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Uveal melanoma (UM) is the most common primary intraocular tumor in adults. New diagnostic procedures and basic science discoveries continue to change our patient management paradigms. A recent meeting of the European Vision Institute (EVI) special interest focus group was held on "Outcome Measures of New Technologies in Uveal Melanoma," addressing the latest advances in UM, starting with genetic developments, then moving on to imaging and treatment of the primary tumor, as well as to investigating the most recent developments in treating metastases, and eventually taking care of the patient's well-being. This review highlights the meeting's presentations in the context of the published literature. [ABSTRACT FROM AUTHOR]

In recent years, the catalyst pellets made of open‐cell metallic foams have been identified as a promising alternative in fixed‐bed reactors. A reliable modeling tool is necessary to investigate the suitability of different foam properties and the shapes of foam pellets. In this article, a workflow for a detailed computational fluid dynamics (CFD) model is presented, which aims to study the flow characteristics in the slender packed beds made of metal foam pellets. The CFD model accounts for the actual random packing structure and the fluid flow throughout the interstitial regions is fully resolved, whereas flow through the porous foam pellets is represented by the closure equations for the porous media model. The bed structure is generated using rigid body dynamics (RBD) and the influence of the catalyst loading method is also considered. The mean bed voidage and the pressure drop predicted by the simulations show good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

Attention in social interactions is directed by social cues such as the face or eye region of an interaction partner. Several factors that influence these attentional biases have been identified in the past. However, most findings are based on paradigms with static stimuli and no interaction potential. Therefore, the current study investigated the influence of one of these factors, namely facial affect in natural social interactions using an evaluated eye-tracking setup. In a sample of 35 female participants, we examined how individuals' gaze behavior responds to changes in the facial affect of an interaction partner trained in affect modulation. Our goal was to analyze the effects on attention to facial features and to investigate their temporal dynamics in a natural social interaction. The study results, obtained from both aggregated and dynamic analyses, indicate that facial affect has only subtle influences on gaze behavior during social interactions. In a sample with high measurement precision, these findings highlight the difficulties of capturing the subtleties of social attention in more naturalistic settings. The methodology used in this study serves as a foundation for future research on social attention differences in more ecologically valid scenarios.

Abstract In this paper, we examine in a systematic manner how investors react to the sentiment of instant ESG news. Instead of acquiring proprietary ESG news or events datasets directly from specific ESG data providers, we extract fresh ESG news directly from a plethora of raw news articles. We showcase how the latest development in NLP (i.e. the BERT model) can be applied to build a comprehensive and fresh ESG news dataset, and how company ESG news sentiment can be efficiently recognized by a machine. Overall, we find that the market reacts to ESG news based on news sentiment. On the event day, positive ESG news has an average abnormal return of 0.31% while negative ESG news leads to a mean value of $$-0.75$$ - 0.75 %. More interestingly, we find that the impact of ESG news may depend on the company’s historical ESG record. The negative impact of negative ESG news has less severe consequences for companies with an overall better ESG record, while the positive impact of positive ESG news may be more pronounced for companies with a worse ESG record.

Several arenaviruses, including Lassa virus (LASV), can cause severe viral haemorrhagic fevers in humans with high morbidity and mortality, to which there is no vaccine and limited treatment (1-4). These [...], Lassa virus, the causative agent of Lassa fever, causes thousands of deaths annually and is a biological threat agent, for which there is no vaccine and limited therapy. The nucleoprotein (NP) of Lassa virus has essential roles in viral RNA synthesis and immune suppression, the molecular mechanisms of which are poorly understood. Here we report the crystal structure of Lassa virus NP at 1.80 A resolution, which reveals amino (N)- and carboxy (C)-terminal domains with structures unlike any of the reported viral NPs. The N domain folds into a novel structure with a deep cavity for binding the m7GpppN cap structure that is required for viral RNA transcription, whereas the C domain contains 3'-5' exoribonuclease activity involved in suppressing interferon induction. To our knowledge this is the first X-ray crystal structure solved for an arenaviral NP, which reveals its unexpected functions and indicates unique mechanisms in cap binding and immune evasion. These findings provide great potential for vaccine and drug development.

Background: This study aims to analyze potential risk factors that may influence the clinical outcomes following surgical treatment of traumatic peroneal nerve lesions. Methods: We conducted a retrospective analysis of patients with traumatic peroneal nerve injuries treated with decompression, split repair, or nerve grafting between 2010 and 2020. Motor function and potential risk factors were evaluated. Results: Out of 93 patients, 42 (45%) underwent decompression, 15 (16%) received split repair, and 36 (39%) required autologous nerve grafting. Up to one year after surgery, weakness of the anterior tibial muscle improved from a median of M0 to M3. After one year following nerve decompression, functional recovery was observed in 28 (65%) cases, in 9 (21%) cases after split repair, and in 7 (16%) cases following autologous nerve grafting. A defect greater than 8 cm was associated with significantly poorer improvement of extensor hallucis longus (p = 0.037, HR 0.109). We found no significant associations between age, diabetes mellitus, arterial hypertension, obesity, and postoperative outcomes. Conclusions: According to the present data, a significant number of patients achieved functional improvement following surgical treatment, indicating that this procedure should be considered an important treatment option in selected cases.

The transient operation of methanation reactors can become desirable when coupled with fluctuating renewable energies in Power‐to‐Gas scenarios, which requires suitable kinetic approaches that can describe the transient catalytic phenomena. A combined experimental and theoretical investigation of the transient CO2 methanation is conducted using concentration forcing to derive a suitable microkinetic model. Methanation experiments are performed with a Ni/SiO2 catalyst in a Berty‐type reactor at industrially relevant conditions. The microkinetics are based on previous work and were automatically constructed for the Ni(111) facet using the Reaction Mechanism Generator. A feasible set of energetic parameters of the microkinetic models was identified in a theory‐constrained optimization procedure within the DFT uncertainty space that can accurately reproduce the experimental results on a first‐principles basis. The microkinetic model unravels that the formation of H2O* and CH3* control the activity and selectivity of Ni(111) under the investigated conditions. [ABSTRACT FROM AUTHOR]

Male specific lethal 1(MSL1) protein binding was visualized along the X chromosome by combining chromatin immunoprecipitation with high-resolution microarrays, to identify bound genes and attempt to isolate the targeting cues. The results demonstrate gene-specific binding of the dosage compensation complex (DCC) and identify several sequence elements that may partly direct its targeting.

Direct target genes for the male-specific-lethal (MSL) complex are identified and transcription in absolute terms after knockdown of MSL-2 is quantified. The results unequivocally document an appropriate twofold activation of target genes by the MSl complex.

Gathered from a real-world discrete manufacturing floor, this dataset features measurements of pneumatic pressure and electrical current during production. Spanning 7 days and encompassing approximately 150 processed units, the data is organized into time series sampled at 100 Hz. The observed machine performs 24 steps to process each unit. Each measurement in the time series, is annotated, linking it to one of the 24 processing steps performed by the machine for processing of a single piece. Segmenting the time series into contiguous regions of constant processing step labels results in 3674 labeled segments, each encompassing one part of the production process. The dataset enriched with labels facilitates the use of supervised learning techniques, like time series classification, and supports the testing of unsupervised methods, such as clustering of time series data.The focus of this dataset is on an end-of-line testing machine for small consumer-grade electric drive assemblies (device under test – DUT). The machine performs multiple actions in the process of evaluating each DUT, with the dataset capturing the pneumatic pressures and electrical currents involved. These measurements are segmented in alignment with the testing machine's internal state transitions, each corresponding to a distinct action undertaken in manipulating the device under observation.The included segments offer distinct signatures of pressure and current for each action, making the dataset valuable for developing algorithms for the non-invasive monitoring of industrial (specifically discrete) processes.

Abstract Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI‐based decision‐making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision‐making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision‐making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.