Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimensions of the Precautionary Principle

This essay attempts to provide an analytical apparatus which may be used for finding an authoritative formulation1 of the Precautionary Principle. Several formulations of the Precautionary Principle are examined. Four dimensions of the principle are identified: (1) the threat dimension, (2) the uncertainty dimension, (3) the action dimension, and (4) the command dimension. It is argued that the Precautionary Principle can be recast into the following if-clause, containing these four dimensions: “If there is (1) a threat, which is (2) uncertain, then (3) some kind of action (4) is mandatory. ” The phrases expressing these dimensions may vary in (a) precision and (b) strength. It is shown that it is the dimension containing the weakest phrase that determines the strength of the entire principle. It is suggested that the four-dimensional if-clause be used as an analytical apparatus in negotiations of the Precautionary Principle.
Orpinolide disrupts a leukemic dependency on cholesterol transport by inhibiting the oxysterol-binding protein OSBP

Metabolic alterations in cancer precipitate in associated dependencies that can be therapeutically exploited. To meet this goal, natural product inspired small molecules can provide a resource of invaluable chemotypes. Here, we identify orpinolide, a synthetic withanolide analog with pronounced anti-leukemic properties via orthogonal chemical screening. Through multi-omics profiling and genome-scale CRISPR/Cas9 screens, we identify that orpinolide disrupts Golgi homeostasis via a mechanism that requires active phosphatidylinositol 4-phosphate (PI4P) signaling at the endoplasmic reticulum (ER)-Golgi membrane interface. Thermal proteome profiling and genetic validation studies reveal the oxysterol-binding protein OSBP as the direct and phenotypically relevant target of orpinolide. Collectively, these data reaffirm sterol transport as a therapeutically actionable dependency in leukemia and motivate ensuing translational investigation via the probe-like compound orpinolide.
A novel ground truth dataset enables robust 3D nuclear instance segmentation in early mouse embryos

For investigations into fate specification and cell rearrangements in time-lapse images of preimplantation embryos, automated 3D instance segmentation of nuclei and subsequent nuclear tracking are invaluable. Often, the images’ low signal-to-noise ratio and high voxel anisotropy and the nuclei’s dense packing and variable shapes limit the performance of many segmentation methods, while subsequent tracking of nuclear instances is complicated by low frame rates and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of difficult-to-obtain annotated 3D data. Here we report a novel mouse line expressing near-infrared nuclear reporter H2B-miRFP720. H2B-miRFP720 is the longest wavelength nuclear reporter in mice and can be imaged simultaneously with other reporters with minimal overlap. We then generate a dataset, which we call BlastoSPIM, of 3D microscopy images of H2B-miRFP720-expressing embryos with ground truth for nuclear instance segmentation. Using BlastoSPIM, we benchmark the performance of seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method across preimplantation development. We then construct a complete pipeline for nuclear instance segmentation with our BlastoSPIM-trained Stardist-3D models and lineage tracking from the 8-cell stage to the end of preimplantation development (> 100 nuclei). Finally, we demonstrate BlastoSPIM’s usefulness as pre-train data for related problems, both for a different imaging modality and for different model systems. BlastoSPIM, its corresponding Stardist-3D models, and documentation of the full associated analysis pipeline are available at: blastospim.flatironinstitute. org.
Field-induced resistive switching based on space-charge-limited current

Polycrystalline (Ba,Sr)(Zr,Ti)O3 thin films sandwiched between two Pt electrodes have been revealed to exhibit hysteretic current-voltage (I-V) characteristics and resistive switching at room temperature. High- and low-resistance states, as well as a less abrupt state transition, occur during the voltage cycle. The maximum ratio between these two resistance states is about 230. Analyses of I-V behaviors have been executed, and it is proposed that space-charge-limited-current conduction in higher voltage region caused by asymmetric electron trapping centers is responsible for such transition of resistance states.
Human pluripotent stem cell-derived kidney organoids reveal tubular epithelial pathobiology of heterozygous HNF1B-associated dysplastic kidney malformations

Hepatocyte nuclear factor 1B (HNF1B) encodes a transcription factor expressed in developing human kidney epithelia. Heterozygous HNF1B mutations are the commonest monogenic cause of dysplastic kidney malformations (DKMs). To understand their pathobiology, we generated heterozygous HNF1B mutant kidney organoids from CRISPR-Cas9 gene-edited human ESCs and iPSCs reprogrammed from a family with HNF1B-asscociated DKMs. Mutant organoids contained enlarged malformed tubules and displayed deregulated cell turnover. Numerous genes implicated in Mendelian kidney tubulopathies were downregulated, and mutant tubules resisted the cAMP-mediated dilatation seen in controls. Bioinformatic analyses indicated abnormal WNT, calcium, and glutamatergic pathways, the latter hitherto unstudied in developing kidneys. Glutamate ionotropic receptor kainate type subunit 3 was upregulated in mutant organoids and was detected in their tubules and in fetal human DKM dysplastic epithelia. These results reveal morphological, molecular, and physiological roles for HNF1B in human kidney tubule morphogenesis and functional differentiation. They additionally suggest druggable targets to ameliorate disease.
Histone modifications regulate pioneer transcription factor binding and cooperativity

Pioneer transcription factors have the ability to access DNA in compacted chromatin. Multiple transcription factors can bind together to a regulatory element in a cooperative way and cooperation between pioneer transcription factors Oct4 and Sox2 is important for pluripotency and reprogramming. However, the molecular mechanisms by which pioneer transcription factors function and cooperate remain unclear. Here we present cryo-EM structures of human Oct4 bound to a nucleosome containing human Lin28B and nMatn1 DNA sequences, which bear multiple binding sites for Oct4. Our structural and biochemistry data reveal that Oct4 binding induces changes to the nucleosome structure, repositions the nucleosomal DNA and facilitates cooperative binding of additional Oct4 and of Sox2 to their internal binding sites. The flexible activation domain of Oct4 contacts the histone H4 N-terminal tail, altering its conformation and thus promoting chromatin decompaction. Moreover, the DNA binding domain of Oct4 engages with histone H3 N-terminal tail, and posttranslational modifications at H3K27 modulate DNA positioning and affect transcription factor cooperativity. Thus, our data show that the epigenetic landscape can regulate Oct4 activity to ensure proper cell reprogramming.
Perturbomics of tumor-infiltrating NK cells

Natural killer (NK) cells are an innate immune cell type that serves at the first level of defense against pathogens and cancer. NK cells have clinical potential, however, multiple current limitations exist that naturally hinder the successful implementation of NK cell therapy against cancer, including their effector function, persistence, and tumor infiltration. To unbiasedly reveal the functional genetic landscape underlying critical NK cell characteristics against cancer, we perform perturbomics mapping of tumor infiltrating NK cells by joint in vivo AAV-CRISPR screens and single cell sequencing. We establish a strategy with AAV-SleepingBeauty(SB)- CRISPR screening leveraging a custom high-density sgRNA library targeting cell surface genes, and perform four independent in vivo tumor infiltration screens in mouse models of melanoma, breast cancer, pancreatic cancer, and glioblastoma. In parallel, we characterize single-cell transcriptomic landscapes of tumor-infiltrating NK cells, which identifies previously unexplored sub-populations of NK cells with distinct expression profiles, a shift from immature to mature NK (mNK) cells in the tumor microenvironment (TME), and decreased expression of mature marker genes in mNK cells. CALHM2, a calcium homeostasis modulator that emerges from both screen and single cell analyses, shows both in vitro and in vivo efficacy enhancement when perturbed in chimeric antigen receptor (CAR)-NK cells. Differential gene expression analysis reveals that CALHM2 knockout reshapes cytokine production, cell adhesion, and signaling pathways in CAR- NKs. These data directly and systematically map out endogenous factors that naturally limit NK cell function in the TME to offer a broad range of cellular genetic checkpoints as candidates for future engineering to enhance NK cell-based immunotherapies.
Aversive experience drives offline ensemble reactivation to link memories across days

Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, linking the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory-linking through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.
Regulatable assembly of synthetic microtubule architectures using engineered MAP-IDR condensates

Microtubules filaments are assembled into higher-order structures and machines critical for cellular processes using microtubule-associated proteins (MAPs). However, the design of synthetic MAPs that direct the formation of new structures in cells is challenging, as nanoscale biochemical activities must be organized across micron length-scales. Here we develop synthetic MAP-IDR condensates (synMAPs) that provide tunable and regulatable assembly of higher-order microtubule structures in vitro and in mammalian cells. synMAPs harness a small microtubule-binding domain from oligodendrocytes (TPPP) whose activity can be synthetically rewired by interaction with condensate-forming IDR sequences. This combination allows synMAPs to self-organize multivalent structures that bind and bridge microtubules into synthetic architectures. Regulating the connection between the microtubule-binding and condensate-forming components allows synMAPs to act as nodes in more complex cytoskeletal circuits in which the formation and dynamics of the microtubule structure can be controlled by small molecules or cell-signaling inputs. By systematically testing a panel of synMAP circuit designs, we define a two-level control scheme for dynamic assembly of microtubule architectures at the nanoscale (via microtubule-binding) and microscale (via condensate formation). synMAPs provide a compact and rationally engineerable starting point for the design of more complex microtubule architectures and cellular machines.
Global quantitative understanding of nonequilibrium cell fate decision making in response to pheromone

Cell cycle arrest and polarized cell growth are commonly used to qualitatively characterize the fate of yeast in response to pheromone. However, the quantitative decision-making process underlying the time-dependent changes in cell fate remains unclear. Here, by observing the multi-dimensional responses at the single-cell level experimentally, we find that yeast cells have various fates. Multiple states are revealed, along with the kinetic switching rates and pathways among them, giving rise to a quantitative landscape of mating response. We developed a theoretical framework using a nonequilibrium landscape and flux theory to account for the cell morphology observed experimentally and performed a stochastic simulation of biochemical reactions to explain the signal transduction and cell growth. Our experimental results established the first global quantitative demonstration of the real-time synchronization of intracellular signaling with their physiological growth and morphological functions which reveals the underlying physical mechanism. This study provides an emerging mechanistic approach for understanding the nonequilibrium global pheromone-regulated cell fate decision-making in growth and morphology.
AI Decision Systems with Feedback Loop Active Learner

Making precise decisions for high-stakes applications such as finance, health, and self-driving is critical for increasing the economy of an entity or the quality of life. In most scenarios, decision quickness is also as essential as accuracy. This is particularly true in the case of event detection problems, where late detection can cause financial or physical damage. While recent work focuses on combining fast unsupervised AI decision systems and precise human decisions to solve this problem, the quality of this cooperation remains questionable. A human can generate ground-truth labels for the AI decision systems for future improvements. However, having noisy ground truth can worsen the performance. To address this challenge, this paper proposes FLAL (Feedback Loop Active Learner), a novel bridge system between the AI decision system and human/s, designed to understand human expertise and interest using a recommender mechanism and improve AI system performance using an active learning mechanism. FLAL is able to identify human behavior and makes entity recommendations to users who can generate better ground-truth labels for these entities. Our experiments show that FLAL performs better than competing baselines and converges fast.
Digital Transformation of Software Companies - Brilliant Idea or Unnecessary Action? (keynote)

Novel digital technologies in the form of digital innovations drive digital transformation in companies across all industries. While the topic of digital transformation has already been studied for many industries, studies on the software industry can hardly be found. Moreover, software companies are particularly interesting in the context of digital transformation because these organizations were forced to deal with the opportunities and risks of novel digital technologies much earlier than others. Hence, they can be referred to as “digital pioneers” and represent an excellent opportunity for researchers and managers to see best practices and gain valuable insights. Therefore, this short paper introduces the concepts of digital innovation and emphasizes the need for a systematic approach toward the digital transformation by presenting two dedicated cases of one software company and one software-based company. Compared to other companies, we see in both cases a quicker “run” to the status of a Digital defined Organization and no need of new management roles like a CDO in the transformation phase.
Computing Grounded Semantics of Uncontroversial Acyclic Constellation Probabilistic Argumentation in Linear Time

We propose a new and faster (linear instead of exponential) way to compute the acceptability probability of an argument (in uncontroversial acyclic graph) with the grounded semantics in the constellations approach of probabilistic argumentation frameworks. Instead of computing all the worlds of the constellation (which is exponential) we show that it is possible to compute the probability of an argument only according to the acceptability probability of its direct attackers and the probability of its attacks by using a function.
Argumentation Ranking Semantics as a Feature for Classification - On Automatic Evaluation of Argumentative Essays

In this paper we focus on the automatic evaluation of argumentative essays, for scaffolding improvements in writing skills. Our goal is providing an automated approach to classify argumentative elements as "effective", "adequate", or "ineffective". We propose the usage of an additional feature, called ranking score, in the training process of a text-based classifier. The ranking score is obtained by performing argumentative reasoning on the different argumentative elements of an essay. We experimentally show that the introduction of this feature leads to improved performance of both Ada boost classifier and biLSTM neural network.
Support System for Exchange Students for the Validation of Courses using Text Mining

Universities usually have a service to carry out student exchanges through the agreements established between the institutions. This service follows specific steps, including reviewing the syllabi, which generally takes a long time due to inconvenience. That is why an alternative solution is proposed with the use of text mining to compare the syllabus of each course and thus ensure an optimal and effective validation of courses. For this, the proposal has been based on the Rational Unified Process following all its phases and the R language for its implementation with a case study of a university in the city of Arequipa-Peru. The results showed that it was possible to find the most frequent and related words in the similarity of the syllabic documents. Therefore, it is concluded that the contribution provided by Text Mining helped improve the process of validating courses for student exchange between institutions.
Startup Digi-Dojo: A Digital Space Supporting Practice and Research of Startup Remote Work

Many companies, including startup companies, are transitioning or pondering about the transition to remote work. However, with the various benefits of remote work also come specific challenges. These challenges tend to be exacerbated in the startup context. The existing body of knowledge on remote work neglects the specifics of the startup context. Similarly, the research on startups does not provide knowledge on their challenges in the remote work setting. Even though online support for startups does exist, it is not clear how the support tools should be designed to purposefully address the specifics of startup remote work. In this article, we introduce a research project that aims at obtaining a comprehensive understanding of the challenges and characteristics of startup remote work, and building a digital space, which we call Startup Digi-dojo, that can support startup remote work as well as research on remote startups. Startup Digi-dojo will offer a unique opportunity to build a novel kind of collaboration and synergy between startups and researchers. We will develop Startup Digi-dojo using the Design Science Research approach. Since the project is in its inception phase, we present the research design and initial architecture of the system in this article in order to get early feedback from the scientific audience.
Diving Deep into Human Centric Issues within Cyber Security

Computer security is more than just about the technological systems; it also relates to the people that use the systems and how their different behaviours may be exploited. Organizations are prone to security breaches, which sometimes are caused by human error. As a result, organizations should seek to improve their employees’ knowledge about cyber security and their capability to engage in secure cyber behaviours. It is possible to target groups ranging from basic users who need some basic understanding of the current threat environment and how to utilize the associated preventive mechanisms, to security experts who need practical exposure in responding to security incidents. Risk-taking preferences, decision-making styles, demographics, and personality characteristics, such as gender, age, culture and emotions, have been found to significantly affect the predictive ability of good security behavior. How gender and age mediate the influences on cyber security beliefs and behaviours among employees is quite interesting. Using behavioural cyber security and human factors to provide insight into relevant theories and principles, this paper proposes an interdisciplinary framework that combines these disciplines.
IRQ Coloring: Mitigating Interrupt-Generated Interference on ARM Multicore Platforms

Mixed-criticality systems, which consolidate workloads with different criticalities, must comply with stringent spatial and temporal isolation requirements imposed by safety-critical standards (e.g., ISO26262). This, per se, has proven to be a challenge with the advent of multicore platforms due to the inner interference created by multiple subsystems while disputing access to shared resources. With this work, we pioneer the concept of Interrupt (IRQ) coloring as a novel mechanism to minimize the interference created by co-existing interrupt-driven workloads. The main idea consists of selectively deactivating specific (“colored”) interrupts if the Quality of Service (QoS) of critical workloads (e.g., Virtual Machines) drops below a well-defined threshold. The IRQ Coloring approach encompasses two artifacts, i.e., the IRQ Coloring Design-Time Tool (IRQ DTT) and the IRQ Coloring Run-Time Mechanism (IRQ RTM). In this paper, we focus on presenting the conceptual IRQ coloring design, describing the first prototype of the IRQ RTM on Bao hypervisor, and providing initial evidence about the effectiveness of the proposed approach on a synthetic use case.
Efficient Abstraction of Clock Synchronization at the Operating System Level

Distributed embedded systems are emerging and gaining importance in various domains, including industrial control applications where time determinism – hence network clock synchronization – is fundamental. In modern applications, moreover, this core functionality is required by many different software components, from OS kernel and radio stack up to applications. An abstraction layer devoted to handling time needs therefore introducing, and to encapsulate time corrections at the lowest possible level, the said layer should take the form of a timer device driver offering a Virtual Clock to the entire system. In this paper we show that doing so introduces a nonlinearity in the dynamics of the clock, and we design a controller based on feedback linearization to handle the issue. To put the idea to work, we extend the Miosix RTOS with a generic interface allowing to implement virtual clocks, including the newly designed controller that we call FLOPSYNC-3 after its ancestor. Also, we introduce the resulting virtual clock in the TDMH [20] real-time wireless mesh protocol. 2012 ACM