High adherence of oral streptococcus to polylactic acid might explain implant infections associated with PLA mesh implantation

Abstract The aim of this study was to evaluate and compare the biofilm formation properties of common pathogens associated with implant-related infections on two different implant material types. Bacterial strains tested in this study were Staphylococcus aureus, Streptococcus mutans, Enterococcus faecalis, and Escherichia coli. Implant materials tested and compared were PLA Resorb × polymer of Poly DL-lactide (PDLLA) comprising 50% poly-L-lactic acid and 50% poly-D-lactic acid) and Ti grade 2 (tooled with a Planmeca CAD-CAM milling device). Biofilm assays were done with and without saliva treatment to evaluate the effect of saliva on bacterial adhesion and to mimic the intraoral and extraoral surgical routes of implant placement, respectively. Five specimens of each implant type were tested for each bacterial strain. Autoclaved material specimens were first treated with 1:1 saliva-PBS solution for 30 min, followed by washing of specimens and the addition of bacterial suspension. Specimens with bacterial suspension were incubated for 24 h at 37 °C for biofilm formation. After 24 h, non-adhered bacteria were removed, and specimens were washed, followed by removal and calculation of adhered bacterial biofilm. S. aureus and E. faecalis showed more attachment to Ti grade 2, whereas S. mutans showed higher adherence to PLA in a statistically significant manner. The salivary coating of specimens enhanced the bacterial attachment by all the bacterial strains tested. In conclusion, both implant materials showed significant levels of bacterial adhesion, but saliva treatment played a vital role in bacterial attachment, therefore, saliva contamination of the implant materials should be minimized and considered when placing implant materials inside the body

B4GALNT3 regulates glycosylation of sclerostin and bone mass

Summary Background: Global sclerostin inhibition reduces fracture risk efficiently but has been associated with cardiovascular side effects. The strongest genetic signal for circulating sclerostin is in the B4GALNT3 gene region, but the causal gene is unknown. B4GALNT3 expresses the enzyme beta-1,4-N-acetylgalactosaminyltransferase 3 that transfers N-acetylgalactosamine onto N-acetylglucosaminebeta-benzyl on protein epitopes (LDN-glycosylation). Methods: To determine if B4GALNT3 is the causal gene, B4galnt3−/− mice were developed and serum levels of total sclerostin and LDN-glycosylated sclerostin were analysed and mechanistic studies were performed in osteoblast-like cells. Mendelian randomization was used to determine causal associations. Findings: B4galnt3−/− mice had higher circulating sclerostin levels, establishing B4GALNT3 as a causal gene for circulating sclerostin levels, and lower bone mass. However, serum levels of LDN-glycosylated sclerostin were lower in B4galnt3−/− mice. B4galnt3 and Sost were co-expressed in osteoblast-lineage cells. Overexpression of B4GALNT3 increased while silencing of B4GALNT3 decreased the levels of LDN-glycosylated sclerostin in osteoblast-like cells. Mendelian randomization demonstrated that higher circulating sclerostin levels, genetically predicted by variants in the B4GALNT3 gene, were causally associated with lower BMD and higher risk of fractures but not with higher risk of myocardial infarction or stroke. Glucocorticoid treatment reduced B4galnt3−/− expression in bone and increased circulating sclerostin levels and this may contribute to the observed glucocorticoid-induced bone loss. Interpretation: B4GALNT3 is a key factor for bone physiology via regulation of LDN-glycosylation of sclerostin. We propose that B4GALNT3-mediated LDN-glycosylation of sclerostin may be a bone-specific osteoporosis target, separating the anti-fracture effect of global sclerostin inhibition, from indicated cardiovascular side effects

Archival analysis of slash-and-burn agriculture in the Northern Ural Mountains at the end of the nineteenth century

Abstract At the end of the nineteenth century, the northern territories of the Russian Plain and western piedmont of Northern Ural Mountains were under various land-use systems, including slash-and-burn (SAB) agriculture. Using archival materials for the years 1880–1910 as data sources, we analyse the the location and extent of SAB agriculture, it timing, and its impact on the landscapes at the turn of the nineteenth and twentieth centuries, and present a review of the historical and ethnographic literature. The study area is in the western piedmont of the Northern Ural Mountains, between the Kama and Pechora rivers in dark conifer dominated forests. The population is sparse (14 people per 100 km² in 1900) and settled along the rivers. In 1885 the practice of SAB agriculture was totally prohibited, but the unusually large crop harvest during the first years of the ban led people to risk breaking the law. Between the years 1885 and 1894 a total number of 175 SAB cases was recorded in the study area. The SAB sites were used only once before being abandoned. The recorded plots were usually cultivated by one family. They small (0.03 ha to 2.70 ha, with a median of 0.55 ha) and located within 7.5 km of the settlements. The practice SAB agriculture led to the formation of mosaics of multiple-aged pyrogenic forest associations near the settlements

A 5‑year suicide rate of adolescents who enrolled to an Open Dialogue‑based services:a nationwide longitudinal register‑based comparison

Abstract In the Open Dialogue (OD) based psychiatric services adolescent patients receive less medication and are more often treated within an outpatient setting as compared to standard services. An evaluation of the possible risks of implementing OD are required. The aim of this longitudinal register-based study was to evaluate how treatment under OD is associated with the probability of suicide as compared standard psychiatric care. Study included all 13- to 20-year-old adolescents who enrolled to a psychiatric service in Finland in 2003–2013. The OD-group included adolescents whose treatment commenced in the Western Lapland area (n = 2107), this being the only region where OD covered all psychiatric services. The comparison group (CG) included rest of Finland (n = 121,658). Information was gathered from onset of treatment to the end of the 5-year follow-up or death. In a multivariate Cox regression there were no statistically significant differences in 5-year suicide hazard ratios between OD and CG

An analytical approach to converting vibration signal to combustion characteristics of homogeneous charge compression ignition engines

Abstract Homogeneous charge compression ignition (HCCI) is a promising low-temperature combustion technique for low-emission internal combustion engines. Unlike conventional engines, HCCI lacks a direct ignition control mechanism, necessitating closed-loop combustion control. This study proposes a phenomenological-based, cost-effective, and non-intrusive approach using vibration data analysis to determine essential combustion parameters. Experiments were conducted on a single-cylinder research engine with an accelerometer attached to the engine head. The engine operation envelope covered the whole engine’s operating area in naturally aspirated HCCI mode. Wavelet analysis revealed that combustion-related frequencies centered around 500 Hz, independent of operating conditions. The correlation-seeking analysis included peak acceleration amplitude and its crank angle with peak heat release rate (HRR) data. The peak HRR location was accurately identified within one degree when vibration amplitude exceeded the 100 m/s2 threshold. This encompassed 98.5% of the analyzed combustion cycles. The peak HRR prediction accuracy had a maximum error below 21% and was suitable to monitor reaction rates, especially in incomplete combustion and high ringing cycles

Cloud condensation nuclei activation properties of Mediterranean pollen types considering organic chemical composition and surface tension effects

Abstract Wind-dispersed pollen grains emitted from vegetation are directly injected into the atmosphere being an important source of natural aerosols globally. These coarse particles of pollen can rupture into smaller particles, known as subpollen particles (SPPs), that may act as cloud condensation nuclei (CCN) and affect the climate. In this study, we characterize and investigate the ability of SPPs of 10 Mediterranean-climate pollen types to activate as CCN. A continuous flow CCN counter (CCNC) was used to measure the activation of size-selected (80, 100 and 200 nm dry mobility diameter) particles at different supersaturations (SS). Hygroscopicity parameter (κ) for each SPP type and size has been calculated using κ-Köhler theory. Organic chemical speciation and protein content has been determined to further characterize pollen solutions. Furthermore, the surface activity of SPPs has also been investigated by using pendant drop tensiometry. All studied SPP samples show critical supersaturation (SSCrit) values that are atmospherically relevant SS conditions. Hygroscopicity κ values are in the range characteristic of organic compounds (0.1–0.3). We found that organic speciation and protein content vary substantially among pollen types, with saccharides and fatty acids being the only organic compounds found in all pollen types. A clear relationship between SPP activation and its organic composition was not observed. This study also reveals that all SPPs investigated reduce the surface tension of water at high concentrations but at diluted concentrations (such as those of activation in the CCNC), the water surface tension value is a good approximation in Köhler theory. Overall, this analysis points out that pollen particles might be an important source of CCN in the atmosphere and should be considered in aerosol-cloud interactions processes

Assessing mental disorders with digital biomarkers

Abstract Mental disorders such as depression and anxiety significantly contribute to the global disease burden. The World Health Organization estimates that mental disorders affect one in eight people globally. Mental disorders lead to adverse health outcomes and have a direct cost impact on society. Despite the availability of effective therapy and medication, a key challenge in diagnosing, monitoring and treating mental disorders is the inadequacy of assessment methods. This article-based doctoral thesis develops and investigates the feasibility of tools leveraging smartphones and wearables, statistics and machine learning technology to augment the traditional methods of mental disorder care. We developed a smartphone-based application for passive and active data collection leveraging embedded smartphone sensors and a data analysis and behaviour modelling pipeline for quantifying digital biomarkers from smartphone data, predictive analysis, and monitoring mental disorder symptoms. We found statistically significant differences in digital biomarkers and moods of people with and without symptoms of depression. We found a statistically significant relationship between digital biomarkers, mood, and symptoms of depression and anxiety. We show that digital biomarkers and mood can predict symptoms of depression, and that it is feasible to passively monitor fluctuations in mental disorder symptoms to inform clinical decisions. The key findings in this thesis show the feasibility of augmenting the current mental disorder care methods with evidence-based and continuous assessment of symptoms in general and clinical populations in everyday life. The tools developed in this thesis could be tailored for various mental disorders such as schizophrenia, post-traumatic stress disorder, bipolar disorder, and anomalous human behaviours such as sleep disorders, sedentary behaviours and problematic smartphone use. Collaborating with public health policymakers and clinicians, we see the potential to impact mental disorder care with just-in-time clinical interventions based on automated early detection of mental disorders and flagging deterioration of mental disorder symptoms.Tiivistelmä Mielenterveyden häiriöt, kuten masennus ja ahdistuneisuus, vaikuttavat merkittävästi maailman sairausrasitteeseen. Maailman terveysjärjestö arvioi, että mielenterveyden häiriöistä kärsii maailmanlaajuisesti yksi kahdeksasta ihmisestä. Mielenterveyden häiriöt johtavat haitallisiin terveysvaikutuksiin, ja niillä on suora taloudellinen vaikutus yhteiskuntaan. Tehokkaiden hoitojen ja lääkkeiden saatavuudesta huolimatta mielenterveyden häiriöiden diagnosoinnissa, seurannassa ja hoidossa keskeisenä haasteena on arviointimenetelmien riittämättömyys. Tämä artikkeliperustainen väitöskirja kehittää ja tutkii älypuhelinten ja älyrannekkeiden, tilastotieteen ja koneoppimisteknologian hyödyntämisen mahdollisuuksia perinteisten mielenterveyden häiriöiden hoitomenetelmien täydentämisessä. Kehitimme älypuhelinsovelluksen, joka kerää tietoa passiivisesti ja aktiivisesti puhelimen sisäisten sensorien avulla. Sovellus analysoi keräämänsä tiedot käyttäytymismallinnusta hyödyntäen. Kehitetyn sovelluksen avulla pystyttiin määrittämään älypuhelinten keräämästä datasta digitaalisia biomarkkereita, suorittamaan ennakoivaa analyysiä ja seuraamaan mielenterveyden häiriöiden oireita. Havaitsimme digitaalisissa biomarkkereissa ja ihmisten mielialoissa tilastollisesti merkittäviä eroja vertailtaessa ajanjaksoja, jolloin henkilöt kokivat tai eivät kokeneet masennuksen oireita. Löysimme myös tilastollisesti merkittävän yhteyden digitaalisten biomarkkereiden, mielialojen sekä masennuksen ja ahdistuksen oireiden välillä. Osoitamme, että digitaaliset biomarkkerit ja mieliala voivat ennustaa masennuksen oireita, ja mielenterveysoireiden vaihtelun passiivinen seuranta on toteutettavissa kliinisten päätösten tueksi. Tämän väitöskirjan keskeiset tulokset osoittavat, että nykyisiä mielenterveyden häiriöiden hoitomenetelmiä voidaan täydentää näyttöön perustuvalla ja jatkuvalla arkielämän oireiden arvioinnilla yleisessä ja kliinisessä väestössä. Tässä väitöskirjassa kehitetyt työkalut voidaan räätälöidä eri mielenterveyden häiriöihin, kuten skitsofreniaan, traumaperäiseen stressihäiriöön, kaksisuuntaiseen mielialahäiriöön sekä poikkeuksellisiin käyttäytymismalleihin, kuten unihäiriöihin, istumatyön aiheuttamiin käyttäytymismuutoksiin ja ongelmalliseen älypuhelimen käyttöön. Yhteistyössä kansanterveyspäättäjien ja kliinikoiden kanssa näemme potentiaalia vaikuttaa mielenterveyden häiriöiden hoitoon mahdollistamalla juuri oikea-aikaiset kliiniset toimenpiteet mielenterveyden häiriöiden varhaisen havaitsemisen ja mielenterveysoireiden pahentumisen automaattisen havaitsemisen avulla

Highly efficient export of a disulfide-bonded protein to the periplasm and medium by the Tat pathway using CyDisCo in Escherichia coli

Abstract High-value heterologous proteins produced in Escherichia coli that contain disulfide bonds are almost invariably targeted to the periplasm via the Sec pathway as it, among other advantages, enables disulfide bond formation and simplifies downstream processing. However, the Sec system cannot transport complex or rapidly folding proteins, as it only transports proteins in an unfolded state. The Tat system also transports proteins to the periplasm, and it has significant potential as an alternative means of recombinant protein production because it transports fully folded proteins. Most of the studies related to Tat secretion have used the well-studied TorA signal peptide that is Tat-specific, but this signal peptide also tends to induce degradation of the protein of interest, resulting in lower yields. This makes it difficult to use Tat in the industry. In this study, we show that a model disulfide bond-containing protein, YebF, can be exported to the periplasm and media at a very high level by the Tat pathway in a manner almost completely dependent on cytoplasmic disulfide formation, by other two putative Tat SPs: those of MdoD and AmiC. In contrast, the TorA SP exports YebF at a low level

Speech separation algorithm using gated recurrent network based on microphone array

Abstract Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position information. Since the GRU network has the advantage of processing time series data with faster training speed and fewer training parameters, the GRU model is adopted to process the separation features of several sequential frames in the same sub-band to estimate the ideal Ratio Masking (IRM). The proposed algorithm decomposes the mixture signals into time-frequency (TF) units using gammatone filter bank in the frequency domain, and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM. The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost. Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech separation in noisy and reverberant environments, provides good performance in terms of speech quality and intelligibility, and has the generalization capacity to reverberate

Oral exposure to thiacloprid-based pesticide (Calypso SC480) causes physical poisoning symptoms and impairs the cognitive abilities of bumble bees

Abstract Background: Pesticides are identified as one of the major reasons for the global pollinator decline. However, the sublethal effects of pesticide residue levels found in pollen and nectar on pollinators have been studied little. The aim of our research was to study whether oral exposure to the thiacloprid levels found in pollen and nectar affect the learning and long-term memory of bumble bees. We tested the effects of two exposure levels of thiacloprid-based pesticide (Calypso SC480) on buff-tailed bumble bee (Bombus terrestris) in laboratory utilizing a learning performance and memory tasks designed to be difficult enough to reveal large variations across the individuals. Results: The lower exposure level of the thiacloprid-based pesticide impaired the bees’ learning performance but not long-term memory compared to the untreated controls. The higher exposure level caused severe acute symptoms, due to which we were not able to test the learning and memory. Conclusions: Our results show that oral exposure to a thiacloprid-based pesticide, calculated based on residue levels found in pollen and nectar, not only causes sublethal effects but also acute lethal effects on bumble bees. Our study underlines an urgent demand for better understanding of pesticide residues in the environment, and of the effects of those residue levels on pollinators. These findings fill the gap in the existing knowledge and help the scientific community and policymakers to enhance the sustainable use of pesticides