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Antti Häkkinen lähestyy artikkelissaan prostituutiota Suomessa kolmesta näkökulmasta. Ensimmäiseksi hän luo kuvanprostituoiduista henkilöinä ja ihmisinä kertoen heidän taustansa, integroitumisensa, elämänsä prostituoituina ja prostituution jälkeen. Toiseksi hän käsittelee prostituutiota epävirallisena taloutena ja eräänlaisena alakulttuurina. Näin hän esittelee sen prostituution toimintalogiikan, jota määrittävät ulkopuoliset kontrollit ja paineet, joustavat sosiaaliset verkostot ja raha. Tämä tarkastelu sijoittuu suurelta osin historialliseen Helsinkiin. Kolmanneksi Häkkinen hahmottaa vielä sitä diskurssia, joka suomalaisessa lehdistössä on prostituutiosta esiintynyt. Framing theory -metodin avulla hän tavoittelee sen logiikkaa ja diskurssissa eri aikoina esiintyneitä trendejä.

Motivation RNA sequencing and other high-throughput technologies are essential in understanding complex diseases, such as cancers, but are susceptible to technical factors manifesting as patterns in the measurements. These batch patterns hinder the discovery of biologically relevant patterns. Unbiased batch effect correction in heterogeneous populations currently requires special experimental designs or phenotypic labels, which are not readily available for patient samples in existing datasets. Results We present POIBM, an RNA-seq batch correction method, which learns virtual reference samples directly from the data. We use a breast cancer cell line dataset to show that POIBM exceeds or matches the performance of previous methods, while being blind to the phenotypes. Further, we analyze The Cancer Genome Atlas RNA-seq data to show that batch effects plague many cancer types; POIBM effectively discovers the true replicates in stomach adenocarcinoma; and integrating the corrected data in endometrial carcinoma improves cancer subtyping. Availability and implementation https://bitbucket.org/anthakki/poibm/ (archived at https://doi.org/10.5281/zenodo.6122436). Supplementary information Supplementary data are available at Bioinformatics online.

The aim of this work is to study changes in the properties of aqueous corundum suspensions during ultrafine grinding with and without the addition of an activator. Corundum suspensions were prepared in distilled water in a ratio of 1:5 with and without the addition of triethanolamine (TEA) and ground using a planetary ball mill. Particle-size distribution, ζ-potential, rheological behavior and turbidity were then measured. TEA was not proved to be a suitable activator for wet grinding of corundum as finer products were obtained in grinding without it. The suspensions showed pseudoplastic behavior and the ζ-potential of all samples ranged from −30 to +30 mV, so the suspensions were unstable. During grinding, the originally negative ζ-potential was converted to positive values and the viscosity and turbidity of the samples increased.

Understanding the reactions taking place in the hematite-oxalic acid system is important in order to clean iron oxides from filters and to remove iron from mineral concentrates. Previous studies reported the formation of an unwanted solid phase during this process. The objective of the current work, therefore, was to visualize and rationalize the iron dissolution steps taking place in the hematite–oxalic acid reaction by combining density functional theory (DFT) calculations and experimental data. The results of DFT calculations indicated that a precipitate was formed in this reaction; XRD analysis of the solid phase after the dissolution experiment revealed the formation of humboldtine as the precipitate. The attachment of oxalate on the hematite surface and the reduction of Fe(III) to Fe(II) were key steps for humboldtine formation. Both simulations and the experimental results showed that greater oxalic acid concentrations yielded more precipitate, suggesting a simple and novel route to synthesize humboldtine, a material which is relevant to the demand for clean energy.

Mutations in hundreds of genes have been associated with formation of human cancer, with different oncogenic lesions prevalent in different cancer types. Yet, the malignant phenotype is simple, characterized by unrestricted growth of cells that invade neighboring healthy tissue and in many cases metastasize to distant organs. One possible hypothesis explaining this dichotomy is that the cancer genes regulate a common set of target genes, which then function as master regulators of essential cancer phenotypes, such as growth, invasion and metastasis. To identify mechanisms that drive the most fundamental feature shared by all tumors – unrestricted cell proliferation – we used a multiomic approach to identify common transcriptional and posttranslational targets of major oncogenic pathways active in different cancer types, and combined this analysis with known regulators of the cell cycle. We identified translation and ribosome biogenesis as common targets of both transcriptional and posttranslational oncogenic pathways. By combining proteomic analysis of clinical samples with functional studies of cell cultures, we also establish NOLC1 as a key node whose convergent regulation both at transcriptional and posttranslational level is critical for tumor cell proliferation. Our results indicate that lineage-specific oncogenic pathways commonly regulate the same set of targets important for growth control, revealing novel key downstream nodes that could be targeted for cancer therapy or chemoprevention.

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we characterized HGSC evolutionary states using whole-genome sequencing data from 214 samples of 55 HGSC patients in the prospective, longitudinal, multiregion DECIDER study. Comparison of the tissues revealed that site-of-origin samples have 70% more unique clones than the metastatic tumors or ascites. By integrating clonal composition and topology of HGSC tumors we discovered three evolutionary states that represent a continuum from genomically highly variable to stable tumors with significant association to treatment response. The states and their evolutionary trajectories were characterized by unique, targetable pathways, which were validated with RNA-seq data. Our study reveals that genomic heterogeneity is unaffected by the current standard-of-care and pinpoints effective treatment targets for each group. All genomics data are available via an interactive visualization platform for rapid exploration.

Dysregulated DNA methylation (DNAm) is a hallmark of all cancer types, though the precise role of these aberrations in tumor progression and treatment response remains elusive. Advances in whole-genome bisulfite sequencing (WGBS) have enabled profiling methylomes at full resolution and stimulated interest to explore dynamics of DNAm across tumor sites and cancer treatment. A major challenge in the analysis of patient data is that a surgically removed tumor sample represents a mixture of cancer cells and microenvironment, and that the insights on DNAm changes are intertwined due to mixed effects from patient epigenomic background, tissue context, and treatment pressure. By using a computational approach, these effects can be decomposed into patient background, tissue site, and treatment phase specific changes, and controlled for natural microenvironment variation. retion, and action, as well as in several oncogenic signaling cascades. The analysis of the tissue profiles indicated that the progression of HGSC from the tissues-of-origin to the metastatic deposits is accompanied by drastic changes in DNAm profiles. Thus, when comparing ovaries and peritoneum, intra-abdominal tissues mesentery and omentum featured prominent change of promoter DNAm: loss in 10% and gain in 20% of genes. Interestingly, the genes with aberrant DNAm included ABC transporters implicated in pumping drugs across the cell membrane, as well as the members of MAPK and PI3K signaling cascades. The joint analysis of both treatment and tissue contributions suggests that the overall modest effect of NACT is further reduced in metastases, mediated via DNAm changes in genes involved in multidrug resistance. Citation Format: Alexandra Lahtinen, Giovanni Marchi, Daria Afenteva, Susanna Holmström, Kaisa Huhtinen, Anni Virtanen, Johanna Hynninen, Antti Häkkinen, Sampsa Hautaniemi. Characterizing dynamic landscape of DNA methylation in ovarian high-grade serous carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6002.

Motivation A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. Results To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. Availabilityand implementation https://bitbucket.org/anthakki/prism. Supplementary information Supplementary data are available at Bioinformatics online.

Dissolution mechanisms and kinetics have a key role in better understanding of processes. In this work, magnetite and hematite powder were dissolved in oxalic and nitric acid mixtures at different temperatures. Higher temperature and higher amounts of oxalic acid in the system accelerated the dissolution kinetics but did not result in higher solubility levels. Oxalic acid had also drawbacks in the process since higher amounts in the system promoted formation of a solid product, humboldtine (Fe(II)C2O4∙2H2O), which, in turn, inhibited the dissolution. This problem may be overcome by adding even a small amount of nitric acid to the system. Kinetic analysis showed, in the variable-rate-controlling step, that two linear fits of the Kabai model described the dissolution better in an oxalic acid and acid mixture of 70/30. Thermodynamic data and special cubic models showed that the nitric acid concentration had a significant role in the solubility, whereas the concentration of oxalic acid had only minor effects on solubility. The results also showed that measuring the oxalate and nitrate concentrations did not provide additional information about the dissolution mechanism itself. The pH, however, might be a tool for following the extent of dissolution, even though it is not a direct indicator of the dissolution mechanism.

Motivation Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. Results We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. Availabilityand implementation The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. Supplementary information Supplementary data are available at Bioinformatics online.

The dissolution of iron oxides in mixtures of acids is fairly uncommon but can result in a more efficient dissolution process. The objective in this work was to investigate the dissolution of synthetic hematite powder in mixtures of oxalic and sulfuric acid. Experiments were done at different acid ratios and temperatures. An increase in temperature from 15 to 35 °C increased solubility, whereas an increase from 35 to 50 °C did not change the solubility but had a profound effect on the kinetics. An important finding was that oxalic acid advanced the dissolution process since increasing the amount of oxalic acid in the system resulted in faster kinetics and higher solubilities. The dissolution kinetics were well described with the Kabai model, which was the only studied model able to describe the whole reaction time. However, the solid specific constant a varied for the different acid ratios and this is argued to be a result of changes in the solid phase. The changes in the constant a were not in line with the original study of Kabai, which indicates that a cannot be the solid specific constant but it can be the constant connected to dissolving media describing the changes in the dissolution mechanism.

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent unmet challenge in oncology. To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, we prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, we found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer–associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, we have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene’s numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

Pore size distribution is one of the most important structural characteristics of filter cloths and can, in theory, be used to predict filtration performance. Capillary flow porometry is a common method to determine the pore diameter(s) and the pore size distribution of a porous material. Equipment applying this method has many advantages, but data processing and further calculations still require refining to be fit for purpose in calculations of flow capacity. The present study aims to develop a new procedure for processing volumetric flow rate data and corresponding pressure, which are measured by capillary flow porometry. The processed data are further used to calculate the pore size distribution of textile materials. Six industrial samples of polypropylene filter cloth were examined using scanning electron microscopy and capillary flow porometry. The raw data measured by the capillary flow porometer were processed using the developed method that considers changes in flow regimes as pressure increases. In addition, the nonideality of pores, originating from tortuosity, an irregular cross-sectional shape of the pore, and the number of pores, were considered in calculations by combining all these characteristics in one single parameter. This work shows that pores in a textile material are not ideal cylinders. The standard pore size distribution calculations should consider an imperfect pore structure and, therefore, be corrected.

Each patient’s cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

The influence of the slurry pH on the characteristics of a topical unit operation, pressure filtration of bauxite residue slurries, was investigated in this experimental study. The primary aim of pH adjustment is to precipitate aluminate off from the liquid phase and to facilitate safe disposal of the residue. In the investigated cases, pH adjustment was performed with hydrochloric acid. The experiments were carried out by using two types of pressure filters, a Nutsche filter unit used for the acquisition of average filtration data, and a piston press used for measuring local cake characteristics. The Nutsche filter was used to separate the bauxite residue slurry with a pH of 13.3 and 11.0 at three different filtration pressures (300, 450 and 600 kPa), while the piston press was operated under a constant filtration pressure of 1200 kPa and with a wider pH range (13.3, 11.0 and 7.0). The average particle/agglomerate size increased as the pH was reduced, however, the width of particle size distribution increased somewhat as well when the pH was adjusted to 7.0. Based on the average cake properties, it could be concluded that the specific cake resistance and cake compressibility index increased, while cake solidosity decreased as the slurry pH was reduced from 13.3 to 11.0. The final average solidosity (at 300 kPa) decreased from 0.43 to 0.31 when the pH was adjusted from 13.3 to 11.0. The local filtration properties also showed that the cake became more compressible and the structure of the cake more porous as a consequence of pH reduction. The local hydrostatic pressure measurements indicated that the expression rate of the cakes was decreased for filter cakes formed from a slurry with a lower pH level (pH 11 and 7 compared to a pH of 13.3). Post-print / Final draft

Horizontal vacuum belt filters are used for continuous solid–liquid separation in a wide variety of industrial processes. Despite the low pressure difference (usually Δp

The main purposes of this research work were to realize the potential of sludge valorization by converting the sludge into adsorbents and to investigate the performance of sludge-based adsorbents for rare earth elements (REEs) recovery from dilute aqueous solutions. Hydrochloric acid and Fenton reagents were employed as pretreatment procedures to enhance the surface area and adsorption yield. Following this, sludge-based alginate beads were prepared by using HCl treated (HT) and Fenton treated (FT) sludge materials. The chemical composition and surface chemistry of sludge-based adsorbent beads were thoroughly analyzed by state-of-the-art analytical techniques, such as Scanning Electron Microscope (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Brunauer-Emmett-Teller (BET) analysis. The prepared beads were then investigated for the recovery of REEs. The batch adsorption studies revealed that the FT sludge beads performed better in comparison to the HT sludge beads. Among REEs, the HT- and FT-sludge beads demonstrated higher affinity towards Sm3+ ions, displaying the maximum adsorption capacities of 2.83 mg/g and 4.16 mg/g, respectively, in a multi-component system (C o = 25 ppm of each REE; pH = 5, t = 24 h and dosage = 5 g/L). In conclusion, the results from this work showed that the prepared sludge-based alginate beads can be used for REEs recovery from diluted waste streams and the tested sludge treatment options were also found effective in converting the waste to resource i.e., sludge to adsorbent for REEs recovery.

This study focuses on characterisation of side streams including biomass fly ash, biomass bottom ash, coal fly ash, green liquor dregs, limestone mine tailings, and electric arc furnace steel slag from different industrial locations in Finland. It was found that the fly ash samples contained the highest Al2O3 and SiO2 concentrations, a large number of spherical particles of small sizes and high specific surface areas. Fly ashes and steel slag were observed to contain higher amounts of amorphous phases compared to the other side streams. The high loss on ignition value of the coal fly ash and green liquor dregs was found to exceed the limitations for their application in geopolymer composites. Despite their relatively high concentrations in ashes and steel slag, the leaching tests have shown that no hazardous metal leached out from the streams. Finally, test specimens of geopolymer composites (GP2) were prepared by the application of biomass fly ash, bottom ash, and limestone mine tailings without any pre-treatment process, in addition to the ordinary Portland cement-(R) and metakaolin-based geopolymer composites (GP1). The measured compressive (14.1 MPa) and flexural strength (3.5 MPa) of GP2 suggest that it could be used in concrete kerbs and paving flags. The data has also shown that over 500% of the compressive strength was developed between 7 and 28 days in GP2, whereas in the case of reference concrete (R) and the metakaolin-based geopolymer composite (GP1) it was developed in the first 7 days.

This research explores the influence of fly ash source, alkali concentration and temperature on the dissolution kinetics of Al and Si. The characteristics of the reaction products and the solid residue were evaluated with ICP-MS, SEM-EDS, and quantitative XRD. It was found that Al dissolution occurs faster with a higher yield compared to Si for both ash types. The temperature and the NaOH concentration had a similar effect on the dissolution of Al, while the dissolution of Si was influenced more by the NaOH concentration. The simultaneous dissolution of aluminosilicates was achieved by keeping the temperature at 30 °C, where the precipitation of Al was prevented, and the concentration of the dissolved Si was close to its maximum value. The Kabai Model was the most suitable to explain the dissolution of Al for both ash sources, while the Si dissolution was described with 1-dimensional diffusion, Jander and Ginstling-Brounsthein models. Post-print / Final draft

Aims: socio-economic conditions in early life are important contributors to cardiovascular disease – the leading cause of mortality globally – in later life. We studied coronary heart disease (CHD) and stroke in adulthood among people born out of wedlock in two historical periods: before and during World War II in Finland. Methods: we compared offspring born out of wedlock before (1934–1939) and during (1940–1944) World War II with the offspring of married mothers in the Helsinki Birth Cohort Study. The war affected the position of unmarried mothers in society. We followed the study subjects from 1971 to 2014 and identified deaths and hospital admissions from CHD and stroke. Data were analysed using a Cox regression, adjusting for other childhood and adulthood socio-economic circumstances. Results: the rate of out-of-wedlock births was 240/4052 (5.9%) before World War II and 397/9197 (4.3%) during World War II. Among those born before World War II, out-of-wedlock birth was associated with an increased risk of stroke (hazard ratio (HR)=1.44; 95% confidence interval (CI) 1.00–2.07) and CHD (HR=1.37; 95% CI 1.02–1.86). Among those born out of wedlock during World War II, the risks of stroke (HR=0.89; 95% CI 0.58–1.36) and CHD (HR=0.70; 95% CI 0.48=1.03) were similar to those observed for the offspring of married mothers. The p-values for interaction of unmarried×World War II were (p=0.015) for stroke and (p=0.003) for CHD. Conclusions: in a society in which marriage is normative, being born out of wedlock is an important predictor of lifelong health disadvantage. However, this may change rapidly when societal circumstances change, such as during a war.

We address challenges in longitudinal RNA-seq analysis of patient tumors, which can reveal powerful insights on cancer evolution at personalized level, proposing methods for unbiased analysis and integration Experimental procedures: 214 whole-genome bulk RNA-seq samples from 61 high-grade serous ovarian cancer (HGSC) patients were collected before and after chemotherapy, along with single-cell RNA-seq data from >93,000 cells in 11 patients. 308 TCGA treatment-naive bulk RNA-seq samples from HGSC, 474 from melanoma (SKCM), and 581 from endometrial carcinoma (UCEC) were used for validation. Results: We show that in a longitudinal analysis, cancer progression and chemotherapy exert both microenvironmental and phenotypic changes. We developed PRISM [1] to factor the expression data at individual bulk level, and show the adjustment improves the association between expression profiles and patient survival. Our findings extend to pathway and expression-derived tumor subtype levels in HGSC and SKCM. Technical batch effects pose challenges for multi-institute or long-running sample collections. Unbiased correction requires replicates, which are typically unavailable for patient data. We developed POIBM [submitted] to simultaneously infer a suitable reference and factor out the batch effects. We show that POIBM effectively discovers true replicates, batch effects plague many cancer types in TCGA data, and batch correction allows more meaningful expression subtyping in UCEC. Distinct genomic backgrounds of the patients hinders stratification and discovering shared functional states. We developed PRIMUS [submitted] to simultaneously factor the patient background (or other confounders) and a sample clustering, which is necessary when the underlying stratification is uneven or shared by patient subsets. Among the commonly aberrant cell states, such as EMT, PRIMUS analysis on HGSC scRNA identified a novel stress signature, enriched by chemotherapy and indicating poor survival, which were validated in PRISM-factored TCGA bulk ovarian data. Conclusions: Our methodology facilitates unbiased longitudinal expression analysis and integration. The more accurate phenotypic changes at gene, pathway, and expression subtype level may confer sensitivity/resistance chemotherapy, and, as shown, allow enhance patient survival prediction and discovery of novel chemotherapy related subtypes. Consequently, our work aids ranking putative intervention strategies for overcoming ovarian cancer chemoresistance for future validation experiments. [1]: Hakkinen et al., Bioinformatics 37: 2882 (2021) Citation Format: Antti Häkkinen, Kaiyang Zhang, Susanna Holmström, Sanaz Jamaldazeh, Johanna Hynninen, Sakari Hietanen, Kaisa Huhtinen, Sampsa Hautaniemi. Factoring expression data of high-grade serous ovarian cancer tumors for unbiased longitudinal analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2707.