Miracle-dz/UniFAR-FTUs huggingface dataset:
Can a Resting-State Functional Connectivity Index Identify Patients with Alzheimer's Disease and Mild Cognitive Impairment Across Multiple Sites?

Resting-state functional connectivity is one promising biomarker for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution, and compared performance for AD and MCI identification with multi-voxel pattern analysis. The multi-voxel pattern analysis using a connectivity map of the default mode network showed good performance, with an accuracy of 81.9% for AD and MCI identification within the ADNI, but the classification model obtained from the ADNI failed to classify AD, MCI, and healthy elderly adults from our site, with an accuracy of only 43.1%. In contrast, a functional connectivity index of the medial temporal lobe based on the frequency distribution showed moderate performance, with an accuracy of 76.5 - 80.3% for AD identification within the ADNI. The performance of this index was similar for our data, with an accuracy of 73.9 - 82.6%. The frequency distribution-based index of functional connectivity could be a good biomarker for AD across multiple sites.
Ginsenosides as Anticancer Agents: In vitro and in vivo Activities, Structure–Activity Relationships, and Molecular Mechanisms of Action

Conventional chemotherapeutic agents are often toxic not only to tumor cells but also to normal cells, limiting their therapeutic use in the clinic. Novel natural product anticancer compounds present an attractive alternative to synthetic compounds, based on their favorable safety and efficacy profiles. Several pre-clinical and clinical studies have demonstrated the anticancer potential of Panax ginseng, a widely used traditional Chinese medicine. The anti-tumor efficacy of ginseng is attributed mainly to the presence of saponins, known as ginsenosides. In this review, we focus on how ginsenosides exert their anticancer effects by modulation of diverse signaling pathways, including regulation of cell proliferation mediators (CDKs and cyclins), growth factors (c-myc, EGFR, and vascular endothelial growth factor), tumor suppressors (p53 and p21), oncogenes (MDM2), cell death mediators (Bcl-2, Bcl-xL, XIAP, caspases, and death receptors), inflammatory response molecules (NF-κB and COX-2), and protein kinases (JNK, Akt, and AMP-activated protein kinase). We also discuss the structure–activity relationship of various ginsenosides and their potentials in the treatment of various human cancers. In summary, recent advances in the discovery and evaluation of ginsenosides as cancer therapeutic agents support further pre-clinical and clinical development of these agents for the treatment of primary and metastatic tumors.
An Energy-based and Distance-based Uneven Clustering Algorithm for Wireless Sensor Networks

In recent years, energy efficient clustering routing protocols and algorithms have been proposed for wireless sensor networks (WSNs). In this paper, we propose an energy-based and distance-based uneven clustering algorithm (EDUCA), which adopts multi-hop communication manner, to balance energy consumption and prolong the network lifetime. Cluster heads and relay nodes are chosen mainly according to the distance between nodes and the energy of sensor nodes and clusters. Simulation results show that EDUCA has a better performance on the clustering and can largely reduce the energy consumption so that prolong network lifetime, compared to LEACH protocol.
Implied threat or part of the scenery: Americans’ perceptions of open carry

Abstract Existing empirical studies of American perceptions of open gun carrying are limited to polls that often omit key background characteristics of respondents. The present study examined American perceptions of open carry by studying perceptions of safety across locations such as home, work, and shopping areas and by level of familiarity with the individual engaging in open carry. Data were drawn from a 45-question, nationwide online survey conducted in 2016. Respondents included more than 250 household gun owners and 250 non-owners. The data were analyzed using ordinal logistic regression models. Overall, results showed that respondents felt uncomfortable in a situation involving open carry, and even less comfortable when the person engaging in open carry was a stranger. Gun owners, whites, and those with higher levels of education all expressed greater feelings of perceived safety. Respondents also reported that concern over mental state was a key factor in their perceived safety. Explanations for these findings, practical implications, and directions for future research are discussed.
Transformational Leadership, Creative Self-Efficacy, Trust in Supervisor, Uncertainty Avoidance, and Innovative Work Behavior of Nurses

This study examines how nurse managers’ transformational leadership relates to innovative work behavior of subordinate nurses through creative self-efficacy, trust in supervisor, and uncertainty avoidance. On the basis of an interactional approach, this study hypothesized that (a) there is an interaction between transformational leadership, trust in supervisor, and uncertainty avoidance that affects innovative work behavior, such that transformational leadership has the strongest positive relationship with innovative work behavior when nurses have high levels of trust and uncertainty avoidance; and (b) creative self-efficacy mediates the effect that this three-way interaction between transformational leadership, trust in supervisor, and uncertainty avoidance has on innovative work behavior. In Study 1, we used a time-lagged research design, collecting multisource data from 322 nurses and their respective head nurses (supervisors), working in public sector hospitals. The results of Study 1 supported our hypotheses. In Study 2, we used a more temporally rigorous research design in which data were collected in three stages, with a 3-month time interval separating Stages 1 and 2, and Stages 2 and 3. On the basis of the time-lagged and multisource data from 371 nurses and their respective head nurses (supervisors), from private sector hospitals, we found that Study 2 produced the same results as Study 1.
CHANGES IN HUMAN BRAIN POTENTIALS DURING THE ONSET OF SLEEP.

A-interrupted alpha: the normal waking 10.per-second rhythm dominates the pattern B-Zozu voltage: the alpha rhythm is lost C-spindles: short groups (“spindles”) of 14-per-second waves appear and also random “delta” * waves 0.2 second or more in length D-spindles plus random: both types of wave increase in voltage and the delta waves become longer E-random: the 14.per-second waves become inconspicuous, but delta waves continue to increase in voltage and wavelength
A ug 2 00 3 Regularly Spaced Subsums of Integer Partitions

Abstract For integer partitions λ : n = a1 + . + ak, where a1 ≥ a2 ≥ . ≥ ak ≥ 1, we study the sum a1 + a3 + . of the parts of odd index. We show that the average of this sum, over all partitions λ of n, is of the form n/2 + ( √ 6/(8π)) √ n log n + c2,1 √ n + O(log n). More generally, we study the sum ai + am+i + a2m+i + . of the parts whose indices lie in a given arithmetic progression and we show that the average of this sum, over all partitions of n, is of the form n/m + bm,i √ n log n + cm,i √ n + O(log n), with explicitly given constants bm,i, cm,i. Interestingly, for m odd and i = (m+1)/2 we have bm,i = 0, so in this case the error term is of lower order. The methods used involve asymptotic formulas for the behavior of Lambert series and the Zeta function of Hurwitz. We also show that if f(n, j) is the number of partitions of n the sum of whose parts of even index is j, then for every n, f(n, j) agrees with a certain universal sequence, Sloane’s sequence #A000712, for j ≤ n/3 but not for any larger j.
Fast lp-sparse Bayesian learning for compressive sensing reconstruction

The problem of compressive sensing reconstruction can be come down to the problem of solving a sparse linear model. In this paper, the lp(0&#60;p≤1)-type prior that encourages sparsity of the weights is used to the sparse linear model. Motivated by maximum a posteriori estimation and sparse Bayesian learning, a stage-wise fast lp-sparse Bayesian learning (SF-lp-SBL) algorithm is proposed through integrating with a fast sequential learning scheme and a stage-wise strategy. The experiments demonstrate that SF-lp-SBL is a fast and effective CS reconstruction algorithm.
Cerebral infarction in perinatal and childhood bacterial meningitis.

BACKGROUND Cerebral infarction is an important neurological complication of childhood bacterial meningitis, but little is known about its epidemiology and outcomes. AIM To determine the predictive factors, clinical features, causative pathogens, and outcomes of cerebral infarction secondary to perinatal and childhood bacterial meningitis. DESIGN Retrospective analysis METHODS Over the period 1986-2001, 166 perinatal and childhood patients were identified as having culture-proven bacterial meningitis, of whom 14 had cerebral infarction at admission. The clinical and CSF data of patients with and without cerebral infarctions on admission were compared. RESULTS Cerebral infarction patients accounted for 10% (14/166) of bacterial meningitis cases, mostly in the first year of life (11/14, 79%). Salmonella species (n = 4) and Streptococcus pneumoniae (n = 4) were the most frequent causative pathogens, accounting for 57% (8/14) of episodes. Single infarctions were found in four patients and multiple infarctions in 10. At 1 year follow-up, outcome was good in three, but poor in 11. Significant differences between the two patient groups at admission included age bands, presence of seizures, hydrocephalus, disturbed consciousness on admission, and CSF lactate concentration. DISCUSSION There was a high prevalence of cerebral infarctions when the disease was caused by S. pneumoniae and Salmonella species. Occurrence was highest in the first year of life, and the prognosis in this patient group is poor. Risk factors associated with cerebral infarction in our patients included age 28-365 days, seizures, hydrocephalus, disturbed consciousness on admission, and high CSF lactate concentrations.
Understanding Chinese language teachers’ language ideologies in teaching South Asian students in Hong Kong

ABSTRACT This qualitative study investigates the language ideology of a groups of Chinese language teachers working in Hong Kong’s secondary schools when they teach ethnic minority students. Drawing on interview data, this study reports on how these teachers’ language ideologies are in accordance/discordance with dominant discourse, and in what ways their ideologies impact their teaching approach and attitudes towards the students’ heritage language and culture. Key findings indicate that instead of promoting homogeneity as a prerequisite for success, the teachers hold a pluralistic language ideology and outline the potential for ethnic minority students to become linguistic and cultural brokers between the heritage communities and mainstream society. An inconsistence was found between the teachers’ positive language attitude towards heritage language and their self-distancing from the heritage language learning and use in school contexts. The findings implied that teacher education courses could enable teachers, as active agents, to re-negotiate the separate language ideology of the language policy at the macro level, deconstruct it in classroom settings and develop knowledge and beliefs to challenge and resist the ideologies that sustain the marginalisation of heritage language and culture.
Growth of neurites without filopodial or lamellipodial activity in the presence of cytochalasin B

To examine the role in neurite growth of actin-mediated tensions within growth cones, we cultured chick embryo dorsal root ganglion cells on various substrata in the presence of cytochalasin B. Time-lapse video recording was used to monitor behaviors of living cells, and cytoskeletal arrangements in neurites were assessed via immunofluorescence and electron microscopic observations of thin sections and whole, detergent-extracted cells decorated with the S1 fragment of myosin. On highly adhesive substrata, nerve cells were observed to extend numerous (though peculiarly oriented) neurites in the presence of cytochalasin, despite their lack of both filopodia and lamellipodia or the orderly actin networks characteristic of typical growth cones. We concluded that growth cone activity is not necessary for neurite elongation, although actin arrays seem important in mediating characteristics of substratum selectivity and neurite shape.
A Learning System for Selective Dissemination of Information

New methods and new systems are needed to lter or to selectively distribute the increasing volume of electronic information being produced nowadays. An e ective information ltering system is one that provides the exact information that ful lls a user's interest with the minimum e ort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we present a learning system for information ltering and selective information dissemination. The learning algorithm is described and the e ectiveness of the system is evaluated in a true information ltering style.
A 1.2-V, 12-bit, 200M sample/s current-steering D/A converter in 90-nm CMOS

This paper describes a 12-bit, 200-MSample/s current-steering CMOS D/A converter for wireless-communication terminals. To reduce the effects of voltage drops (IR drops) along power supplies, 3-dimensional wiring is applied. The converter was implemented in a 90-nm CMOS technology, and it operates at a low supply voltage of 1.2 V. The modulator achieves the intrinsic accuracy of 12 bits and a spurious-free dynamic range (SFDR) above 55 dB over a 100-MHz bandwidth
Algebraic geometry of discrete dynamics. The Case of one variable

We argue that discrete dynamics has natural links to the theory of analytic functions. Most important, bifurcations and chaotic dynamical properties are related to intersections of algebraic varieties. This paves the way to identification of boundaries of Mandelbrot sets with discriminant varieties in moduli spaces, which are the central objects in the worlds of chaos and order (integrability) respectively. To understand and exploit this relation one needs first to develop the theory of discrete dynamics as a solid branch of algebraic geometry, which so far did not pay enough attention to iterated maps. The basic object to study in this context is Julia sheaf over the universal Mandelbrot set. The base has a charateristic combinatorial structure, which can be revealed by resultant analysis and represented by a basic graph. Sections (Julia sets) are contractions of a unit disc, related to the action of Abelian $\bb{Z}$ group on the unit circle. Their singularities (bifurcations) are located at the points of the universal discriminant variety.
Improved protein–ligand docking using GOLD

The Chemscore function was implemented as a scoring function for the protein–ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, “Goldscore‐CS” and “Chemscore‐GS,” in terms of docking accuracy, prediction of binding affinities, and speed. In the “Goldscore‐CS” protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the “Chemscore‐GS” protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a “clean” set of 224 protein–ligand complexes, and for two subsets of this set, one for which the ligands are “drug‐like,” the other for which they are “fragment‐like. ” For “drug‐like” and “fragment‐like” ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. “Goldscore‐CS” gives success rates of up to 81% (top‐ranked GOLD solution within 2.0 Å of the experimental binding mode) for the “clean list,” but at the cost of long search times. For most virtual screening applications, “Chemscore‐GS” seems optimal; search settings that give docking speeds of around 0.25–1.3 min/compound have success rates of about 78% for “drug‐like” compounds and 85% for “fragment‐like” compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1–2 min/compound, the Goldscore function predicts binding energies with a standard deviation of ∼10.5 kJ/mol. Proteins 2003;52:609–623. © 2003 Wiley‐Liss, Inc.
Genetic and environmental influences on Anxious/Depression: A longitudinal study in 3- to 12-year-old children.

Pathways of developmental psychopathology have long been of interest to scientists, clinicians, and patients. To date it is unclear why some children are never affected by psychopathological illness, why others become ill and re-Principal Investigator). We would like to thank Christel Middeldorp for her critical reading of this chapter.
Statistical analysis on KATAN Block Cipher

In this research, we present the analysis on Lightweight Block Cipher; KATAN. This block cipher consists of three algorithms with block sizes of 32/48/64-bit and utilizes an 80-bit key. NIST Statistical Test Suite is used to determine the randomness of output sequence produced by all algorithms. Nine data categories are applied to generate the output sequence. From our analysis, we found out that all three algorithms are not random based on the 1% significance level.
Minimum Dependencies Energy-Efficient Scheduling in Data Centers

This work presents an on-line, energy- and communication-aware scheduling strategy for SaaS applications in data centers. The applications are composed of various services and represented as workflows. Each workflow consists of tasks related to each other by precedence constraints and represented by Directed Acyclic Graphs (DAGs). The proposed scheduling strategy combines advantages of state-of-the-art workflow scheduling strategies with energy-aware independent task scheduling approaches. The process of scheduling consists of two phases. In the first phase, virtual deadlines of individual tasks are set in the central scheduler. These deadlines are determined using a novel strategy that favors tasks which are less dependent on other tasks. During the second phase, tasks are dynamically assigned to computing servers based on the current load of network links and servers in a data center. The proposed approach, called Minimum Dependencies Energy-efficient DAG (MinD+ED) scheduling, has been implemented in the GreenCloud simulator. It outperforms other approaches in terms of energy efficiency, while keeping a satisfiable level of tardiness.
Feasibility Conditions of SIR-based Power Control in TDMA Wireless Systems

In this paper, the problem of SIR-based power control in narrowband wireless systems is re- visited. Results on feasibility conditions of SIR-based power control are given. We stated and proved the relations between the achievable SIR and the link gain matrix in a narrowband system. Taking into ac- count the orders of the realistic values for the prob- lem quantities and assuming the desired SIR is cho- sen properly, then there exist innite many solutions of the SIR-based power control problem. Otherwise, there is hardly a solution since the existence condi- tions are very restrictive. It is noticeable that these feasibility conditions have important implications on systems capacity and can be applied to call admission control(CAC). Based on the feasibility conditions, an improved stepwise removal algorithm (ISRA) is pro- posed for TDMA systems to achieve a better perfor- mance. I. Review of SIR based Power Control in Narrowband Wireless Networks In a cellular wireless network, certain quality-of-service (QoS) should be maintained for all active users in the network. A quantity that measures the user's provided QoS is the signal- to-interference ratio (SIR). In narrowband systems, it is also called carrier-to-interference ratio (CIR). A general idea to achieve the desired SIR for all the active users is to allocate the network resources in the most ecien t way. Resource allocation in a cellular wireless network include channel allo- cation, power control, etc. The transmitter power is the most valuable resource in the network. By proper control of each transmitter power, the interference can be minimized. At the same time, power control extends the battery life in the hand- set. In this paper, only narrowband wireless networks are con- sidered. Time and frequency slots are partitioned in a narrow- band wireless network in order to avoid mutual interference between users. Nevertheless, users can share the same time or frequency slot provided they are sucien tly far apart, and such users are termed as cochannel users. The issue of power control in narrowband wireless networks arises because the cochannel users interfere with each other. The optimum open loop power allocation problem is for- mulated as min pi X
Three-Dimensional FDTD Analysis of the Dual-Band Implantable Antenna for Continuous Glucose Monitoring

The flnite difierence time domain (FDTD) method is widely used as a computational tool to simulate the electromagnetic wave propagation in biological tissues. When expressed in terms of Debye parameters, dispersive biological tissues dielectric properties can be e-ciently incorporated into FDTD codes. In this paper, FDTD formulation with nonuniform grid is presented to simulate a dual medical implant communications service (MICS) (402{405MHz) and industrial, scientiflc, and medical (ISM) (2.4{2.48GHz) band implantable antenna for continuous glucose-monitoring applications. In addition, we present computationally simpler two-pole Debye models that retain the high accuracy of the Cole-Cole Model for dry skin in MICS and ISM bands. The re∞ection coe-cient simulation result with Debye dispersion is presented and compared with the published results. FDTD was also applied to analyze antenna's far- fleld.
Developing Decision Trees for Handling Uncertain Data

Classification is one of the most efficient and widely used data mining technique. In classification, Decision trees can handle high dimensional data, and their representation is intuitive and generally easy to assimilate by humans. Decision trees handle the data whose values are certain. We extend such classifiers i.e, decision trees to handle uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution (pdf’s). Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted that show that the resulting classifiers are more accurate than those using value averages.