Design-Education: Die Siemens HMI-Design Masterclass

Wie kann man die internationale Anlagen - und Maschinenindustrie zum „Besseren Entwerfen von Mensch-Maschine-Benutzeroberflächen“ verführen. Das war die Ausgangssituation und Aufgabenstellung. [...] Mit der Erkenntnis, dass nutzerorientiertes HMI-Design ein wesentlicher Erfolgsfaktor ist, stellte sich das Unternehmen Siemens die Frage, wie es nachhaltig und schrittweise die weltweiten Kunden zum „besseren Design“ anleiten und die tägliche Arbeit der Anwender in den Maschinen-Produktionshallen der Welt spürbar verbessern könnte. In Zusammenarbeit mit chilli mind entstand mit der HMI-Design Masterclass eine erfolgversprechende Antwort: Man braucht ein zeiteffizientes und unterhaltsames Lernformat, verbunden mit praktischen und pragmatischen Lerninhalten, um die Zielgruppe der weltweiten Maschinenbauer für eine „bessere HMI-Gestaltung“ zu gewinnen und zur Teilnahme zu bewegen. (Kranert et al. 2018) Dieser Beitrag beantwortet u.a. folgende drei Fragen: 1. Welche Charakteristika von Content Marketing, Microlearning und Storytelling greifen ineinander, um zum Lernerfolg der Zielgruppe zu führen? 2. Wie sieht das Konzept der HMI-Design Masterclass konkret aus? 3. Welche messbaren Erfolge konnten mit der HMI-Design Masterclass erzielt werden? [... aus der Einleitung

Genetic variations in bile acid homeostasis are not overrepresented in alcoholic cirrhosis compared to patients with heavy alcohol abuse and absent liver disease

Increased serum bile salt levels have been associated to a single-nucleotide polymorphism in the bile salt export pump (BSEP; ABCB11) in several acquired cholestatic liver diseases but there is little evidence in alcoholic liver disease (ALD). Furthermore, a crosstalk between vitamin D and bile acid synthesis has recently been discovered. Whether this crosstalk has an influence on the course of ALD is unclear to date. Our aim was to analyse the role of genetic polymorphisms in BSEP and the vitamin D receptor gene (NR1I1) on the emergence of cirrhosis in patients with ALD. Therefore, 511 alcoholic patients (131 with cirrhosis and 380 without cirrhosis) underwent ABCB11 genotyping (rs2287622). Of these, 321 (131 with cirrhosis and 190 without cirrhosis) were also tested for NR1I1 polymorphisms (bat-haplotype: BsmI rs1544410, ApaI rs7975232 and TaqI rs731236). Frequencies of ABCB11 and NR1I1 genotypes and haplotypes were compared between alcoholic patients with and without cirrhosis and correlated to serum bile salt, bilirubin and aspartate aminotransferase levels in those with cirrhosis. Frequencies of ABCB11 and NR1I1 genotypes and haplotypes did not differ between the two subgroups and no significant association between genotypes/haplotypes and liver function tests could be determined for neither polymorphism. We conclude that ABCB11 and NR1I1 polymorphisms are obviously not associated with development of cirrhosis in patients with AL

Retardation of arsenic transport through a Pleistocene aquifer

Groundwater drawn daily from shallow alluvial sands by millions of wells over large areas of south and southeast Asia exposes an estimated population of over a hundred million people to toxic levels of arsenic. Holocene aquifers are the source of widespread arsenic poisoning across the region. In contrast, Pleistocene sands deposited in this region more than 12,000 years ago mostly do not host groundwater with high levels of arsenic. Pleistocene aquifers are increasingly used as a safe source of drinking water and it is therefore important to understand under what conditions low levels of arsenic can be maintained. Here we reconstruct the initial phase of contamination of a Pleistocene aquifer near Hanoi, Vietnam. We demonstrate that changes in groundwater flow conditions and the redox state of the aquifer sands induced by groundwater pumping caused the lateral intrusion of arsenic contamination more than 120 metres from a Holocene aquifer into a previously uncontaminated Pleistocene aquifer. We also find that arsenic adsorbs onto the aquifer sands and that there is a 16–20-fold retardation in the extent of the contamination relative to the reconstructed lateral movement of groundwater over the same period. Our findings suggest that arsenic contamination of Pleistocene aquifers in south and southeast Asia as a consequence of increasing levels of groundwater pumping may have been delayed by the retardation of arsenic transport

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

More mentoring needed? A cross-sectional study of mentoring programs for medical students in Germany

<p>Abstract</p> <p>Background</p> <p>Despite increasing recognition that mentoring is essential early in medical careers, little is known about the prevalence of mentoring programs for medical students. We conducted this study to survey all medical schools in Germany regarding the prevalence of mentoring programs for medical students as well as the characteristics, goals and effectiveness of these programs.</p> <p>Methods</p> <p>A definition of mentoring was established and program inclusion criteria were determined based on a review of the literature. The literature defined mentoring as a steady, long-lasting relationship designed to promote the mentee's overall development. We developed a questionnaire to assess key characteristics of mentoring programs: the advocated mentoring model, the number of participating mentees and mentors, funding and staff, and characteristics of mentees and mentors (e.g., level of training). In addition, the survey characterized the mentee-mentor relationship regarding the frequency of meetings, forms of communication, incentives for mentors, the mode of matching mentors and mentees, and results of program evaluations. Furthermore, participants were asked to characterize the aims of their programs. The questionnaire consisted of 34 questions total, in multiple-choice (17), numeric (7) and free-text (10) format. This questionnaire was sent to deans and medical education faculty in Germany between June and September 2009. For numeric answers, mean, median, and standard deviation were determined. For free-text items, responses were coded into categories using qualitative free text analysis.</p> <p>Results</p> <p>We received responses from all 36 medical schools in Germany. We found that 20 out of 36 medical schools in Germany offer 22 active mentoring programs with a median of 125 and a total of 5,843 medical students (6.9 - 7.4% of all German medical students) enrolled as mentees at the time of the survey. 14 out of 22 programs (63%) have been established within the last 2 years. Six programs (27%) offer mentoring in a one-on-one setting. 18 programs (82%) feature faculty physicians as mentors. Nine programs (41%) involve students as mentors in a peer-mentoring setting. The most commonly reported goals of the mentoring programs include: establishing the mentee's professional network (13 programs, 59%), enhancement of academic performance (11 programs, 50%) and counseling students in difficulties (10 programs, 45%).</p> <p>Conclusions</p> <p>Despite a clear upsurge of mentoring programs for German medical students over recent years, the overall availability of mentoring is still limited. The mentoring models and goals of the existing programs vary considerably. Outcome data from controlled studies are needed to compare the efficiency and effectiveness of different forms of mentoring for medical students.</p

Retardation of arsenic transport through a Pleistocene aquifer

Groundwater drawn daily from shallow alluvial sands by millions of wells over large areas of south and southeast Asia exposes an estimated population of over a hundred million people to toxic levels of arsenic1. Holocene aquifers are the source of widespread arsenic poisoning across the region2, 3. In contrast, Pleistocene sands deposited in this region more than 12,000 years ago mostly do not host groundwater with high levels of arsenic. Pleistocene aquifers are increasingly used as a safe source of drinking water4 and it is therefore important to understand under what conditions low levels of arsenic can be maintained. Here we reconstruct the initial phase of contamination of a Pleistocene aquifer near Hanoi, Vietnam. We demonstrate that changes in groundwater flow conditions and the redox state of the aquifer sands induced by groundwater pumping caused the lateral intrusion of arsenic contamination more than 120 metres from a Holocene aquifer into a previously uncontaminated Pleistocene aquifer. We also find that arsenic adsorbs onto the aquifer sands and that there is a 16–20-fold retardation in the extent of the contamination relative to the reconstructed lateral movement of groundwater over the same period. Our findings suggest that arsenic contamination of Pleistocene aquifers in south and southeast Asia as a consequence of increasing levels of groundwater pumping may have been delayed by the retardation of arsenic transport.National Science Foundation (U.S.) (NSF grant EAR09-11557)Swiss Agency for Development and Cooperation (Grant NAFOSTED 105-09-59-09 to CETASD, the Centre for Environmental Technology and Sustainable Development (Vietnam))National Institute of Environmental Health Sciences (NIEHS grant P42 ES010349)National Institute of Environmental Health Sciences (NIEHS grant P42 ES016454