Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Soil Extraction Method for Cultivating Previously Uncultured Soil Bacteria

Both metagenomics and single-cell sequencing can detect unknown genes from uncultured microbial strains in environments, and either method may find the significant potential metabolites and roles of these strains. However, such gene/genome-based techniques do not allow detailed investigations that are possible with cultures. To solve this problem, various approaches for cultivation of uncultured bacteria have been developed, but there are still difficulties in maintaining pure cultures by subculture. ABSTRACT Here, a new medium, named intensive soil extract medium (ISEM), based on new soil extract (NSE) using 80% methanol, was used to efficiently isolate previously uncultured bacteria and new taxonomic candidates, which accounted for 49% and 55% of the total isolates examined (n = 258), respectively. The new isolates were affiliated with seven phyla (Proteobacteria, Acidobacteria, Firmicutes, Actinobacteria, Verrucomicrobia, Planctomycetes, and Bacteroidetes). The result of chemical analysis showed that NSE included more diverse components of low-molecular-weight organic substances than two conventional soil extracts made using distilled water. Cultivation of previously uncultured bacteria is expected to extend knowledge through the discovery of new phenotypic, physiological, and functional properties and even roles of unknown genes. IMPORTANCE Both metagenomics and single-cell sequencing can detect unknown genes from uncultured microbial strains in environments, and either method may find the significant potential metabolites and roles of these strains. However, such gene/genome-based techniques do not allow detailed investigations that are possible with cultures. To solve this problem, various approaches for cultivation of uncultured bacteria have been developed, but there are still difficulties in maintaining pure cultures by subculture.
Correlation of Maternal Stress Because of Positive Aneuploidy Screening Serum Analytes and Uterine Arteries’ Doppler Ultrasound Index: A Prospective Cohort Study

Background Antenatal anxiety or maternal stress is a prevalent chronic mental disorder in pregnant women. We have assessed the effect of maternal stress from positive aneuploidy screening results on the changes in uterine artery blood flow. Materials and Methods We performed a prospective cohort (one sample) pilot study at a hospital in Tehran, Iran. A total of 60 pregnant women who were candidates for amniocentesis due to abnormal sequential screening test results entered the study. We conducted 2 standard psychological tests, the Spielberger’s State-Trait Anxiety Inventory and the Beck Anxiety Inventory, to determine anxiety levels in the participants before amniocentesis and two weeks after amniocentesis. The uterine artery resistance index was also measured before and two weeks after amniocentesis. The level of maternal stress was compared with the uterine artery resistance index. Results Patients had a mean State Trait Anxiety Inventory score before amniocentesis of greater than 40, which meant that the mothers experienced high anxiety. There were no correlations between both inventories’ anxiety scores and uterine artery blood flow before amniocentesis. However, two weeks after amniocentesis, we observed significant negative correlations between the State Anxiety (P=0.0041) and Trait Anxiety (P=0.010) Inventory scores and the uterine artery resistance indexes. Also, there was an association between the decreased right uterine artery resistance index and State Anxiety scores (P=0.036). There were significant correlations between State and Trait Anxiety scores and second trimester analytes of β-human chorionic gonadotropin (β-hCG, P<0.001), α-fetoprotein (P<0.001), and unconjugated estriol (P=0.048). Conclusion Maternal anxiety because of positive aneuploidy screening serum analytes and amniocentesis can af- fect perinatal outcomes via mood-based alterations in blood flow of the uterine arteries and the screening markers β-hCG,unconjugated estriol, and α-fetoprotein.
Prepregnancy obesity status and risks on pregnancy outcomes in Shanghai

Abstract Obesity in women of reproductive age is not only associated with numerous adverse maternal and fetal effects prenatally but also exerts a negative influence on female fertility. The aim of this study was to investigate the situation of prepregnant obesity in Shanghai and explore the impact of prepregnant obesity on gestational weight gain as well as other pregnancy outcomes. A prospective hospital-based pregnant women cohort was established in Shanghai since January 2015. All pregnant women who were registered and expected to deliver in this hospital were included in the cohort. Nearly one fourth of pregnant women in Shanghai were overweight/obese and the prevalence of overweight/obesity was more common among women with advancing age (P < .001). Women prepregnancy overweight/obesity was associated with 3.5-fold higher risk of excessive gestational weight gain (odds ratio, OR 3.58; 95% confidence interval, CI, 2.82–4.55; P < .001). Women prepregnancy BMI was statistically related to pregnancy outcomes as macrosomia (OR 2.24; 95% CI, 1.55–3.23; P < .001), cesarean delivery (OR 2.04; 95% CI, 1.60–2.62; P < .001), maternal complications (OR 1.53; 95% CI, 1.18–1.98; P < .001). Prepregnancy obesity is associated with a much higher risk of excessive gestational weight gain and pregnancy outcomes in Shanghai. Further interventions targeting maternal obesity, especially prepregnancy obesity are required.
Hawkmoth flight in the unsteady wakes of flowers

ABSTRACT Flying animals maneuver and hover through environments where wind gusts and flower wakes produce unsteady flow. Although both flight maneuvers and aerodynamic mechanisms have been studied independently, little is known about how these interact in an environment where flow is already unsteady. Moths forage from flowers by hovering in the flower's wake. We investigated hawkmoths tracking a 3D-printed robotic flower in a wind tunnel. We visualized the flow in the wake and around the wings and compared tracking performance with previous experiments in a still-air flight chamber. As in still air, moths flying in the flower wake exhibit near-perfect tracking at the low frequencies at which natural flowers move. However, tracking in the flower wake results in a larger overshoot between 2 and 5 Hz. System identification of flower tracking reveals that moths also display reduced-order dynamics in wind compared with still air. Smoke visualization of the flower wake shows that the dominant vortex shedding corresponds to the same frequency band as the increased overshoot. Despite these large effects on tracking dynamics in wind, the leading edge vortex (LEV) remains bound to the wing throughout the wingstroke and does not burst. The LEV also maintains the same qualitative structure seen in steady air. Persistence of a stable LEV during decreased flower tracking demonstrates the interplay between hovering and maneuvering. Highlighted Article: Moths maneuvering in the wake of flowers display simplified flower tracking dynamics. In the flower wake, the leading edge vortex does not burst and extends continuously across the wings and thorax.
C-DLSI: An Extended LSI Tailored for Federated Text Retrieval

As the web expands in data volume and in geographical distribution, centralized search methods become inefficient, leading to increasing interest in cooperative information retrieval, e.g., federated text retrieval (FTR). Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself. To process a query, FTR requires firstly the identification of promising peers that host the relevant documents and secondly the retrieval of the most relevant documents from the selected peers. Most of the existing methods only apply traditional IR techniques that treat each text collection as a single large document and utilize term matching to rank the collections. In this paper, we formalize the problem and identify the properties of FTR, and analyze the feasibility of extending LSI with clustering to adapt to FTR, based on which a novel approach called Cluster-based Distributed Latent Semantic Indexing (C-DLSI) is proposed. C-DLSI distinguishes the topics of a peer with clustering, captures the local LSI spaces within the clusters, and consider the relations among these LSI spaces, thus providing more precise characterization of the peer. Accordingly, novel descriptors of the peers and a compatible local text retrieval are proposed. The experimental results show that C-DLSI outperforms existing methods.
Performance Analysis of Multi-Cell Millimeter-Wave Massive MIMO Networks With Low-Precision ADCs

In this paper, we investigate a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station. Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio. It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.
Cyber-Physical Correlation Effects in Defense Games for Large Discrete Infrastructures

In certain critical infrastructures, correlations between cyber and physical components can be exploited to launch strategic attacks, so that disruptions to one component may affect others and possibly the entire infrastructure. Such correlations must be explicitly taken into account in ensuring the survival of the infrastructure. For large discrete infrastructures characterized by the number of cyber and physical components, we characterize the cyber–physical interactions at two levels: (i) the cyber–physical failure correlation function specifies the conditional survival probability of the cyber sub-infrastructure given that of the physical sub-infrastructure (both specified by their marginal probabilities), and (ii) individual survival probabilities of both sub-infrastructures are characterized by first-order differential conditions expressed in terms of their multiplier functions. We formulate an abstract problem of ensuring the survival probability of a cyber–physical infrastructure with discrete components as a game between the provider and attacker, whose utility functions are composed of infrastructure survival probability terms and cost terms, both expressed in terms of the number of components attacked and reinforced. We derive Nash equilibrium conditions and sensitivity functions that highlight the dependence of infrastructure survival probability on cost terms, correlation functions, multiplier functions, and sub-infrastructure survival probabilities. We apply these analytical results to characterize the defense postures of simplified models of metro systems, cloud computing infrastructures, and smart power grids.
Poster Abstract: Building IoT Nodes - A Flexible Approach

Ubiquitous sensing enabled by Wireless Sensor Network(WSN) technologies offers the ability to measure, infer and understand environmental parameters and respond to changes. In this paper, we describe our Internet of Things(IoT)-based intelligent solution designed to reduce the energy consumption in existing buildings without affecting the thermal comfort of the users. We have designed IoT nodes which can be used for sensing necessary parameters in any environment. Based on the measured values they send appropriate control commands to the appliances such as lights, fans or ACs, making the infrastructure smart, which helps us achieve our goal of reducing the energy consumption. We have deployed our IoT nodes in four classrooms in our academic building to make them smart.
A Socio-Technical System Based on Gamification Towards Energy Savings

This paper presents a gamification methodology and ICT solution currently under development in the enCOMPASS European project, which aims at implementing and validating an integrated socio-technical approach to behavioral change for energy saving. To this aim, innovative user-friendly digital tools are being developed to make energy data consumption available and understandable for different categories of users and stakeholders (household residents, office employees, school pupils, building managers, and utilities operators) and to enable their collaboration to achieve energy savings in efficient, costeffective and comfort-preserving ways. The enCOMPASS platform supports user-centered visualization of energy data from smart meters and sensors and uses the gathered information to provide its users with context-aware adaptive recommendations for energy savings. Moreover, it relies on gamification elements to increase effectiveness and produce greater and more durable behaviour change.
Admission 25-Hydroxyvitamin D Levels Are Associated With Functional Status at Time of Discharge from Intensive Care Unit in Critically Ill Surgical Patients.

BACKGROUND Vitamin D status is associated with length of stay (LOS) and discharge destination in critically ill patients. To further understand this relationship, we investigated whether admission 25-hydroxyvitaminD (25OHD) levels are associated with discharge functional status in the intensive care unit (ICU). METHODS In this retrospective study, data from 2 surgical ICUs at a large teaching hospital were analyzed. 25OHD levels were measured within 24 hours of ICU admission and Functional Status Score for the ICU (FSS-ICU) was calculated within 24 hours of ICU discharge for all patients. To investigate the association of vitamin D status with FSS-ICU, we constructed linear and logistic regression models, controlling for body mass index, Nutrition Risk in the Critically Ill score, ICU LOS, and cumulative protein or caloric deficit during ICU admission. RESULTS Mean 25OHD level and FSS-ICU was 19 (SD 8) ng/mL and 17 (SD 4), respectively, in the analytic cohort (n = 300). Each unit increase in 25OHD level was associated with a 0.2 increment in FSS-ICU (β = .20; 95% CI 0.14-0.25). Patients with 25OHD levels <20 ng/mL had >3-fold risk of low FSS-ICU (<17) compared with patients with 25OHD >20 ng/mL (OR 3.45; 95% CI 1.96-6.08). CONCLUSIONS Our results suggest that vitamin D status at admission is associated with discharge FSS-ICU in critically ill surgical patients. Future studies are needed to validate our results, to build upon our findings, and to determine whether optimizing 25OHD levels can improve functional status and other important clinical outcomes in ICU patients.
Hybrid Active Inference

We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that, in addition to enhancing human cognitive functions with an intelligent and adaptive interface, integrated cognitive processing could accelerate emergent properties within artificial intelligence. To establish this, a machine learning part learns to integrate into human cognition by explaining away multi-modal sensory measurements from the environment and physiology simultaneously with the brain signal. With ongoing training, the amount of predictable brain signal increases. This lends the agent the ability to self-supervise on increasingly high levels of cognitive processing in order to further minimize surprise in predicting the brain signal. Furthermore, with increasing level of integration, the access to sensory information about environment and physiology is substituted with access to their representation in the brain. While integrating into a joint embodiment of human and machine, human action and perception are treated as the machine's own. The framework can be implemented with invasive as well as non-invasive sensors for environment, body and brain interfacing. Online and offline training with different machine learning approaches are thinkable. Building on previous research on shared representation learning, we suggest a first implementation leading towards hybrid active inference with non-invasive brain interfacing and state of the art probabilistic deep learning methods. We further discuss how implementation might have effect on the meta-cognitive abilities of the described agent and suggest that with adequate implementation the machine part can continue to execute and build upon the learned cognitive processes autonomously.
Speed Control System Based on Fuzzy Neural Network of BLDCM

The controller uses the BP network as the basic structure. Fuzzy rules, fuzzy variables and membership functions are integrated by Mamdani fuzzy control method in the network structure and parameters; BP algorithm is applied to adjust the weights of the network, which is equal with membership, enabling the features of fuzzy control with fuzzy reasoning, fuzzy decision-making, self-learning neural networks, adaptive and parallel data processing. The simulation result shows fuzzy neural network control method improves the static and dynamic performance compared to the other two control methods (traditional PI and fuzzy control). The speed increases faster, the overshoot reduces significantly, the motor speed reaches stability quickly at rated speed, added anti-interference capability. The speed control system overall shows high reliability, stability and robustness. In conclusion, the fuzzy neural network control, when used in permanent magnet brushless DC motor speed control system, greatly improves the dynamic and static performance and the anti-interference capability of the system. It is with high practical value.
The mobile Internet underclass: Reality or hyperbole?

Scholars have warned that because mobile Internet access offers lower levels of functionality and content availability, operates on less open and flexible platforms and contributes to diminished levels of user engagement, content creation and information seeking compared with traditional fixed solutions, mobile Internet has created a mobile Internet ‘underclass’. As a growing proportion of the online population is now ‘mobile only’, it is imperative to understand the extent to which mobile Internet may represent a meaningful substitute for traditional PC-based Internet access. Based on operational data from a major Chinese telecommunications carrier, this study provides a comparative analysis of the mobile Internet usage patterns associated with different access channels. Our results show that, in terms of mobile Internet usage, there is not enough evidence to support the ‘mobile underclass’ argument. However, our analysis reveals that the primary concern about the mobile ‘underclass’ remains as the first-order digital divide ‘access’ issue.
Hyper-Parameter Optimization for Emotion Detection using Physiological Signals

Emotion recognition systems are an automatic way to detect user’s temporary emotional states. Nowadays, there is a high volume of work done regarding the emotion recognition task. However, this task remains to be complex and challenging. In this work, we employed optimization algorithm known as sequential model-based optimization (SMBO) algorithms for segmentation and feature selection to predict affect from physiological signals, such as electrocardiogram and electro-dermal activity, directly from the raw time representation. We presented empirical results for the configuration of a physiological signal-based emotion recognition system. This information may be helpful for people developing emotion recognition systems and for further research in this field.
Face Pattern Recognition Using Convolutional Macropixel Approach

Convolutional Neural Network (CNN) is a widely used deep learning framework and is applied in the field of face recognition achieving outstanding results. Macropixel Comparison Approach is a shallow mathematical approach that recognizes face by comparing original pixel blocks of face images. In this paper, we are inspired by ideas of the currently popular deep neural network framework and introduce two features into the mathematical approach: deep overlap and weighted filter. The aim is to explore if the idea of deep learning could benefit mathematical recognition method, which might extend the scope of face recognition research. Results from our experiments show that the new proposed approach achieves markedly better recognition rates than the original macropixel methods.
Prediction of User Behavior in Smart Home Based on Improved ARIMA Model

In the research of prediction on smart home user behavior, the method of improving ARIMA model to predict user behavior is proposed. The water heater's turn-on time is taken as a case, by analyzing the original data of the water heater turn-on time for 12 consecutive weeks, an improved ARIMA model is established to predict the time when the user turns on the water heater in the 13th week. The results of the comparison between the improved ARIMA model and the ARIMA model shows that the improved ARIMA model has better prediction performance and meet the demand on accurate short-term prediction. This model can be extended to predict the time when users turn on other smart home appliances and smart home user behavior.
A High-Gain Circularly Polarized U-Slot Patch Antenna Array [Antenna Designers Notebook]

Patch antennas are widely applied in modern wireless communication systems. However, conventional patch antennas have the disadvantage of narrow bandwidth and are not suitable for Ku-band satellite broadcast reception. Many researchers have proposed various techniques to enhance the bandwidth of probe-fed patch antennas, e.g., employing thick substrate [1], adding parasitic patches either in the same layer (coplanar) [2]-[3] or in another layer (stacked) [4], and using capacitor-loaded patches [5]. With the first technique, the substrate thickness increases and induces the excitation of surface waves [6]. Apart from reducing the radiation efficiency, these surface waves diffract at the substrate edges and deteriorate the radiation patterns. The presence of coplanar and stacked geometry has the disadvantage of increasing the area and thickness of the antenna, respectively. Additional capacitors cause antenna gain reduction because of the ohmic loss of the loading chip resistor.
Radar Signal Recognition Based on FCBF and AdaBoost Algorithm

In order to identify radar emitter signals of complex new systems, a frequency domain analysis method and a recognition method based on FCBF (Fast Correlation-Based Filter Solution) feature selection and AdaBoost (Adaptive Boosting) are proposed. After discretization preprocessing of the frequency domain amplitude set, the FCBF algorithm is introduced to extract the main frequency components to form a new subset and then the subset will be used as the input of the subsequent classifier. In the classifier design, an AdaBoost algorithm based on weak classifier integration is introduced. The algorithm selects C4.5 decision tree as the corresponding weak classifier and by training the model, the strong classifier obtained realizes the recognition of radar signal data set. The theoretical analysis proves the feasibility of the proposed method. The simulation results show that the proposed method is more effective than the SVM algorithm and the AdaBoost algorithm classifier, which are classified by artificial extraction features at low signal-to-noise rate (SNR). The accuracy is obviously improved and the stability is better. Experiments fully prove the superiority of the method on the recognition effect of the radar signal set.