Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins

Domain insertion engineering is a promising approach to recombine the functions of evolutionarily unrelated proteins. Insertion of light‐switchable receptor domains into a selected effector protein, for instance, can yield allosteric effectors with light‐dependent activity. However, the parameters that determine domain insertion tolerance and allostery are poorly understood. Here, an unbiased screen is used to systematically assess the domain insertion permissibility of several evolutionary unrelated proteins. Training machine learning models on the resulting data allow to dissect features informative for domain insertion tolerance and revealed sequence conservation statistics as the strongest indicators of suitable insertion sites. Finally, extending the experimental pipeline toward the identification of switchable hybrids results in opto‐chemogenetic derivatives of the transcription factor AraC that function as single‐protein Boolean logic gates. The study reveals determinants of domain insertion tolerance and yielded multimodally switchable proteins with unique functional properties

An alternative method to determine the share of fossil carbon in solid refuse-derived fuels – Validation and comparison with three standardized methods

Today different types of wastes are used as refuse-derived fuels (RDF) either in waste-to-energy plants or as fuel substitutes in energy-intensive industrial processes. In order to quantify their greenhouse-gas relevance (fossil carbon content), reliable and practical analytical methods are required, which allow differentiation between biogenic and fossil organic carbon. In the present paper, an alternative method to determine the fossil share in RDFs is examined and validated. The so-called “adapted Balance Method” (aBM) is applied to three different RDFs and the results are compared to three standardized methods, namely the Radiocarbon Method (14CMethod), the Selective Dissolution Method (SDM), and the Manual Sorting Method (MS). The aBM is based on the distinctly different elemental composition of water-and-ash-free biogenic and of fossil matter (TOXBIO and TOXFOS). Within the study, these compositional data are derived by manual sorting of the RDFs. The results show that the values obtained by the aBM are in excellent agreement with the results of the 14C-Method (considered as reference method). Mean deviations between the two methods of −0.9 to +1.9% absolute for the share of fossil carbon are found which are statistically insignificant. High trueness and reliability of the aBM can be expected, independent of the RDF type. In contrast, the reliability of the other standardized methods (SDM and MS) appears to strongly depend on the type and composition of the RDF. The results further indicate that the generation of RDF-specific data on TOXFOS is important for the aBM if significant shares of polymers with comparably high oxygen content might be present in the RDF and if low uncertainties of the results (<3% relative) are required. The findings demonstrate that the alternative method has advantages compared to standardized methods with respect to reliability and/or costs

Klimarelevanz von Ersatzbrennstoffen – Anwendung und Vergleich verschiedener Bestimmungsmethoden

Der Einsatz von alternativen Brennstoffen – wie aus Abfall hergestellte Ersatzbrennstoffe (EBS) – kann in industriellen thermischen Verwertungsanlagen (beispielsweise in Zementwerken) neben der Einsparung von Primärrohstoffen auch zu einer Reduzierung der klimarelevanten CO2-Emissionen führen. Um diese CO2-Einsparungen nachzuweisen, bedarf es einer Methode, die der Heterogenität der Abfallgemische gerecht wird und den fossilen Kohlenstoffanteil bzw. den fossilen CO2-Emissionsfaktor der EBS zuverlässig bestimmen lässt. Die Studie befasst sich mit der Erprobung einer alternativen Bestimmungsmethode, der sogenannten adaptierten Bilanzenmethode (aBM), die auf der Bestimmung der Elementarzusammensetzung des EBS beruht. Insgesamt wurden sechs verschiedene EBS auf ihre Klimarelevanz mittels aBM untersucht und mit Ergebnissen standardisierter Verfahren verglichen. Dabei zeigte sich eine sehr gute Übereinstimmung der aBM-Werte mit denen der Radiokarbonmethode (14C-Methode), die als Vergleichsmethode herangezogen werden kann. Die mittlere Abweichung von der 14C-Methode lag bei 0,6 ± 1,4 %absolut bezogen auf den mittleren fossilen Kohlenstoffanteil. Für die zwei weiteren standardisierten Methoden (Selektive Lösemethode und Manuelle Sortierung) zeigten sich beim Vergleich der Ergebnisse deutliche methodische Einschränkungen in Abhängigkeit von der Zusammensetzung der EBS. Die aBM ist damit neben der analytisch aufwendigen 14C-Methode das einzige Bestimmungsverfahren, das unabhängig vom EBS-Typ zuverlässige Werte zur Klimarelevanz generiert. Zudem weist die Praktikabilität (Zeit- und Kostenaufwand) der aBM, insbesondere bei Routineanwendungen, Vorteile gegenüber standardisierten Verfahren auf. Einzig bei erstmaliger Anwendung ist eine Ermittlung von EBS-spezifischen Eingangswerten zur Elementarzusammensetzung der enthaltenen biogenen und fossilen Materialien notwendig, welche mit erhöhtem Aufwand verbunden sein kann.Refuse-derived fuels (RDF) are utilized in industrial processes (e. g. cement plants) to reduce costs for primary energy carriers, to lessen natural resource consumption, but also to lower the amount of climate-relevant CO2 emissions associated with the production process. In order to account for the CO2 savings, a practical method is required which accounts for the heterogeneity of RDF and allows the share of fossil carbon and fossil CO2 emissions to be reliably determined. The study examines an alternative method, the so-called adapted Balance Method (aBM), which relies on the determination of the elementary composition of the RDF. Six different RDFs were investigated by means of aBM and the results were compared to three standardized methods. The results of aBM are in excellent agreement with the ones of 14C-Method which is regarded as reference method (mean deviation of 0.6 ± 1.4%absolute based on the share of fossil carbon). Both other standardized methods, the Selective Dissolution Method and the method of Manual Sorting show methodological limitations leading to allegedly false estimates for single RDFs. Thus, the aBM is—besides the 14C-Method, which requires highly specialist equipment and personnel—the only determination method which delivers reliable data on the climate-relevant CO2 emissions independently of the type of RDF. Further benefits of the aBM in comparison to the standardized methods include its practicability for routine application (expenditure of time and money). Only during initial application of the aBM the determination of RDF-specific input parameters (elemental composition of biogenic and fossil materials in the RDF) may lead to increased efforts.Austrian Science Fund (FWF

Characterization of Blast Furnace Sludge with Respect to Heavy Metal Distribution

Blast furnace sludge is a heterogeneous material generated by the wet top gas cleaning process. It consists mainly of Fe, O, and C and contains major amounts of Si, Al, and Mg. Besides these elements, the sludge also contains trace metals such as Cd, Zn, and Pb. Reliable information about the total contents and distribution of the elements present in blast furnace sludge is of importance for hot metal production as some of them should preferably be recycled (Fe, C) and others (Cd, Zn, or Pb) are of environmental concern. Hence, the aim of the present study was to investigate the composition of blast furnace sludge with respect to the separation of desirable from undesirable elements. For this purpose, blast furnace sludge samples have been taken and analyzed with regard to particle size distribution (using wet sieving) and their respective contents of Fe, Zn, Pb, Cr, Ni, Cd, and C. The results of the analyses demonstrate that the concentration of the elements investigated significantly depends on the particle size. Coarser particles (>100 μm) are characterized by higher contents of C, whereas fine grained particles (<20 μm) show an enrichment of Fe and a significant accumulation of the heavy metals Zn, Pb, and Cd, which is attributed to their condensation on the particles’ surfaces. The content of Cr and Ni in blast furnace sludge is largely independent of the particle size

Differential Gene Expression in Circulating CD14+ Monocytes Indicates the Prognosis of Critically Ill Patients with Sepsis

Critical illness and sepsis are characterized by drastic changes in the systemic innate immune response, particularly involving monocytes. The exact monocyte activation profile during sepsis, however, has remained obscure. Therefore, we prospectively analyzed the gene expression profile of circulating CD14+ monocytes from healthy volunteers (n = 54) and intensive care unit (ICU) patients (n = 76), of which n = 36 had sepsis. RNA sequencing of selected samples revealed that monocytes from septic ICU patients display a peculiar activation pattern, which resembles characteristic functional stages of monocyte-derived macrophages and is distinct from controls or non-sepsis ICU patients. Focusing on 55 highly variable genes selected for further investigation, arachidonate 5-lipoxygenase-activating protein (ALOX5AP) was highly upregulated in monocytes of ICU patients and only normalized during 7 days in the ICU in non-sepsis patients. Strikingly, low monocytic guanine nucleotide exchange factor 10-like protein (ARHGEF10L) mRNA expression was associated with the disease severity and mortality of ICU patients. Collectively, our comprehensive analysis of circulating monocytes in critically ill patients revealed a distinct activation pattern, particularly in ICU patients with sepsis. The association with disease severity, the longitudinal recovery or lack thereof during the ICU stay, and the association with prognosis indicate the clinical relevance of monocytic gene expression profiles during sepsis

An alternative method to determine the share of fossil carbon in solid refuse-derived fuels – Validation and comparison with three standardized methods

Today different types of wastes are used as refuse-derived fuels (RDF) either in waste-to-energy plants or as fuel substitutes in energy-intensive industrial processes. In order to quantify their greenhouse-gas relevance (fossil carbon content), reliable and practical analytical methods are required, which allow differentiation between biogenic and fossil organic carbon. In the present paper, an alternative method to determine the fossil share in RDFs is examined and validated. The so-called “adapted Balance Method” (aBM) is applied to three different RDFs and the results are compared to three standardized methods, namely the Radiocarbon Method (14CMethod), the Selective Dissolution Method (SDM), and the Manual Sorting Method (MS). The aBM is based on the distinctly different elemental composition of water-and-ash-free biogenic and of fossil matter (TOXBIO and TOXFOS). Within the study, these compositional data are derived by manual sorting of the RDFs. The results show that the values obtained by the aBM are in excellent agreement with the results of the 14C-Method (considered as reference method). Mean deviations between the two methods of −0.9 to +1.9% absolute for the share of fossil carbon are found which are statistically insignificant. High trueness and reliability of the aBM can be expected, independent of the RDF type. In contrast, the reliability of the other standardized methods (SDM and MS) appears to strongly depend on the type and composition of the RDF. The results further indicate that the generation of RDF-specific data on TOXFOS is important for the aBM if significant shares of polymers with comparably high oxygen content might be present in the RDF and if low uncertainties of the results (<3% relative) are required. The findings demonstrate that the alternative method has advantages compared to standardized methods with respect to reliability and/or costs