Miracle-dz/UniFAR-FTUs huggingface dataset:
Medicinal chemistry lens on patent data in SureChEMBL

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded particular importance because of the high costs of research and development (R&D), associated with the considerable risks inherent in the creation of novel drugs. Consequently, patents play a pivotal role in ensuring a suitable return on R&D investments, especially those directed toward clinical trials. The patent corpus has been analyzed by researchers to identify freedom-to-operate spaces for novel drug candidates. Several well-established public patent data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this manuscript, we delve into one such publicly available patent database, SureChEMBL, which catalogues patents related to life sciences. Our exploration begins by evaluating the coverage of compounds within SureChEMBL in comparison to other chemical data resources. Additionally, we pinpoint critical sections of patent documents where detailed chemical annotations can be found. Next, we exhibit the compounds’ potential to serve as drug candidates by evaluating their conformity to the Rule of Five (Ro5) and its extension, the beyond Ro5 (bRo5). Furthermore, we examine the development stage (preclinical or clinical) reported for these compounds as documented in public databases like ChEMBL. In summation, our investigation aims to provide an estimate of the relevance of the compounds catalogued within the SureChEMBL database in the context of the drug discovery domain.
Regulators of health and lifespan extension in genetically diverse mice on dietary restriction

Caloric restriction (CR) delays aging and extends healthy lifespan in multiple species. Alternative forms of dietary restriction (DR) such as intermittent fasting (IF) have drawn significant interest as a more sustainable regimen, but the landscape of longevity-promoting dietary interventions remains largely unexplored. Identifying the most robust, efficacious, and experimentally tractable modes of DR is key to better understanding and implementing effective longevity interventions for human healthspan. To that end, we have performed an extensive assessment of DR interventions, investigating the effects of graded levels of CR (20% and 40%) and IF (1 day and 2 days of fasting per week) on the health and survival of 960 genetically diverse female mice. All interventions extended lifespan, although only CR significantly reduced the mortality doubling time. Notably, IF did not extend lifespan in mice with high pre-intervention bodyweight. We carried out extensive phenotyping to determine the health effects of long-term DR and to better understand the mechanisms driving within-diet heterogeneity in lifespan. The top within-diet predictor of lifespan was the ability of mice to maintain bodyweight through periods of handling, an indicator of stress resilience. Additional predictors of long lifespan include specific changes in immune cells, red blood cell distribution width (RDW), and retention of adiposity in late life. We found that lifespan is heritable (h2 = 0.24), and that genetic background has a larger influence on lifespan than dietary interventions. We identified a significant association for lifespan and RDW on chromosome 18 that explained 4.3% of the diet-adjusted variation in lifespan. Diet-induced changes on metabolic traits, although beneficial, were relatively poor predictors of lifespan, arguing against the long-standing notion that DR works by counteracting the negative effects of obesity. These findings indicate that improving health and extending lifespan are not synonymous and that metabolic parameters may be inappropriate endpoints for evaluating aging interventions in preclinical models and clinical trials.
Together, Neuropilin 1 and Neuropilin 2 direct α5 integrin trafficking through GTPase-activating-protein p120RasGAP in endothelial cells to promote fibronectin fibrillogenesis

Integrin trafficking to and from membrane adhesions is a crucial mechanism that dictates many aspects of a cell’s behaviour, including motility, polarisation, and invasion. In endothelial cells (ECs), the intracellular traffic of α5 integrin is regulated by both neuropilin 1 (NRP1) and neuropilin 2 (NRP2), yet the redundancies in function between these co-receptors remain unclear. Moreover, the endocytic complexes that participate in NRP-directed traffic remain poorly annotated. Using label-free quantitative mass spectrometry of α5 integrin associations in ECs we identify α5 trafficking pathways that depend on NRP1, NRP2, or both NRPs. We identify a trafficking pathway that depends on both NRPs: one that impinges on the GTPase-activating protein p120RasGAP. This pathway promotes the recycling of α5 integrin from early endosomes. Mechanistically, p120RasGAP enables transit of endocytosed α5 integrin-NRP1-NRP2 complexes to Rab11+ recycling endosomes, promoting cell polarisation and fibronectin (FN) fibrillogenesis. Silencing of both NRP receptors, or p120RasGAP, results in the accumulation of α5 integrin in early endosomes, a loss of α5 integrin from surface adhesions, and attenuated EC polarisation. Importantly, endothelial-specific deletion of both NRP1 and NRP2 in the postnatal retina recapitulated our in vitro findings, severely impairing FN fibrillogenesis and polarised sprouting. Our data assign an essential role for p120RasGAP during integrin traffic in ECs and support a hypothesis that NRP receptors can co-traffic internalised cargoes.
Rate of brain aging associates with future executive function in Asian children and older adults

Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55 − 88 years old) and children (4 − 11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.
Coarse-to-Fine Active Segmentation of Interactable Parts in Real Scene Images

We introduce the first active learning (AL) framework for high-accuracy instance segmentation of dynamic , in-teractable parts from RGB images of real indoor scenes . As with most human-in-the-loop approaches, the key criterion for success in AL is to minimize human effort while still attaining high performance. To this end, we employ a transformer-based segmentation network that utilizes a masked-attention mechanism. To enhance the network, tailoring to our task, we introduce a coarse-to-fine model which first uses object-aware masked attention and then a pose-aware one, leveraging a correlation between interactable parts and object poses and leading to improved handling of multiple articulated objects in an image. Our coarse-to-fine active segmentation module learns both 2D instance and 3D pose information using the transformer, which supervises the active segmentation and effectively reduces human effort. Our method achieves close to fully accurate (96% and higher) segmentation results on real images, with 77% time saving over manual effort, where the training data consists of only 16.6% annotated real photographs. At last, we contribute a dataset of 2,550 real photographs with annotated interactable parts, demonstrating its superior quality and diversity over the current best alternative.
On Agile Leadership and Project Sustainability

: Sustainability is the predominant topic right now and will remain so in the coming decades, which adds another layer to the already existing complexity accompanying digitalization. At the same time, many organizations embrace agile leadership on project or organizational level, to better deal with complexity and constant changes. The objective of this paper is to investigate potential impacts of agile leadership on project sustainability. This is done by mapping categories of agile leadership to the three sustainability dimensions “people, profit, and planet” (triple bottom line). While certain sustainability aspects can be addressed primarily by top level decision making and the mindset of leaders, other aspects are directly influenced by an agile environment and way of working. Overall, the results highlight natural synergies between agile leadership and sustainability and provide an impulse for further research.
Towards the Accuracy of Cybernetic Strategy Planning Models : Causal Proof and Function Approximation

All kind of strategic tasks within an enterprise require a deep understanding of its critical key success factors and their interrelations as well as an in-depth analysis of relevant environmental influences. Due to the openness of the underlying system, there seems to be an indefinite number of unknown variables influencing strategic goals. Cybernetic or systemic planning techniques try to overcome this intricacy by modeling the most important cause-and-effect relations within such a system. Although it seems to be obvious that there are specific influences between business variables, it is mostly impossible to identify the functional dependencies underlying such relations. Hence simulation or evaluation techniques based on such hypothetically assumed models deliver inaccurate results or fail completely. This paper addresses the need for accurate strategy planning models and proposes an approach to prove their cause-andeffect relations by empirical evidence. Based on this foundation an approach for the approximation of the underlying cause-andeffect function by the means of Artificial Neural Networks is developed.
The Humpty Dumpty Scale: cross-cultural adaptation and validation for Brazilian culture

Objective: To adapt and validate The Humpty Dumpty Scale for Brazilian culture. Methods: Methodological study that followed the steps of translation, synthesis, back-translation, evaluation by specialists, pre-test and evaluation by the author of the original instrument for the cross-cultural adaptation. A total of 103 children/adolescents admitted to a public hospital in the countryside of the state of Sao Paulo were selected. We used a characterization form, The Humpty Dumpty Scale and the Pediatric Patient Classification Instrument, which was used to assess construct validity, with Spearman’s correlation coefficient. Interobserver reliability was assessed using the Intraclass Correlation Coefficient and the Kappa
Urban change and the mesh: an ethnography of Deptford’s Open Wireless Network

This chapter engages in a trans-disciplinary discussion on urban wireless networks. It presents findings from my longitudinal participant observation of Open Wireless Network (OWN), a Wi-Fi mesh network in Deptford, inner-city London, as well as of the related 'Wireless Wednesday' workshops. It wants to do two things, while narrating some of the dense exchange I had with users, owners, and developers of the network. It firstly makes visible, on the ground, the socio-technical context in which taken-for-granted wireless works. It then investigates the inherent paradox at the centre of urban mesh networks: between localised tactics of connectedness—always in the geographically limited range of the wireless reach—and the anonymity implied by mesh networking, which the city of strangers massively expands. What does it mean for its members and developers to participate to an anonymous but open network? What are the possibilities and limitations of such an engagement in these rapidly changing neighbourhoods?
Multitask Classiﬁcation across Psychological Models of Emotion and Aﬀect

The classiﬁcation of emotions from text is usually done on basis of a single psychological model of emotion and aﬀect with a singletask classiﬁer. The goal of this bachelor thesis is to evaluate the merit of multitask learning across emotion models. Here, a multitask approach is taken, where multiple data sets, each annotated on basis of a diﬀer-ent psychological model, are trained in a multiheaded neural network. Comparing the F1-score / mean-squared-error of the multitask classi-ﬁers to the singletask classiﬁers show no improvement. Abstract Die Klassiﬁzierung von Emotionen aus Text wird normalerweise auf Basis eines einzelnen psychologischen Modells von Emotion und Aﬀekt mit einem singletask Klassiﬁzierer erwirkt. Das Ziel dieser Bach-elorarbeit ist es, den Vorzug vom multitask Lernen von Emotionsmod-ellen zu evaluieren. Hierbei wird ein multitask Ansatz genommen, bei dem mehrere Datens¨atze, die jeweils auf Basis unterschiedlicher psy-chologischer Modelle annotiert wurden, in einem mehrk¨opﬁgen neu-ronalem Netzwerk trainiert werden. Der Vergleich des F-Maß / mit-tleren quadratischen Fehlers der multitask Klassiﬁzierer und der sin-gletask Klassiﬁzierer zeigt keine Verbesserung.
Current Harmonics and Unbalance Suppression of Dual Three-Phase PMSM Based on Adaptive Linear Neuron Controller

A strategy of current harmonics and unbalance suppression based on the vector space decomposition control with adaptive linear neuron (ALN) controller for the dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive system is proposed in this article. According to the conventional vector space decomposition theory, the compensation voltages from the ALN controller in the dq-frame and dqz-frame are employed to suppress the 5th and 7th current harmonics and unbalances. Compared with the VSD control using only the PI controller, the current harmonics and unbalance are sufficiently suppressed in the proposed strategy. In addition, unlike the resonant controller discretized by conventional Tustin transformation, the ALN controller has no resonant pole shift to the desired resonant frequency. Therefore, the harmonic suppression effect of the proposed strategy exhibits better performance than the VSD control with the resonant controller, especially in the high-speed region. Finally, the feasibility and effectiveness of the proposed strategy are validated by experimental results under both steady and transient states.
SDN Lullaby: VM Consolidation for SDN using Transformer-Based Deep Reinforcement Learning

This study introduces Virtual Machine (VM) Consolidation using a Transformer-based Deep Reinforcement Learning (DRL) method, to address the complexity and inefficiency in operating Software Defined Networks-enabled Network Function Virtualization (SDN-enabled NFV). The distribution of Virtual Network Functions (VNFs) as VMs across servers often leads to energy loss due to irregular deployment. The proposed approach enhances energy efficiency while maintaining the performance of Service Function Chains (SFCs). By refining the VM consolidation process and leveraging a more sophisticated DRL method, this approach promises a more efficient solution to VM consolidation in SDN-enabled NFV environments.
Artistic Rendering of Natural Environments

Mountain panorama maps are aerial view paintings that depict complex, three-dimensional landscapes in a pleasing and understandable way. Painters and cartographers have developed techniques to create such artistic landscapes for centuries, but the process remains difficult and time-consuming. In this dissertation, we derive principles and heuristics for panorama map creation of mountainous terrain from a perceptual and artistic analysis of a panorama map of Yellowstone National Park painted by two different artists, Heinrich Berann and James Niehues. We then present methods to automatically produce landscape renderings in the visual style of the panorama map. Our algorithms rely on USGS terrain and its corresponding classification data. Our surface textures are generated using perceptual metrics and artistic considerations, and use the structural information present in the terrain to guide the automatic placement of image space strokes for natural surfaces such as forests, cliffs, snow, and water. An integral part of automatic rendering is choosing a viewpoint that is meaningful and representative of the landscape. In this dissertation we examine the automatic generation of well-composed and purposeful images in the context of mountainous terrain. We explore a set of criteria based on utility, perception, and aesthetics applicable to natural outdoor scenes. We also propose a method that uses the criteria to produce renderings of terrain scenes automatically. Finally, arising from difficulties we encountered using color transfer to improve the final rendered colors of our panorama map renderings, we propose a novel color model, oRGB, that is based on opponent color theory. Like HSV, it is designed specifically for computer graphics. However, it is also designed to work well for computational applications such as color transfer, where HSV falters. Despite being geared towards computation, oRGB's natural axes facilitate HSV-style color selection and manipulation. oRGB also allows for new applications such as a quantitative cool-to-warm metric, intuitive color manipulations and variations, and simple gamut mapping. This new color model strikes a balance between simplicity and the computational qualities of color spaces such as CIE L*a*b*.
Reduced SnO2-x for Low Power NO2 Gas Sensors: From First Principles Simulations to Sensing Performance

Semiconducting metal oxides are the most studied materials for gas sensing application due to their outstanding high sensitivity, good stability and low production costs. However, despite these advantages, the devices suffer weakness in their performance, including the lack of selectivity and high working temperature, which results in high-power consumption. In this work, a simple and low-cost method was used to synthesize reduced SnO2-x to produce gas sensors that can be operated at low operation temperatures. The combined use of theoretical Density Functional Theory and in-depth characterization of SnO2-x samples allowed to investigate both superficial and bulk oxygen vacancies created. The results achieved confirm that the simple and low-cost method, used in this work, successfully reduces SnO2 samples, and based on the theoretical investigation the reduction of this kind of materials may lower the sensor operating temperature. Indeed, the electrical characterizations of the sensor showed the highest sensitivity at 70 °C instead of the common operating temperature of SMOX-based gas sensors (ranging from 300 to 600 °C).
Quantifying the Impact of Automated Vehicles on Traffic

One of the major challenges in the development of Automated Driving is its assessment. It is expected that Automated Vehicles behave differently than human drivers. Therefore, mixed human-robot traffic will yield different and new driving situations as human-only traffic. It is important to know how this mixed traffic will change the composition of traffic situations to be able to quantify the impact Automated Vehicles will have on everyday traffic. This paper presents a methodology on how to find metrics that quantify traffic in order to detect changes in the traffic space that will come with the introduction of Automated Vehicles. Additionally, this methodology provides tools to help with the validation of virtual testing platforms such as simulation.
Initial Investigation of Physically Tightly-Coupled Single-Chip MEMS IMU for Mutual Compensation of Two Differential Resonant Accelerometers and a Mode-Matched Donut-Mass Gyroscope

In this study, we report an initial study on mutual compensation between a differential resonant accelerometer (DRA) and a donut-mass gyroscope (DMG). High-precision inertial measurement units (IMUs) require compensation for temperature drift and multi-axis sensitivity in addition to the development of high-precision sensors. One effective means for achieving this is to integrate each sensor into a single chip. DRAs are suitable for IMUs because they can separate the main sensitivity acceleration and temperature drift components from the resonance frequencies of the two beams. We developed the first single-chip that integrates two DRAs with XY-axis sensitivity and a high-precision DMG capable of acquiring Z-axis angular velocity and conducted an initial evaluation using a microcontroller-based module. We demonstrated linear operation of the DMG by applying angular velocities of +1, 5, 10, and 50 deg/s, and found high agreement ($\mathrm{R}^{2}=0.9826,\ 0.9965$) with the frequencies of DMG and DRA corrected for multi-axis sensitivity of the DRA when ±1 G acceleration was applied in the X, Y, and Z-axes.
Gas Mixture Estimation: Using Power-Law Models of Arrayed Chemiresistive Metal-Oxide Sensors

This paper presents a model for the resistance of micromachined semiconducting metal-oxide (SMOx) sensors exposed to ternary gas mixtures. This paper also presents a corresponding method for estimating gas concentrations using the measured resistances of an array of on a chip. To our knowledge, this approach has not been reported for multi-gas estimation.
Identification of Axon Bendings in Neurons by Multiphysics FEM Simulations of High-Density MEA Extracellular Recordings

A three-dimensional finite element method (FEM) simulation of a neuron (retinal ganglion cell) interfaced to a high-density CMOS-based microelectrode array (MEA) is performed and compared to recordings. The adopted multiscale simulation approach accurately reproduces the signals recorded by the patch clamp and the MEA sensors. The simulation leads us to identify bendings of the axon initial segment of a few neurons embedded in tissue, which are reported here for the first time.
3-D Reconstruction Method Based on RGB-Assisted ICP

Due to the advantages of high accuracy and rich informa-tion, point cloud-based geometric reconstruction has been widely applied in 3-D reconstruction for various scenarios. However, the classic iterative closest point (ICP) algorithm, relying on threshold permission, imposes limitations on the distance and generalizability of point cloud registration. In this letter, we propose a 3-D reconstruction system based on RGB-assisted ICP, leveraging the advantages of RGB calibration to achieve long-range registration. We establish an experimental platform in a laboratory setting to verify the feasibility of the algorithm, calculating the distances between points and the reconstructed plane through plane parameter fitting. The proposed method is further applied to actual airport pallet measurement scenarios, achieving a reconstruction accuracy of 3.6 cm within a working range of 8 m × 8 m × 8 m.