Miracle-dz/UniFAR-FTUs huggingface dataset:
Gastric Mucosa-Associated Microbial Signatures of Early Gastric Cancer

Alterations in the microbiome are associated with the development of gastric cancer. Our study aimed to identify dysbiotic features in early gastric cancer (EC). The gastric microbiome was assessed in EC (n = 30), advanced gastric cancer (AC) (n = 30), and chronic gastritis (CG) (n = 60). The results demonstrated significant differences in the microbial profile and composition between EC and AC, suggesting alterations associated with gastric cancer progression. Linear discriminant analysis (LDA) effect size (LEfSe) analyses identified 32 bacterial genera that were associated with EC. Functional analyses of the gastric microbiome showed that the production of urease and synthesis of bacterial flagella were weakened in EC, while the glycolysis of fructose and hydrolysis of glycosides were enhanced. A classifier based on a random forest (RF) machine learning algorithm identified a microbial signature that distinguished EC from CG or AC with high accuracy. The correct identification of the signature was further validated in independent cohorts. This signature enriched of bacteria with varied abundance, high degree of bacterial interactions and carcinogenic potentials. Constrained principal coordinate analyses revealed that the presence of Helicobacter pylori and the cagA and vacA virulence genotypes influenced the structure of the gastric microbiome. To determine the impacts of host genetic variations on the gastric microbiome, six previously reported single nucleotide polymorphisms (SNPs) were examined. The minor allele of MUC1 rs4072037 was associated with an increased abundance of Ochrobactrum. The gastric microbiome altered in EC, which might be attributed in part to host genetic variations, H. pylori infection, bacterial virulence and environmental adaptations. The identified microbial signature could serve as biomarkers for clinical assessment of gastric cancer risk in high-risk patients.
Soil Ecosystem Changes During the Transition to No-Till Cropping

Summary Growers in the United States and worldwide are adopting no-tillage (no-till) cropping to reduce soil erosion, improve soil quality, increase water infiltration, and reduce number of passes with farm equipment over their fields. Soil erosion from dry farmed (i.e., non-irrigated) cropland in most regions of the United States exceeds the tolerable rate. An understanding of the changes in the soil ecosystem with changing tillage practices is needed to minimize the impact of agriculture on the environment and foster the use of sustainable agricultural practices. The soil biota is critical to the functioning of any agro-ecosystem, but studying the soil biota is difficult due to the diversity and the challenges associated with isolating and identifying these organisms. Soil disturbance or lack of disturbance can have a profound effect on biotic populations, processes and community structure. This contribution examines changes that occur in soil during the transition to no-till cropping, interrelations among organisms in the soil food web, and the relationships between organisms and their environment. As interest grows in sustainable cropping systems that mimic processes and soil organic matter turnover of native, undisturbed systems, it is imperative to understand how the transition to no-till affects an organism's niche, or functional role within the soil environment. Ecosystem investigations will enhance the understanding of changes that occur with the adoption of reduced tillage and no-till cropping systems so that these systems become increasingly viable.
The Approach to Design of Problem-Oriented Reconfigurable Hardware Computational Units

The article presents an approach to designing specialized hardware computational units (CmUs) with the possibility of coarse-grained reconfiguration at several levels: the CmU structure, individual data processing units, and the target algorithm. The article discusses options for the design flow of CmU in the framework of the proposed approach and the organization of automated design tools. Two practical examples of the approach implementation with various features of the CmU structure organization and tools are presented. The application range of the proposed CmUs and the differences from alternative solutions are discussed. The proposed approach can be successfully used in the design of CmUs and accelerators for digital signal processing, the implementation of real-time control algorithms, hardware-in-the-loop modeling.
Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus

Objective To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of classifiers trained to discriminate a comprehensive basis set for speech: 39 English phonemes. We classified neural correlates of spoken-out-loud words in the “hand knob” area of precentral gyrus, which we view as a step towards the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak. Approach Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode’s binned action potential counts or high-frequency local field potential power. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes’ onset times. Main results A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while a recurrent neural network classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Significance The ability to decode a comprehensive set of phonemes using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.
RNAxplorer: Harnessing the Power of Guiding Potentials for Sampling of RNA Landscapes

Motivation Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the structure space. Abstractions are therefore needed to simplify downstream analyses, and make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method We introduce RNAxplorer, a novel adaptive sampling method which uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials reflecting the similarity to already well-sampled structures. These potentials are accumulated into pseudo-energy terms that effectively steer sampling towards underrepresented or unexplored regions of the structure space. Results RNAxplorer allows us to efficiently explore RNA state space. It yields rare conformations that may be inaccessible to other sampling methods. We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape that is well suited to compute better approximations of RNA folding kinetics. Availability https://github.com/ViennaRNA/RNAxplorer/ Contact ronny@tbi.univie. ac.at Supplementary information Supplementary data are available at Bioinformatics online.
TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

The computational prediction of disease-associated genetic variation is of fundamental importance for the genomics, genetics and clinical research communities. Whereas the mechanisms and disease impact underlying coding single nucleotide polymorphisms (SNPs) and small Insertions/Deletions (InDels) have been the focus of intense study, little is known about the corresponding impact of structural variants (SVs), which are challenging to detect, phase and interpret. Few methods have been developed to prioritise larger chromosomal alterations such as Copy Number Variants (CNVs) based on their pathogenicity. We address this issue with TADA, a method to prioritise pathogenic CNVs through manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigorous enrichment analysis. We demonstrate that our machine-learning classifiers for deletions and duplications are able to accurately predict pathogenic CNVs (AUC: 0.8042 and 0.7869, respectively) and produce a well-calibrated pathogenicity score. The combination of enrichment analysis and classifications suggests that prioritisation of pathogenic CNVs based on functional annotation is a promising approach to support clinical diagnostic and to further the understanding of mechanisms that control the disease impact of larger genomic alterations.
The nitrogen cycle: perturbations due to man and their impact on atmospheric N2O and O3

Features of the global cycle of fixed nitrogen are reviewed with an emphasis on perturbations due to man. It is argued that agricultural practices and combustion may lead to an increase in the concentration of atmospheric N2_O with consequent effects on O_3. The level of O_3 may drop by about 20% over the next 100 years if world population and the demand for food should continue to grow at anything like rates which prevailed in the recent past. Uncertainties in the model are highlighted and note is taken of areas where there is need for additional data.
A Novel High-Density and Low-Power Ternary Content Addressable Memory Design Based on 3D NAND Flash

A novel ternary content addressable memory (TCAM) design based on 3D NAND Flash is proposed, in which two adjacent flash cells consist of one TCAM cell. HSPICE simulations show that the proposed TCAM has the ultra-low energy consumption of 0.298 fJ/bit/search for a 64-bit word and the cell density with 96-layer 3D NAND is reduced ~582 times compared to the conventional CMOS-based TCAMs.
Modeling of collaterally sensitive drug cycles, and optimization of the drug effect in the spirit of adaptive therapy

In previous work, we focused on the optimal therapeutic strategy with a pair of drugs which are collaterally sensitive to each other, that is, a situation in which evolution of resistance to one drug induces sensitivity to the other, and vice versa. [1] Here, we have extended this exploration to the optimal strategy with a collaterally sensitive drug sequence of an arbitrary length, N(≥2). To explore this, we have developed a dynamical model of sequential drug therapies with N drugs. In this model, tumor cells are classified as one of N subpopulations represented as {Ri|i = 1, 2, …, N}. Each subpopulation, Ri, is resistant to ‘Drug i’ and each subpopulation, Ri−1 (or RN, if i = 1), is sensitive to it, so that Ri increases under ‘Drug i’ as it is resistant to it, and after drug-switching, decreases under ‘Drug i + 1’ as it is sensitive to that drug(s). Similar to our previous work examining optimal therapy with two drugs, we found that there is an initial period of time in which the tumor is ‘shaped’ into a specific makeup of each subpopulation, at which time all the drugs are equally effective . After this shaping period, all the drugs are quickly switched with duration relative to their efficacy in order to maintain each subpopulation, consistent with the ideas underlying adaptive therapy. [2] Additionally, we have developed methodologies to administer the optimal regimen under clinical or experimental situations in which no drug parameters and limited information of trackable populations data (all the subpopulations or only total population) are known. The therapy simulation based on these methodologies showed consistency with the theoretical effect of optimal therapy.
The effect of copper in excess of the requirement during the starter period on subsequent performance of broiler chicks

SUMMARY Several researchers have explored the potential of Cu as a growth-promoting agent in the diets of broiler chicks in excess of the current recommended nutritional level (up to 300 mg/kg of the diet). However, it is not clear whether the reported beneficial or toxic effects of extra Cu last until market age if Cu is supplemented to the diet only during the starter period. Therefore, an experiment was conducted using 1-d-old male broiler chicks to investigate the effects of different levels of Cu [0, 125, and 250 ppm of Cu from cupric sulfate pentahydrate (CuSO4∙5H2O)] in excess of the requirement during the starter period (1 to 21 d) on broiler chick performance, bone development, and gastrointestinal development. The addition of 250 ppm of Cu had significant (P 0.05) by additional Cu supplementation; however, the addition of 250 ppm of Cu to the basal diet significantly (P < 0.05) increased liver Cu and decreased blood hematocrit (%) at 20 d. Although feeding 250 ppm of Cu during the starter period had negative consequences on bird performance, moderate levels of Cu (125 ppm) had beneficial effects on FCR without having deleterious effects on bird performance at market age. Caution is advised when considering higher levels of Cu (250 ppm) as a feed supplement in broiler diets.
Performance Evaluation of RPL protocol in a 6LoWPAN based Smart Home Environment

The advancement in technologies like IoT, deviceto-device communication lead to concepts like smart home and smart cities, etc. In smart home architecture, different devices such as home appliances, personal computers, surveillance cameras, etc. are connected to the Internet and enable the user to monitor and control irrespective of time and location. IPv6-enabled 6LoWPAN is a low-power, low-range communication protocol designed and developed for the short-range IoT applications. 6LoWPAN is based on IEEE 802.15. 4 protocol and IPv6 network protocol for low range wireless applications. Although 6LoWPAN supports different routing protocols, RPL is the widely used routing protocol for low power and lossy networks. In this work, we have taken an IoT enabled smart home environment, in which 6LoWPAN is used as a communication and RPL as a routing protocol. The performance of this proposed network model is analyzed based on the different performance metrics such as latency, PDR, and throughput. The proposed model is simulated using Cooja simulator running over the Contiki OS. Along with the Cooja simulator, the network analyzer tool Wireshark is used to analyze the network behaviors.
Design and Simulation of a Soft Robotic Device for Wrist Rehabilitation

Recently, soft robotics have become very popular in providing robot-assisted rehabilitation because of their compliant nature, which makes their interactions with human users safe. In task-specific rehabilitative exercises, several upper limb joints have to be mobile, but soft robotic exoskeletons have generally focused on hand rehabilitation. Soft pneumatic actuator (SPA) is a kind of soft robotic technique that enabling diverse and complex motion of the soft robotic devices. In this work, an assistive and portable soft robot has been designed and simulated for the rehabilitation of wrist in individuals suffering from wrist dysfunction. The robotic device uses soft actuators with molded elastomeric chambers and shell reinforcement plates which induce specific bending trajectories under pneumatic pressurizations. A kind of actuator with three different section-shapes -rectangle, trapezoid and arc-shaped-are simulated and compared, the efficiency of different section shape actuators used in wrist rehabilitation equipment is discussed.
In vivo phage display: identification of organ-specific peptides using deep sequencing and differential profiling across tissues

In vivo phage display is widely used for identification of organ- or disease-specific homing peptides. However, the current in vivo phage biopanning approaches fail to assess biodistribution of specific peptide phages across tissues during the screen, thus necessitating laborious and time-consuming post-screening validation studies on individual peptide phages. Here, we adopted bioinformatics tools used for RNA sequencing for analysis of high throughput sequencing (HTS) data to estimate the representation of individual peptides during biopanning in vivo. The data from in vivo phage screen were analyzed using differential binding – relative representation of each peptide in the target organ vs. in a panel of control organs. Application of this approach in a model study using low-diversity peptide T7 phage library with spiked-in brain homing phage, demonstrated brain-specific differential binding of brain homing phage and resulted in identification of novel lung and brain specific homing peptides. Our study provides a broadly applicable approach to streamline in vivo peptide phage biopanning and to increase its reproducibility and success rate. Graphic abstract In vivo phage display using differential binding approach
Molecular phylogeny, taxonomy and evolution of arbuscular mycorrhizal fungi

45 Introduction . 45 Materials and Methods . 46 Results . 48 Discussion . 53 Acknowledgements . 56 References . 56 Legends to figures . 59 7. Revealing natural relationships among arbuscular mycorrhizal fungi: culture line BEG47 represents Diversispora epigaea , not Glomus versiforme . 63
Calcium Permeable Channels in Cancer Hallmarks

Cancer, the second cause of death worldwide, is characterized by several common criteria, known as the “cancer hallmarks” such as unrestrained cell proliferation, cell death resistance, angiogenesis, invasion and metastasis. Calcium permeable channels are proteins present in external and internal biological membranes, diffusing Ca2+ ions down their electrochemical gradient. Numerous physiological functions are mediated by calcium channels, ranging from intracellular calcium homeostasis to sensory transduction. Consequently, calcium channels play important roles in human physiology and it is not a surprise the increasing number of evidences connecting calcium channels disorders with tumor cells growth, survival and migration. Multiple studies suggest that calcium signals are augmented in various cancer cell types, contributing to cancer hallmarks. This review focuses in the role of calcium permeable channels signaling in cancer with special attention to the mechanisms behind the remodeling of the calcium signals. Transient Receptor Potential (TRP) channels and Store Operated Channels (SOC) are the main extracellular Ca2+ source in the plasma membrane of non-excitable cells, while inositol trisphosphate receptors (IP3R) are the main channels releasing Ca2+ from the endoplasmic reticulum (ER). Alterations in the function and/or expression of these calcium channels, as wells as, the calcium buffering by mitochondria affect intracellular calcium homeostasis and signaling, contributing to the transformation of normal cells into their tumor counterparts. Several compounds reported to counteract several cancer hallmarks also modulate the activity and/or the expression of these channels including non-steroidal anti-inflammatory drugs (NSAIDs) like sulindac and aspirin, and inhibitors of polyamine biosynthesis, like difluoromethylornithine (DFMO). The possible role of the calcium permeable channels targeted by these compounds in cancer and their action mechanism will be discussed also in the review.
Building a tRNA thermometer to access the world’s biochemical diversity

Because ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA, and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a model using tRNA sequence to predict OGT. We used tRNA sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum r2 of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps, and widening the scope of valid downstream analyses.
A self-regenerating synthetic cell model

Self-regeneration is a fundamental function of all living systems. Here we demonstrate molecular self-regeneration in a synthetic cell model. By implementing a minimal transcription-translation system within microfluidic reactors, the system was able to regenerate essential protein components from DNA templates and sustained synthesis activity for over a day. By mapping genotype-phenotype landscapes combined with computational modeling we found that minimizing resource competition and optimizing resource allocation are both critically important for achieving robust system function. With this understanding, we achieved simultaneous regeneration of multiple proteins by determining the required DNA ratios necessary for sustained self-regeneration. This work introduces a conceptual and experimental framework for the development of a self-replicating synthetic cell.
Prey escorting behavior and possible convergent evolution of foraging recruitment mechanisms in an invasive ant

The longhorn crazy ant Paratrechina longicornis is a pest ant species with worldwide distribution. It tends honeydew-producing Hemiptera to obtain carbohydrates and is also an effective predator and scavenger. What strategies does P. longicornis use to aid in hunting and scavenging? Our results show that as well as long-term recruitment to stable food sources, this species uses specialized short-term recruitment pheromones to recruit nest mates to assist in the exploitation of large food items. By mass, 88% of externally carried food was retrieved cooperatively. Recruitment to large items is via a pheromone trail laid by the discovering ant. This trail is initially followed with few errors by naive recruits (82% correct choices at a T-bifurcation) but decays very rapidly (within 6min). We also show that food-discovering ants can recruit nearby nest mates to a large food item without returning to the nest. These properties of the recruitment system are strikingly similar to 2 unrelated ant species, which also specialize in cooperative retrieval of large food items, suggesting convergent evolution. Lastly, we describe a novel "escort" behavior in which additional workers accompany a transported item but do not necessarily assist in carrying it. Both local recruitment and escorting are much more pronounced (3 and 4 times greater, respectively) when handling live prey. When an "escort" is present, live prey presented to the ants is 50% more likely to be captured and is captured 4 times sooner and 50% closer to the release point.