Recent publications
The critical need for awareness and genetic testing of the SAMHD1 deletion in Ashkenazi Jewish patients is highlighted owing to its relatively high carrier frequency. Early detection can prevent severe disease complications through targeted therapy.
Transmembrane protein 175 (TMEM175) is an endolysosomal cation channel, which has attracted much attention recently from academics and the pharmaceutical industry alike since human mutations in TMEM175 were found to be associated with the development of Parkinson's disease (PD). Thus, gain‐of‐function mutations were identified, which reduce and loss‐of‐function mutations, which increase the risk of developing PD. After having been characterized as an endolysosomal potassium channel initially, soon after TMEM175 was claimed to act as a proton channel. In fact, recent evidence suggests that depending on the conditions, TMEM175 can act as either a potassium or proton channel, without acting as an antiporter or exchanger. A recent work has now identified amino acid H57 to be directly involved in gating, increasing proton conductance of the channel while leaving the potassium conductance unaffected. We review here the current knowledge of TMEM175 function, pharmacology, physiology, and pathophysiology. We discuss the potential of this ion channel as a novel drug target for the treatment of neurodegenerative diseases such as PD, and we discuss the discovery of H57 as proton sensor.
Irrigation is often celebrated as a means of intensifying agricultural production and improving food and nutrition security. In the context of semi-subsistence smallholder agriculture irrigation can have a positive impact on dietary diversity through various pathways. However, studies on the linkages between irrigation and rural household nutrition show mixed results. This study argues that irrigation is not a simple agricultural input factor but is embedded in socio-technical conditions. It compares two different irrigation arrangements to understand how irrigation can contribute to transforming local food systems through different pathways. The impact of irrigation on dietary diversity and the potential impact pathways (agricultural income, production diversity and women’s empowerment) are analyzed using a propensity score matching (PSM) approach. The analysis is repeated for subsets of farmer-led and public irrigation to explore how different irrigation arrangements lead to different outcomes. The results show that both farmer-led and public irrigation have a positive impact on agricultural income and dietary diversity. The positive effect on dietary diversity was stronger in farmer-led irrigation while the income effect was stronger in public irrigation arrangement. However, the positive impact on dietary diversity appears to be dampened by a reduction in production diversity, particularly in the case of public irrigation. This study highlights that irrigation development may lead to a more diverse diet, strengthen the income pathway but weaken the production diversity pathway with the extent of this effect depending on the irrigation arrangement. Therefore, policy makers should be aware of this trade-off and seek to support irrigation that allows increased production for urban markets without compromising the dietary intake of rural households.
Investigating digital privacy behavior requires consideration of its contextual nuances and the underlying social norms. This study delves into users' joint articulation of such norms by probing their implicit assumptions and "common sense" surrounding privacy conventions. To achieve this, we introduce Privacy Taboo, a card game designed to serve as a playful breaching interview method, fostering discourse on unwritten privacy rules. Through nine interviews involving pairs of participants (n=18), we explore the decision-making and collective negotiation of privacy's vagueness. Our findings demonstrate individuals' ability to articulate their information needs when consenting to fictive data requests, even when contextual cues are limited. By shedding light on the social construction of privacy, this research contributes to a more comprehensive understanding of usable privacy, thereby facilitating the development of democratic privacy frameworks. Moreover, we posit Privacy Taboo as a versatile tool adaptable to diverse domains of application and research.
Illegal wildlife trade is a growing problem internationally. Poaching of animals not only leads to the extinction of populations and species but also has serious consequences for ecosystems and economies. This study introduces a molecular marker system that authorities can use to detect and substantiate wildlife trafficking. SNPSTR markers combine short tandem repeats with single nucleotide polymorphisms within an amplicon to increase discriminatory power. Within the FOGS (Forensic Genetics for Species Protection) project, we have established SNPSTR marker sets for 74 vertebrate species. On average, each set consists of 19 SNPSTR markers with 82 SNPs per set. More than 1300 SNPSTR markers and over 300 STR markers were identified. Also, through its biobanking pipeline, the FOGS project enabled the cryopreservation of somatic cells from 91 vertebrate species as well as viable tissues for later cell initiation from a further 109 species, providing future strategies for ex situ conservation. In addition, many more fixed tissues and DNA samples of endangered species were biobanked. Therefore, FOGS was an interdisciplinary study, combining molecular wildlife forensics and conservation tools. The SNPSTR sets and cell culture information are accessible through the FOGS database ( https://fogs‐portal.de/data ) that is open to scientists, researchers, breeders and authorities worldwide to protect wildlife from illegal trade.
The widespread adoption of electric vehicles (EVs) and large-scale energy storage has necessitated advancements in Battery management systems (BMS) so that the complex dynamics of batteries under various operational conditions...
As generative Artificial Intelligence (AI) is seen as a catalyst for a new learning and examination culture in higher education, it urges universities to reinvent themselves and to adapt to these changes effectively. By analysing the content of 67 university guidelines on generative AI, this study investigates how universities in Germany position themselves towards the rise of generative AI. Findings reveal that a majority of university guidelines explicitly permit both university lecturers and students to engage with generative AI, emphasising the importance of building AI literacy among both groups and preparing students for changing demands in the world of work. Importantly, 56.7% of university guidelines posit that the opportunities of generative AI for higher education outweigh the risks, underscoring the potential transformative impact on teaching and research. In addition, the results of a workshop with 25 faculty members were scrutinised to deepen and specify the findings of the content analysis.
Engineering schools and colleges around the world have developed a variety of innovative models to train their undergraduate (post-secondary, tertiary) students and graduate students (post-baccalaureate, masters, doctoral) for global engagement. This chapter shares global student mobility models from Africa, China, Germany, India, Latin America, and the United States that are both internal to the curriculum, and external to the campuses. Collaborations between the leaders of the models throughout these featured regions have been fostered by engineering education organizations such as the Latin and Caribbean Consortium of Engineering Institutions, and the Global Engineering Deans Council, which connect through the International Federation of Engineering Education Societies and convene annually at locations across the globe. Collaborations formed during the convenings yielded promising practices that can be utilized by professors around the world as they create their own sustainable programs and learning opportunities to develop new generations of global engineers.
Neuromorphic computing mimics computational principles of the brain in silico and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer superior power consumption, response latencies, and dynamic ranges. SNNs replicate biological neuronal dynamics and have demonstrated potential as alternatives to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Nevertheless, these novel paradigms remain scarcely explored outside the domain of aerial robots. To investigate the utility of brain-inspired sensing and data processing, we developed a neuromorphic approach to obstacle avoidance on a camera-equipped manipulator. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans using a dynamic motion primitive. We conducted experiments with a Kinova Gen3 arm performing simple reaching tasks that involve obstacles in sets of distinct task scenarios and in comparison to a non-adaptive baseline. Our neuromorphic approach facilitated reliable avoidance of imminent collisions in simulated and real-world experiments, where the baseline consistently failed. Trajectory adaptations had low impacts on safety and predictability criteria. Among the notable SNN properties were the correlation of computations with the magnitude of perceived motions and a robustness to different event emulation methods. Tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation. Our results motivate incorporating SNN learning, utilizing neuromorphic processors, and further exploring the potential of neuromorphic methods.
Transport Layer Security (TLS) is a widely used protocol for secure channel establishment. However, TLS lacks any inherent mechanism for validating the security state of the endpoint software and its platform. To overcome this limitation, recent works have combined remote attestation (RA) and TLS, named attested TLS. The most popular attested TLS protocol for confidential computing is Intel's RA-TLS, which is used in multiple industrial projects. However, Intel has neither specified the protocol nor the desired properties. Moreover, despite its wide usage in security-critical use cases, there is no formal reasoning for the security of attested TLS for confidential computing in general and RA-TLS in particular. Using the state-of-the-art symbolic security analysis tool ProVerif, we formalized and found vulnerabilities in RA-TLS from both RA and TLS perspectives. We also propose mitigations for the vulnerabilities. During the formalization process, we found that despite several formal verification efforts for TLS to ensure its security, the validation of corresponding formal models has been largely overlooked. We demonstrate that a simple validation framework could discover crucial issues in state-of-the-art formalization of TLS 1.3 key schedule in ProVerif. Particularly, we found that 11 out of 14 keys in that formalization deviate from the corresponding TLS specifications. These issues have been acknowledged and fixed by the authors. Finally, we provide recommendations for protocol designers and the formal verification community based on the lessons learned in the formal verification and validation.
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