ResearchPad - Biotechnology Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Enhancing flavonoid production by promiscuous activity of prenyltransferase, BrPT2 from Boesenbergia rotunda]]>

Flavonoids and prenylated flavonoids are active components in medicinal plant extracts which exhibit beneficial effects on human health. Prenylated flavonoids consist of a flavonoid core with a prenyl group attached to it. This prenylation process is catalyzed by prenyltranferases (PTs). At present, only a few flavonoid-related PT genes have been identified. In this study, we aimed to investigate the roles of PT in flavonoid production. We isolated a putative PT gene (designated as BrPT2) from a medicinal ginger, Boesenbergia rotunda. The deduced protein sequence shared highest gene sequence homology (81%) with the predicted homogentisate phytyltransferase 2 chloroplastic isoform X1 from Musa acuminata subsp. Malaccensis. We then cloned the BrPT2 into pRI vector and expressed in B. rotunda cell suspension cultures via Agrobacterium-mediated transformation. The BrPT2-expressing cells were fed with substrate, pinostrobin chalcone, and their products were analyzed by liquid chromatography mass spectrometry. We found that the amount of flavonoids, namely alpinetin, pinostrobin, naringenin and pinocembrin, in BrPT2-expressing cells was higher than those obtained from the wild type cells. However, we were unable to detect any targeted prenylated flavonoids. Further in-vitro assay revealed that the reaction containing the BrPT2 protein produced the highest accumulation of pinostrobin from the substrate pinostrobin chalcone compared to the reaction without BrPT2 protein, suggesting that BrPT2 was able to accelerate the enzymatic reaction. The finding of this study implied that the isolated BrPT2 may not be involved in the prenylation of pinostrobin chalcone but resulted in high yield and production of other flavonoids, which is likely related to enzyme promiscuous activities.

<![CDATA[Transient expression and purification of β-caryophyllene synthase in Nicotiana benthamiana to produce β-caryophyllene in vitro]]>

The sesquiterpene β-caryophyllene is an ubiquitous component in many plants that has commercially been used as an aroma in cosmetics and perfumes. Recent studies have shown its potential use as a therapeutic agent and biofuel. Currently, β-caryophyllene is isolated from large amounts of plant material. Molecular farming based on the Nicotiana benthamiana transient expression system may be used for a more sustainable production of β-caryophyllene. In this study, a full-length cDNA of a new duplicated β-caryophyllene synthase from Artemisia annua (AaCPS1) was isolated and functionally characterized. In order to produce β-caryophyllene in vitro, the AaCPS1 was cloned into a plant viral-based vector pEAQ-HT. Subsequently, the plasmid was transferred into the Agrobacterium and agroinfiltrated into N. benthamiana leaves. The AaCPS1 expression was analyzed by quantitative PCR at different time points after agroinfiltration. The highest level of transcripts was observed at 9 days post infiltration (dpi). The AaCPS1 protein was extracted from the leaves at 9 dpi and purified by cobalt–nitrilotriacetate (Co-NTA) affinity chromatography using histidine tag with a yield of 89 mg kg−1 fresh weight of leaves. The protein expression of AaCPS1 was also confirmed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and western blot analyses. AaCPS1 protein uses farnesyl diphosphate (FPP) as a substrate to produce β-caryophyllene. Product identification and determination of the activity of purified AaCPS1 were done by gas chromatography–mass spectrometry (GC–MS). GC–MS results revealed that the AaCPS1 produced maximum 26.5 ± 1 mg of β-caryophyllene per kilogram fresh weight of leaves after assaying with FPP for 6 h. Using AaCPS1 as a proof of concept, we demonstrate that N. benthamiana can be considered as an expression system for production of plant proteins that catalyze the formation of valuable chemicals for industrial applications.

<![CDATA[Impact of Phellinus gilvus mycelia on growth, immunity and fecal microbiota in weaned piglets]]>


Antibiotics are the most commonly used growth-promoting additives in pig feed especially for weaned piglets. But in recent years their use has been restricted because of bacterial resistance. Phellinus, a genus of medicinal fungi, is widely used in Asia to treat gastroenteric dysfunction, hemrrhage, and tumors. Phellinus is reported to improve body weight on mice with colitis. Therefore, we hypothesize that it could benefit the health and growth of piglets, and could be used as an alternative to antibiotic. Here, the effect of Phellinus gilvus mycelia (SH) and antibiotic growth promoter (ATB) were investigated on weaned piglets.


A total of 72 crossbred piglets were randomly assigned to three dietary treatment groups (n = 4 pens per treatment group with six piglets per pen). The control group was fed basal diet; the SH treatment group was fed basal diet containing 5 g/kg SH; the ATB treatment group was feed basal diet containing 75 mg/kg aureomycin and 20 mg/kg kitasamycin. The experiment period was 28 days. Average daily gain (ADG), average daily feed intake (ADFI), and feed intake to gain ratio were calculated. The concentrations of immunoglobulin G (IgG), interleukin-1β (IL-1β), tumor necrosis factor (TNF)-α and myeloperoxidase (MPO) in serum were assessed. Viable plate counts of Escherichia coli in feces were measured. Fecal microbiota was analyzed via the 16S rRNA gene sequencing method.


The ADG (1–28 day) of piglets was significantly higher in SH and ATB treatment groups (P < 0.05) compared to the control, and the ADG did not show significant difference between SH and ATB treatment groups (P > 0.05). Both SH and ATB treatments increased the MPO, IL-1β, and TNF-α levels in serum compared to the control (P < 0.05), but the levels in SH group were all significantly higher than in the ATB group (P < 0.05). Fecal microbiological analysis showed that viable E. coli counts were dramatically decreased by SH and ATB. The 16S rRNA gene sequencing analysis showed that ATB shifted the microbiota structure drastically, and significantly increased the relative abundance of Prevotella, Megasphaera, and Faecalibacterium genera. But SH slightly influenced the microbiota structure, and only increased the relative abundance of Alloprevotella genus.


Our work demonstrated that though SH slightly influenced the microbiota structure, it markedly reduced the fecal E. coli population, and improved growth and innate immunity in piglets. Our finding suggested that SH could be an alternative to ATB in piglet feed.

<![CDATA[Tissue Engineering Using Vascular Organoids From Human Pluripotent Stem Cell Derived Mural Cell Phenotypes]]>

Diffusion is a limiting factor in regenerating large tissues (100–200 μm) due to reduced nutrient supply and waste removal leading to low viability of the regenerating cells as neovascularization of the implant by the host is a slow process. Thus, generating prevascularized tissue engineered constructs, in which endothelial (ECs) and mural (MCs) cells, such as smooth muscle cells (SMCs), and pericytes (PCs), are preassembled into functional in vitro vessels capable of rapidly connecting to the host vasculature could overcome this obstacle. Toward this purpose, using feeder-free and low serum conditions, we developed a simple, efficient and rapid in vitro approach to induce the differentiation of human pluripotent stem cells-hPSCs (human embryonic stem cells and human induced pluripotent stem cells) to defined SMC populations (contractile and synthetic hPSC-SMCs) by extensively characterizing the cellular phenotype (expression of CD44, CD73, CD105, NG2, PDGFRβ, and contractile proteins) and function of hPSC-SMCs. The latter were phenotypically and functionally stable for at least 8 passages, and could stabilize vessel formation and inhibit vessel network regression, when co-cultured with ECs in vitro. Subsequently, using a methylcellulose-based hydrogel system, we generated spheroids consisting of EC/hPSC-SMC (vascular organoids), which were extensively phenotypically characterized. Moreover, the vascular organoids served as focal starting points for the sprouting of capillary-like structures in vitro, whereas their delivery in vivo led to rapid generation of a complex functional vascular network. Finally, we investigated the vascularization potential of these vascular organoids, when embedded in hydrogels composed of defined extracellular components (collagen/fibrinogen/fibronectin) that can be used as scaffolds in tissue engineering applications. In summary, we developed a robust method for the generation of defined SMC phenotypes from hPSCs. Fabrication of vascularized tissue constructs using hPSC-SMC/EC vascular organoids embedded in chemically defined matrices is a significant step forward in tissue engineering and regenerative medicine.

<![CDATA[Therapeutic Potential of Extracellular Vesicles in Degenerative Diseases of the Intervertebral Disc]]>

Extracellular vesicles (EVs) are lipid membrane particles carrying proteins, lipids, DNA, and various types of RNA that are involved in intercellular communication. EVs derived from mesenchymal stem cells (MSCs) have been investigated extensively in many different fields due to their crucial role as regeneration drivers, but research for their use in degenerative diseases of the intervertebral disc (IVD) has only started recently. MSC-derived EVs not only promote extracellular matrix synthesis and proliferation in IVD cells, but also reduce apoptosis and inflammation, hence having multifunctional beneficial effects that seem to be mediated by specific miRNAs (such as miR-233 and miR-21) within the EVs. Aside from MSC-derived EVs, IVD-derived EVs (e.g., stemming from notochordal cells) also have important functions in IVD health and disease. This article will summarize the current knowledge on MSC-derived and IVD-derived EVs and will highlight areas of future research, including the isolation and analysis of EV subpopulations or exposure of MSCs to cues that may enhance the therapeutic potential of released EVs.

<![CDATA[Gene Editing Regulation and Innovation Economics]]>

Argentina was the first country that enacted regulatory criteria to assess if organisms resulting from new breeding techniques (NBTs) are to be regarded as genetically modified organisms (GMOs) or not. The country has now accumulated 4 year of experience applying such criteria, reaching a considerable number of cases, composed mostly of gene-edited plants, animals, and microorganisms of agricultural use. This article explores the effects on economic innovation of such regulatory experience. This is done by comparing the cases of products derived from gene editing and other NBTs that have been presented to the regulatory system, against the cases of GMOs that have been deregulated in the country. Albeit preliminary, this analysis suggests that products from gene editing will have different profiles and market release rates compared with the first wave of products from the so called “modern biotechnology.” Gene editing products seems to follow a much faster development rate from bench to market. Such development is driven by a more diverse group of developers, and led mostly by small and medium enterprises (SMEs) and public research institutions. In addition, product profiles are also more diversified in terms of traits and organisms. The inferences of these findings for the agricultural and biotechnology sectors, particularly in developing countries, are discussed.

<![CDATA[Synthetic Biology Tools for Genome and Transcriptome Engineering of Solventogenic Clostridium]]>

Strains of Clostridium genus are used for production of various value-added products including fuels and chemicals. Development of any commercially viable production process requires a combination of both strain and fermentation process development strategies. The strain development in Clostridium sp. could be achieved by random mutagenesis, and targeted gene alteration methods. However, strain improvement in Clostridium sp. by targeted gene alteration method was challenging due to the lack of efficient tools for genome and transcriptome engineering in this organism. Recently, various synthetic biology tools have been developed to facilitate the strain engineering of solventogenic Clostridium. In this review, we consolidated the recent advancements in toolbox development for genome and transcriptome engineering in solventogenic Clostridium. Here we reviewed the genome-engineering tools employing mobile group II intron, pyrE alleles exchange, and CRISPR/Cas9 with their application for strain development of Clostridium sp. Next, transcriptome engineering tools such as untranslated region (UTR) engineering and synthetic sRNA techniques were also discussed in context of Clostridium strain engineering. Application of any of these discussed techniques will facilitate the metabolic engineering of clostridia for development of improved strains with respect to requisite functional attributes. This might lead to the development of an economically viable butanol production process with improved titer, yield and productivity.

<![CDATA[Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model]]>

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called “modes of variation” (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes.

<![CDATA[Comparative Transcriptome Analysis Reveals New lncRNAs Responding to Salt Stress in Sweet Sorghum]]>

Long non-coding RNAs (lncRNAs) can enhance plant stress resistance by regulating the expression of functional genes. Sweet sorghum is a salt-tolerant energy crop. However, little is known about how lncRNAs in sweet sorghum respond to salt stress. In this study, we identified 126 and 133 differentially expressed lncRNAs in the salt-tolerant M-81E and the salt-sensitive Roma strains, respectively. Salt stress induced three new lncRNAs in M-81E and inhibited two new lncRNAs in Roma. These lncRNAs included lncRNA13472, lncRNA11310, lncRNA2846, lncRNA26929, and lncRNA14798, which potentially function as competitive endogenous RNAs (ceRNAs) that influence plant responses to salt stress by regulating the expression of target genes related to ion transport, protein modification, transcriptional regulation, and material synthesis and transport. Additionally, M-81E had a more complex ceRNA network than Roma. This study provides new information regarding lncRNAs and the complex regulatory network underlying salt-stress responses in sweet sorghum.

<![CDATA[Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy]]>

Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.

<![CDATA[Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis]]>

Concussion is a significant public health problem affecting 1.6–2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

<![CDATA[Bioengineering of Pulmonary Epithelium With Preservation of the Vascular Niche]]>

The shortage of transplantable donor organs directly affects patients with end-stage lung disease, for which transplantation remains the only definitive treatment. With the current acceptance rate of donor lungs of only 20%, rescuing even one half of the rejected donor lungs would increase the number of transplantable lungs threefold, to 60%. We review recent advances in lung bioengineering that have potential to repair the epithelial and vascular compartments of the lung. Our focus is on the long-term support and recovery of the lung ex vivo, and the replacement of defective epithelium with healthy therapeutic cells. To this end, we first review the roles of the lung epithelium and vasculature, with focus on the alveolar-capillary membrane, and then discuss the available and emerging technologies for ex vivo bioengineering of the lung by decellularization and recellularization. While there have been many meritorious advances in these technologies for recovering marginal quality lungs to the levels needed to meet the standards for transplantation – many challenges remain, motivating further studies of the extended ex vivo support and interventions in the lung. We propose that the repair of injured epithelium with preservation of quiescent vasculature will be critical for the immediate blood supply to the lung and the lung survival and function following transplantation.

<![CDATA[Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning]]>

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

<![CDATA[Preservation of Microalgae, Lignocellulosic Biomass Blends by Ensiling to Enable Consistent Year-Round Feedstock Supply for Thermochemical Conversion to Biofuels]]>

Seasonal variation in microalgae productivity is a significant barrier to economical production of algae biofuels and chemicals. Summer production can be 3–5 times higher than in the winter resulting in uneven feedstock supplies at algae biorefineries. A portion of the summer production must be preserved for conversion in the winter in order to maintain a biorefinery running at capacity. Ensiling, a preservation process that utilizes lactic acid fermentation to limit microbial degradation, has been demonstrated to successfully stabilize algae biomass (20% solids) and algae-lignocellulosic blends (40% algae-60% lignocellulosic biomass, dry basis) for over 6 months, resulting in fuel production cost savings with fewer emissions. Preservation of algae as blends could be beneficial to biorefineries that utilize thermochemical approaches to fuel production as co-processing of algae and lignocellulosic biomass has been observed to enhance biocrude yield and improve oil quality. This study conducts a resource assessment of biomass residues in the southern United States to identify materials available during peak algae productivity and in sufficient quantity to meet the algae storage needs of an algae biofuel industry. Eight feedstocks met the quantity threshold but only three, distillers grains, haylage, and yard waste, were also available in season. Storage experiments utilizing both freshwater and marine strains of microalgae – Scenedesmus acutus, Chlorella vulgaris, Chlorella zofingiensis, Nannochloropsis gaditana, and Porphyridium purpureum – and yard waste were conducted for 30 days. Storage losses were less than 10% in all but one case, and the pH of all but one blend was reduced to less than 4.7, indicating that yard waste is a suitable feedstock for blending with algae prior to storage. To better understand whether the benefits to conversion realized by processing blends might be affected by storage, elemental analysis and bomb calorimetry of pre- and post-storage algae-yard waste blends were conducted to characterize changes occurring during storage. Storing algae biomass as blends with lignocellulosic biomass could be an effective method of mitigating seasonal variability in algae biomass production while retaining the synergistic effect of co-processing algae blends in thermochemical conversion.

<![CDATA[An Engineered Pathway for Production of Terminally Sialylated N-glycoproteins in the Periplasm of Escherichia coli]]>

Terminally sialylated N-glycoproteins are of great interest in therapeutic applications. Due to the inability of prokaryotes to carry out this post-translational modification, they are currently predominantly produced in eukaryotic host cells. In this study, we report a synthetic pathway to produce a terminally sialylated N-glycoprotein in the periplasm of Escherichia coli, mimicking the sialylated moiety (Neu5Ac-α-2,6-Gal-β-1,4-GlcNAc-) of human glycans. A sialylated pentasaccharide, Neu5Ac-α-2,6-Gal-β-1,4-GlcNAc-β-1,3-Gal-β-1,3-GlcNAc-, was synthesized through the activity of co-expressed glycosyltransferases LsgCDEF from Haemophilus influenzae, Campylobacter jejuni NeuBCA enzymes, and Photobacterium leiognathi α-2,6-sialyltransferase in an engineered E. coli strain which produces CMP-Neu5Ac. C. jejuni oligosaccharyltransferase PglB was used to transfer the terminally sialylated glycan onto a glyco-recognition sequence in the tenth type III cell adhesion module of human fibronectin. Sialylation of the target protein was confirmed by lectin blotting and mass spectrometry. This proof-of-concept study demonstrates the successful production of terminally sialylated, homogeneous N-glycoproteins with α-2,6-linkages in the periplasm of E. coli and will facilitate the construction of E. coli strains capable of producing terminally sialylated N-glycoproteins in high yield.

<![CDATA[Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph]]>

Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.

<![CDATA[A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU]]>

Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.

<![CDATA[Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma]]>

Long non-coding RNAs (lncRNAs) are recently emerging as a novel promising biomarker for cancer diagnosis and prognosis. Despite these previous investigations, the expression pattern and diagnostic role of lncRNAs in oral squamous cell carcinoma (OSCC) remain unclear. In this study, we identified six novel lncRNA biomarkers (LINC01697, LINC02487, LOC105376575, AC005083.1, SLC8A1-AS1, and U62317.1) from a list of 29 differentially expressed lncRNAs using the least absolute shrinkage and selection operator (LASSO) method in the discovery dataset of 167 OSCC samples and 45 normal oral tissues. Using the logistic regression method, we constructed a six lncRNAs-based diagnostic risk model (6lncRNAScore) which was able to differentiate between OSCC samples and control samples with high performance with AUC of 0.995 and high diagnostic specificity of 88.9% and sensitivity of 98.2% in the discovery dataset. The diagnostic performance of the 6lncRNAScore was further validated in another two independent OSCC dataset with AUC of 0.968 and 1.0. Functional enrichment analysis for lncRNA biomarkers-related mRNAs suggested that lncRNAs biomarkers tended to be involved in the lipid metabolic process. Together, our study highlighted the importance of lncRNAs in OSCC and demonstrated the utility of lncRNA expression as a promising biomarker for early diagnosis of OSCC.

<![CDATA[Fruits By-Products – A Source of Valuable Active Principles. A Short Review]]>

The growing demand for more sustainable, alternative processes leading to production of plant-derived preparations imposes the use of plants waste generated mainly by agri-food and pharmaceutical industries. These mostly unexploited but large quantities of plants waste also increase the interest in developing alternative approaches for sustainable production of therapeutic molecules. In order to reduce the amount of plant waste by further processing, different novel extraction techniques can be applied. Fruits and their industrial by-products are rich sources of different classes of compounds with therapeutic properties. The processed fruits waste can be reused and lead to novel pharmaceuticals, food supplements or functional foods. This review intends to briefly summarize recent aspects regarding the production of different active compounds from fruit by-products, and their therapeutic properties. The potential use of fruits by-products in different industries will be also discussed.

<![CDATA[Selecting for lactic acid producing and utilising bacteria in anaerobic enrichment cultures]]>


Lactic acid‐producing bacteria are important in many fermentations, such as the production of biobased plastics. Insight in the competitive advantage of lactic acid bacteria over other fermentative bacteria in a mixed culture enables ecology‐based process design and can aid the development of sustainable and energy‐efficient bioprocesses. Here we demonstrate the enrichment of lactic acid bacteria in a controlled sequencing batch bioreactor environment using a glucose‐based medium supplemented with peptides and B vitamins. A mineral medium enrichment operated in parallel was dominated by Ethanoligenens species and fermented glucose to acetate, butyrate and hydrogen. The complex medium enrichment was populated by Lactococcus, Lactobacillus and Megasphaera species and showed a product spectrum of acetate, ethanol, propionate, butyrate and valerate. An intermediate peak of lactate was observed, showing the simultaneous production and consumption of lactate, which is of concern for lactic acid production purposes. This study underlines that the competitive advantage for lactic acid‐producing bacteria primarily lies in their ability to attain a high biomass specific uptake rate of glucose, which was two times higher for the complex medium enrichment when compared to the mineral medium enrichment. The competitive advantage of lactic acid production in rich media can be explained using a resource allocation theory for microbial growth processes.