Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data to the Bench: Transcriptome Analysis for Undergraduates

Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experiments is crucial for training young scientists. However, significant challenges impede the introduction of these methods into the undergraduate classroom, including the need for specialized computer programs and knowledge of computer coding. Here, we describe a semester-long, course-based undergraduate research experience at a liberal arts college combining RNA-sequencing (RNA-seq) analysis with student-driven, wet-lab experiments to investigate plant responses to light. Students derived hypotheses based on analysis of RNA-seq data and designed follow-up studies of gene expression and plant growth. Our assessments indicate that students acquired knowledge of big data analysis and computer coding; however, earlier exposure to computational methods may be beneficial. Our course requires minimal prior knowledge of plant biology, is easy to replicate, and can be modified to a shorter, directed-inquiry module. This framework promotes exploration of the links between gene expression and phenotype using examples that are clear and tractable and improves computational skills and bioinformatics self-efficacy to prepare students for the “big data” era of modern biology.
Superradiance and Exciton Delocalization in Bacterial Photosynthetic Light-Harvesting Systems

We present temperature-dependent fluorescence quantum yield and lifetime measurements on the LH-1 and LH-2 complexes of Rhodobacter sphaeroides and on the isolated B820 subunit of Rhodospirillum rubrum. From these measurements the superradiance is calculated, which is related to the delocalization of excitations in these complexes. In the B820 preparation we find a radiative rate that is 30% higher than that of monomeric bacteriochlorophyll, in agreement with a dimer model of this subunit. At room temperature both LH-1 and LH-2 are superradiant relative to monomeric Bchl-a with enhancement factors of 3.8 and 2.8, respectively. In LH-2 the radiative rate does not change significantly upon lowering the temperature to 4 K. LH-1 however exhibits a strong temperature dependence, giving rise to a 2.4 times higher radiative rate at 4 K relative to room temperature. From modeling of the superradiance using a Hamiltonian based on the LH-2 structure and including site inhomogeneity, we conclude that the ratio of in.
Protective effects of interactional justice on job insecurity of Chinese workers: Evidence from a large-scale state-owned telecom company

The study attempted to explore protective effects of procedural justice on job insecurity and job attitudes of Chinese workers in the face of an impending organizational change. In a large-scale state-owned telecom company and its four subsidiary companies in China where a fundamental organizational change was about to take place, 592 employees were randomly sampled and surveyed. The results of hierarchical regression analysis show that procedural justice could bolster employees' job satisfaction and organizational commitment partially through reducing their job insecurity. Further analysis of this partial mediating effect, in terms of two components of procedural justice, revealed a protective effect of interactional justice instead of formal procedure on job insecurity of Chinese employees in the face of the forthcoming organizational change. Implications for measures protecting employees ' psychological well-being in the decision-making process of human resource management leading up to organizational change are discussed later.
Near-Optimal Scheme for Cognitive Radio Networks With Heterogeneous Mobile Secondary Users

In this paper, we study the throughput and delay scaling laws for cognitive radio network with static primary users and heterogeneous mobile secondary users coexist in the unit planar region for both without base-station case and with base-station case. The primary network consists of n randomly and uniformly distributed static primary users with higher priority to access the spectrum. The secondary network consists of m = (h + 1)n1+ϵ heterogeneous mobile secondary users with h = O(log n)1 and ϵ > 0, which should access the spectrum opportunistically. Each secondary user moves within a circular region, centered at its home-point with a restricted speed. The moving region of each mobile SU is n-α, where α is a random variable following the discrete uniform distribution with h + 1 different values, ranging from 0 to α0(α0 > 0). We propose a scheme which consists of routing and scheduling schemes and show that the primary network and secondary network can achieve near-optimal throughput and delay scalings if we increase the heterogeneity of secondary users. In this near-optimal condition, both the primary network and part of the secondary network can achieve constant throughput and delay scalings except for poly-logarithmic factor. Furthermore, we study the case that there are nβ uniformly distributed static base-stations (BSs) where β > 0 and propose the corresponding scheme for PUs, SUs and BSs. Finally, the theoretical results indicate that if β is large enough, BSs can improve the throughput and delay performance by relaying packets through cable backhaul.
An Improved Technique for Frequent Itemset Mining

An association rule mining is important in data mining. Two Steps important in association rule mining. First, find the frequent itemset from dataset and Second, find the association rule from frequent itemsets. A frequent itemsets mining is crucial and most expensive step in association rule mining. In Apriori and Apriori-like principle it's known that the algorithms cannot perform efficiently due to high and repeatedly database passes. In this paper we proposed a improved technique for frequent itemset mining. This technique scan the database only once and reduces the number of transaction.
A Cognitive Framework for Battlefield Commanders' Situation Assessment

Abstract : Situation assessment is an essential component of battlefield planning. This report describes a cognitive framework for situation assessment, based on research in cognitive psychology and tactical planning and on interviews with command staff. The report describes the framework in terms of three key components: memory and knowledge structures; actions, goals, and values; and monitoring and regulating cognition. It defines structural constraints and modes of processing (procedural versus knowledge-based and analytic versus intuitive) in terms of these components. It describes paths through the framework, illustrating them with concrete examples based on experiences of command staff. Finally, the report discusses characteristics of proficient situation assessors. Among the knowledge structures used by situation assessors to frame situations are enemy plan structures, enemy goal structures, temporal plan execution structures, enemy planning/command and control structures, and terrain structures. Situations may be framed proactively, predictively, or reactively. Metacognitive processes test the completeness, reliability, and consistency of situation models and plans and guide the activation of additional knowledge to flesh them out, replace weak assumptions, or resolve conflict. The framework provides a foundation for improved situation assessment performance through training, materiel, computer aids, procedures, organizational structures, or personnel selection. Command and control, Staff operations, Decision making, Situation assessment, Cognitive processes, Expertise, Knowledge representation, Tactical planning, Metacognition
Models and Separation Logics for Resource Trees

In this article, we propose a new data structure, called resource tree, that is a node-labelled tree in which nodes contain resources which belong to a partial monoid. We define the resource tree model and a new separation logic (BI-Loc) that extends the Bunched Implications logic (BI) with a modality for locations. In addition, we consider quantifications on locations and paths and then we study decidability by model-checking in these models and logics. Moreover, we define a language to deal with resource trees and also an assertion logic derived from BI-Loc. Then soundness and completeness issues are studied, and we show how the model and its associated language can be used to manage heap structures and also permission accounting.
Crystal structures of Ca2+–calmodulin bound to NaV C-terminal regions suggest role for EF-hand domain in binding and inactivation

Significance Calmodulin (CaM) is a Ca2+-sensing protein that endows several voltage-gated sodium (NaV) and calcium (CaV) channels with Ca2+-dependent inactivation (CDI). Although this phenomenon has been known to exist for decades, the exact mechanism remains at large. Several high-resolution structures have captured complexes between Ca2+/CaM and NaV or CaV C-terminal IQ domains, but structures are scarce for a larger (Ca2+)4/CaM complex that also includes the upstream EF-hand domain, a domain that is critical for CDI. We show crystal structures of the C-terminal domain of the cardiac Nav1.5 and the IQ domain of Nav1.4, two NaVs that are differentially regulated by CaM. The results uncover a role for the EF-hand domain in dictating binding of CaM and suggest a conformational switch in CDI. Voltage-gated sodium (NaV) and calcium channels (CaV) form targets for calmodulin (CaM), which affects channel inactivation properties. A major interaction site for CaM resides in the C-terminal (CT) region, consisting of an IQ domain downstream of an EF-hand domain. We present a crystal structure of fully Ca2+-occupied CaM, bound to the CT of NaV1.5. The structure shows that the C-terminal lobe binds to a site ∼90° rotated relative to a previous site reported for an apoCaM complex with the NaV1.5 CT and for ternary complexes containing fibroblast growth factor homologous factors (FHF). We show that the binding of FHFs forces the EF-hand domain in a conformation that does not allow binding of the Ca2+-occupied C-lobe of CaM. These observations highlight the central role of the EF-hand domain in modulating the binding mode of CaM. The binding sites for Ca2+-free and Ca2+-occupied CaM contain targets for mutations linked to long-QT syndrome, a type of inherited arrhythmia. The related NaV1.4 channel has been shown to undergo Ca2+-dependent inactivation (CDI) akin to CaVs. We present a crystal structure of Ca2+/CaM bound to the NaV1.4 IQ domain, which shows a binding mode that would clash with the EF-hand domain. We postulate the relative reorientation of the EF-hand domain and the IQ domain as a possible conformational switch that underlies CDI.
Content and Usability Evaluation of Patient Oriented Drug-Drug Interaction Websites

Drug-Drug Interactions (DDI) are an important source of preventable adverse drug events and a common reason for hospitalization among patients on multiple drug therapy regimens. DDI information systems are important patient safety tools with the capacity to identify and warn health professionals of clinically significant DDI risk. While substantial research has been completed on DDI information systems in professional settings such as community, hospital, and independent pharmacies; there has been limited research on DDI systems offered through online websites directly for use by ambulatory patients. The focus of this project is to test patient oriented website capacity to correctly identify drug interactions among well established and clinically significant medication combinations and convey clinical risk data to patients. The patient education capability was assessed by evaluating website Information Capacity, Patient Usability and Readability. The study results indicate that the majority of websites identified which met the inclusion and exclusion criteria operated similarly, but vary in risk severity assessment and are not optimally patient oriented to effectively deliver risk information. The limited quality of information and complex medical term content complicate DDI risk data conveyance and the sites may not provide optimal information delivery to allow medication consumers to understand and manage their medication regimens.
Disruption of the exocyst induces podocyte loss and dysfunction

Although the slit diaphragm proteins in podocytes are uniquely organized to maintain glomerular filtration assembly and function, little is known about the underlying mechanisms that participate in trafficking these proteins to the correct location for development and homeostasis. Identifying these mechanisms will likely provide novel targets for therapeutic intervention to preserve podocyte function following glomerular injury. Analysis of structural variation in cases of human nephrotic syndrome identified rare heterozygous deletions of EXOC4 in two patients. This suggested that disruption of the highly-conserved eight-protein exocyst trafficking complex could have a role in podocyte dysfunction. Indeed, mRNA profiling of injured podocytes identified significant exocyst down-regulation. To test the hypothesis that the exocyst is centrally involved in podocyte development/function, we generated homozygous podocyte-specific Exoc5 (a central exocyst component that interacts with Exoc4) knockout mice that showed massive proteinuria and died within 4 weeks of birth. Histological and ultrastructural analysis of these mice showed severe glomerular defects with increased fibrosis, proteinaceous casts, effaced podocytes, and loss of the slit diaphragm. Immunofluorescence analysis revealed that Neph1 and Nephrin, major slit diaphragm constituents, were mislocalized and/or lost. mRNA profiling of Exoc5 knockdown podocytes showed that vesicular trafficking was the most affected cellular event. Mapping of signaling pathways and Western blot analysis revealed significant up-regulation of the mitogen-activated protein kinase and transforming growth factor-β pathways in Exoc5 knockdown podocytes and in the glomeruli of podocyte-specific Exoc5 KO mice. Based on these data, we propose that exocyst-based mechanisms regulate Neph1 and Nephrin signaling and trafficking, and thus podocyte development and function.
Subsurface pipeflow dynamics of north-coastal California swale systems

Pipeflow dynamics are being studied at Caspar Creek Experimental Watershed in north-coastal California near Ft. Bragg. Pipes have been observed at depths to 2 m within trenched swales and at the heads of gullied channels in small (0.8 to 2 ha) headwater drainages. Digital data loggers connected to pressure transducers monitor discharge using calibrated standpipes. During storms, pipeflow up to 8 l s -1 has been measured, while, within the same swales, no surface channel flow occurred. Pipeflow discharge has been correlated with antecedent precipitation.
Stability-Based Generalization Analysis of Distributed Learning Algorithms for Big Data

As one of the efficient approaches to deal with big data, divide-and-conquer distributed algorithms, such as the distributed kernel regression, bootstrap, structured perception training algorithms, and so on, are proposed and broadly used in learning systems. Some learning theories have been built to analyze the feasibility, approximation, and convergence bounds of these distributed learning algorithms. However, less work has been studied on the stability of these distributed learning algorithms. In this paper, we discuss the generalization bounds of distributed learning algorithms from the view of algorithmic stability. First, we introduce a definition of uniform distributed stability for distributed algorithms and study the distributed algorithms’ generalization risk bounds. Then, we analyze the stability properties and generalization risk bounds of a kind of regularization-based distributed algorithms. Two generalization distributed risks obtained show that the generalization distributed risk bounds for the difference between their generalization distributed and empirical distributed/leave-one-computer-out risks are closely related to the size of samples <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and the amount of working computers <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> as <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(m/{n}^{1/2})$ </tex-math></inline-formula>. Furthermore, the results in this paper indicate that, for a good generalization regularized distributed kernel algorithm, the regularization parameter <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> should be adjusted with the change of the term <inline-formula> <tex-math notation="LaTeX">$m/{n}^{1/2}$ </tex-math></inline-formula>. These theoretic discoveries provide the useful guidance when deploying the distributed algorithms on practical big data platforms. We explore our theoretic analyses through two simulation experiments. Finally, we discuss some problems about the sufficient amount of working computers, nonequivalence, and generalization for distributed learning. We show that the rules for the computation on one single computer may not always hold for distributed learning.
Effect of Drought Stress and Developmental Stages on Microbial Community Structure and Diversity in Peanut Rhizosphere Soil

Background: Peanut (Arachis hypogaea L.), an important oilseed and food legume, is widely cultivated in the semi-arid tropics. Drought is the major stress in this region which limits productivity. Microbial communities in the rhizosphere are of special importance to stress tolerance. However, relatively little is known about the relationship between drought and microbial communities in peanuts. Method: In this study, deep sequencing of the V3-V4 region of the 16S rRNA gene was performed to characterize the microbial community structure of drought-treated and untreated peanuts. Results: Taxonomic analysis showed that Actinobacteria, Proteobacteria, Saccharibacteria, Chloroflexi, Acidobacteria and Cyanobacteria were the dominant phyla in the peanut rhizosphere. Comparisons of microbial community structure of peanuts revealed that the relative abundance of Actinobacteria and Acidobacteria dramatically increased in the seedling and podding stages in drought-treated soil, while that of Cyanobacteria and Gemmatimonadetes increased in the flowering stage in drought-treated rhizospheres. Metagenomic profiling indicated that sequences related to metabolism, signaling transduction, defense mechanism and basic vital activity were enriched in the drought-treated rhizosphere, which may have implications for plant survival and drought tolerance. Conclusion: This microbial communities study will form the foundation for future improvement of drought tolerance of peanuts via modification of the soil microbes.
The selection of Mongolian stop words

In this paper, by investigating the methods of selecting Mongolian stop words based on statistical methods in Mongolian information retrieval, an improved method is proposed. The main idea of this method is to obtain Mongolian stop words through algorithms have been used, and then remove the noun or verb as the main component in the sentences and Mongolian homonyms from the stop words. The experiments show that using this method select stop words and the existing methods select the stop words for Mongolian document retrieval, this method can improve the retrieval results.
Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping

Image cropping aims at improving the quality of images by removing unwanted outer areas, which is widely used in the photography and printing industry. Most of the previous cropping methods that do not need bounding box supervision rely on the sliding window mechanism. The sliding window method results in fixed aspect ratios and limits the shape of the cropping region. Moreover, the sliding window method usually produces lots of candidates on the input image, which is very time-consuming. Motivated by these challenges, we formulate image cropping as a sequential decision-making process and propose a reinforcement learning-based framework to address this problem, namely, Fast Aesthetics-Aware Adversarial Reinforcement Learning (Fast A3RL). Particularly, the proposed method develops an aesthetics-aware reward function that is dedicated for image cropping. Similar to human’s decision-making process, we use a comprehensive state representation, including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The adversarial learning process is also applied during the training stage. The proposed method is evaluated on several popular cropping datasets, in which the images are unseen during training. The experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with related methods.
Modeling and Analysis of Multipath Video Transport over Lossy Networks Using Packet-Level FEC

The use of forward error correction (FEC) coding is often proposed to combat the effects of network packet losses for error-resilient video transmission on packet-switched networks. On the other hand, path diversity has recently been proposed to improve network transport for both singledescription (SD) and multiple-description (MD) coded video. In this work we model and analyze an SD coded video transmission system employing packet-level FEC in combination with path diversity. In particular, we provide a precise analytical approach to evaluating the efficacy of path diversity in reducing the burstiness of network packet-loss processes. We use this approach to quantitatively demonstrate the advantages of path diversity in improving end-to-end video transport performance using packet-level FEC.
Sweroside‐mediated mTORC1 hyperactivation in bone marrow mesenchymal stem cells promotes osteogenic differentiation

This paper aims to probe into the effect of sweroside (SOS) in osteoporosis (OP) and explains mechanisms of its molecular. Applying the ovariectomized (OVX) mouse model investigates the preventive effect of SOS against postmenopausal OP after 3 months of SOS treatment (120 mg/kg/day). Using hematoxylin and eosin (HE) staining and micro computed tomography (CT) observed the morphology of OP in each group. Immunohistochemical staining (IHC) was used to examine osteoblast markers. Experiments in vitro, bone marrow mesenchymal stem cells (BMSCs) from C57/BL6 mice were treated with SOS for 14 days. The staining of alizarin red and alkaline phosphatase activity were measured, and the presentation of osteoblast markers was detected by quantitative reverse transcription PCR. BMSCs were also treated with 1 μg/mL SOS with or without rapamycin, the expression of protein S6 (PS6), P‐mTOR, runt‐related transcription factor 2 (RUNX2), OSX, and osteocalcin (OCN) was detected by Western blotting. Experiments in vivo, HE results show that SOS can alleviate OP, CT results show that there are lower trabecular thickness, bone mineral density, and trabecular number in control OVX mice than those in the OVX + SOS group. IHC results showed that SOS can promote the expression of osteogenic markers and immunofluorescent results show that SOS can promote mTORC1 signal activation. Experiments in vitro revealed that SOS stimulated the activation of the mTORC1 signaling pathway and upregulated RUNX2, OSX, and OCN, rapamycin can reverse it. Our findings demonstrated that differentiated BMSCs into osteoblasts can be promoted by SOS via upregulating the expression of P‐mTOR, PS6, RUNX2, OSX, and OCN. SOS effectively prevented OP by hyperactivation of the mTORC1/PS6 signaling pathway.