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Abstract Africa lags behind the world on operational and financial progress to maintain safe drinking water services. In rural Mali, we explore the implications of monthly flat fee contributions and volumetric (pay-as-you-fetch) payments for water use and revenue generation. By assessing 4413 months of data across 177 handpumps, we find that once payment modalities switch from volumetric payments to monthly flat fees, a waterpoint registers a more than three-times higher monthly revenue. While flat fees cover a higher share of the operational costs of providing reliable water services, a subsidy gap persists. Flat fees appear to stimulate daily water use which more than doubles compared to volumetric payments. We estimate that a 1 °C increase in average monthly temperature is associated with 180 more litres of water used every day per handpump, emphasising the importance of climate-resilient water supplies. Based on these insights, we discuss the role of professional service delivery models to support reliable drinking water services for rural communities.

Although Machine Learning (ML) in supply chain management (SCM) has become a popular topic, predictive uses of ML in SCM remain an understudied area. A specific area that needs further attention is the prediction of late deliveries by suppliers. Recent approaches showed promising results but remained limited in their use of classification algorithms and struggled with the curse of dimensionality, making them less applicable to low-volume-high-variety production settings. In this paper, we show that a prediction model using a regression algorithm is capable to predict the severity of late deliveries of suppliers in a representative case study of a low-volume-high-variety machinery manufacturer. Here, a detailed understanding of the manufacturer’s procurement process is built, relevant features are identified, and different ML algorithms are compared. In detail, our approach provides three key contributions: First, we develop an ML-based regression model predicting the severity of late deliveries by suppliers. Second, we demonstrate that prediction within the earlier phases of the purchasing process is possible. Third, we show that there is no need to reduce the dimensionality of high-dimensional input features. Nevertheless, our approach has scope for improvement. The inclusion of information such as component identifiers may improve the prediction quality.

As a severe weather phenomenon, thunderstorms can cause casualties and economic loss to the human society. A reliable forecast of these weather events would help to avoid or at least mitigate this damage. To date, the forecasting of thunderstorms however is still a challenge, especially for lead times of one hour and beyond. In this study we present a methodology to forecast deep-convection for several hours lead time: Cb‑Fusion. Cb‑Fusion estimates the likelihood of thunderstorm occurrence for up to 6 hours in advance over a part of Central Europe, using a data fusion technique that blends data of multiple sources from observations, nowcasts, and numerical weather predictions with a high update rate. The Cb‑Fusion is set up to operate in near real time. The skill of Cb‑Fusion is evaluated based on 1743 hours of thunderstorm observations collected during the months April to October, 2019. Three categories of thunderstorm size have been distinguished: ‘large’ for a coverage area larger than 5000 km2, ‘medium’ for a coverage area between 5000 km2 and 500 km2, and ‘small’ for a coverage area smaller than 500 km2. Compared to thunderstorm forecasts from numerical models alone, the combination of data from various sources by Cb‑Fusion results in a significantly better forecast skill. The study reveals that the forecast is reliable for up to 3 hours lead time for ‘medium’ and ‘large’ scale thunderstorms (median POD of 0.6–0.9) but little skill is found for ‘small’ scale thunderstorms and lead times between 3 and 6 hours (median POD of 0.05). It is argued that Cb‑Fusion provides meaningful improvements in forecasting thunderstorms for various users.

For manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms – six prediction models on every level of detail – and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.

Facility Layout Problems (FLP) are concerned with finding efficient factory layouts. Numerous resolution approaches are known in literature for layout optimization. Among those, intelligent approaches are less researched than solutions from exact or approximating approaches. The recent surge of research interest in Artificial Intelligence, and specifically Machine Learning (ML) techniques, presages an increase of such techniques' usage in FLP. However, previous reviews on FLP research induce that, to date, this trend has not yet emerged. Utilizing a systematic literature review coupled with a k-Means based clustering algorithm, we analyzed 25 relevant publication full-texts from an original sample of 1,425 papers. Our findings corroborate the statement that ML techniques have attracted substantially less research interest than most other resolution approaches. While a few papers used Unsupervised Learning algorithms directly as a solution to the FLP, Supervised and Reinforcement Learning were found to be practically irrelevant. ML usage was significantly higher in FLP-adjacent planning tasks such as group technology. Drawing from experiences with other NP-hard combinatorial optimization problems in manufacturing research, we conclude that Reinforcement Learning is most promising to bridge the evident gap between FLP and ML research. Our study further contributes to FLP research by extending established classification frameworks.

The interest of manufacturing companies in a sufficient prediction of lead times is continuously increasing - especially in engineer to order environments with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to provide guidance at setting up prediction models and developing new approaches, a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-two publications were analyzed and synthetized: Based on a developed framework considering the used data class (e.g. product data or system status), the data origin (master data or real data) and the used method and algorithm from OR and ML, the publications are classified. Based on the classification, a descriptive analysis is performed to identify common approaches in the existing literature as well as implications for further research. One result is, that mostly order data and the status of the production system are used for predicting lead times whereas material data are used seldom. Additionally, ML approaches primarily use artificial neural networks and regression models for predicting lead times, while OR approaches use mainly combinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of real data decreased. Thus, we identified as an implication for further research to set up a complex data model considering material data, which uses real data as data origin.

Abstract Background By definition, effect of synonymous single-nucleotide variants (SNVs) on protein folding and function are neutral, as they alter the codon and not the encoded amino acid. Recent examples indicate tissue-specific and transfer RNA (tRNA)-dependent effects of some genetic variations arguing against neutrality of synonymous SNVs for protein biogenesis. Results We performed systematic analysis of tRNA abunandance across in various models used in cystic fibrosis (CF) research and drug development, including Fischer rat thyroid (FRT) cells, patient-derived primary human bronchial epithelia (HBE) from lung biopsies, primary human nasal epithelia (HNE) from nasal curettage, intestinal organoids, and airway progenitor-directed differentiation of human induced pluripotent stem cells (iPSCs). These were compared to an immortalized CF bronchial cell model (CFBE41o−) and two widely used laboratory cell lines, HeLa and HEK293. We discovered that specific synonymous SNVs exhibited differential effects which correlated with variable concentrations of cognate tRNAs. Conclusions Our results highlight ways in which the presence of synonymous SNVs may alter local kinetics of mRNA translation; and thus, impact protein biogenesis and function. This effect is likely to influence results from mechansistic analysis and/or drug screeining efforts, and establishes importance of cereful model system selection based on genetic variation profile.

Abstract Background With the introduction of Olaparib treatment for BRCA-deficient recurrent ovarian cancer, testing for somatic and/or germline mutations in BRCA1/2 genes in tumor tissues became essential for treatment decisions. In most cases only formalin-fixed paraffin-embedded (FFPE) samples, containing fragmented and chemically modified DNA of minor quality, are available. Thus, multiplex PCR-based sequencing is most commonly applied in routine molecular testing, which is predominantly focused on the identification of known hot spot mutations in oncogenes. Methods We compared the overall performance of an adjusted targeted capture-based enrichment protocol and a multiplex PCR-based approach for calling of pathogenic SNVs and InDels using DNA extracted from 13 FFPE tissue samples. We further applied both strategies to seven blood samples and five matched FFPE tumor tissues of patients with known germline exon-spanning deletions and gene-wide duplications in BRCA1/2 to evaluate CNV detection based solely on panel NGS data. Finally, we analyzed DNA from FFPE tissues of 11 index patients from families suspected of having hereditary breast and ovarian cancer, of whom no blood samples were available for testing, in order to identify underlying pathogenic germline BRCA1/2 mutations. Results The multiplex PCR-based protocol produced inhomogeneous coverage among targets of each sample and between samples as well as sporadic amplicon drop out, leading to insufficiently or non-covered nucleotides, which subsequently hindered variant detection. This protocol further led to detection of PCR-artifacts that could easily have been misinterpreted as pathogenic mutations. No such limitations were observed by application of an adjusted targeted capture-based protocol, which allowed for CNV calling with 86% sensitivity and 100% specificity. All pathogenic CNVs were confirmed in the five matched FFPE tumor samples from patients carrying known pathogenic germline mutations and we additionally identified somatic loss of the second allele in BRCA1/2. Furthermore we detected pathogenic BRCA1/2 variants in four the eleven FFPE samples from patients of whom no blood was available for analysis. Conclusions We demonstrate that an adjusted targeted capture-based enrichment protocol is superior to commonly applied multiplex PCR-based protocols for reliable BRCA1/2 variant detection, including CNV-detection, using FFPE tumor samples.

The data provided in the present article provides information on the importance of a list of monetary and non-monetary influencing factors on decisions regarding the automation of an assembly system. A survey among German industry representatives conducted between July 2018 and October 2018 is the basis for this dataset. It contains the characteristics of industrial companies based in Germany that participated in the survey as well as their attitude towards the development of the automation level in assembly systems. The focus of the survey lies on the influencing factors of the production and production environment and their influence on the automation level. The participants were able to evaluate the factors on a six-step ordinal scale from “no influence” to “very strong influence”. Interpretation of this data can be found in the research article titled “Automation decisions in flow-line assembly systems based on a cost-benefit analysis” [1].

[This corrects the article DOI: 10.1371/journal.pone.0201041.].

ABSTRACT: Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology. • NLP and k-means clustering to filter large article corpora during systematic literature reviews. • Automated clustering allows filtering very efficiently as well as effectively compared to manual selection. • Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.

Due to the exponential increase of failure cost during the product development process, problems have to be effectively remedied as early as possible and with shortened innovation cycles, increasingly efficient. For the manufacturing of complex products at low maturity levels (referred to as physical product development), nonconformance problem solving constitutes a major difficulty in this regard (Camarillo et al., 2017; Walter et al., 2010). The data presented in this article was collected from German companies, differing in size and industry sector, manufacturing highly complex products at low maturity. Selected and consulted companies therefore operate in (or comparable to) the automotive prototyping, air and space, shipbuilding, special machinery or electronics domain. The survey comprises the answers of 46 participants, gathered via online questionnaire. It subdivides into 18 questions covering the companies’ characteristics, knowledge management and documentation systems within the product development process, as well as the appraisal of technological potentials. The obtained data gives an insight into the industrial status quo of nonconformance problem solving. The data allows to derive existing deficits and dedicated research on solutions. Keywords: Manufacturing problem solving, Product development, Knowledge based systems

Synonymous single nucleotide polymorphisms (sSNPs), which change a nucleotide, but not the encoded amino acid, are perceived as neutral to protein function and thus, classified as benign. We report a patient who was diagnosed with cystic fibrosis (CF) at an advanced age and presented very mild CF symptoms. The sequencing of the whole cystic fibrosis transmembrane conductance regulator (CFTR) gene locus revealed that the patient lacks known CF-causing mutations. We found a homozygous sSNP (c.1584G>A) at the end of exon 11 in the CFTR gene. Using sensitive molecular methods, we report that the c.1584G>A sSNP causes cognate exon skipping and retention of a sequence from the downstream intron, both of which, however, occur at a relatively low frequency. In addition, we found two other sSNPs (c.2562T>G (p.Thr854=) and c.4389G>A (p.Gln1463=)), for which the patient is also homozygous. These two sSNPs stabilize the CFTR protein expression, compensating, at least in part, for the c.1584G>A-triggered inefficient splicing. Our data highlight the importance of considering sSNPs when assessing the effect(s) of complex CFTR alleles. sSNPs may epistatically modulate mRNA and protein expression levels and consequently influence disease phenotype and progression.

Despite being a pan-cultural phenomenon, laughter is arguably the least understood behaviour deployed in social interaction. As well as being a response to humour, it has other important functions including promoting social affiliation, developing cooperation and regulating competitive behaviours. This multi-functional feature of laughter marks it as an adaptive behaviour central to facilitating social cohesion. However, it is not clear how laughter achieves this social cohesion. We consider two approaches to understanding how laughter facilitates social cohesion – the ‘representational’ approach and the ‘affect-induction’ approach. The representational approach suggests that laughter conveys information about the expresser’s emotional state, and the listener decodes this information to gain knowledge about the laugher’s felt state. The affect-induction approach views laughter as a tool to influence the affective state of listeners. We describe a modified version of the affect-induction approach, in which laughter is combined with additional factors – including social context, verbal information, other social signals and knowledge of the listener’s emotional state – to influence an interaction partner. This view asserts that laughter by itself is ambiguous: the same laughter may induce positive or negative affect in a listener, with the outcome determined by the combination of these additional factors. Here we describe two experiments exploring which of these approaches accurately describes laughter. Participants judged the genuineness of audio–video recordings of social interactions containing laughter. Unknown to the participants the recordings contained either the original laughter or replacement laughter from a different part of the interaction. When replacement laughter was matched for intensity, genuineness judgements were similar to judgements of the original unmodified recordings. When replacement laughter was not matched for intensity, genuineness judgements were generally significantly lower. These results support the affect-induction view of laughter by suggesting that laughter is inherently underdetermined and ambiguous, and that its interpretation is determined by the context in which it occurs.

Although intellectual disability is one of the major indications for genetic counselling, there are no homogenous diagnostic algorithms for molecular testing. While whole exome sequencing is increasingly applied, we questioned whether analyzing a partial exome, enriched for genes associated with Mendelian disorders, might be a valid alternative approach that yields similar detection rates but requires less sequencing capacities. Within this context 106 patients with different intellectual disability forms were analyzed for mutations in 4.813 genes after pre-exclusion of copy number variations by array-CGH. Subsequent variant interpretation was performed in accordance with the ACMG guidelines. By this, a molecular diagnosis was established in 34% of cases and candidate mutations were identified in additional 24% of patients. Detection rates of causative mutations were above 30%, regardless of further symptoms, except for patients with seizures (23%). We did not detect an advantage from partial exome sequencing for patients with severe intellectual disability (36%) as compared to those with mild intellectual disability (44%). Specific clinical diagnoses pre-existed for 20 patients. Of these, 5 could be confirmed and an additional 6 cases could be solved, but showed mutations in other genes than initially suspected. In conclusion partial exome sequencing solved >30% of intellectual disability cases, which is similar to published rates obtained by whole exome sequencing. The approach therefore proved to be a valid alternative to whole exome sequencing for molecular diagnostics in this cohort. The method proved equally suitable for both syndromic and non-syndromic intellectual disability forms of all severity grades.

A protein at equilibrium is commonly thought of as a fully relaxed structure, with the intra-molecular interactions showing fluctuations around their energy minimum. In contrast, here we find direct evidence for a protein as a molecular tensegrity structure, comprising a balance of tensed and compressed interactions, a concept that has been put forward for macroscopic structures. We quantified the distribution of inter-residue prestress in ubiquitin and immunoglobulin from all-atom molecular dynamics simulations. The network of highly fluctuating yet significant inter-residue forces in proteins is a consequence of the intrinsic frustration of a protein when sampling its rugged energy landscape. In beta sheets, this balance of forces is found to compress the intra-strand hydrogen bonds. We estimate that the observed magnitude of this pre-compression is enough to induce significant changes in the hydrogen bond lifetimes; thus, prestress, which can be as high as a few 100 pN, can be considered a key factor in determining the unfolding kinetics and pathway of proteins under force. Strong pre-tension in certain salt bridges on the other hand is connected to the thermodynamic stability of ubiquitin. Effective force profiles between some side-chains reveal the signature of multiple, distinct conformational states, and such static disorder could be one factor explaining the growing body of experiments revealing non-exponential unfolding kinetics of proteins. The design of prestress distributions in engineering proteins promises to be a new tool for tailoring the mechanical properties of made-to-order nanomaterials.