Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrogen addition increases the contents of glomalin-related soil protein and soil organic carbon but retains aggregate stability in a Pinus tabulaeformis forest

Background Glomalin-related soil protein (GRSP) and soil organic carbon (SOC) contribute to the formation and stability of soil aggregates, but the mechanism by which global atmospheric nitrogen (N) deposition changes soil aggregate stability by altering the distribution of GRSP and SOC in different aggregate fractions remains unknown. Methods We used a gradient N addition (0–9 g N m−2 y−1) in Pinus tabulaeformis forest for two years in northeast China and then examined the changes in SOC contents, total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents in three soil aggregate fractions (macro-aggregate: >250 μm, micro-aggregate: 250–53 μm, and fine material: <53 μm) and their relationship with aggregate stability. Results (1) The soil was dominated by macro-aggregates. Short term N addition had no significant effect on mean weight diameter (MWD) and geometric mean diameter (GMD). (2) GRSP varied among aggregate fractions, and N addition had different effects on the distribution of GRSP in aggregate fractions. The EE-GRSP content in the macro-aggregates increased initially and then decreased with increasing N addition levels, having a peak value of 0.480 mg g−1 at 6 g N m−2 y−1. The micro-aggregates had the lowest EE-GRSP content (0.148 mg g−1) at 6 g N m−2 y−1. Furthermore, the T-GRSP content significantly increased in the aggregate fractions with the N addition levels. (3) The macro-aggregate had the highest SOC content, followed by the micro-aggregate and the fine material had the lowest SOC content. N addition significantly increased the SOC content in all the aggregate fractions. (4) GRSP and SOC contents were not significantly correlated with MWD. Conclusion Glomalin-related soil protein and SOC contents increased by N addition, but this increase did not enhance aggregate stability in short term, and the improvement of stability might depend on binding agents and incubation time.
Using Machine Learning Algorithm to Predict Student Pass Rates In Online Education

In online education, the quality evaluation of education is crucial importance to schools and even to the entire range of educational institutions. There are many ways to evaluate online education. Taking the prediction of student pass rates as an example, many researchers have used machine Learning algorithms to predict student pass rates and find out important student features affecting learning. However, they did not establish feature model for online education that predicts the student pass rates and introduce deep neural network (DNN) algorithms - a new method in machine learning into online education. Therefore, this study first explores how to establish a feature model that predicts the student pass rates for online education, and then uses the grid search (GS) algorithm to optimize the decision tree algorithm (DT) and support vector machine (SVM) algorithm to improve the prediction accuracy. Finally, compared the improved algorithm with the DNN algorithm, we find a suitable algorithm for student pass rate prediction. The purpose of this study is to improve the quality of online teaching by predicting the student pass rates, increasing students' academic performance and strengthening online educational management.
Generating term weighting schemes through genetic programming

Term-Weighting Scheme (TWS) is an important step in text classification. It determines how documents are represented in Vector Space Model (VSM). Even though state-of-the-art TWSs exhibit good behaviors, a large number of new works propose new approaches and new TWSs that improve performances. Furthermore, it is still difficult to tell which TWS is well suited for a specific problem. In this paper, we are interested in automatically generating new TWSs with the help of evolutionary algorithms and especially genetic programming (GP). GP evolves and combines different statistical information and generates a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.
Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the local coefficient of variation (CV2), reflect variability on long and short time scales, respectively. We co-analyzed FF and CV2 in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that FF significantly decreases from wait to movement, while CV2 increases. The more regular firing (low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF≈CV2. This expectation was met during movement, but not during wait where FF≫CV22. We conclude that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. We discuss our results in the light of recent computational models that generate non-stationary network conditions.
Towards Non-Parametric Learning to Rank

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$ and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be ranked high by the agent. Our goal is to find neighbors of an arbitrary agent or alternative in the latent space. We first show that the Kendall-tau distance based kNN produces incorrect results in our model. Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix. Our approach is in sharp contrast to most existing feature engineering methods. Finally, we design another new algorithm identifying similar alternatives. The construction of alternative features can be done using "local information," highlighting the algorithmic difference between finding similar agents and similar alternatives.
The Antidiabetic Agent Sodium Tungstate Induces Abnormal Glycogen Accumulation in Renal Proximal Tubules from Diabetic IRS2-Knockout Mice

The kidney is an insulin-sensitive organ involved in glucose homeostasis. One major effect of insulin is to induce glycogen storage in the liver and muscle. However, no significant glycogen stores are detected in normal kidneys, but diabetic subjects present a characteristic renal histopathological feature resulting from extensive glycogen deposition mostly in nonproximal tubules. The mechanism of renal glycogen accumulation is yet poorly understood. Here, we studied in situ glycogen accumulation in the kidney from diabetic IRS2-knockout mice and the effect of the insulin-mimetic agent sodium tungstate (NaW). IRS2-knockout mice displayed hyperglycemia and hyperinsulinemia. NaW only normalized glycemia. There was no evident morphological difference between kidneys from untreated wild-type (WT), NaW-treated WT, and untreated IRS2-knockout mice. However, NaW-treated IRS2-knockout mice showed tubular alterations resembling clear cells in the cortex, but not in the outer medulla, that were correlated with glycogen accumulation. Immunohistochemical detection of the gluconeogenic enzyme phosphoenolpyruvate carboxykinase, mostly expressed by renal proximal tubules, showed that altered tubules were of proximal origin. Our preliminary study suggests that IRS2 differentially regulates glycogen accumulation in renal tubules and that NaW treatment in the context of IRS2 ablation induces abnormal glycogen accumulation in cortical proximal tubules.
Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks

Energy disaggregation is the process of extracting the power consumptions of multiple appliances from the total consumption signal of a building. Artificial Neural Networks (ANN) have been very popular for this task in the last decade. In this paper we propose two recurrent network architectures that use sliding window for real-time energy disaggregation. We compare this approach to existing techniques using six metrics and find that it scores better for multi-state devices. Finally, we compare ANNs that use Gated Recurrent Unit neurons against those using Long Short-Term Memory neurons and find that they perform equally.
Internalized HIV stigma, bullying, major depressive disorder, and high-risk suicidality among HIV-positive adolescents in rural Uganda

Background. Studies conducted in sub-Saharan Africa suggest a high prevalence of depression and suicidality among adolescents living with HIV (ALWH). This is an important public health issue because depression is known to compromise HIV treatment adherence. However, the drivers of depression and suicidality in this population are unclear. We conducted a cross-sectional study to estimate the associations between internalized stigma, bullying, major depressive disorder, and suicidality. Methods. We conducted a cross-sectional survey between November 2016 and March 2017, enrolling a consecutive sample of 224 ALWH aged 13–17 years. We collected information on demographic characteristics, internalized HIV-related stigma (using the six-item Internalized AIDS-Related Stigma Scale), bullying victimization (using the nine-item Social and Health Assessment Peer Victimization Scale), major depressive disorder [using the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID)], and suicidality (also using the MINI-KID). We fitted multivariable logistic regression models to estimate the associations between stigma, bullying, major depressive disorder, and suicidality. Results. Thirty-seven participants (16%) had major depressive disorder, 30 (13%) had suicidality, and nine (4%) had high-risk suicidality. Ninety-one participants (41%) had high levels of internalized stigma, while 97 (43%) reported two or more bullying events in the past year. In multivariable logistic regression models, major depressive disorder had a statistically significant association with bullying (AOR = 1.09; 95% CI 1.00–1.20; p = 0.04); while suicidality (low, moderate, high risk) had statistically significant associations with both bullying (AOR = 1.09; 95% CI 1.01–1.17; p = 0.02) and stigma (AOR = 1.30; 95% CI 1.03–1.30; p = 0.02). Conclusions. Among ALWH in rural Uganda, stigma and bullying are strongly associated with major depressive disorder and suicidality. There is a need to incorporate psychological interventions in the mainstream HIV care to address these challenges for optimal management of HIV among ALWH.
Troubling Trends in Machine Learning Scholarship

Flawed scholarship threatens to mislead the public and stymie future research by compromising ML’s intellectual foundations. Indeed, many of these problems have recurred cyclically throughout the history of AI and, more broadly, in scientific research. In 1976, Drew McDermott chastised the AI community for abandoning self-discipline, warning prophetically that "if we can’t criticize ourselves, someone else will save us the trouble. " The current strength of machine learning owes to a large body of rigorous research to date, both theoretical and empirical. By promoting clear scientific thinking and communication, our community can sustain the trust and investment it currently enjoys.
CoReCast: Collision Resilient Broadcasting in Vehicular Networks

Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization. Due to low coherence time, the channel changes rapidly in vehicular networks. CoReCast embraces packet collisions and takes advantage of the channel dynamics to decode collided packets. The design of CoReCast is based on a preamble detection scheme that estimates channels from multiple transmitters without any prior information about them. The proposed scheme reduces the space and time requirement exponentially than the existing schemes. The system is evaluated through experiments with USRP N210 and GPS devices placed in vehicles driven on roads in different environments as well as using trace-driven simulations. It provides 15x and 2x lower delay than 802.11p and OCP (Omniscient Clustering Protocol), respectively. Reliability of CoReCast is 8x and 2x better than 802.11p and OCP, respectively.
Achievements and challenges in developing health leadership in South Africa: the experience of the Oliver Tambo Fellowship Programme 2008–2014

Abstract The Oliver Tambo Fellowship Programme is convened by the School of Public Health and Family Medicine, University of Cape Town, South Africa. It is a health leadership training programme with a post-graduate Diploma at its core, supplemented by management seminars, mentorship and alumni networking. An external evaluation was conducted in 2015 for the period since 2008. This rapid, descriptive study made use of mixed methods—including a document review of existing Programme material (management reports, anonymized alumni’s implementation project reports, exit interviews, field interviews and e-mailed questionnaires), a brief e-mailed questionnaire, and 18 semi-structured telephonic interviews conducted by the evaluator with Programme alumni, convenors and senior government line managers. Data were analysed according to indicators and associated criteria developed by the evaluator on the basis of the Programme’s objectives, international experience, the nature of the South African health system and the particular philosophy of the Programme. The evaluation found that the Diploma offered a unique contribution. This is because it sought less to convey new technical knowledge, than to empower and galvanize students to become change agents in the complex settings of their workplaces. Reflective practice was an important part of this process. Alumni were able to point to a number of positive changes in their management practice and motivation, translating these into improved performance by their teams and more effective health services. Alumni also helped to build the capacity of their own and other staff, sharing the knowledge and skills they had gained through the Programme, and leading by example. However, the Programme found it difficult to arrange adequate mentorship or peer support for alumni once they returned to their workplaces, pointing to the need for human resource development units in government to become more active in supporting alumni and holding them accountable for improving practice.
Comparison the Effectiveness of Breastfeeding, Oral 25% Dextrose, Kangaroo-Mother Care Method, and EMLA Cream on Pain Score Level Following Heal Pick Sampling in Newborns: a randomized clinical trial

Background Today, it is considered that infants can feel pain, and it can have several adverse effects on their development. Although different interventions, such as skin-to-skin contact (kangaroo mother care method), glucose solutions, breastfeeding, and local anesthetic agents, have been evaluated to reduce infants’ pain, there is no clinical trial available to identify the best method. Objective The aim of this study was to compare the efficacy of four methods of relieving infants’ pain, i.e., breastfeeding, oral 25% dextrose, kangaroo mother care method (KMCM), and EMLA cream based on a pain score level following heal-prick sampling in term newborns. Methods In this double-blind, controlled, randomized clinical trial, full term, and healthy infants in Shahid Motahhari Hospital, Marvdasht, Shiraz Province, Iran were studied. Infants were randomly allocated to four groups of interventions, i.e., A: breast milk feeding (n=42), B: oral 25% dextrose (n=40), C: KMCM (n=38), and D: KMCM ointment (n=40). All interventions were applied 15 minutes before heel-prick sampling. To evaluate the pain level in infants, the Neonatal Infant Pain Scale (NIPS) questionnaire was used. The data were analyzed with SPSS version 16 by ANOVA and the chi-squared tests. Results One hundred and sixty-one infants (93 males, 68 females) with an age range of 3 to 5 days were studied. There was no significant difference between the groups in terms of gender (p=0.113), weight (p=0.059), and baseline pain score level (p=0.904). The breastfeeding method showed the lowest pain score in comparison to the other interventions (A=5.52±2.22, B=6.45±1.88, C=6.84±1.96, D=7.37±1.95; p=0.001) after the heel-prick sampling. Conclusion It seems that, among the four methods of interventions in this study, the most effective method of lowering perceived pain in infants undergoing painful procedures was proven to be breastfeeding. Trial Registration This study is registered in the Iranian Registry of Clinical Trials with the registration number of IRCT20151201253256N1. Funding This research was supported financially by the Research Council of the International Campus of Shiraz University of Medical Sciences.
Genetic diversity of Staphylococcus aureus strains from a teaching hospital in Isfahan, Iran: The emergence of MRSA ST639- SCCmec III and ST343- SCCmec III

Background and Objectives: Staphylococcus aureus is an important pathogen that can be colonized in the nose and increase the risk of spreading infections in hospitals. The present study aimed at determining the phenotypic and genotypic characterization of S. aureus strains isolated from patients and healthcare workers (HCWs) from a teaching hospital in Isfahan, Iran. Materials and Methods: This cross-sectional study was performed on 262 nasal swabs and 23 clinical isolates that were collected from a teaching hospital during February and April 2016. Staphylococcal cassette chromosome mec (SCCmec) and multilocus sequence typing (MLST) were performed for selected isolates. Results: Overall, 23% and 18% of healthcare workers and patients were carriers, respectively. Methicillin-resistant S. aureus (MRSA) rate was 13%, 33% and 52% in nasal HCWs, nasal patients, and clinical samples, respectively. The molecular typing of MRSA isolates revealed that the most common SCCmec type is SCCmec type III (88%). The highest rate of resistance was observed against tetracycline and erythromycin, with 48.7%. The most frequently detected toxin genes among S. aureus isolates were hla (99%) and sea (44%), moreover, pvl genes were detected in (40%) of MRSA isolates. The results of MLST showed 7 different sequence types (STs): ST859 (2/9), ST6 (2/9), ST639 (1/9), ST343 (1/9), ST239 (1/9), ST291 (1/9) and ST25 (1/9). Conclusion: Our results revealed that ST clones associated with healthcare-associated MRSA (HA-MRSA) are actively circulating among nasal carriage in our healthcare setting, and thus, effective infection control policies are needed to reduce nasal carriage in healthcare settings.
Secrecy Energy Efficiency Maximization for UAV-Enabled Mobile Relaying

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle (UAV) enabled mobile relaying system, where a high-mobility UAV is exploited to assist transmitting confidential information from a ground source to a legitimate ground destination, in the presence of a potential eavesdropper. We aim to maximize the SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a given time horizon. The formulated problem is non-convex that is challenging to be optimally solved. To make the problem more tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes each block of the variables with the others fixed. Moreover, the practical scenario with uncertain eavesdropper location is investigated to evaluate the performance of the proposed solution. Double circular flight and running track shape flight cases are considered to drawn more insights. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmark schemes.
Optimal network topology for responsive collective behavior

Collectives must tune their interaction network to adapt and respond to external perturbations at different time scales. Animals, humans, and multi-robot systems operate in dynamic environments, where the ability to respond to changing circumstances is paramount. An effective collective response requires suitable information transfer among agents and thus critically depends on the interaction network. To investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making and study the capacity of the system to follow a driving signal for varying topologies and system sizes. Experiments with a swarm of robots reveal a nontrivial relationship between frequency of the driving signal and optimal network topology. The emergent collective response to slow-changing perturbations increases with the degree of the interaction network, but the opposite is true for the response to fast-changing ones. These results have far-reaching implications for the design and understanding of distributed systems: a dynamic rewiring of the interaction network is essential to effective collective operations at different time scales.
Present and Future Therapy of Cryptococcus Infections

Cryptococcal infections burden the immunocompromised population with unacceptably high morbidity and mortality. This population includes HIV-infected individuals and those undergoing organ transplants, as well as seemingly immunocompetent patients (non-HIV, non-transplant). These groups are difficult to manage with the current therapeutic options and strategies, particularly in resource-limited settings. New trials aimed at providing the best treatment strategies for resource-limited countries that will reduce costs and adverse reactions have focused on decreasing the length of therapy and using more readily accessible antifungal agents such as fluconazole. Furthermore, the emergence of antifungal resistance poses another challenge for successful treatment and may require the development of new agents for improved management. This review will discuss the principles of management, current and future antifungal agents, as well as emerging techniques and future directions of care for this deadly infection.