With the steady rise of the use of AI in bio-technical applications and the widespread adoption of genomics sequencing, an increasing amount of AI-based algorithms and tools is entering the research and production stage affecting critical decision-making streams like drug discovery and clinical outcomes. This paper demonstrates the vulnerability of AI models often utilized downstream tasks on recognized public genomics datasets. We undermine model robustness by deploying an attack that focuses on input transformation while mimicking the real data and confusing the model decision-making, ultimately yielding a pronounced deterioration in model performance. Further, we enhance our approach by generating poisoned data using a variational autoencoder-based model. Our empirical findings unequivocally demonstrate a decline in model performance, underscored by diminished accuracy and an upswing in false positives and false negatives. Furthermore, we analyze the resulting adversarial samples via spectral analysis yielding conclusions for countermeasures against such attacks.
Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever
et al.
Phenology, the timing of cyclical plant life events such as leaf emergence and coloration, is crucial in the bio-climatic system. Climate change drives shifts in these phenological events, impacting ecosystems and the climate itself. Accurate phenology models are essential to predict the occurrence of these phases under changing climatic conditions. Existing methods include hypothesis-driven process models and data-driven statistical approaches. Process models account for dormancy stages and various phenology drivers, while statistical models typically rely on linear or traditional machine learning techniques. Research shows that process models often outperform statistical methods when predicting under climate conditions outside historical ranges, especially with climate change scenarios. However, deep learning approaches remain underexplored in climate phenology modeling. We introduce PhenoFormer, a neural architecture better suited than traditional statistical methods at predicting phenology under shift in climate data distribution, while also bringing significant improvements or performing on par to the best performing process-based models. Our numerical experiments on a 70-year dataset of 70,000 phenological observations from 9 woody species in Switzerland show that PhenoFormer outperforms traditional machine learning methods by an average of 13% R2 and 1.1 days RMSE for spring phenology, and 11% R2 and 0.7 days RMSE for autumn phenology, while matching or exceeding the best process-based models. Our results demonstrate that deep learning has the potential to be a valuable methodological tool for accurate climate-phenology prediction, and our PhenoFormer is a first promising step in improving phenological predictions before a complete understanding of the underlying physiological mechanisms is available.
Understanding species dynamics in heterogeneous environments is essential for ecosystem studies. Traditional models assumed homogeneous habitats, but recent approaches include spatial and temporal variability, highlighting species migration. We adopt starvation-driven diffusion (SDD) models as nonlinear diffusion to describe species dispersal based on local resource conditions, showing advantages for species survival. However, accurate prediction remains challenging due to model simplifications. This study uses multi-agent reinforcement learning (MARL) with deep Q-networks (DQN) to simulate single species and predator-prey interactions, incorporating SDD-type rewards. Our simulations reveal evolutionary dispersal strategies, providing insights into species dispersal mechanisms and validating traditional mathematical models.
Navigation of male moths toward females during the mating search offers a unique perspective on the exploration-exploitation (EE) model in decision-making. This study uses the EE model to explain male moth pheromone-driven flight paths. We leverage wind tunnel measurements and 3D tracking using infrared cameras to gain insights into male moth behavior. During the experiments in the wind tunnel, we add disturbance to the airflow and analyze the effect of increased fluctuations on moth flights in the context of the proposed EE model. We separate the exploration and exploitation phases by applying a genetic algorithm to the dataset of moth 3D trajectories. First, we demonstrate that the exploration-to-exploitation rate (EER) increases with distance from the source of the female pheromone, which can be explained in the context of the EE model. Furthermore, our findings reveal a compelling relationship between EER and increased flow fluctuations near the pheromone source. Using the open-source pheromone plume simulation and our moth-inspired navigation model, we explain why male moths exhibit an enhanced EER as turbulence levels increase, emphasizing the agent's adaptation to dynamically changing environments. This research extends our understanding of optimal navigation strategies based on general biological EE models and supports the development of advanced, theoretically supported bio-inspired navigation algorithms. We provide important insights into the potential of bio-inspired navigation models for addressing complex decision-making challenges.
Bicelle is one of the most stable phospholipid assemblies, which has tremendous applications in the research areas for drug delivery or structural studies of membrane proteins owing to its bio-membrane mimicking characteristics and high thermal stability. However, the conventional preparation method for bicelle demands complicated manufacturing processes and a long time so that the continuous synthesis method of bicelle using microfluidic chip has been playing an important role to expand its feasibility. We verified the general availability of hydrodynamic focusing method with microfluidic chip for bicelle synthesis using various kinds of lipids which have a phase transition temperature ranged from − 2 to 41 °C. Bicelle can be formed only when the inside temperature of microfluidic chip was over the phase transition temperature. Moreover, the concentration condition for bicelle formation varied depending on the lipids. Furthermore, the transition process characteristics from bicelle to vesicle were analyzed by effective q-value, mixing time and dilution condition. We verified that the size of transition vesicles was controlled according to the effective q-value, mixing time, and temperature.
In an effort to upgrade and provide a unified and improved instrument control and data acquisition system for the Oak Ridge National Laboratory (ORNL) small-angle neutron scattering (SANS) instrument suite—biological small-angle neutron scattering instrument (Bio-SANS), the extended q-range small-angle neutron scattering diffractometer (EQ-SANS), the general-purpose small-angle neutron scattering diffractometer (GP-SANS)—beamline scientists and developers teamed up and worked closely together to design and develop a new system. We began with an in-depth analysis of user needs and requirements, covering all perspectives of control and data acquisition based on previous usage data and user feedback. Our design and implementation were guided by the principles from the latest user experience and design research and based on effective practices from our previous projects. In this article, we share details of our design process as well as prominent features of the new instrument control and data acquisition system. The new system provides a sophisticated Q-Range Planner to help scientists and users plan and execute instrument configurations easily and efficiently. The system also provides different user operation interfaces, such as wizard-type tool Panel Scan, a Scripting Tool based on Python Language, and Table Scan, all of which are tailored to different user needs. The new system further captures all the metadata to enable post-experiment data reduction and possibly automatic reduction and provides users with enhanced live displays and additional feedback at the run time. We hope our results will serve as a good example for developing a user-friendly instrument control and data acquisition system at large user facilities.
Abstract Objective The aim of the present systematic review meta-analysis is to assess the effect of olfactory stimulation on reducing dysmenorrhea. Methods Systematic search was conducted in several databases, such as PubMed, Web of Science, Cochrane, and Scopus, to identify relevant research up to October 26, 2019. The identified studies were evaluated based on a modified Jadad scale. The intervention involves aromatherapy alone or in combination with essential oils. There was no restriction for the control group such as a placebo group or other common treatments. The Comprehensive Meta-Analysis Version 2 (Bio stat, Englewood, NJ, USA) was used for meta-analysis. Cochran's Q and I2 tests were utilized. Results The findings of our meta-analysis, which contained 13 trials (15 data), showed that dysmenorrhea decreased significantly in the group receiving aromatherapy with herbal compared with the control group (standardized mean difference [SMD] = -0.795; 95% confidence interval [CI]: -0.922 to- 0.667; 17 trials O < 0.001); heterogeneity; I2 = 19.47%; p = 0.236). In addition, four studies with insufficient data were not included in our meta-analysis. The results of all studies suggested that aromatherapy with herbal medicine group compared with control group is effective. Conclusion Aromatherapy with herbal medicine decreased dysmenorrhea. This treatment was particularly effective when aroma oil was combined with massage or when a mixture of aroma oil was used for the treatment of dysmenorrhea.
Abstract In this article, we describe a novel method for the modification of nanocellulose (MNC) produced from coconut coir (a bio-waste) by acetylization applied to the removal of vanadium (V) and chromium (III) metal ions from an aqueous phase in a batch study. The structure of the MNC formed was confirmed using Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray (EDX), and atomic force microscopy (AFM) analysis. The adsorptive capacity of MNC was enhanced upon modification, achieving 93.6% removal for V V and 84.6% for Cr III at pH 5 within 30 min. The sorption data followed the Langmuir isotherm model (Q max V V; 32.88 and Cr III; 114 mg g−1) with the fast adsorption rate following the pseudo-first-order model (k 1 V V; 0.06 and Cr III 0.073 min−1, respectively). Thus, the MNC from coconut coir very much displayed the desired potential as a sorbent for the removal of these toxic pollutants from wastewater. The sorbent has a promising capability owing to its high sorption capacity which can be used at the industrial scale.
The Field experiment was conducted to evaluate the effect of fertility management on growth, yield attributes and yield of pearlmillet in a Randomized Block Design (RBD) with ten treatments and four replications during summer, 2019 at Anand, Gujarat. The experiment comprises of different nutrient management practices including 100% and 75% RDF with 15 t and 10 t FYM along with Bio NP consortia. A significant higher growth and yield parameters enhancement with the application of 100% RDF + 15 t FYM ha-1 + Bio NP Consortia was recorded in plant height, number of tillers, length of ear head, protein content and biological yield. The treatment T5 produced maximum (91.5 q ha-1) biological yield and statistically it was on par with T9 and T5. However, the lowest biomass production (73.0 q ha-1) was reported in treatment T1. Results of different nutrient management practices on days to 50% flowering, days to maturity, ear head girth and test weight were found non-significant. Protein content of pearlmillet was increased from 7.5% to 9.06% under different nutrient management practices.
Satoshi Kume, Hiroshi Masuya, Yosky Kataoka
et al.
Imaging data is one of the most important fundamentals in the current life sciences. We aimed to construct an ontology to describe imaging metadata as a data schema of the integrated database for optical and electron microscopy images combined with various bio-entities. To realise this, we applied Resource Description Framework (RDF) to an Open Microscopy Environment (OME) data model, which is the de facto standard to describe optical microscopy images and experimental data. We translated the XML-based OME metadata into the base concept of RDF schema as a trial of developing microscopy ontology. In this ontology, we propose 18 upper-level concepts including missing concepts in OME such as electron microscopy, phenotype data, biosample, and imaging conditions.
Abstract Objectives Hemoglobin (Hb) variant is one of the most common monogenic inherited disorders. We aimed to explore the prevalence and hematological and molecular characteristics of Hb variants in southern China. Methods We collected blood samples from all patients with suspected variants found during HbA1c measurement via a cation-exchange high-performance liquid chromatography system (Bio-Rad Variant II Turbo 2.0) or a capillary electrophoresis method (Sebia Capillarys). Hematological analysis, Sanger sequencing, and gap-PCR were performed for these samples. Results Among the 311,024 patients tested, we found 1,074 Hb variant carriers, including 823 identified using Capillarys and 251 using Variant II Turbo 2.0, with a total carrier rate of 0.35%. We discovered 117 types of Hb variants (52 HBB, 47 HBA, and 18 HBD mutations) containing 18 new mutations. The most common variant found was Hb E, followed by Hb New York, Hb J-Bangkok, Hb Q-Thailand, Hb G-Coushatta, Hb G-Honolulu, Hb G-Taipei, and Hb Broomhill. Most heterozygotes for the Hb variant exhibited normal hematological parameters. However, most patients with compound heterozygotes for the Hb variant and thalassemia showed varied degrees of microcytic hypochromic anemia. Conclusions The prevalence of hemoglobin variants remains high and exhibits genetic diversity and widespread distribution in the population of southern China.
Background Mahasudarshan Churna (MC) is a polyherbal Ayurvedic medicine that is employed in fever (especially chronic type), cold and malaria, improvement of digestion and appetite, removes toxins from the blood, boosts immunity and protects against common bacterial infections. Methods Validation and quantification of oleanolic acid (OA), ursolic acid (UA), mangiferin (M), gallic acid (GA), quercetin (Q) and curcumin (C) in commercial MC formulations by HPTLC method. Mobile phase, hexane: ethyl acetate: acetone (16.4: 3.6: 0.2, v/v) was used for the separation of OA and UA; ethyl acetate: glacial acetic acid: formic acid: water (20: 2.2: 2.2: 5.2 v/v) for the development of M; and toluene: ethyl acetate: formic acid (13.5: 9: 0.6 v/v) for the separation of GA, Q and C in crude sample extracts. Visualization and scanning were performed at λ = 530 nm for OA and UA, at λ = 254 nm for M and at λ = 366 nm for GA, Q and C. In addition, HPLC-PDA analysis was used to confirm the HPTLC results. Results Major bio-active compounds in MC formulations were oleanolic acid (1.54–1.78%), mangiferin (1.38–1.52%) and gallic acid (1.01–1.15%); followed by ursolic acid (0.79–0.98%), curcumin (0.45–0.67%) and quercetin (0.22–0.34%). Conclusion Analysis of bio-active compounds in the present study was performed using HPTLC methods and later HPTLC results were compared with HPLC. These two methods give comparable results and there was no statistically significant difference between the mean values for all extracts. Present study concluded that this HPTLC technique is low cost, fast, precise, and accurate which can be employed for the quantification of xanthonoid (M), triterpenoids (OA, UA) and phenolics (GA, Q and C) in samples/formulations. Furthermore, present HPTLC method can be conveniently employed for routine quality control analysis of all the six marker compounds in marketed Ayurvedic/herbal formulations.
In this work, efficient, sensitive bifunctional-monomer chitosan magnetic molecularly imprinted polymers (BCMMIPs) were fabricated and successfully applied to concentrate the metabolites of Epimedium flavonoids in rat testis and bone that were later analyzed using UPLC-Q-TOF-MS. Using chitosan and methacrylic acid as co-functional monomers, BCMMIPs exhibited a large adsorption capacity (7.60 mg/g), fast kinetics (60 min), and good selectivity. Chitosan is bio-compatible and non-toxic, and methacrylic acid provides multiple hydrogen bond donors. The BCMMIPs were injected into rat testis to specifically enrich the total flavonoid metabolites in vivo and were used to extract metabolites from bone in vitro. The results showed that the BCMMIPs coupled with UPLC-Q-TOF-MS successfully identified 28 compounds from testis and 18 compounds from bone, including 19 new compounds. This study provided a reliable protocol for the concentration of metabolites from complex biological samples, and several new metabolites of Epimedium flavonoids were found in vivo and in vitro.
In this research we present a quantum circuit for estimating algorithmic complexity using the coding theorem method. This accelerates inferring algorithmic structure in data for discovering causal generative models. The computation model is restricted in time and space resources to make it computable in approximating the target metrics. The quantum circuit design based on our earlier work that allows executing a superposition of automata is presented. As a use-case, an application framework for protein-protein interaction ontology based on algorithmic complexity is proposed. Using small-scale quantum computers, this has the potential to enhance the results of classical block decomposition method towards bridging the causal gap in entropy based methods.
Breaking a complex bio-social phenomenon (epidemic) into its components, considering the processes that determine its dynamics, formalizing the accepted hypotheses in mathematical equations, selecting appropriate experimental and statistical material, and constructing a mathematical model - those are typical tasks of scientific research. A specific data processing method (balanced identification) and appropriate information technology made it possible to consider a number of models, determine the general biological laws of the virus-human interaction (common to all populations), and the country specific social features of epidemic management in the countries (or cities) under consideration. As the initial data, only new cases are used. Data from different countries is taken from official sources and processed in a uniform way. The obtained estimates of the number of undetected infected are lower estimates.
Abstract As a renewal energy source for bioethanol production, the residuum of apple wood likely offers a major benefit by having the lignin portion transformed into biofuels firstly. In this study, we used the residuum of Raney Ni and Ru/C catalyzed and hydro-deoxygenated (for jet-fuel production) apple wood as the start substrate to produce bio-ethanol. Cellulose content could reach 83.38% for residuum of Ni catalyzed substrates (Ni-cs for short), and the enzymatic hydrolysis rate increased to 88% which is in reasonable high scale. Lignin left in residuum showed more negative effect on cellulose enzymatic hydrolysis than cellulose II. Different catalysts (Ni or Ru/C) concentration influenced the enzymatic hydrolysis and fermentation. Using quasi-simultaneous enzymatic saccharification and combined fermentation (Q-SScombF), the inhibit effect from catalysts was obvious and the ethanol production efficiency were lower than 35%. But for separate hydrolysis and fermentation process (SHF), the ethanol conversion of raw material and catalyzed residuum improved from 33% to 75% (for residuum of Ni-cs) and increased from 25% to 73% for residuum of Ru/C catalyzed substrates (Ru/C-cs). This research provided a good basis for the feasibility of bio-ethanol production from residuum of bio-refined apple wood.
Detection and identification of the in vivo metabolites of traditional Chinese medicine by untargeted profiling strategies are often confronted with severe interference from complex endogenous substances. Here we developed an integral approach, by combining untargeted data-dependent MS2 (dd-MS2) of Q-Orbitrap mass spectrometry and predictive multiple reaction monitoring-information dependent acquisition-enhanced product ion scan (pMRM-IDA-EPI) of triple quadrupole-linear ion trap (QTRAP) mass spectrometry, aiming to detect and identify more extensive metabolites in bio-samples. Ecliptae Herba (EH) is a widely consumed medicinal herb with the effects of nourishing liver/kidney, but its metabolites in vivo have not been fully elucidated. Firstly, after UHPLC separation on an HSS T3 column, chemical fingerprinting of 70% ethanolic extract of EH was performed by untargeted dd-MS2 in negative ion mode. We could characterize 41 compounds from EH, and 24 were detectable in the plasma of rats (prototypes) after oral administration of EH extract (1 g/kg). Secondly, using echinocystic acid (triterpene), wedelolactone (coumarin), and apigenin (flavonoid) as the different parent templates, an MRM list containing 150 predicted ion-pairs was established to enhance MS2 scan by pMRM-IDA-EPI, which enabled the primary identification of up to 200 metabolites. The biotransformations mainly involve oxidation, hydrogenation, methylation, glucuronidation, sulfonation etc. Thirdly, the rat plasma samples obtained after oral administration of three pure compounds (echinocystic acid, wedelolactone and apigenin) were analyzed to verify the reliability of metabolites identification, and 11, 4, and 10 metabolites were found individually. This is the first comprehensive research on the metabolism of EH in vivo.