Motivation: Predicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. GDA prediction can be framed as a ranking problem where genes are ranked for a query disease, based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. Results: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementation: https://github.com/bio-ontology-research-group/indigena
The remarkable adaptability of humans in response to complex environments is often demonstrated by the context-dependent adoption of different behavioral modes. However, the existing game-theoretic studies mostly focus on the single-mode assumption, and the impact of this behavioral multimodality on the evolution of cooperation remains largely unknown. Here, we study how cooperation evolves in a population with two behavioral modes. Specifically, we incorporate Q-learning and Tit-for-Tat (TFT) rules into our toy model, where prisoner's dilemma game is played and we investigate the impact of the mode mixture on the evolution of cooperation. While players in Q-learning mode aim to maximize their accumulated payoffs, players within TFT mode repeat what their neighbors have done to them. In a structured mixing implementation where the updating rule is fixed for each individual, we find that the mode mixture greatly promotes the overall cooperation prevalence. The promotion is even more significant in the probabilistic mixing, where players randomly select one of the two rules at each step. Finally, this promotion is robust when players are allowed to adaptively choose the two modes by real-time comparison. In all three scenarios, players within the Q-learning mode act as catalyzer that turns the TFT players to be more cooperative, and as a result drive the whole population to be highly cooperative. The analysis of Q-tables explains the underlying mechanism of cooperation promotion, which captures the ``psychologic evolution" in the players' mind. Our study indicates that the variety of behavioral modes is non-negligible, and could be crucial to clarify the emergence of cooperation in the real world.
Ruya Zhang, Charles Brooker, Laura L. E. Whitehouse
et al.
The mechanical competence and suturing ability of collagen-based membranes are paramount in Guided Bone Regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane. UV-cured networks were obtained via either single functionalisation of AC with 4-vinylbenzyl chloride (4VBC) or sequential functionalisation of AC with both 4VBC and methacrylic anhydride (MA). The wet-state compression modulus (Ec), Atomic Force Microscopy elastic modulus (EAFM) and swelling ratio (SR) were significantly affected by the UV-cured network architecture, leading up to a three-fold reduction in SR, and about two-fold increase in both Ec and EAFM, in the sequentially functionalised, compared to the single-functionalised, samples. Electron microscopy, dimensional analysis and compression testing revealed the direct impact of the ethanol series dehydration process on membrane microstructure, yielding densification of the freshly synthesised porous samples and a pore-free microstructure with increased Ec. Noteworthy, the single-functionalised, but not the sequentially functionalised, samples displayed higher suture retention strength in both the dry state and following 1 hour in Phosphate Buffered Saline (PBS), compared to Bio-Gide(r). These structure-property relationships confirm the key role played by the molecular architecture of covalently crosslinked collagen, aimed towards long-lasting resorbable membranes for predictable GBR.
Nesrine Bannour, Christophe Servan, Aurélie Névéol
et al.
Background: Transformer-based language models have shown strong performance on many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adaptedto different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighterthan modern Large Language Models (LLMs). Recently, several MLMs have been released for the biomedicaldomain in French, and experiments suggest that they outperform standard French counterparts. However, nosystematic evaluation comparing all models on the same corpora is available. Objective: This paper presentsan evaluation of masked language models for biomedical French on the task of clinical named entity recognition.Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them tostandard French models CamemBERT, FlauBERT and FrALBERT as well as multilingual mBERT using three publicallyavailable corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standardcorpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperformsDrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbonfootprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for Frenchclinical entity recognition that compares model performance consistently on nested entity recognition using metricscovering performance and environmental impact.
Behavioral experiments on the trust game have shown that trust and trustworthiness are universal among human beings, contradicting the prediction by assuming \emph{Homo economicus} in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations however need to resort to some factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals update their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one's decision making as trustor and trustee respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human's psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness without external factors involved. More importantly, the proposed paradigm shows the potential in deciphering many puzzles in human behaviors.
Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej
et al.
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and brain-state-specific activation mechanisms, namely, apical amplification, isolation, and drive, which are associated with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model that incorporates features essential for supporting brain-state-specific learning. This model includes a piece-wise linear transfer function (ThetaPlanes) at the highest abstraction level, making it suitable for use in large-scale bio-inspired artificial intelligence systems. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected the parameters that define neurons expressing the desired apical mechanisms.
Living systems are hierarchical control systems that display a small world network structure, in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution (a mereology). Apart from their structure, the dynamics of living systems also shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or states (treble) that are nested within lower frequencies or traits (bass), producing a power-law frequency spectrum that is known as a state-trait continuum in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms vertically encode the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies produce physical as well as behavioral traits (i.e. body plans and personalities). Nested-modular structure then causes higher frequencies to be embedded within lower frequencies, producing a power law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.g. earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g. during maturation and disease) should leave specific traces in the dynamics of nested modular systems that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.
H. H. Mai, Trong Tam Nguyen, Tien Thinh Nguyen
et al.
In this study, we demonstrate a simple approach to fabricate a high-performance random laser (RL) from the natural inverse photonic glass structure of Artemia eggshells. Herein, the three-dimensional structures of Artemia eggshells provide an ideal scattering medium with a significantly high-reflectance stopband which facilitates resonance feedback for random lasing action. By doping organic dye molecules into the Artemia eggshells, RLs are realized by optical pumping with a threshold of 79 μJ mm−2, and a quality (Q) factor of 2328. In comparison with other works on RLs from natural photonic crystals such as butterfly wings, our RLs demonstrate a significantly lower lasing threshold and a comparable Q factor. Our results indicate that the natural inverse photonic glass structure is not only served as an effective scattering medium for random lasing but also paves a novel approach in designing and fabricating bio-controlled photonic devices.
Nai-hsuan Hu, Cory B. Sims, Tyler V Schrand
et al.
Octa(dimethylsiloxy)silica cages (Q8M8H) undergo rapid self-polymerization in the presence of a fluoride catalyst to form complex 3D porous structural network materials with specific surface areas up to 650 m2 g-1. This establishes a new method to form bio-derived high inorganic content soft silicas with potential applications in filtration, carbon capture, catalysis, or hydrogen source.
Adrien Journé, Hector Garcia Rodriguez, Qinghai Guo
et al.
Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals -- which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.
Reinforcement learning (RL) using deep Q-networks (DQNs) has shown performance beyond the human level in a number of complex problems. In addition, many studies have focused on bio-inspired hardware-based spiking neural networks (SNNs) given the capabilities of these technologies to realize both parallel operation and low power consumption. Here, we propose an on-chip training method for DQNs applicable to hardware-based SNNs. Because the conventional backpropagation (BP) algorithm is approximated, a performance evaluation based on two simple games shows that the proposed system achieves performance similar to that of a software-based system. The proposed training method can minimize memory usage and reduce power consumption and area occupation levels. In particular, for simple problems, the memory dependency can be significantly reduced given that high performance is achieved without using replay memory. Furthermore, we investigate the effect of the nonlinearity characteristics and two types of variation of non-ideal synaptic devices on the performance outcomes. In this work, thin-film transistor (TFT)-type flash memory cells are used as synaptic devices. A simulation is also conducted using fully connected neural network with non-leaky integrated-and-fire (I&F) neurons. The proposed system shows strong immunity to device variations because an on-chip training scheme is adopted.
A novel optical BioNEMS (Bio Nano-Electro-Mechanical-Systems) sensor based on a tunable 2D photonic crystal (PhC) cavity is proposed in this paper. The present device includes a functionalized BioNEMS cantilever and a tunable PhC structure. This optical detection system is a rod-type silicon photonic crystal cavity in which a designed defect allows the transmission of highly confined wave modes. While the immobilized capture probes are exposed to a solution containing the target biomarkers (i.e., DNA, mRNA or proteins), binding events cause a stress on the surface of the nanocantilever because of biological interactions. Therefore, the NEMS movable part is displaced as a result of an induced differential surface stress. This changes the position of the defect nanorods in the PhC cavity which leads to the transmitted wavelength variations. Finally, the concentration of biological quantities is measured by detecting the optical spectrum changes of the proposed biosensor. Furthermore, the presented BioNEMS sensor is analyzed by numerical and analytical approaches to obtain its functional characteristics as follows: optical sensitivity of 5140 nm/RIU, FOM of 10280 RIU-1, Q-factor of 3078, mechanical sensitivity of 1.05 µm/Nm-1 and resonant frequency of 23.45 kHz. Based on the obtained results, the proposed structure relied on Fano resonances provides a high-precision biosensor which has a great potential for highly sensitive detection of biomarkers in early diseases diagnosis as well as drug delivery test.
Abstract Over the past several decades, it has been demonstrated that cannabinoids offer a wide range of therapeutic benefits. Their oral administration, however, while arguably the most convenient and discrete, has been associated with low bioavailability, delayed onset of action and poor reproducibility resulting from the extracts' strongly lipophilic character. To overcome these obstacles, cannabinoids can be incorporated into oil-in-water nanoemulsions: a process known to enhance the delivery of lipophilic bio-actives by making them behave like water-soluble (hydrophilic) compounds. In this manuscript, formulation development and production scale-up procedures for a cannabis extract (CBDX, 55% cannabidiol)-containing nanoemulsion are described. Nanoemulsion samples were prepared by high-intensity ultrasonic liquid processing, and the formulation was optimized for carrier oil and surfactant(s) contents as well as for the hydrophilic-lipophilic balance (HLB) of the surfactant mixture. Translucent CBDX-containing nanoemulsions with median droplet sizes well below 100 nm were possible to form with synthetic surfactants (Tween 80/Span 80 mixture), but not with a natural surfactant (Q-naturale®). By utilizing Barbell Horn® Ultrasonic Technology (BHUT), the nano-emulsification process was successfully scaled up, achieving a commercial-level processing rate equivalent to one million nanoemulsified 10 mg CBDX doses made per month with a single bench-scale ultrasonic liquid processor (BSP-1200).
Level of contaminants (Nickel and Lead) in aquatic ecosystems has increased due to discharge of industrial effluents in water. Hence, there is a need to remove heavy metals (Nickel and Lead) from the water. For removing heavy metals from water, hybrid biosorbent (Almond shell and Pleurotus sajor caju) was prepared. To prepare a novel hybrid biosorbent (Almond shell and Pleurotus sajor caju) for the removal of nickel and lead from waste water the study was conducted in the department of chemistry, university of agriculture Faisalabad. The biomass was collected from local market of Chiniot. Hybrid matrix (Almond shell and Pleurotus sajor caju) and heavy metals (Nickel and Lead) were prepared. Waste water was interacted with the developed hybrid metals (Nickel and Lead) and hybrid bio sorbent (almond shell and P.sajor caju).The maximum adsorption capacity q(mg/g) of nickel and lead obtained at l0mgL-l concentration is in the following order; hybrid biosorbent(87)>P.sajor caju(65)> almond shell(54) and hybrid biosorbent(85)>P.sajor caju(57)>almond shell(45). The maximum uptake for nickel obtained by almond shell, P.sajor caju, hybrid biosorbent (56%), (66%), (90%) for lead and (47%), (61%), (89%) for nickel. The adsorption of nickel and lead follows the 2nd order kinetic model. FTIR spectra show that there are various functional groups, active sites present in hybrid biosorbent (Almond shell and Pleurotus sajor caju). Maximum absorption of lead occurs at pH 5 and nickel at pH 3. The sorptions of heavy metals (Lead and Nickel) follow the pseudo 2nd order kinetic model. From the whole analysis it is concluded that Hybrid biosorbent calm of microbial and plant waste biomass was extremely functional in exclusion of lead and Nickel from wastewater.
A. Prasannan, Jittrakorn Udomsin, Hsieh-Chih Tsai
et al.
Abstract Special wettability materials from bio-renewable and bioinspired materials can efficiently be utilized for the treatment of water insoluble pollutants but these materials mostly failed to remove water-soluble organic pollutants because of oil contamination, low water forbearance and complex synthetic procedures. In this view, we have prepared a biocompatible, superwetting polymer composite from natural gel forming materials ĸ-carrageenan (CGN) and poly(acrylamide-co-diallyldimethylammonium chloride) (PAm-DADAc) with graphene oxide (GO) possessing superhydrophilic and underwater superoleophobic properties. Prepared CGN-PAm-DADAc-GO composite were applied to separate oil from oil-water mixture, water-soluble dye effluents and dye containing emulsion separation. The CGN-PAm-DADAc-GO coated stainless-steel meshes demonstrated efficient oil-water separation exhibiting good superhydrophilicity, antifouling property and underwater superoleophobicity. Superhydrophilic nature of CGN-PAm-DADAc-GO composite has been utilized to remove water-soluble cationic and anionic azo dyes contamination at high efficiency 96% with freeze-dried samples. Hydrated layer formation with abundant surface functional groups like sulphate, amine and hydroxyl groups may boosts the simultaneous removal of insoluble oils and soluble dyes from the dye-contaminated emulsion through simply passing via CGN-PAm-DADAc-GO coated filter paper with emulsion flux about 40 L m-2 h-1 and 96% of separation efficiency. These composite materials may hold promising multipurpose applications as featured membrane fabrication material for simultaneous removal of various types of contaminations.
Schaftoside is a flavone-C-glycoside isolated from Herba Desmodii Styracifolii with valuable anti-kidney stones efficacies. In this study, a six-step strategy was first developed to detect and identify the metabolites in plasma, urine, bile, feces and rat intestinal bacteria samples of healthy and model rats administrated with schaftoside using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). The number and the relative peak area of metabolites in healthy rats and model rats were compared, and it was noticed that metabolites in bio-samples of healthy and model rats both had obvious differences. A total of 28 metabolites of schaftoside in healthy rats and 30 metabolites in model rats were initially indentified. The relative peak area of the parent drug and every metabolite in model rat plasma samples were larger than those in healthy rat plasma. Those metabolites with high blood concentrations might be beneficial for the treatment of calcium oxalate stones in the kidney. The results are valuable and important for understanding the metabolic process of schaftoside in clinical application, and especially the metabolism study in calcium oxalate kidney stone model rats could provide a beneficial reference for the further search of effective substances associated with the treatment of kidney stones.
Background Primary hyperoxaluria type 1 (PH1) is associated with nephrocalcinosis (NC) and calcium oxalate (CaOx) kidney stones (KS). Populations of urinary extracellular vesicles (EVs) can reflect kidney pathology. The aim of this study was to determine whether urinary EVs carrying specific biomarkers and proteins differ among PH1 patients with NC, KS or with neither disease process. Methods Mayo Clinic Rare Kidney Stone Consortium bio-banked cell-free urine from male and female PH1 patients without (n = 10) and with NC (n = 6) or KS (n = 9) and an eGFR > 40 mL/min/1.73 m 2 were studied. Urinary EVs were quantified by digital flow cytometer and results expressed as EVs/ mg creatinine. Expressions of urinary proteins were measured by customized antibody array and results expressed as relative intensity. Data were analyzed by ANCOVA adjusting for sex, and biomarkers differences were considered statistically significant among groups at a false discovery rate threshold of Q < 0.20. Results Total EVs and EVs from different types of glomerular and renal tubular cells (11/13 markers) were significantly (Q < 0.20) altered among PH1 patients without NC and KS, patients with NC or patients with KS alone. Three cellular adhesion/inflammatory (ICAM-1, MCP-1, and tissue factor) markers carrying EVs were statistically (Q < 0.20) different between PH1 patients groups. Three renal injury (β2-microglobulin, laminin α5, and NGAL) marker-positive urinary EVs out of 5 marker assayed were statistically (Q < 0.20) different among PH1 patients without and with NC or KS. The number of immune/inflammatory cell-derived (8 different cell markers positive) EVs were statistically (Q < 0.20) different between PH1 patients groups. EV generation markers (ANO4 and HIP1) and renal calcium/phosphate regulation or calcifying matrixvesicles markers (klotho, PiT1/2) were also statistically (Q < 0.20) different between PH1 patients groups. Only 13 (CD14, CD40, CFVII, CRP, E-cadherin, EGFR, endoglin, fetuin A, MCP-1, neprilysin, OPN, OPGN, and PDGFRβ) out of 40 proteins were significantly (Q < 0.20) different between PH1 patients without and with NC or KS. Conclusions These results imply activation of distinct renal tubular and interstitial cell populations and processes associated with KS and NC, and suggest specific populations of urinary EVs and proteins are potential biomarkers to assess the pathogenic mechanisms between KS versus NC among PH1 patients.