Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem

Cell type identification, differential expression analysis and trajectory inference in single-cell transcriptomics

Single-cell RNA-sequencing (scRNA-seq) is a cutting-edge technology that enables to quantify the transcriptome, the set of expressed RNA transcripts, of a group of cells at the single-cell level. It represents a significant upgrade from bulk RNA-seq, which measures the combined signal of thousands of cells. Measuring gene expression by bulk RNA-seq is an invaluable tool for biomedical researchers who want to understand how cells alter their gene expression due to an illness, differentiation, ternal stimulus, or other events. Similarly, scRNA-seq has become an essential method for biomedical researchers, and it has brought several new applications previously unavailable with bulk RNA-seq. scRNA-seq has the same applications as bulk RNA-seq. However, the single-cell resolution also enables cell annotation based on gene markers of clusters, that is, cell populations that have been identified based on machine learning to be, on average, dissimilar at the transcriptomic level. Researchers can use the cell clusters to detect cell-type-specific gene expression changes between conditions such as case and control groups. Clustering can sometimes even discover entirely new cell types. Besides the cluster-level representation, the single-cell resolution also enables to model cells as a trajectory, representing how the cells are related at the cell level and what is the dynamic differentiation process that the cells undergo in a tissue. This thesis introduces new computational methods for cell type identification and trajectory inference from scRNA-seq data. A new cell type identification method (ILoReg) was proposed, which enables high-resolution clustering of cells into populations with subtle transcriptomic differences. In addition, two new trajectory inference methods were developed: scShaper, which is an accurate and robust method for inferring linear trajectories; and Totem, which is a user-friendly and flexible method for inferring tree-shaped trajectories. In addition, one of the works benchmarked methods for detecting cell-type-specific differential states from scRNA-seq data with multiple subjects per comparison group, requiring tailored methods to confront false discoveries. KEYWORDS: Single-cell RNA sequencing, transcriptome, cell type identification, trajectory inference, differential expressionYksisoluinen RNA-sekvensointi on huipputeknologia, joka mahdollistaa transkriptomin eli ilmentyneiden RNA-transkriptien laskennallisen määrittämisen joukolle soluja yhden solun tarkkuudella, ja sen kehittäminen oli merkittävä askel eteenpäin perinteisestä bulkki-RNA-sekvensoinnista, joka mittaa tuhansien solujen yhteistä signaalia. Bulkki-RNA-sekvensointi on tärkeä työväline biolääketieteen tutkijoille, jotka haluavat ymmärtää miten solut muuttavat geenien ilmentymistä sairauden, erilaistumisen, ulkoisen ärsykkeen tai muun tapahtuman seurauksena. Yksisoluisesta RNA-sekvensoinnista on vastaavasti kehittynyt tärkeä työväline tutkijoille, ja se on tuonut useita uusia sovelluksia. Yksisoluisella RNA-sekvensoinnilla on samat sovellukset kuin bulkki-RNA-sekvensoinnilla, mutta sen lisäksi se mahdollistaa solujen tunnistamisen geenimarkkerien perusteella. Geenimarkkerit etsitään tilastollisin menetelmin solupopulaatioille, joiden on tunnistettu koneoppimisen menetelmin muodostavan transkriptomitasolla keskenään erilaisia joukkoja eli klustereita. Tutkijat voivat hyödyntää soluklustereita tutkimaan geeniekspressioeroja solutyyppien sisällä esimerkiksi sairaiden ja terveiden välillä, ja joskus klusterointi voi jopa tunnistaa uusia solutyyppejä. Yksisolutason mittaukset mahdollistavat myös solujen mallintamisen trajektorina, joka esittää kuinka solut kehittyvät dynaamisesti toisistaan geenien ilmentymistä vaativien prosessien aikana. Tämä väitöskirja esittelee uusia laskennallisia menetelmiä solutyyppien ja trajektorien tunnistamiseen yksisoluisesta RNA-sekvensointidatasta. Väitöskirja esittelee uuden solutyyppitunnistusmenetelmän (ILoReg), joka mahdollistaa hienovaraisia geeniekspressioeroja sisältävien solutyyppien tunnistamisen. Sen lisäksi väitöskirjassa kehitettiin kaksi uutta trajektorin tunnistusmenetelmää: scShaper, joka on tarkka ja robusti menetelmä lineaaristen trajektorien tunnistamiseen, sekä Totem, joka on käyttäjäystävällinen ja joustava menetelmä puumallisten trajektorien tunnistamiseen. Lopuksi väitöskirjassa vertailtiin menetelmiä solutyyppien sisäisten geeniekspressioerojen tunnistamiseen ryhmien välillä, joissa on useita koehenkilöitä tai muita biologisia replikaatteja, mikä vaatii erityisiä menetelmiä väärien positiivisten löydösten vähentämiseen. ASIASANAT: yksisoluinen RNA-sekvensointi, klusterointi, trajektorin tunnistus, geeniekspressi

Supply chain optimization of palm oil mill effluent to biocompressed natural gas for industrial usage

Virtual distribution of Biocompressed Natural Gas (BioCNG) is economically attractive to industries which are remotely located from natural gas pipeline. However, this concept poses some issues concerning logistics due to scattered spatial distribution of palm oil mills. Addressing these aspects requires an integrated spatial planning and optimization to synthesise location and allocate network of BioCNG virtual transportation to the respective industry. This study presented the development of integrated spatial planning and optimization of BioCNG supply and distribution network through virtual pipeline to meet on-site energy demand of specific industry. This study also aimed to investigate the contribution of optimized BioCNG supply chain towards systematic energy hub among other energy alternatives. The data from network analysis of aeronautical reconnaissance coverage geographic information system were coded into generalized algebraic modelling system and advanced interactive multidimensional modelling system modelling to generate supply cost curve for multiple source of energy carrier i.e. liquefied natural gas import, natural gas (NG) through pipeline network, and BioCNG supply chain through virtual pipeline. The results show that standardised optimum compression pressures of BioCNG without and with biogas upgrading are 53.8 bar and 215 bar respectively. Minimum total cost per energy of decentralised BioCNG supply chain is 3.57 USD/GJ while that of centralised BioCNG supply chain is 3.64 USD/GJ. Decentralised production pathway was found to be more economically effective compared to centralised production at the study area of Johor. To achieve a 20 % greenhouse gas (GHG) emission reduction, energy mix with a combination of NG from natural gas grid extension, BioCNG production with upgrading and coal is required for the demand locations considered. BioCNG production with upgrading is a cost effective mitigation method on GHG emission reduction. The optimum energy mix not only has lower emission level than baseline but also reduces the total energy supply cost by 19.1 %

O problema do caminho mais curto com restrições de capacidade

Mestrado em Matemática e AplicaçõesNeste trabalho estuda-se o problema do caminho mais curto com capacidades (PCMCRC). O PCMCRC é uma variante do problema do caminho mais curto onde existe uma restrição de capacidade associada aos arcos. Este problema tem variadas aplicações, nomeadamente na área das telecomunicações e no planeamento de rotas de veículos. Na sua forma geral o PCMCRC é NP-difícil. É feita uma descrição do problema, uma breve referência às principais técnicas de resolução e é proposto um novo algoritmo heurístico baseado na relaxação da restrição de capacidade. É efectuado um estudo computacional com o objectivo de identificar as instâncias mais difíceis do PCMCRC e, também, de testar o novo algoritmo.This work studies the shortest path problem with capacities (SPPC). The SPPC is a variation of the shortest path problem, where there is a capacity constraint associated with the arcs. This problem has multiple applications in areas such as telecommunications and traffic routing planning. In it’s general form, it’s a NP-hard problem. It is made a description of the problem, a slight reference to the main resolution techniques, and it’s proposed a new heuristic algorithm, based on the relaxation of the capacity constraint. It is reported a computational study in order to identify the hard instances for the SPPC and in order to test the new algorithm

Problemas do caminho mais curto com restrições adicionais

Mestrado em Matemática e AplicaçõesNeste trabalho estudam-se problemas do caminho mais curto com restrições adicionais. Este tipo de problemas tem variadas aplicações práticas onde é destacado o planeamento de rotas de veículos e o encaminhamento de mensagens em redes de comunicações. O problema de caminho mais curto com restrições adicionais tem tido também grande aplicação como sub-problema de outros problemas. É o caso do problema de caminho mais curto com janelas temporais que surge como sub-problema do problema de determinação de rotas de veículos com janelas temporais. É feita uma descrição das várias variantes do problema de caminho mais curto com restrições adicionais, é apresentada uma revisão da literatura sobre os métodos usados na resolução deste tipo de problemas e são descritas algumas aplicações deste tipo de problemas.In this work, shortest path problems with additional constraints are studied. This type of problems has several practical applications, such as vehicle routing planning and the routing of messages in communications networks. The shortest path problem with additional constrains has also application as a sub-problem of other problems. This is the case of the shortest path problem with time windows occurring as sub-problem of the vehicle routing problem with time windows. We present a description of several variants of the shortest path problem with additional constraints, a short literature review on resolution methods for these problems and a description of some applications of this type of problems

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Improving & applying single-cell RNA sequencing

The cell is the fundamental building block of life. With the advent of single-cell RNA sequencing (scRNA-seq), we can for the first time assess the transcriptome of many individual cells. This has profound implications for biological and medical questions and is especially important to characterize heterogeneous cell populations and rare cells. However, the technology is technically and computationally challenging as complementary DNA (cDNA) needs to be generated and amplified from minute amounts of mRNA and sequenceable libraries need to be efficiently generated from many cells. This requires to establish different protocols, identify important caveats, benchmark various methods and improve them if possible. To this end, we analysed amplification bias and its effect on detecting differentially expressed genes in several bulk and a single-cell RNA sequencing methods. We found that correcting for amplification bias is not possible computationally but improves the power of scRNA-seq considerably, though neglectable for bulk-RNA-seq. In the second study we compared six prominent scRNA-seq protocols as more and more single-cell RNA-sequencing are becoming available, but an independent benchmark of methods is lacking. By using the same mouse embryonic stem cells (mESCs) and exogenous mRNA spike-ins as common reference, we compared six important scRNA-seq protocols in their sensitivity, accuracy and precision to quantify mRNA levels. In agreement with our previous study, we find that the precision, i.e. the technical variance, of scRNA-seq methods is driven by amplification bias and drastically reduced when using unique molecular identifiers to remove amplification duplicates. To assess the combined effects of sensitivity and precision and to compare the cost-efficiency of methods we compared the power to detect differentially expressed genes among the tested scRNA-seq protocols using a novel simulation framework. We find that some methods are prohibitively inefficient and others show trade-offs depending on the number of cells per sample that need to be analysed. Our study also provides a framework for benchmarking further improvements of scRNA-seq protocol and we published an improved version of our simulation framework powsimR. It uniquely recapitulates the specific characteristics of scRNA-seq data to enable streamlined simulations for benchmarking both wet lab protocols and analysis algorithms. Furthermore, we compile our experience in processing different types of scRNA-seq data, in particular with barcoded libraries and UMIs, and developed zUMIs, a fast and flexible scRNA-seq data processing software overcoming shortcomings of existing pipelines. In addition, we used the in-depth characterization of scRNA-seq technology to optimize an already powerful scRNA-seq protocol even further. According to data generated from exogenous mRNA spike-ins, this new mcSCRB-seq protocol is currently the most sensitive scRNA-seq protocol available. Single-cell resolution makes scRNA-seq uniquely suited for the understanding of complex diseases, such as leukemia. In acute lymphoblastic leukemia (ALL), rare chemotherapy-resistant cells persist as minimal residual disease (MRD) and may cause relapse. However, biological mechanisms of these relapse-inducing cells remain largely unclear because characterisation of this rare population was lacking so far. In order to contribute to the understanding of MRD, we leveraged scRNA-seq to study minimal residual disease cells from ALL. We obtained and characterised rare, chemotherapy-resistant cell populations from primary patients and patient cells grown in xenograft mouse models. We found that MRD cells are dormant and feature high expression of adhesion molecules in order to persist in the hematopoietic niche. Furthermore, we could show that there is plasticity between resting, resistant MRD cells and cycling, therapy-sensitive cells, indicating that patients could benefit from strategies that release MRD cells from the niche. Importantly, we show that our data derived from xenograft models closely resemble rare primary patient samples. In conclusion, my work of the last years contributes towards the development of experimental and computational single-cell RNA sequencing methods enabling their widespread application to biomedical problems such as leukemia