Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunogenetic metabolomics revealed key enzymes that modulate CAR-T metabolism and function

Immune evasion is a critical step of cancer progression that remains a major obstacle for current T cell-based immunotherapies. Hence, we seek to genetically reprogram T cells to exploit a common tumor-intrinsic evasion mechanism, whereby cancer cells suppress T cell function by generating a metabolically unfavorable tumor microenvironment (TME). Specifically, we use an in silico screen to identify ADA and PDK1 as metabolic regulators, in which gene overexpression (OE) enhances the cytolysis of CD19-specific CD8 CAR-T cells against cognate leukemia cells, and conversely, ADA or PDK1 deficiency dampens such effect. ADA-OE in CAR-T cells improves cancer cytolysis under high concentrations of adenosine, the ADA substrate and an immunosuppressive metabolite in the TME. High-throughput transcriptomics and metabolomics in these CAR-Ts reveal alterations of global gene expression and metabolic signatures in both ADA- and PDK1- engineered CAR-T cells. Functional and immunological analyses demonstrate that ADA-OE increases proliferation and decreases exhaustion in α-CD19 and α-HER2 CAR-T cells. ADA-OE improves tumor infiltration and clearance by α-HER2 CAR-T cells in an in vivo colorectal cancer model. Collectively, these data unveil systematic knowledge of metabolic reprogramming directly in CAR-T cells, and reveal potential targets for improving CAR-T based cell therapy. Synopsis The authors identify the adenosine deaminase gene (ADA) as a regulatory gene that reprograms T cell metabolism. ADA-overexpression (OE) in α-CD19 and α-HER2 CAR-T cells increases proliferation, cytotoxicity, memory, and decreases exhaustion, and ADA-OE α-HER2 CAR-T cells have enhanced clearance of HT29 human colorectal cancer tumors in vivo.
Step-wise mechanical unfolding and dissociation of the GAIN domains of ADGRG1/GPR56, ADGRL1/Latrophilin-1 and ADGRB3/BAI3: insights into the mechanical activation hypothesis of adhesion G protein-coupled receptors

Adhesion G protein-coupled receptors (aGPCRs) are a large family within the superfamily of G protein-coupled receptors involved in various physiological processes. One unique feature of aGPCRs is their long N-terminal extracellular regions (ECRs), which contain adhesive domains and a GPCR autoproteolysis-inducing (GAIN) domain. This GAIN domain promotes autoproteolytic cleavage of aGPCRs into N- and C-terminal fragments (NTF, CTF, respectively) after receptor biosynthesis. aGPCR signaling involves an interplay between the NTF and CTF that can be me-chanically activated or modulated. However, how force affects the conformation/structure of the GAIN domain as a central structural element in aGPCR activation remains largely unknown. In this study, we investigated the mechanical stability of the GAIN domains of three aGPCRs from subfamilies B, G and L at a loading rate of 1 pN/s. Our findings demonstrate that the GAIN domains can be destabilized by forces from a few to 20 piconewtons (pN). Specifically, for the autocleaved aGPCRs, ADGRG1/GPR56 and ADGRL1/Latrophilin-1, forces over this range can cause detachment of the GAIN domain from the membrane-proximal Stachel element, which serves as an endogenous tethered agonist to aGPCRs, typically preceded with GAIN domain unfolding. For the non-cleavable aGPCR ADGRB3/BAI3, the GAIN domain undergoes complex mechanical unfolding over a similar force range. We also demonstrate that detachment of the GAIN domain can take place during cell migration, provided that the linkage between aGPCR and extracellular matrix is sufficiently stable. These results suggest that both structural stability of the GAIN domain and NTF/CTF dissociation are sensitive to physiological ranges of tensile forces, providing insights into the mechanical activation hypothesis of aGPCRs.
Predatory Bacteria can Reduce Pseudomonas aeruginosa Induced Corneal Perforation and Proliferation in a Rabbit Keratitis Model

Purpose Pseudomonas aeruginosa keratitis is a severe ocular infection that can lead to perforation of the cornea. In this study we evaluated the role of bacterial quorum sensing in generating corneal perforation and bacterial proliferation and tested whether co-injection of the predatory bacteria Bdellovibrio bacteriovorus could alter the clinical outcome. P. aeruginosa with lasR mutations were observed among keratitis isolates from a study collecting samples from India, so an isogenic lasR mutant strain of P. aeruginosa was included. Methods Rabbit corneas were intracorneally infected with P. aeruginosa strain PA14 or an isogenic ΔlasR mutant and co-injected with PBS or B. bacteriovorus. After 24 h, eyes were evaluated for clinical signs of infection. Samples were analyzed by scanning electron microscopy, optical coherence tomography, sectioned for histology, and corneas were homogenized for CFU enumeration and for inflammatory cytokines. Results We observed that 54% of corneas infected by wild-type PA14 presented with a corneal perforation (n=24), whereas only 4% of PA14 infected corneas that were co-infected with B. bacteriovorus perforate (n=25). Wild-type P. aeruginosa proliferation was reduced 7-fold in the predatory bacteria treated eyes. The ΔlasR mutant was less able to proliferate compared to the wild-type, but was largely unaffected by B. bacteriovorus. Conclusion These studies indicate a role for bacterial quorum sensing in the ability of P. aeruginosa to proliferate and cause perforation of the rabbit cornea. Additionally, this study suggests that predatory bacteria can reduce the virulence of P. aeruginosa in an ocular prophylaxis model.
Optineurin deficiency impairs autophagy to cause interferon beta overproduction and increased survival of mice following viral infection

Background Optineurin (OPTN) is associated with several human diseases, including amyotrophic lateral sclerosis (ALS), and is involved in various cellular processes, including autophagy. Optineurin regulates the expression of interferon beta (IFNβ), which plays a central role in the innate immune response to viral infection. However, the role of optineurin in response to viral infection has not been fully clarified. It is known that optineurin-deficient cells produce more IFNβ than wild-type cells following viral infection. In this study, we investigate the reasons for, and effects of, IFNβ overproduction during optineurin deficiency both in vitro and in vivo. Methods To investigate the mechanism of IFNβ overproduction, viral nucleic acids in infected cells were quantified by RT-qPCR and the autophagic activity of optineurin-deficient cells was determined to understand the basis for the intracellular accumulation of viral nucleic acids. Moreover, lethal viral infection experiments using optineurin-disrupted (Optn-KO) animals were performed. Results IFNβ overproduction following viral infection was observed not only in several types of optineurin-deficient cell lines but also in Optn-KO mice and human ALS patient cells carrying mutations in OPTN. IFNβ overproduction in Optn-KO cells was revealed to be caused by excessive accumulation of viral nucleic acids, which was a consequence of reduced autophagic activity caused by the loss of optineurin. Additionally, IFNβ overproduction in Optn-KO mice suppressed viral proliferation, resulting in increased mouse survival following lethal viral challenge. Conclusion Our findings indicate that the combination of optineurin deficiency and viral infection leads to IFNβ overproduction in vitro and in vivo. The effects of optineurin deficiency are elicited by viral infection, therefore, viral infection may be implicated in the development of optineurin-related diseases.
A Simulation Analysis of Distortion Operators of Wang, NIGand Cauchy: A Comparative Approach

The problem of pricing contingent claims has been extensively studied for non-Gaussian models and in particular, BlackScholes formula has been derived for the NIG asset pricing model. This approach was originally studied in Insurance pricing where the distortion function was defined in terms of the normal distribution. It was also used to compare the standard Black-Scholes contingent pricing and distortion based contingent pricing. So, in this paper, we aim at using the Cauchy simulation analysis via MATLAB to compare the Wang distortion and NIG distortion operator with their pricing model. The results show that we can recuperate the Black-Scholes and NIG pricing model using the simulation of Cauchy distortion operator.
Reciprocal discoidin domain receptor signaling strengthens integrin adhesion to connect adjacent tissues

Separate tissues connect through adjoining basement membranes to carry out molecular barrier, exchange, and organ support functions. Cell adhesion at these connections must be robust and balanced to withstand independent tissue movement. Yet, how cells achieve synchronized adhesion to connect tissues is unknown. Here, we have investigated this question using the C. elegans utse-seam tissue connection that supports the uterus during egg-laying. Through genetics, quantitative fluorescence, and cell specific molecular disruption, we show that type IV collagen, which fastens the linkage, also activates the collagen receptor discoidin domain receptor 2 (DDR-2) in both the utse and seam. RNAi depletion, genome editing, and photobleaching experiments revealed that DDR-2 signals through LET-60/Ras to coordinately strengthen an integrin adhesion in the utse and seam that stabilizes their connection. These results uncover a synchronizing mechanism for robust adhesion during tissue connection, where collagen both affixes the linkage and signals to both tissues to bolster their adhesion.
The ancestry of Antennapedia-like homeobox genes

I show that broad evolutionarily significant sub-groups of Antennapedia-like homeobox genes can be distinguished by consideration of 4 amino acids. The absence of proline at position 26 is a synapomorphy of the class; HOX-like homeodomains contain a 19,30 salt-bridge of inverted polarity with respect to the same residues in typical NK-like homeodomains, and residue 28 is highly conserved within but not necessarily between orthologous groups. None of these residues has an obvious role in sequence specific DNA recognition. The EH1 and hexapeptide sequence motifs outside the homeodomain are not well correlated with sub-type. From the discovery of a hexapeptide motif in a sponge NK-like gene, and identification of new instances of longer engrailed-like (EH2) variants of the hexapeptide, I infer that scattered motif distribution is unlikely to be due to convergent evolution, but rather multiple independent loss events. I reconcile these features and the species distribution of current genes to propose a scheme for the ordering of duplication events in the cnidarian-bilaterian stem group.
Conjunctive Queries with Free Access Patterns Under Updates

We study the problem of answering conjunctive queries with free access patterns (CQAP) under updates. A free access pattern is a partition of the free variables of the query into input and output. The query returns tuples over the output variables given a tuple of values over the input variables. We introduce a fully dynamic evaluation approach for CQAP queries. We also give a syntactic characterisation of those CQAP queries that admit constant time per single-tuple update and whose output tuples can be enumerated with constant delay given a tuple of values over the input variables. Finally, we chart the complexity trade-off between the preprocessing time, update time and enumeration delay for CQAP queries. For a class of CQAP queries, our approach achieves optimal, albeit non-constant, update time and delay. Their optimality is predicated on the Online Matrix-Vector Multiplication conjecture. Our results recover prior work on the dynamic evaluation of conjunctive queries without access patterns. We also illustrate an application of our dynamic evaluation approach to tractable CQAP queries over probabilistic databases.
Shuffling-Based Data Augmentation for Argument Mining

Argument Mining is an emerging research area in natural language processing; it is concerned with extracting arguments and their structure from text documents. Deep neural networks and contextualized word embeddings have been recently obtaining state-of-the-art results on various classification and relation extraction tasks including argument mining, but they tend to learn spurious correlations and to memorize high-frequency patterns possibly undermining systems’ predictions. In this paper we illustrate how transformers-based models fine-tuned on argumentative student essays are biased (and their performance are thereby affected) by their structure; additionally, we show that adopting data augmentation by shuffling sentences may be helpful in reducing structure dependency and to improve generalization.
Software Architecture for Distributed Edge Computing Systems

The world is progressing towards the hyper-connected era where every object in our physical world will possess built-in computing and connectivity. As the number of these smart devices grows, these Systems of Systems will transition from integrated domain-specific solutions to horizontal platforms where all systems will be designed for knowledge-based automation, driving the decentralization of computing, communications and business processes. The recent unpredictability in various business process scenarios has high-lighted the need for collaboration and the advantages of participating. Edge computing facilitates segregation of computing responsibilities between edge devices and cloud infrastructure to process and analyze data. Researchers are working on reconfigurable edge computing systems based on varied technology solutions. We propose a software architectural framework to facilitate sustainability and evolvability in such ecosystems.
Realizing a Denial of Expectation in Pipelined Neural Data-To-Text Generation

This paper aims at the generation of denials of expectations in the domain of vehicle reviews. A denial of expectation expresses an apparent contradiction between some probabilistically motivated rule and a current circumstance expressed by a contrastive sentence. For generating such an argumentative sentence, we present a new approach for content selection in a neural data-to-text generation framework. In addition to selecting relevant information from tabular data that should appear in the text, further methods are required for determining evaluations that are rooted in this data, but express individual appraisals of the respective vehicle. We show how a content selection module is able to decide when expressing a denial of expectation. We use multi-label and binary classification for content selection on automatically extracted training data and Random Forest Regression with varying knowledge limitation for predicting expectations about feature values. These predictions are compared to manually annotated corpus instances of contrast relations in order to show that the concept of denial of expectation is a reasonable approach to determining contrasts and evaluative content at the early stage of content selection.
Human Centric Framework for Customising and Producing Effective Cybersecurity Training Materials

These days organizational security breaches are widespread, and many of them can be traced back to human errors. As a result, companies must improve employee security awareness and their ability to engage in safe cybersecurity practices. To accomplish so, organizations should spend on cybersecurity training and awareness programs to urge employees to take an active role in adhering to security policies. Numerous organizations’ cybersecurity training and awareness activities, on the other hand, fall short of their goals since most training programs focus solely on technical issues, leaving many human factors unaddressed, resulting in training failure. Unlike most other cybersecurity training programs, this study emphasizes the importance of human factors in cybersecurity and the need for successful cybersecurity training and awareness programs in businesses and provides best practices that will assist organizations in developing and implementing effective cybersecurity programs that account for human factors. Also, the end objective of this research is to implement a framework that will suggest personalized cybersecurity training materials based on the end-user’s knowledge about cybersecurity.
Self-motivational Beliefs that Enhance Self-regulation of Students in Virtual Courses

The present research exposes the way in which eight students selected from virtual environments of a Peruvian institute of higher education perceive their self-regulation processes in the planning stage. The approach is qualitative with a phenomenological interpretative design. Based on the interview of eight students enrolled in the distance learning modality. The main finding is that students have their own beliefs about themselves that help motivate them permanently in the assignments they must complete. Determined by both external and internal factors, these help academic tasks to be valued in a positive way, so that their completion gives them satisfaction
Some Vignettes on Subgraph Counting Using Graph Orientations (Invited Talk)

Subgraph counting is a fundamental problem that spans many areas in computer science: database theory, logic, network science, data mining, and complexity theory. Given a large input graph G and a small pattern graph H , we wish to count the number of occurrences of H in G . In recent times, there has been a resurgence on using an old (maybe overlooked? ) technique of orienting the edges of G and H , and then using a combination of brute-force enumeration and indexing. These orientation techniques appear to give the best of both worlds. There is a rigorous theoretical explanation behind these techniques, and they also have excellent empirical behavior (on large real-world graphs). Time and again, graph orientations help solve subgraph counting problems in various computational models, be it sampling, streaming, distributed, etc. In this paper, we give some short vignettes on how the orientation technique solves a variety of algorithmic problems.
Software-Supported Product Backlog Prioritization in Scrum Software Development Projects

Agile software development methods (e.g., Scrum) have gained increasing significance in recent years. In contrast to traditional plan-driven methods, they offer the necessary flexibility to react to continuously changing requirements. As the most established agile development method, Scrum involves the Product Owner (PO) as the sole representative of all project relevant stakeholders. A central task of the PO is the management of the Product Backlog (PB) that contains all relevant information to maximize the product value, i.e., requirements in the form of user stories. During PB management, creating a shared understanding of the underlying requirements is necessary. However, the PO is often a bottleneck, e.g., due to requirements complexity or limited access to relevant stakeholders. Especially the task of PB prioritization appears to be a challenging while success critical one. Appropriate software support can help to overcome challenges such as communication and collaboration barriers, complex calculations, or in-transparency regarding the prioritization process and result. While much research on manual or (semi-)automated prioritization techniques has been conducted, no software-supported PB prioritization currently exists that is based on a comprehensive methodological approach. The PhD project addresses this knowledge gap in developing a conceptual model and a therewith-aligned software prototype.
Response to van der Lans

We wish to thank Ralf van der Lans for pointing out corrections to part of the pseudo-code in Appendix A.5 provided in Holmes and Held (2006) concerning the polychotomous regression. The pseudo-code was inconsistent, and we have highlighted below the required corrections in a compact fashion making clear the extent of the necessary changes. It is also important to note that the theory and methods presented in the paper are correct.
A Survey of the Southern Sky at 85 MHz

A survey at 85 MHz is presented of the sky south of declination 0 = _20°,and of the northern galactic spur region to 0 = +20°. The survey was made withthe 210 ft telescope at Parkes, New South Wales. At this frequency the observedbeamwidth was 3.5° by 3· 8° to half-power points. The radiation characteristics ofthe survey aerial and of scaled aerial models were studied to enable corrections to bemade for radiation received by the aerial from regions outside the main beam.Absolute beam temperatures derived from the observed aerial temperatures had anr.m.s. accuracy of better than 7%.
Identification of seismically excited buildings with two orthogonal horizontal components

An algorithm is presented here to identify parameters of linear models of buildings under orthogonal bi-dimensional seismic excitation, using acceleration measurements of sensors arbitrarily arranged on the stories, in real time. The model considers three degrees of freedom for each story and uses an efficient reparameterization of the least-squares algorithm that allows a very important reduction in the number of calculations, when compared with the standard formulation. Results obtained from simulations and with data obtained from instrumented buildings confirm the efficacy of the proposed method.
General theory of Maker fringes in crystals of low symmetry

A set of general equations for Maker fringes, applicable to all crystal structures and orientations, has been derived. Alternative forms of the equations are given, suitable for numerical work or for analytical reduction in cases where symmetry or other factors reduce their complexity. Formulae for special cases have been obtained from the general equations and the results compared with previously published work. The case of monoclinic symmetry has been discussed in more detail and a numerical example of the application of the formulae to the study of the d-coefficients is presented.
Prediction in Some Poisson Errors in Variables Models

Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs sampling routines are used to derive the predictive and posterior probabilities. Examples of analyses are provided with data from road accidents in Sweden.
Measurement of Ocean Wave Heights with the Random-Signal Radar

When a radar antenna is pointed directly down at the sea surface, the return signal is caused mainly by specular points on the surface. In this case, the sea surface can be modeled as a collection of Poisson-distributed point scatterers, each having a certain radar range and a certain vertical velocity. This model can be characterized by a deterministic function, called the scatterer density function, which is the density of specular point scatterers in the two-dimensional range-velocity space. This function can be estimated by using a high-resolution random-signal radar and provides information relating the dynamic aspects of the ocean surface to the distribution of wave heights. A special case of this problem is the estimation of average wave height, or sea state, and a specific technique to estimate this quantity is proposed and analyzed. This technique has the advantage of being a direct estimation procedure; i.e., the quantity measured is wave height, not a quantity that has been empirically related to wave height.