Miracle-dz/UniFAR-FTUs huggingface dataset:
Giardia intestinalis deoxyadenosine kinase has a unique tetrameric structure that enables high substrate affinity and makes the parasite sensitive to deoxyadenosine analogues

Giardia intestinalis is a protozoan parasite causing giardiasis, a severe, sometimes even life-threatening, diarrheal disease. Giardia is one of only a few known organisms that lack de novo synthesis of DNA building blocks, and the parasite is therefore completely dependent on salvaging deoxyribonucleosides from the host. The deoxyribonucleoside kinases (dNKs) needed for this salvage are generally divided into two structurally distinct families, thymidine kinase 1 (TK1)-like dNKs and non-TK1-like dNKs. We have characterized the G. intestinalis deoxyadenosine kinase and found that it, in contrast to previously studied non-TK1-like dNKs, has a tetrameric structure. Deoxyadenosine was the best natural substrate of the enzyme (KM=1.12 μM; Vmax=10.3 μmol·min-1·mg-1), whereas the affinities for deoxyguanosine, deoxyinosine and deoxycytidine were 400-2000 times lower. Deoxyadenosine analogues halogenated at the 2- and/or 2’ s-positions were also potent substrates, with comparable EC50 values as the main drug used today, metronidazole, but with the advantage of being usable on metronidazole-resistant parasites. Cryo-EM and 2.1 Å X-ray structures of the enzyme in complex with the product dAMP (and dADP) showed that the tetramer is kept together by extended N- and C-termini that reach across from one canonical dimer to the next in a novel dimer-dimer interaction. Removal of the two termini resulted in lost ability to form tetramers and a 100-fold decreased deoxyribonucleoside substrate affinity. This is the first example of a non-TK1-like dNK that has a higher substrate affinity as the result of a higher oligomeric state. The development of high substrate affinity could be an evolutionary key factor behind the ability of the parasite to survive solely on deoxyribonucleoside salvage. Authors summary The human pathogen Giardia intestinalis is one of only a few organisms that lack ribonucleotide reductase and is therefore completely dependent on salvaging deoxyribonucleosides from the host for the supply of DNA building blocks. We have characterized one of the G. intestinalis salvage enzymes, which was named deoxyadenosine kinase based on its substrate specificity. The enzyme also phosphorylated many deoxyadenosine analogues that were equally efficient in preventing parasite growth as the most used drug today, metronidazole, and also usable against metronidazole-resistant parasites. Structural analysis of the enzyme with cryo-EM and X-ray crystallography showed that the enzyme was unique in its family of deoxyribonucleoside kinases by forming a tetramer and mutational analysis showed that tetramerization is a prerequisite for the high substrate affinity of the enzyme. The ability to gain substrate affinity by increasing the number of enzyme subunits could potentially represent an evolutionary pathway that has assisted the parasite to become able to survive entirely on salvage synthesis of DNA building blocks.
An Enhanced PSO Algorithm for Aircraft Stand Reassignment Problem with Multiple Stand Sizes

A well-organized reassignment of aircraft stands in response to unusual disturbances, such as flight delays or cancellations, is a crucial aspect of efficient airport operation, particularly for airports with high daily flight volumes. In contrast to existing literature that emphasizes passenger satisfaction, this paper presents a novel approach that focuses on the objective of minimizing the total cost of recovery. To enhance realism, the model incorporates multi-size aircraft stands to account for practical constraints faced by airports. To efficiently solve the resulting multi-decision variable and multi-constraint model, we propose an Enhanced Particle Swarm Optimization (EPSO) algorithm specifically tailored for this problem. The efficacy of the algorithm is validated using real-world disruption data from Quanzhou Jinjiang International Airport. The results demonstrate that our EPSO algorithm outperforms other methods, providing the best solution for the targeted aircraft stand reassignment problem while slightly reducing the operating time.
Climate change impacts dryland multifunctionality: A cascade of precipitation variability to species replacement in multitrophic microbiota

In the Anthropocene, the pervasive impacts of climate change on arid ecosystems are increasingly evident, resulting in significant alterations in both biodiversity and functionality. Although soil microbes are anticipated to be sensitive to changes in precipitation regimes, the mechanisms through which multifaceted precipitation changes impair the delivery of multiple functions by multitrophic microbiota in soils remain largely unexplored. In the drylands of Northwestern China, we examined the direct effects of historical precipitation regimes on the soil multifunctionality of biocrusts, a model system, and their indirect impacts mediated by microbial communities. Among abiotic predictors, precipitation variability, rather than mean annual precipitation, emerged as the primary driver of multifunctionality, leading to a convergence of less functionally beneficial species. By utilizing a biodiversity-ecosystem function relationship framework, our results underscore the crucial roles of α- and β-diversity of heterotrophic bacteria, fungi, and phototrophic cyanobacteria in maintaining soil functionality. However, the underlying mechanisms are distinct. We found that the local richness of heterotrophs and phylogenetic dissimilarity of photoautotrophs exert positive influences on multifunctionality, while species replacement, a component of β-diversity, primarily enhances the variance in soil multifunctionality. Importantly, the findings illuminate the cascading effect of precipitation variability on dryland ecosystems, amplified by microbial diversity, potentially triggering a silent collapse of vulnerable arid habitats. Our study highlights the importance of considering indirect climate impacts via soil microbiota, contributing to a deeper understanding of the real-world consequences of climate change on drylands, and offering valuable insights for the management of biodiversity theory-inspired ecosystem restoration.
A Paradigm for Collaborative 3D Editing via List Conflict-free Replicated Data Types

In this paper, we have proposed a novel approach for 3D collaborative editing based on Conflict-free Replicated Data Types (CRDTs). Firstly, we use list CRDTs to represent 3D models since each individual part of a model can be expressed as an ordered list of modeling operations. Secondly, we employ incremental updates for real-time online sharing of 3D models. Finally, we propose two strategies for automatically resolving exclusive and compatible concurrency conflicts and merging operations from different users. By conducting performance testing on incremental updates and conflict resolution, our method is demonstrated to have the ability to enable 3D collaborative editing with efficiency and convenience, as well as preserving the intentions of multiple users.
IAVA: Interactive and Adaptive Virtual Agent

During an interaction, partners adapt their behaviors to each other. Adaptation can have several functions such as being a sign of engagement and enhancing human users' interaction experience. It is important that virtual agents acting as interaction partners should continuously adapt their behaviors to those of their interlocutors in real time. This paper focuses on creating an interactive virtual agent that is capable of rendering real-time adaptive behaviors in response to its human interlocutor. It ensures the two aspects: generating real-time adaptive behavior and managing natural dialogue. We propose a system of an adaptive virtual agent and choose the e-health application of Cognitive Behavioral Therapy (CBT), which is a mental health treatment that restructures automatic thoughts into balanced thoughts, as a proof-of-concept to showcase the benefit of endowing behavior adaptation to the agent. The virtual agent adapts to the user via the display of nonverbal behaviors, which are generated via a deep learning model, throughout the whole interaction while acting as a therapist helping human users to detect their negative automatic thoughts.
The Virtual Maze: a Tool for Measuring Trust towards Virtual Humans

This demo presents an innovative and validated method for measuring interpersonal trust in virtual humans as social interaction partners. In our design, participants are tasked to navigate through a virtual reality maze and interact with a virtual human, seeking advice on which path to take. By carefully designing the experiments and context, we induced participants to believe that they are interacting with a real person. A validation study, manipulating virtual humans' appearance, tone of voice, and engagement, revealed that the frequency of advice-seeking in the maze correlates with participants' evaluation of interpersonal trust towards virtual humans. Thus, our paradigm represents a valuable research tool for studying trust in virtual reality.
Automatic Generation of Formal BIP Models for C Programs

The BIP model is based on a combinatorial model-checking technique, which decomposes a software system into behavioral components, interaction components and priority rules. It can effectively formalize the description and verification of complex software systems, and is widely used in industry. However, BIP models require manual modeling and rely heavily on professional knowledge, so there are issues related to high cost, low efficiency, and difficulty in guaranteeing model accuracy. In this paper, an automatic BIP model generation method for C programs is proposed, which can automatically extract key program information to build a middle layer Labelled Transition System (LTS) and finally generate a BIP model. This paper further conducts automatic behavioral modeling of a high-performance dynamic memory allocation algorithm and successfully verifies 12 essential security requirements related to the algorithm. Experimental results show that the proposed automatic formal modeling method can effectively reduce manual dependency and improve modeling efficiency, while ensuring the accuracy of the constructed model.
Flash-Based Solid-State Storage Reduces LDPC Read Retry Scheme

Flash memory, with its high performance and low power consumption, has been widely applied in various fields. The increasing storage density of flash memory has led to a gradual increase in Bit Error Rate (BER). Given the users' requirement for data accuracy, the reliability of flash memory has become a research focus. Currently, a common approach to achieve data error correction is by introducing error-correcting code modules. Among them, Low-Density Parity-Check (LDPC) codes are widely adopted due to their excellent error correction capability. However, using LDPC codes directly for error correction can introduce significant latency. To reduce the decoding latency of LDPC codes, this paper proposes a combined approach of refreshing and caching. Experimental results demonstrate that compared to the original approach, this scheme can achieve a maximum reduction of 28% in average response time and a maximum improvement of 63% in IOPS.
Smart Camera Traps: Enabling Energy-Efficient Edge-AI for Remote Monitoring of Wildlife

We present an energy-efficient and practical solution for adding edge intelligence to camera traps, that aims to make remote monitoring and timely detection of wildlife more feasible. Using a hardware-software co-design approach and low-cost, commercially available, off-the-shelf components, our system has been designed to last for months in the field on a small battery pack, while providing accurate edge intelligence and on-board communication functionality to existing camera trap deployments. Our hardware is compatible with most trail cameras while our embedded edge inference pipeline can be run effectively using the limited computing resources and energy budget on-board. Our prototype has currently been deployed for remote monitoring of wild felids - mountain lions and bobcats - serving the needs of a wild cat research and conservation effort in Northern California and is the outcome of a collaboration between conservation efforts, trained project team members and volunteers, and a `maker culture' approach to affordable and open-source technology solutions for the planet.
Expressive power and data complexity of nonrecursive query languages for lists and trees ( Extended

We extend the traditional query languages by primitives for handling lists and trees. Our main results characterize the expressive power and data complexity of the following extended languages: (1) relational algebra with lists and trees, (2) nonrecursive Datalog with lists and trees, (3) nonrecursive Prolog with lists and trees, (4) rst-order logic over lists and trees. Languages (2){(4) turn out to have the same expressive power; their range-restricted fragments have the same expressive power as (1). Every query in these languages is a boolean combination of range-restricted queries. We also prove that these query languages have polynomial data complexity under any \reasonable" encoding of inputs. Furthermore, under a natural encoding of inputs, languages (2){(4) have the same expressive power as rst-order logic over nite structures, therefore their data complexity is in AC 0. Thus, the use of lists and trees in nonrecursive query languages gives no gain in the expressiveness. This contrasts with a huge di erence between the nonelementary program complexity of extended languages (2){(4) and the PSPACE program complexity of their relational counterparts. Our results partly explain why lists and trees are not so widely used in nonrecursive query languages as other collection types. Partially supported by grants from EPSRC, TFR and INTAS-RFBR. yPartially supported by grants from EPSRC and the Faculty of Science and Technology, University of Manchester
Graph Sketches : Sparsification , Spanners , and Subgraphs

When processing massive data sets, a core task is to construct synopses of the data. To be useful, a synopsis data structure should be easy to construct while also yielding good approximations of the relevant properties of the data set. A particularly useful class of synopses are sketches, i.e., those based on linear projections of the data. These are applicable in many models including various parallel, stream, and compressed sensing settings. A rich body of analytic and empirical work exists for sketching numerical data such as the frequencies of a set of entities. Our work investigates graph sketching where the graphs of interest encode the relationships between these entities. The main challenge is to capture this richer structure and build the necessary synopses with only linear measurements. In this paper we consider properties of graphs including the size of the cuts, the distances between nodes, and the prevalence of dense sub-graphs. Our main result is a sketch-based sparsifier construction: we show that O̅(nε-2) random linear projections of a graph on n nodes suffice to (1 + ε) approximate all cut values. Similarly, we show that O(ε-2) linear projections suffice for (additively) approximating the fraction of induced subgraphs that match a given pattern such as a small clique. Finally, for distance estimation we present sketchbased spanner constructions. In this last result the sketches are adaptive, i.e., the linear projections are performed in a small number of batches where each projection may be chosen dependent on the outcome of earlier sketches. All of the above results immediately give rise to data stream algorithms that also apply to dynamic graph streams where edges are both inserted and deleted. The non-adaptive sketches, such as those for sparsification and subgraphs, give us single-pass algorithms for distributed data streams with insertion and deletions. The adaptive sketches can be used to analyze MapReduce algorithms that use a small number of rounds.
On the power of magic

This paper considers the efficient evaluation of recursive queries expressed using Horn Clauses. We define sideways information passing formally and show how a query evaluation algorithm may be defined in terms of sideways information passing and control. We then consider a class of information passing strategies which suffices to describe most query evaluation algorithms in the database literature, and show that these strategies may always be implemented by rewriting a given program and evaluating the rewritten program bottom-up. We describe in detail several algorithms for rewriting a program. These algorithms generalize the Counting and Magic Sets algorithms to work with arbitrary programs. Safety and optimality of the algorithms are also considered.
Generalizing Data to Provide Anonymity when Disclosing Information

The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi(cid:12)ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi(cid:12)ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de(cid:12)nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity. A table is said to provide k -anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k . We introduce the notions of generalized table and of minimal generalization of a table with respect to a k -anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e(cid:14)ciently computes a preferred minimal generalization to provide anonymity.
Static analysis and optimization of semantic web queries

Static analysis is a fundamental task in query optimization. In this paper we study static analysis and optimization techniques for SPARQL, which is the standard language for querying Semantic Web data. Of particular interest for us is the optionality feature in SPARQL. It is crucial in Semantic Web data management, where data sources are inherently incomplete and the user is usually interested in partial answers to queries. This feature is one of the most complicated constructors in SPARQL and also the one that makes this language depart from classical query languages such as relational conjunctive queries. We focus on the class of well-designed SPARQL queries, which has been proposed in the literature as a fragment of the language with good properties regarding query evaluation. We first propose a tree representation for SPARQL queries, called pattern trees, which captures the class of well-designed SPARQL graph patterns and which can be considered as a query execution plan. Among other results, we propose several transformation rules for pattern trees, a simple normal form, and study equivalence and containment. We also study the enumeration and counting problems for this class of queries.
Changing Parent Attitudes Towards HPV Vaccination by Including Adolescents in Multiparty Counseling using Virtual Agents

Parental permission is required for medical care for children, and decisions may be made without incorporating children's views, even for adolescents. We explore the impact of including adolescents in virtual agent-based multiparty health counseling to promote Human Papillomavirus (HPV) vaccination. The agent is designed to encourage HPV vaccination for children aged 9-12 by engaging co-present parent/adolescent dyads in an online interaction. Several techniques are incorporated, including HPV education, motivational interviewing, persuasion, modeling, and enablement, to address parents' intent to vaccinate their children. We conduct a between-subjects randomized study comparing a version of the agent exclusively for the parent, to one that includes the adolescent in the conversation and incorporates the child's views in counseling strategies. We measure pre- and post-intervention changes in intent to vaccinate, vaccination hesitancy, and knowledge in both the parent and the adolescent, hypothesizing greater improvements in these measures when the adolescent is included in the conversation. We also examine the satisfaction, engagement, and comfort of the parent/child interactions with the virtual agent. We found significant pre-post increases in parent intent to vaccinate their adolescent for both versions of the agent. Our work provides insights into the effectiveness of the virtual agent in promoting HPV vaccination, the impact of child participation on healthcare decision-making, and the user experience of multi-party interaction with the virtual agent.
Alexa, I Do Not Want to Be Patronized: A Qualitative Interview Study to Explore Older Adults' Attitudes Towards Intelligent Voice Assistants

Intelligent voice assistants such as Alexa and Google Home enable individuals who would otherwise be excluded to access the Internet. For instance, older adults have difficulties operating certain interfaces such as a computer or smartphone because of physical or mental constraints. Due to their intuitive, speech-based control in dialogue form, voice assistants hold the potential to assist older adults and promote their independence. However, for a successful adoption, the technology needs to be accepted and trusted by the target group. With the aim to explore older adults' attitudes towards intelligent voice assistants, semi-structured interviews were conducted with 19 adults between 65 and 86 years. The interviews included a wizard-of-oz interaction with a voice assistant and a vignette describing a scenario in which the voice assistant appears to be eavesdropping. On the one hand, the interviewees evaluated the voice assistant as useful, easy to comprehend, and pleasant. On the other hand, they voiced general data protection concerns, fears of being overheard and observed as well as a strong aversion to unprompted recommendations and advertisement. Most strikingly, fears of being patronized or manipulated by the voice assistant were mentioned frequently, which was justified by a strong need to maintain independence -- particularly with advancing age.