3) enabled independent prediction of the composite clinical endpoint post-TAVI (OR=2, 95 % CI 1.52 to 2.64, p=9.2x10-7). No significant difference in the Kaplan-Meier survival analysis was observed for either of the approaches.
Conclusions: The patient-specific calcium detection threshold +100 % HUAorta is more predictive of post-TAVI adverse events included in the combined clinical endpoint than the fixed HU 850 approach. For the +100 % HUAorta approach, a calcium volume cut-off of 1421 mm3 of the aortic valve had the highest predictive value.Hintergrund: Ein wichtiger Auslöser von Komplikationen nach einer Transkatheter-Aortenklappen-Implantation (TAVI) sind ausgeprägte Kalkablagerung an der Aortenklappe. Dennoch erfolgte bisher keine Einigung auf ein standardisiertes Messverfahren zur Quantifizierung der Kalklast der Aortenklappe in einer kontrastverstärkten dynamischen computertomographischen Angiographie (CTA). Die vorliegende Dissertation untersucht, inwieweit die Wahl des Analyseverfahrens zur Quantifizierung von Kalkablagerungen in der Aortenklappe die Prognose von Komplikationen und der Überlebensdauer nach einer TAVI beeinflusst.
Methodik: Der Untersuchung liegt ein umfangreicher Datensatz von 965 Patienten mit 198 Merkmalen pro Patienten zugrunde, welche sich zwischen 2012 und 2019 am Deutschen Herzzentrum Berlin einer TAVI unterzogen haben. Die Quantifizierung der Kalkablagerung an der Aortenklappe mittels CTA wurde einerseits mit einem starren Grenzwert von 850 Hounsfield Einheiten (HU) (HU 850 Verfahren) und andererseits anhand eines individuellen Grenzwertes bemessen. Letzterer ergibt sich aus der HU-Dämpfung in dem Lumen der Aorta ascendens multipliziert mit 2 (+100 % HUAorta Verfahren). Der primäre klinische Endpunkt dieser Dissertation besteht aus einem aus sechs Variablen zusammengesetzten klinischen Endpunkt, welcher ungewünschte Ereignisse nach einer TAVI abbildet (paravalvuläre Leckage ≥mild, Herzrhythmusstörungen nach einer TAVI, Tod innerhalb von 30 Tagen, post-TAVI Schlaganfall, Ruptur des Annulus und Prothesendislokation). Mögliche Störfaktoren, die auf das Eintreten der Komplikationen nach TAVI Einfluss haben, wurden durch den Einsatz des Akaike Informationskriterium ermittelt. Um die Vorhersagekraft von Komplikationen nach einer TAVI durch beide Verfahren zu ermitteln, wurde eine multivariate Regressionsanalyse durchgeführt.
Ergebnisse: Die multivariaten logistischen Regressionen zeigen, dass die Messung der Kalkablagerungen anhand der HU 850 Messung (Kalklast Grenzwert von 146 mm3) die Komplikationen und die Überlebensdauer nicht vorhersagen konnten (OR=1.13, 95 % CI 0.87 bis 1.48, p=0.35). Die nach dem +100 % HUAorta Verfahren (Kalklast Grenzwert von 1421 mm3) individualisierte Kalkmessung erwies sich hingegen als sehr aussagekräftig, da hiermit Komplikationen nach einer TAVI signifikant vorhergesagt werden konnten (OR=2, 95 % CI 1.52 bis 2.64, p=9.2x10-7). In Hinblick auf die postoperative Kaplan-Meier Überlebenszeitanalyse kann auch mit dem +100 % HUAorta Verfahren keine Vorhersage getroffen werden.
Fazit: Aus der Dissertation ergibt sich die Empfehlung, die Messung von Kalkablagerungen nach dem +100 % HUAorta Verfahren vorzunehmen, da Komplikationen wesentlich besser und zuverlässiger als nach der gängigen HU 850 Messmethode vorhergesagt werden können. Für das +100 % HUAorta Verfahren lag der optimale Kalklast Grenzwert bei 1421 mm3
Linear to multi-linear algebra and systems using tensors
In past few decades, tensor algebra also known as multi-linear algebra has
been developed and customized as a tool to be used for various engineering
applications. In particular, with the help of a special form of tensor
contracted product, known as the Einstein Product and its properties, many of
the known concepts from Linear Algebra could be extended to a multi-linear
setting. This enables to define the notions of multi-linear system theory where
the input, output signals and the system are multi-domain in nature. This paper
provides an overview of tensor algebra tools which can be seen as an extension
of linear algebra, at the same time highlighting the difference and advantages
that the multi-linear setting brings forth. In particular, the notion of tensor
inversion, tensor singular value and tensor Eigenvalue decomposition using the
Einstein product is explained. In addition, this paper also introduces the
notion of contracted convolution in both discrete and continuous multi-linear
system tensors. Tensor Networks representation of various tensor operations is
also presented. Also, application of tensor tools in developing transceiver
schemes for multi-domain communication systems, with an example of MIMO CDMA
systems, is presented. Thus this paper acts as an entry point tutorial for
graduate students whose research involves multi-domain or multi-modal signals
and systems
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC
The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this
final state has yielded some of the most precise measurements of the particle. As
measurements of the Higgs boson become increasingly precise, greater import is
placed on the factors that constitute the uncertainty. Reducing the effects of these
uncertainties requires an understanding of their causes. The research presented
in this thesis aims to illuminate how uncertainties on simulation modelling are
determined and proffers novel techniques in deriving them.
The upgrade of the FastCaloSim tool is described, used for simulating events in
the ATLAS calorimeter at a rate far exceeding the nominal detector simulation,
Geant4. The integration of a method that allows the toolbox to emulate the
accordion geometry of the liquid argon calorimeters is detailed. This tool allows
for the production of larger samples while using significantly fewer computing
resources.
A measurement of the total Higgs boson production cross-section multiplied
by the diphoton branching ratio (σ × Bγγ) is presented, where this value was
determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement
with the Standard Model prediction. The signal and background shape modelling
is described, and the contribution of the background modelling uncertainty to the
total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production
mechanism.
A method for estimating the number of events in a Monte Carlo background
sample required to model the shape is detailed. It was found that the size of
the nominal γγ background events sample required a multiplicative increase by
a factor of 3.60 to adequately model the background with a confidence level of
68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate,
0.5 billion additional simulated events were produced, substantially reducing the
background modelling uncertainty.
A technique is detailed for emulating the effects of Monte Carlo event generator
differences using multivariate reweighting. The technique is used to estimate the
event generator uncertainty on the signal modelling of tHqb events, improving the
reliability of estimating the tHqb production cross-section. Then this multivariate
reweighting technique is used to estimate the generator modelling uncertainties
on background V γγ samples for the first time. The estimated uncertainties were
found to be covered by the currently assumed background modelling uncertainty
Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico
Valve replacement remains as the standard therapeutic option for aortic
stenosis patients, aiming at abolishing pressure overload and triggering
myocardial reverse remodeling. However, despite the instant hemodynamic
benefit, not all patients show complete regression of myocardial hypertrophy,
being at higher risk for adverse outcomes, such as heart failure. The current
comprehension of the biological mechanisms underlying an incomplete reverse
remodeling is far from complete. Furthermore, definitive prognostic tools and
ancillary therapies to improve the outcome of the patients undergoing valve
replacement are missing. To help abridge these gaps, a combined myocardial
(phospho)proteomics and pericardial fluid proteomics approach was followed,
taking advantage of human biopsies and pericardial fluid collected during
surgery and whose origin anticipated a wealth of molecular information
contained therein.
From over 1800 and 750 proteins identified, respectively, in the myocardium
and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated
proteins were detected. Gene annotation and pathway enrichment analyses,
together with discriminant analysis, are compatible with a scenario of increased
pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by
complement activity and other extrinsic factors, such as death receptor
activators), acute-phase response, immune system activation and fibrosis.
Specific validation of some targets through immunoblot techniques and
correlation with clinical data pointed to complement C3 β chain, Muscle Ring
Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation
regulated kinase 1A (DYRK1A) as potential markers of an incomplete
response. In addition, kinase prediction from phosphoproteome data suggests
that the modulation of casein kinase 2, the family of IκB kinases, glycogen
synthase kinase 3 and DYRK1A may help improve the outcome of patients
undergoing valve replacement. Particularly, functional studies with DYRK1A+/-
cardiomyocytes show that this kinase may be an important target to treat
cardiac dysfunction, provided that mutant cells presented a different response
to stretch and reduced ability to develop force (active tension).
This study opens many avenues in post-aortic valve replacement reverse
remodeling research. In the future, gain-of-function and/or loss-of-function
studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic
targets. Besides, clinical studies in larger cohorts will bring definitive proof of
complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de
referência para doentes com estenose aórtica e visa a eliminação da
sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica.
Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes
apresentam regressão completa da hipertrofia do miocárdio, ficando com maior
risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os
mecanismos biológicos subjacentes a uma remodelagem reversa incompleta
ainda não são claros. Além disso, não dispomos de ferramentas de
prognóstico definitivos nem de terapias auxiliares para melhorar a condição
dos pacientes indicados para substituição da válvula. Para ajudar a resolver
estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica
para a caracterização, respetivamente, do miocárdio e do líquido pericárdico
foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em
ambiente cirúrgico.
Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio
e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90
proteínas desreguladas foram detetadas. As análises de anotação de genes,
de enriquecimento de vias celulares e discriminativa corroboram um cenário de
aumento da expressão de genes pro-hipertróficos e de síntese proteica, um
sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular
(potencialmente acelerada pela atividade do complemento e por outros fatores
extrínsecos que ativam death receptors), com ativação da resposta de fase
aguda e do sistema imune, assim como da fibrose.
A validação de alguns alvos específicos através de immunoblot e correlação
com dados clínicos apontou para a cadeia β do complemento C3, a Muscle
Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation
regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta
incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma,
sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a
glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição
dos pacientes indicados para intervenção. Em particular, a avaliação funcional
de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo
importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes
responderam de forma diferente ao estiramento e mostraram uma menor
capacidade para desenvolver força (tensão ativa).
Este estudo levanta várias hipóteses na investigação da remodelagem reversa.
No futuro, estudos de ganho e/ou perda de função realizados em
cardiomiócitos isolados ou em modelos animais de banding-debanding da
aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos
encontrados. Além disso, estudos clínicos em coortes de maior dimensão
trarão conclusões definitivas quanto ao valor de prognóstico do complemento
C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
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