Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Community Structure and Functional Potential in Cultivated and Native Tallgrass Prairie Soils of the Midwestern United States

The North American prairie covered about 3.6 million-km2 of the continent prior to European contact. Only 1–2% of the original prairie remains, but the soils that developed under these prairies are some of the most productive and fertile in the world, containing over 35% of the soil carbon in the continental United States. Cultivation may alter microbial diversity and composition, influencing the metabolism of carbon, nitrogen, and other elements. Here, we explored the structure and functional potential of the soil microbiome in paired cultivated-corn (at the time of sampling) and never-cultivated native prairie soils across a three-states transect (Wisconsin, Iowa, and Kansas) using metagenomic and 16S rRNA gene sequencing and lipid analysis. At the Wisconsin site, we also sampled adjacent restored prairie and switchgrass plots. We found that agricultural practices drove differences in community composition and diversity across the transect. Microbial biomass in prairie samples was twice that of cultivated soils, but alpha diversity was higher with cultivation. Metagenome analyses revealed denitrification and starch degradation genes were abundant across all soils, as were core genes involved in response to osmotic stress, resource transport, and environmental sensing. Together, these data indicate that cultivation shifted the microbiome in consistent ways across different regions of the prairie, but also suggest that many functions are resilient to changes caused by land management practices – perhaps reflecting adaptations to conditions common to tallgrass prairie soils in the region (e.g., soil type, parent material, development under grasses, temperature and rainfall patterns, and annual freeze-thaw cycles). These findings are important for understanding the long-term consequences of land management practices to prairie soil microbial communities and their genetic potential to carry out key functions.
Optogenetic dissection of mitotic spindle positioning in vivo

The position of the mitotic spindle determines the plane of cell cleavage, and thereby the location, size, and content of daughter cells. Spindle positioning is driven by dynein-mediated pulling forces exerted on astral microtubules. This process requires an evolutionarily conserved complex of Gα-GDP, GPR-1/2Pins/LGN, and LIN-5Mud/NuMA proteins. It remains unknown whether this complex merely forms a membrane anchor for dynein, or whether the individual components have additional functions, for instance through Gα-GTP or dynein activation. To functionally dissect this system, we developed a genetic strategy for light-controlled localization of endogenous proteins in C. elegans embryos. Controlled germline expression and membrane recruitment of the Gα regulators RIC-8Ric-8A and RGS-7Loco/RGS3, and replacement of Gα with a light-inducible membrane anchor demonstrated that Gα-GTP signaling is dispensable for pulling force generation. In the absence of Gα, cortical recruitment of GPR-1/2 or LIN-5, but not dynein itself, induced high pulling forces. Local recruitment of LIN-5 overruled normal cell-cycle and polarity regulation, and provided experimental control over the spindle and cell cleavage plane. Our results define Gα∙GDP–GPR-1/2Pins/LGN as a regulatable membrane anchor, and LIN-5Mud/NuMA as a potent activator of dynein-dependent spindle positioning forces. This study also highlights the possibilities for optogenetic control of endogenous proteins within an animal system.
Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks

The estimation of systolic and diastolic blood pressure using artificial neural network is considered in the paper. The blood pressure values are estimated using pulse arrival time, and additionally RR intervals of ECG signal together with respiration signal. A single layer recurrent neural network with hyperbolic tangent activation function was used. The average blood pressure estimation error for the data obtained from 21 subjects from MIMIC database was equal to 2.490 mmHg with standard deviation equal to 1.063 mmHg for systolic blood pressure, and was equal to 1.330 mmHg with standard deviation equal to 0.627 mmHg for diastolic blood pressure using vanilla recurrent neural networks. Similar results were obtained for long short term memory cells. The simulation shows that taking into account pulse arrival time together with RR intervals and respiration signal gave better results than pulse arrival time alone.
A review on interventions to reduce medication discrepancies or errors in primary or ambulatory care setting during care transition from hospital to primary care

Introduction: Transition of care from hospital to primary care has been associated with increased medication errors. This review article aims to examine the existing evidence on interventions to reduce medication discrepancies or errors in primary or ambulatory care setting during care transition from hospital to primary care. Methods: We systematically reviewed the articles in primary or ambulatory care setting on patients with care transition that involved medication safety, discrepancy, or error as outcome assessment. Primary research articles were selected. Interventions in nursing homes or long-term care facilities were excluded from the review. Results: We found 6 articles that met the inclusion criteria and 4 are prospective cohort study. The key players were pharmacists, nurse, and primary care physician. The interventions included care communication, medication reconciliation or review, and clarifying medication-related problems. Conclusion: There is evidence that interventions in primary care setting reduce medication discrepancies on patients with the transition of care from hospital to primary care setting. Only one randomized trial involving pharmacist-led medication reconciliation was done in an outpatient setting. More good-quality randomized controlled trials should be carried out to confirm the evidence.
Temporal Segmentation for Capturing Snapshots of Patient Histories in Korean Clinical Narrative

Objectives Clinical discharge summaries provide valuable information about patients' clinical history, which is helpful for the realization of intelligent healthcare applications. The documents tend to take the form of separate segments based on temporal or topical information. If a patient's clinical history can be seen as a consecutive sequence of clinical events, then each temporal segment can be seen as a snapshot, providing a certain clinical context at a specific moment. This study aimed to demonstrate a temporal segmentation method of Korean clinical narratives for identifying textual snapshots of patient history as a proof-of-a-concept. Methods Our method uses pattern-based segmentation to approximate human recognition of the temporal or topical shifts in clinical documents. We utilized rheumatic patients' discharge summaries and transformed them into sequences of constituent chunks. We built 97 single pattern functions to denote whether a certain chunk has attributes that indicate that it can be a segment boundary. We manually defined the relationships between the pattern functions to resolve multiple pattern matchings and to make a final decision. Results The algorithm segmented 30 discharge summaries and processed 1,849 decision points. Three human judges were asked whether they agreed with the algorithm's prediction, and the agreement percentage on the judges' majority opinion was 89.61%. Conclusions Although this method is based on manually constructed rules, our findings demonstrate that the proposed algorithm can achieve fairly good segmentation results, and it may be the basis for methodological improvement in the future.
Robust and Blind Eigenvalue-Based Multiantenna Spectrum Sensing Under IQ Imbalance

We investigate the spectrum sensing problem in a single-input-multiple-output cognitive radio system under in-phase and quadrature imbalance (IQI). To do this, the received signal model is obtained in the presence of IQI at the analog front-ends of both the primary and secondary users. We first use a preprocessing stage to mitigate the effect of receiver IQI (RX-IQI). It is found that the received matrix signal is composed of an unknown rank-2 matrix contaminated by the receiver noise matrix. Then, we apply the likelihood ratio test principle to develop three eigenvalue-based detectors. Analytical expressions for the false alarm probabilities of the proposed detectors are derived to adjust their detection thresholds, especially under noise variance uncertainty. Simulation results are provided to illustrate the spectrum sensing performance of the proposed detectors in comparison with existing detectors in the literature also to verify the analytical expressions derived for the false alarm probability. Our simulation results show that the proposed spectrum sensing algorithms are not susceptible to the RX-IQI effects. In addition, we analytically show that one of the proposed spectrum sensing algorithms has constant false alarm rate property with respect to the noise variance uncertainty.
The Dawn of Geostationary Air Quality Monitoring: Case Studies From Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9th, 2016 and Los Angeles, California on June 27th, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.
Digital versus Analog Procedures for the Prosthetic Restoration of Single Implants: A Randomized Controlled Trial with 1 Year of Follow-Up

Aim To compare the outcome of digital versus analog procedures for the restoration of single implants. Methods Over a two-year period (2014-2016), all patients who had been treated in a dental center with a single implant were randomly assigned to receive either a monolithic zirconia crown, fabricated with digital workflow (test group), or a metal-ceramic crown, fabricated with analog workflow (control group). All patients were followed for 1 year after the delivery of the final crown. The outcomes were success, complications, peri-implant marginal bone loss (PIMBL), patient satisfaction, and time and cost of the treatment. Results 50 patients (22 males, 28 females; mean age 52.6±13.4 years) were randomly assigned to one of the groups (25 per group). Both workflows showed high success (92%) and low complication rate (8%). No significant differences were found in the mean PIMBL between test (0.39±0.29mm) and control (0.54±0.32mm) groups. Patients preferred digital impressions. Taking the impression took half the time in the test group (20±5min) than in the control (50±7min) group. When calculating active working time, workflow in the test group was more time-efficient than in the control group, for provisional (70±15min versus 340±37min) and final crowns (29±9min versus 260±26min). The digital procedure presented lower costs than the analog (€277.3 versus €392.2). Conclusions No significant clinical or radiographic differences were found between digital and analog procedures; however, the digital workflow was preferred by patients; it reduced active treatment time and costs. The present study is registered in the ISRCTN (http://www.isrctn.com/ISRCTN36259164) with number 36259164.
The Peptidisc, a simple method for stabilizing membrane proteins in detergent-free solution

Membrane proteins are difficult to work with due to their insolubility in aqueous solution and quite often their poor stability in detergent micelles. Here, we present the peptidisc for their facile capture into water-soluble particles. Unlike the nanodisc, which requires scaffold proteins of different lengths and precise amounts of matching lipids, reconstitution of detergent solubilized proteins in peptidisc only requires a short amphipathic bi-helical peptide (NSPr) and no extra lipids. Multiple copies of the peptide wrap around to shield the membrane-exposed part of the target protein. We demonstrate the effectiveness of this ‘one size fits all’ method using five different membrane protein assemblies (MalFGK2, FhuA, SecYEG, OmpF, BRC) during ‘on-column’, ‘in-gel’, and ‘on-bead’ reconstitution embedded within the membrane protein purification protocol. The peptidisc method is rapid and cost-effective, and it may emerge as a universal tool for high-throughput stabilization of membrane proteins to advance modern biological studies.
Diversity on State Machine Replication

The dependability and security properties of a system could be impaired by a system failure or by an opponent that exploits its vulnerabilities, respectively. An alternative to mitigate this risk is the implementation of fault- and intrusion-tolerant systems, in which the system properties are ensured even if some of its components fail (e.g., because a software bug or a failure in the runtime environment) or are compromised by a successful attack. State Machine Replication (SMR) is widely used to implement these systems. In SMR, servers are replicated and client requests are deterministically executed in the same order by all replicas in a way that the system behavior remains correct even if some of them are compromised since the correct replicas mask the misbehavior of the faulty ones. Unfortunately, the proposed solutions for SMR do not consider diversity in the implementation and all replicas execute the same software. Consequently, the same attack or software bug could compromise all the system. Trying to circumvent this problem, this work proposes an architecture to allow diversity in SMR, allowing the implementation and execution of replicas in different development languages. The main problems addressed by the proposed architecture are twofold: (1) communication among different languages; and (2) data representation. The proposed architecture was integrated in BFT-SMART, a SMR library, and a set of experiments showed its practical feasibility.
Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer

Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems‐level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant‐driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta‐analysis of human prostate samples using weighted gene co‐expression network analysis combined with experimental AR variant transcriptome analyses. An AR variant‐driven gene module that is upregulated during human PC progression was identified. We filtered this module by identifying genes that functionally interacted with AR variants using a high‐throughput synthetic genetic array screen in Schizosaccharomyces pombe. This strategy identified seven AR variant‐regulated genes that also enhance AR activity and drive cancer progression. Expression of the seven genes predicted poor disease‐free survival in large independent PC patient cohorts. Pharmacologic inhibition of interacting members of the gene set potently and synergistically decreased PC cell proliferation. This unbiased and novel gene discovery strategy identified a clinically relevant, oncogenic, interacting gene hub with strong prognostic and therapeutic potential in PC.
The Association Between the Risk Scores for Cardiovascular Disease and Long-Term Mortality Following an Acute Coronary Event.

BACKGROUND A patient`s individual chance of being diagnosed with cardiovascular disease can be determined by risk scores. OBJECTIVES To determine the risk score profiles of patients presenting with a first acute coronary event according to pre-admission risk factors and to evaluate its association with long-term mortality. METHODS The research was based on a retrospective study of a cohort from the 2010 and 2013 Acute Coronary Syndrome Israeli Surveys (ACSIS). Inclusion criteria included first event and no history of coronary heart disease or cardiovascular disease risk equivalent. The Framingham Risk Score, the European Systematic COronary Risk Evaluation (SCORE), and the American College of Cardiology/American Heart Association/ (ACC/AHA) risk calculator were computed for each patient. The risk profile of each patients was determined by the three scores. The prognostic value of each score for 5 year survival was evaluated. RESULTS The study population comprised 1338 patients enrolled in the prospective ACSIS survey. The ACC/AHA score was the most accurate in identifying patients as high risk based on pre-admission risk factors (73% of the subjects). The Framingham algorithm identified 53%, whereas SCORE recognized only 4%. After multivariate adjustment for clinical factors at presentation, we found that no scores were independently associated with 5 year mortality following the first acute coronary event. CONCLUSIONS Patients with first acute coronary event had a higher pre-admission risk scores according to the ACC/AHA risk algorithm. No risk scores were independently associated with 5 year survival after an event.
Data Security in Cognitive Information Systems

The aim of the article is to discuss security of IT systems and to identify elements that should be protected against unauthorized access. Software protection is created based on the characteristics of cyberspace. The complexity of IT systems makes security more complicated and unreliable. The development of technology is inseparably connected with the development of threats, which means that the improvement of software solutions has become an extremely important aspect for all users. The article is also intended to indicate what security elements should be considered as both the system user and when creating the security system. This paper presents topic of cognitive security aspects dedicated to management processes.
Associative Graph Data Structures with an Efficient Access via AVB+trees

This paper introduces a new efficient associative graph data structure with efficient access to all stored data inspired by biological neural networks and associative brain-like approaches. It also introduces new self-balancing, self-organizing, and self-sorting AVB+trees which not only store data but it also allows for replacing many search operations with this structure. This structure aggregates representations of all duplicated of values and objects. It also allows for faster computation of many useful functions as medians, average, minima, maxima, neighbor values in the defined order etc. It always stores all data in order for all attributes simultaneously. Automatic aggregations of duplicates allow it for access to all stored data in less than logarithmic time. These associative structures can also be successfully used as a kind of artificial neural network for classification, clustering, various inferences, estimation of similarities, differences, or correlations of the stored objects.
Energy Balancing of the Alternate Arm Converter Under Parameter Variations

Energy balancing of the alternate arm converter (AAC) is one of the major challenges due to the absence of continuous circulating current. Overlap period-based circulating current control methods are the most common methods for AAC energy balancing. In practical applications, component parameter variations of modular power electronics converters occur due to tolerances, component aging, and internal faults. Component parameter variation is a key consideration for energy balancing of all sorts of modular converters but has not been addressed in the existing literature for the AAC. This paper investigates the energy balancing capability of overlap period-based circulating current control methods in high-voltage direct current (HVDC) applications and proposes a compensation method to improve the energy balancing performance of the AAC under parameter variations. The effectiveness of the proposed compensation method is demonstrated through real-time simulation results based on an AAC HVDC model derived from the CIGRE benchmark modular multilevel converter model.
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.