Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Cannabinoid-Based Photoaffinity Probe to Determine the Δ8/9-Tetrahydrocannabinol Protein Interaction Landscape in Neuroblastoma Cells

Abstract Introduction: Δ9-Tetrahydrocannabinol (THC), the principle psychoactive ingredient in Cannabis, is widely used for its therapeutic effects in a large variety of diseases, but it also has numerous neurological side effects. The cannabinoid receptors (CBRs) are responsible to a large extent for these, but not all biological responses are mediated via the CBRs. Objectives: The identification of additional target proteins of THC to enable a better understanding of the (adverse) physiological effects of THC. Methods: In this study, a chemical proteomics approach using a two-step photoaffinity probe is applied to identify potential proteins that may interact with THC. Results: Photoaffinity probe 1, containing a diazirine as a photocrosslinker, and a terminal alkyne as a ligation handle, was synthesized in 14 steps. It demonstrated high affinity for both CBRs. Subsequently, two-step photoaffinity labeling in neuroblastoma cells led to identification of four potential novel protein targets of THC. The identification of these putative protein hits is a first step towards a better understanding of the protein interaction profile of THC, which could ultimately lead to the development of novel therapeutics based on THC.
Development of emergency food assistance design: a nutritionally balanced, culturally tailored and cost-effective strategy for flood mitigation

ABSTRACT Emergency food assistance forms an integral component of flood mitigation in Malaysia. A cross-sectional survey investigating emergency food assistance among 202 flood victims was conducted and alternative food assistance plan was developed using linear programming targeting post-disaster food requirement scenarios. From the study, the receipt of food aid was significantly associated with ethnicity, household income, residential area and evacuation into temporary shelters. Linear programming analysis identified nutritionally appropriate food assistance targeting mass feeding, emergency food basket, and immediate food requirement for as low as MYR 6.07 (1.55 USD), MYR 7.14 (1.82 USD), and MYR 8.00 (2.04 USD), respectively. This study provides nutritional guidelines for disaster food assistance policy.
Diagonal Method to Measure Synergy Among Any Number of Drugs

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.
The effects of 12-week progressive strength training on strength, functional capacity, metabolic biomarkers, and serum hormone concentrations in healthy older women: morning versus evening training

ABSTRACT Previous findings suggest that performing strength training (ST) in the evening may provide greater benefit for young individuals. However, this may not be optimal for the older population. The purpose of this study was to compare the effects of a 12-week ST program performed in the morning vs. evening on strength, functional capacity, metabolic biomarker and basal hormone concentrations in older women. Thirty-one healthy older women (66 ± 4 years, 162 ± 4 cm, 75 ± 13 kg) completed the study. Participants trained in the morning (M) (07:30, n = 10), in the evening (E) (18:00, n = 10), or acted as a non-training control group (C) (n = 11). Both intervention groups performed whole-body strength training with 3 sets of 10–12 repetitions with 2–3 minutes rest between sets. All groups were measured before and after the 12-week period with; dynamic leg press and seated-row 6-repetition maximum (6-RM) and functional capacity tests (30-second chair stands and arm curl test, Timed Up and Go), as well as whole-body skeletal muscle mass (SMM) (kg) and fat mass (FM-kg, FM%) assessed by bioelectrical impedance (BIA). Basal blood samples (in the intervention groups only) taken before and after the intervention assessed low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), blood glucose (GLU), triglycerides (TG), high-sensitive C-reactive protein (hsCRP) concentrations and total antioxidant status (TAS) after a 12 h fast. Hormone analysis included prolactin (PRL), progesterone (P) estradiol (ESTR), testosterone (T), follicle stimulating hormone (FSH), and luteinizing hormone (LH). While C showed no changes in any variable, both M and E significantly improved leg press (+ 46 ± 22% and + 21 ± 12%, respectively; p < 0.001) and seated-row (+ 48 ± 21% and + 42 ± 18%, respectively; p < 0.001) 6-RM, as well as all functional capacity outcomes (p < 0.01) due to training. M were the only group to increase muscle mass (+ 3 ± 2%, p < 0.01). Both M and E group significantly (p < 0.05) decreased GLU (–4 ± 6% and –8 ± 10%, respectively), whereas significantly greater decrease was observed in the E compared to the M group (p < 0.05). Only E group significantly decreased TG (–17 ± 25%, p < 0.01), whereas M group increased (+ 15%, p < 0.01). The difference in TG between the groups favored E compared to M group (p < 0.01). These results suggest that short-term “hypertrophic” ST alone mainly improves strength and functional capacity performance, but it influences metabolic and hormonal profile of healthy older women to a lesser extent. In this group of previously untrained older women, time-of-day did not have a major effect on outcome variables, but some evidence suggests that training in the morning may be more beneficial for muscle hypertrophy (i.e. only M significantly increased muscle mass and had larger effect size (M: g = 2 vs. E: g = 0.5).
Quantum Efficiency Enhancement of a GaN-Based Green Light-Emitting Diode by a Graded Indium Composition p-Type InGaN Layer

We propose a graded indium composition p-type InGaN (p-InGaN) conduction layer to replace the p-type AlGaN electron blocking layer and a p-GaN layer in order to enhance the light output power of a GaN-based green light-emitting diode (LED). The indium composition of the p-InGaN layer decreased from 10.4% to 0% along the growth direction. The light intensity of the LED with a graded indium composition p-InGaN layer is 13.7% higher than that of conventional LEDs according to the experimental result. The calculated data further confirmed that the graded indium composition p-InGaN layer can effectively improve the light power of green LEDs. According to the simulation, the increase in light output power of green LEDs with a graded indium composition p-InGaN layer was mainly attributed to the enhancement of hole injection and the improvement of the radiative recombination rate.
Common Weak Linear Copositive Lyapunov Functions for Positive Switched Linear Systems

Lyapunov functions play a key role in the stability analysis of complex systems. In this paper, we study the existence of a class of common weak linear copositive Lyapunov functions (CWCLFs) for positive switched linear systems (PSLSs) which generalize the conventional common linear copositive Lyapunov functions (CLCLFs) and can be used as handy tool to deal with the stability of PSLSs not covered by CLCLFs. We not only establish necessary and sufficient conditions for the existence of CWCLFs but also clearly describe the algebraic structure of all CWCLFs. Numerical examples are also given to demonstrate the effectiveness of the obtained results.
RNAvista: a webserver to assess RNA secondary structures with non-canonical base pairs

Motivation: In the study of 3D RNA structure, information about non‐canonical interactions between nucleobases is increasingly important. Specialized databases support investigation of this issue based on experimental data, and several programs can annotate non‐canonical base pairs in the RNA 3D structure. However, predicting the extended RNA secondary structure which describes both canonical and non‐canonical interactions remains difficult. Results: Here, we present RNAvista that allows predicting an extended RNA secondary structure from sequence or from the list enumerating canonical base pairs only. RNAvista is implemented as a publicly available webserver with user‐friendly interface. It runs on all major web browsers. Availability and implementation: http://rnavista.cs. put.poznan. pl
Incrementally Grounding Expressions for Spatial Relations between Objects

Recognizing, reasoning about, and providing understandable descriptions of spatial relations between objects is an important task for robots interacting with humans. This paper describes an architecture for incrementally learning and revising the grounding of spatial relations between objects. Answer Set Prolog, a declarative language, is used to represent and reason with incomplete knowledge that includes prepositional spatial relations between scene objects. A generic grounding of prepositions for spatial relations, human input (when available), and non-monotonic logical inference, are used to infer spatial relations between 3D point clouds in given scenes, incrementally acquiring a specialized metric grounding of the prepositions and the relative confidence associated with each grounding. The architecture is evaluated on a benchmark dataset of tabletop images and on complex simulated scenes of furniture.
Deep Joint Semantic-Embedding Hashing

Hashing has been widely deployed to large-scale image retrieval due to its low storage cost and fast query speed. Almost all deep hashing methods do not sufficiently discover semantic correlation from label information, which results in the learned hash codes less discriminative. In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that contains LabNet and ImgNet. Specifically, LabNet is explored to capture abundant semantic correlation between sample pairs and supervise ImgNet from semantic level and hash codes level, which is conductive to the generated hash codes being more discriminative and similarity-preserving. Extensive experiments on three benchmark datasets show that the proposed model outperforms the state-of-the-art methods.
Modelling Hegemonic Power Transition in Cyberspace

Cyberspace is the newest domain of conflict and cooperation between states. In cyberspace, as in all other domains, land, sea, air, and space, these interactions often lead to the emergence of hegemons which are characterised by their predominant influence over global world order and all other states. We examined the emergence and collapse of hegemons in a modelled cyberspace world through the notions of power transition and power diffusion. We used Repast Simphony to construct a simple agent-based model (ABM) of a system of states interacting both competitively and cooperatively in this world. Our simple model parsimoniously captures the character of the real international system of states through simple parameters of wealth and power determining the outcome of attack or cooperation amongst pairwise interacting states. We found hegemons of global world order emerged in cyberspace as they do in the other traditional domains from models with these few parameters. And we found that hegemons, contrary to traditional understanding, are not exceptional states but merely occupy the tail of a continuous distribution of power and lifetimes. We also found that hegemony in the system depends on two perhaps unexpected parameters: the difficulty of acquiring power as wealth increases and the amount of cooperation between states. And as a consequence, we argue that cyberspace, as a power-diffuse domain where cooperation is easier than elsewhere, is less suited to the kind of hegemony we see in the traditional domains of state interaction.
Optimal Multi-robot Task Planning: from Synthesis to Execution (and Back)

Integrated task planning and execution is a challenging problem with several applications in AI and robotics. In this work we consider the problem of generating and executing optimal plans for multi-robot systems under temporal and ordering constraints. More specifically, we propose an approach that unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal solutions for task planning, and runtime feedback on their execution.
Recurrent Collaborative Filtering for Unifying General and Sequential Recommender

General recommender and sequential recommender are two commonly applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors; whereas sequential recommender focuses on exploring the item-to-item sequential relations, failing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combine them. However, previous approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.
Improving Data Management using Domain Knowledge

The development of tools and techniques for flexible and reliable data management is a long-standing challenge, ever more pressing in today’s data-rich world. We advocate using domain knowledge expressed in ontologies to tackle it, and summarize some research efforts to this aim that follow two directions. First, we consider the problem of ontology-mediated query answering (OMQA), where queries in a standard database query language are enriched with an ontology expressing background knowledge about the domain of interest, used to retrieve more complete answers when querying incomplete data. We discuss some of our contributions to OMQA, focusing on (i) expressive languages for OMQA, with emphasis on combining the open- and closed-world assumptions to reason about partially complete data; and (ii) OMQA algorithms based on rewriting techniques. The second direction we discuss proposes to use ontologies to manage evolving data. In particular, we use ontologies to model and reason about constraints on datasets, effects of operations that modify data, and the integrity of the data as it evolves.
Micronuclei Formation Is Prevented by Aurora B-Mediated Exclusion of HP1a from Late-Segregating Chromatin in Drosophila

Here, Warecki and Sullivan investigate how late-segregating chromosome fragments pass through nuclear envelope channels to maintain euploidy in the nuclei of Drosophila neuroblasts during telophase. The authors find that Aurora B activity prevents the recruitment of HP1a to. While it is known that micronuclei pose a serious risk to genomic integrity by undergoing chromothripsis, mechanisms preventing micronucleus formation remain poorly understood. Here, we investigate how late-segregating acentric chromosomes that would otherwise form micronuclei instead reintegrate into daughter nuclei by passing through Aurora B kinase-dependent channels in the nuclear envelope of Drosophila melanogaster neuroblasts. We find that localized concentrations of Aurora B preferentially phosphorylate H3(S10) on acentrics and their associated DNA tethers. This phosphorylation event prevents HP1a from associating with heterochromatin and results in localized inhibition of nuclear envelope reassembly on endonuclease- and X-irradiation-induced acentrics, promoting channel formation. Finally, we find that HP1a also specifies initiation sites of nuclear envelope reassembly on undamaged chromatin. Taken together, these results demonstrate that Aurora B-mediated regulation of HP1a-chromatin interaction plays a key role in maintaining genome integrity by locally preventing nuclear envelope assembly and facilitating the incorporation of late-segregating acentrics into daughter nuclei.
On Q-learning Convergence for Non-Markov Decision Processes

Temporal-difference (TD) learning is an attractive, computationally efficient framework for model- free reinforcement learning. Q-learning is one of the most widely used TD learning technique that enables an agent to learn the optimal action-value function, i.e. Q-value function. Contrary to its widespread use, Q-learning has only been proven to converge on Markov Decision Processes (MDPs) and Q-uniform abstractions of finite-state MDPs. On the other hand, most real-world problems are inherently non-Markovian: the full true state of the environment is not revealed by recent observations. In this paper, we investigate the behavior of Q-learning when applied to non-MDP and non-ergodic domains which may have infinitely many underlying states. We prove that the convergence guarantee of Q-learning can be extended to a class of such non-MDP problems, in particular, to some non-stationary domains. We show that state-uniformity of the optimal Q-value function is a necessary and sufficient condition for Q-learning to converge even in the case of infinitely many internal states.
Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.
Emergent Tangled Program Graphs in Multi-Task Learning

We propose a Genetic Programming (GP) framework to address high-dimensional Multi-Task Reinforcement Learning (MTRL) through emergent modularity. A bottom-up process is assumed in which multiple programs self-organize into collective decision-making entities, or teams, which then further develop into multi-team policy graphs, or Tangled Program Graphs (TPG). The framework learns to play three Atari video games simultaneously, producing a single control policy that matches or exceeds leading results from (game-specific) deep reinforcement learning in each game. More importantly, unlike the representation assumed for deep learning, TPG policies start simple and adaptively complexify through interaction with the task environment, resulting in agents that are exceedingly simple, operating in real-time without specialized hardware support such as GPUs.
Show and Tell More: Topic-Oriented Multi-Sentence Image Captioning

Image captioning aims to generate textual descriptions for images. Most previous work generates a single-sentence description for each image. However, a picture is worth a thousand words. Single-sentence can hardly give a complete view of an image even by humans. In this paper, we propose a novel Topic-Oriented Multi-Sentence (\emph{TOMS}) captioning model, which can generate multiple topic-oriented sentences to describe an image. Different from object instances or attributes, topics mined by the latent Dirichlet allocation reflect hidden thematic structures in reference sentences of an image. In our model, each topic is integrated to a caption generator with a Fusion Gate Unit (FGU) to guide the generation of a sentence towards a certain topic perspective. With multiple sentences from different topics, our \emph{TOMS} provides a complete description of an image. Experimental results on both sentence and paragraph datasets demonstrate the effectiveness of our \emph{TOMS} in terms of topical consistency and descriptive completeness.
Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach

Learning vector space representation of words, or word embedding, has attracted much recent research attention. With the objective of better capturing the semantic and syntactic information inherent in words, we propose two new embedding models based on the singular value decomposition of lexical co-occurrences of words. Different from previous work, our proposed models allow for injecting linear constraints when performing the decomposition, with which the desired semantic and syntactic information will be maintained in word vectors. Conceptually the models are flexible and convenient to encode prior knowledge about words. Computationally they can be easily solved by direct matrix factorization. Surprisingly simple yet effective, the proposed models have reported significantly improved performance in empirical word analogy and sentence classification evaluations, and demonstrated high potentials in practical applications.
Ten cold clubfeet

Background and purpose — Idiopathic clubfeet are commonly treated with serial manipulation and casting, known as the Ponseti method. The use of Plaster of Paris as casting material causes both exothermic and endothermic reactions. The resulting temperature changes can create discomfort for patients. Patients and methods — In 10 patients, we used a digital thermometer with a data logger to measure below-cast temperatures to create a thermal profile of the treatment process. Results — After the anticipated temperature peak, a surprisingly large dip was observed (Tmin = 26 °C) that lasted 12 hours. Interpretation — Evaporation of excess water from a cast might be a cause for discomfort for clubfoot patients and subsequently, their caregivers.