Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining and promoting women’s health through a descriptive analysis of a novel health tool: the ‘Circle of Health’

ABSTRACT A comprehensive perspective of women’s health increases healthy equity and broadens the spectrum of care for women. The Circle of Health, a holistic health assessment tool, was created by the National Centers of Excellence in Women’s Health (NCEWH). This initiative focuses on advancing women’s health needs by empowering them to actively engage about their own health care. The tool includes surveys for seven distinct domains of health: physical, social, emotional, intellectual, environmental, financial, and spiritual. The present study reports comprehensive health findings for a sample of 169 females from Region VIII with data collected from February 2014 to April 2019. The Circle of Health tool highlights distinct health needs across the seven domains. For the present sample of mostly female college students, areas of concern included the emotional, financial, and environmental health domains. Further research is needed to examine the holistic health of women from diverse cultural, racial, age, and socioeconomic backgrounds. The Circle of Health tool describes health, for both women and their health providers, more holistically and encourages an integrative model of care.
Depth distribution of heavy metals in lake sediments from lignite mine pit lakes of lusatia (Germany)

Mine pit lakes from lignite mining in Lusatia posses a great environmental problem due to very low pH (< 4) and high acidity of more than 100 of these lakes. To study the range and penetration of possible contamination of the sediments of these lakes by heavy metals the depth distribution of aluminium, iron, manganese, zinc, copper, lead, nickel and chromium were determined in four lakes (pit lake B, pit lake Felixsee, pit lake F, pit lake Waldsee) covering a broad range in pH (2.7–8.0), lake size (0.3–230 ha) and lake depth (5–17.5 m). According to the different acid status and history of the lakes the level of contamination is quite different for the four lakes investigated, especially for aluminium (1,800–39,000 mg/kg), iron (13,000 mg/kg – 45.7 wt-%) and manganese (28 – 1,095 mg/kg). In general, the mass concentrations for zinc, copper, lead, nickel and chromium are low (< 100 mg/kg for zinc and < 50 mg/kg for copper, lead, nickel and chromium) although varying among and within the four pit lakes.
LISEM: a physically-bas ed hydrological and soil erosion model for basin-scale water and sediment management

Abstract A new hydrological and soil erosion model has been developed and tested: LISEM, the Limburg Soil Erosion Model. The model uses physically-based equations to describe interception, infiltration and soil water transport, storage in surface depressions, splash and flow detach­ment, transport capacity and overland and channel flow. The model is fully incorporated in a raster GIS for ease of use. However, it is clear that although the model has several advantages over other models, the results are far from perfect. Based on the sensitivity analysis and field observations, the main reasons for these differences seems to be the spatial and temporal variability of the soil hydraulic conductivity and the initial pressure head at the basin scale. Another reason for the differences between measured and simulated results is the lack of theoretical under­standing of the hydrological and soil erosion processes. INTRODUCTION Between 1991 and 1994 a soil erosion project has been carried out in three catchments in the loess area of South Limburg, The Netherlands. The project was funded by the Province of Limburg, the water board "RoerenOvermaas", the Ministry of Agriculture and 14 municipalities of South Limburg. The Departments of Physical Geography of the Universities of Utrecht and Amsterdam and the Soil Physics Division of the Winand Staring Centre in Wageningen cooperated to develop and validate a new physically-based hydrological and soil erosion model, which can be used for planning and conser­vation purposes: the Limburg Soil Erosion Model (LISEM). In three catchments, discharge and sediment load at the outlet have been measured continuously. Rainfall has been measured at six locations to take into account the spatial variability of rainfall. Furthermore, in six sub-catchments discharge and sediment load measurements have been carried out. Also, at twelve sites in the catchments along four slope profiles, continuous soil suction measurements have been done to assess both vertical and lateral transport of water in the soil. The soils in the catchments were mapped in detail to serve, amongst other purposes, as a basis for the soil hydrological sub-model. Several times during the year, land use and soil cover, surface roughness parameters, soil porosity, soil moisture content, soil shear strength and aggregate stability are measured using advanced methods. Further­more, to assess the effects of soil conservation measures and to validate the simulations
Analog Axon Hillock Neuron Design for Memristive Neuromorphic Systems

Neuromorphic electronics studies the physical realization of neural networks in discrete circuit components. Hardware implementations of neural networks take advantage of highly parallelized computing power with low energy systems. The hardware designed for these systems functions as a low power, low area alternative to computer simulations. With on-line learning in the system, hardware implementations of neural networks can further improve their solution to a given task. In this work, the analog computational system presented is the computational core for running a spiking neural network model. This component of a neural network, the neuron, is one of the building blocks used to create neural networks. The neuron takes inputs from the connected synapses, which each store a weight value. The inputs are stored in the neuron and checked against a threshold. The neuron activates, causing a firing event, when the neuron’s internal storage crosses its threshold. The neuron designed is an Axon-Hillock neuron utilizing memristive synapses for low area and energy operation.
Effect of Rainfall Variability on the Sustainability of Ginger Yield in Jaba Local Government Area of Kaduna State, Nigeria

This study examined the effects of rainfall variability on the sustainability of ginger yield in Jaba Local Government Area of Kaduna State. Rainfall data for 15 years (2001 to 2015) was analyzed in order to establish the pattern of rainfall anomalies in the area. 274 copies of questionnaires were purposively administered to farmers in order to examine their perception on the effects of rainfall variability on ginger yield in the study area. Linear regression was used to determine the linear trends of rainfall and ginger yield, the Standardized Anomaly Index was then used to determine the dry and wet years in the rainfall series. The relationship between rainfall and ginger yield was determined using Pearson’s product moment correlation coefficient, while the perceptions of farmers on the effects of rainfall variation on ginger yield was analyzed using descriptive statistics. The study revealed that as the rainfall is increasing, so also is ginger yield increasing. The Standardized Anomaly Index revealed positive and negative anomalies within the period. The study further observed that fluctuation of rainfall does not have adverse effect on sustainability of ginger yields. The results of the correlation analysis between the annual rainfall amount and ginger yield showed a strong positive monotonic relationship (r= 0.61) between ginger yield and rainfall amount. The study recommends the need for extension agents to sensitize farmers on adjustments of farming calendar to synchronize planting and growing period. Agricultural Research Institutes in the country should develop drought resistant and early maturing variety of ginger
Copy number variation alters local and global mutational tolerance

Copy number variants (CNVs), duplications and deletions of genomic sequences, contribute to evolutionary adaptation but can also confer deleterious effects and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that acquired adaptive CNVs of variable structures and copy numbers following experimental evolution in glutamine-limited chemostats. Although beneficial in the selective environment, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genome-wide genetic interactions in CNV strains. We find that CNVs increase mutational target size, confer increased mutational tolerance in amplified essential genes, and result in novel genetic interactions with unlinked genes. We validated a novel genetic interaction between different CNVs and BMH1 that was common to multiple strains. We also analyzed global gene expression and found that transcriptional dosage compensation does not affect most genes amplified by CNVs, although gene-specific transcriptional dosage compensation does occur for ∼12% of amplified genes. Furthermore, we find that CNV strains do not show previously described transcriptional signatures of aneuploidy. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV-associated diseases, such as cancer.
Monitoring biofilm growth and dispersal in real-time with impedance biosensors

Abstract Microbial biofilm contamination is a widespread problem that requires precise and prompt detection techniques to effectively control its growth. Microfabricated electrochemical impedance spectroscopy (EIS) biosensors offer promise as a tool for early biofilm detection and monitoring of elimination. This study utilized a custom flow cell system with integrated sensors to make real-time impedance measurements of biofilm growth under flow conditions, which were correlated with confocal laser scanning microscopy (CLSM) imaging. Biofilm growth on EIS biosensors in basic aqueous growth media (tryptic soy broth, TSB) and an oil–water emulsion (metalworking fluid, MWF) attenuated in a sigmoidal decay pattern, which lead to an ∼22–25% decrease in impedance after 24 Hrs. Subsequent treatment of established biofilms increased the impedance by ∼14% and ∼41% in TSB and MWF, respectively. In the presence of furanone C-30, a quorum-sensing inhibitor (QSI), impedance remained unchanged from the initial time point for 18 Hrs in TSB and 72 Hrs in MWF. Biofilm changes enumerated from CLSM imaging corroborated impedance measurements, with treatment significantly reducing biofilm. Overall, these results support the application of microfabricated EIS biosensors for evaluating the growth and dispersal of biofilm in situ and demonstrate potential for use in industrial settings. One-Sentence Summary This study demonstrates the use of microfabricated electrochemical impedance spectroscopy (EIS) biosensors for real-time monitoring and treatment evaluation of biofilm growth, offering valuable insights for biofilm control in industrial settings.
The efficacy and safety of sodium tanshinone ⅡA sulfonate injection in the treatment of unstable angina pectoris: A systematic review and meta-analysis

Objective To systematically evaluate the efficacy and safety of Sodium tanshinone ⅡA sulfonate injection (STS) in the treatment of unstable angina pectoris (UAP). Methods CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Web of Science, Embase were searched by computer. The research covers the clinical randomized controlled trials of STS in the treatment of unstable angina pectoris published from the establishment of the library to January 31, 2023. Two researchers independently screened the literature, extracted data and evaluated the risk of research bias, and then conducted meta-analysis with RevMan5.3 software. Results A total of 37 randomized controlled trials were included, involving 3926 patients in total. Meta analysis results showed that, compared with conventional western medicine alone, STS combined with conventional western medicine could reduce the frequency (SMD = -2.61, 95%CI[-4.27, -0.96], P = 0.002) and duration (SMD = -4.01, 95%CI[-6.18, -1.84], P = 0.0003) of angina pectoris, improve ECG efficacy (OR = 3.61, 95%CI[2.79, 4.68], P<0.00001) and clinical symptom efficacy (OR = 4.02, 95%CI[3.32, 4.87], P<0.00001), reduce TG(SMD = -0.60, 95%CI[-1.04, -0.16], P = 0.008), TC(SMD = -3.86, 95%CI[-6.37, -1.34], P = 0.003), and LDL-C(SMD = -1.54, 95%CI[-2.67, -0.42], P = 0.007), decrease plasma viscosity(SMD = -1.02, 95%CI[-1.58, -0.47], P<0.0003), whole blood low shear viscosity(SMD = -0.85, 95%CI[-1.21, -0.49], P<0.00001), whole blood high shear viscosity(SMD = -0.82, 95%CI[-1.44, -0.20], P = 0.009), and erythrocyte aggregation index(SMD = -1.00, 95%CI[-1.75, -0.25], P = 0.009), and bring down CRP(SMD = -1.39, 95%CI[-1.91, -0.86], P<0.00001). The incidence of adverse reactions in the treatment group was higher than that in the control group (OR = 2.26, 95%CI[1.06, 4.85], P = 0.04). Neither of the two groups suffered from abnormal liver and kidney function during the study process. Conclusion STS combined with routine treatment has a definite clinical efficacy and certain safety in the treatment of UAP, but it needs to be further confirmed by high-quality and low-bias randomized controlled trials in the future.
Preoperative chronic kidney disease and complications after pancreatoduodenectomy: a retrospective cohort study

Chronic kidney disease is a prevalent condition in surgical patients. Possible associations with increased postoperative morbidity and mortality have not been clearly demonstrated in patients undergoing pancreatoduodenectomy. The aim of this study was to assess the risk of postoperative complications in patients with reduced kidney function undergoing pancreatoduodenectomy.
A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT

In this work, we test the working of Google Translate’s recently introduced Sanskrit-English translation system using a relatively small set of probe test cases designed to focus on those areas that we expect, based on a knowledge of Sanskrit and English grammar, to pose a challenge for translation between Sanskrit and English. We summarize the findings that point to significant gaps in the current Zero-Shot Neural Multilingual Translation (Zero-Shot NMT) approach to Sanskrit-English translation. We then suggest an approach based on Sanskrit grammar to create a differential parallel corpus as a corrective training data to address such gaps. This approach should also generalize to other pairs of languages that have low availability of learning resources, but a good grammar theory.
Example-Based Machine Translation with a Multi-Sentence Construction Transformer Architecture

Neural Machine Translation (NMT) has now attained state-of-art performance on large-scale data. However, it does not achieve the best translation results on small data sets. Example-Based Machine Translation (EBMT) is an approach to machine translation in which existing examples in a database are retrieved and modified to generate new translations. To combine EBMT with NMT, an architecture based on the Transformer model is proposed. We conduct two experiments respectively using limited amounts of data, one on an English-French bilingual dataset and the other one on a multilingual dataset with six languages (English, French, German, Chinese, Japanese and Russian). On the bilingual task, our method achieves an accuracy of 96.5 and a BLEU score of 98.8. On the multilingual task, it also outperforms OpenNMT in terms of BLEU scores.
Entrenchment Matters: Investigating Positional and Constructional Sensitivity in Small and Large Language Models

The success of large language models (LMs) has also prompted a push towards smaller models, but the differences in functionality and encodings between these two types of models are not yet well understood. In this paper, we employ a perturbed masking approach to investigate differences in token influence patterns on the sequence embeddings of larger and smaller RoBERTa models. Specifically, we explore how token properties like position, length or part of speech influence their sequence embeddings. We find that there is a general tendency for sequence-final tokens to exert a higher influence. Among part-of-speech tags, nouns, numerals and punctuation marks are the most influential, with smaller deviations for individual models. These findings also align with usage-based linguistic evidence on the effect of entrenchment. Finally, we show that the relationship between data size and model size influences the variability and brittleness of these effects, hinting towards a need for holistically balanced models.
Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach

While personalization increases the utility of item recommendation, it also suffers from the issue of popularity bias. However, previous methods emphasize adopting supervised learning models to relieve popularity bias in the static recommendation, ignoring the dynamic transfer of user preference and amplification effects of the feedback loop in the recommender system (RS). In this paper, we focus on studying this issue in the interactive recommendation. We argue that diversification and novelty are both equally crucial for improving user satisfaction of IRS in the aforementioned setting. To achieve this goal, we propose a Diversity-Novelty-aware Interactive Recommendation framework (DNaIR) that augments offline reinforcement learning (RL) to increase the exposure rate of long-tail items with high quality. Its main idea is first to aggregate the item similarity, popularity, and quality into the reward model to help the planning of RL policy. It then designs a diversity-aware stochastic action generator to achieve an efficient and lightweight DNaIR algorithm. Extensive experiments are conducted on the three real-world datasets and an authentic RL environment (Virtual-Taobao). The experiments show that our model can better and full use of the long-tail items to improve recommendation satisfaction, especially those low popularity items with high-quality ones, thus achieving state-of-the-art performance.
Janus: A Unified Distributed Training Framework for Sparse Mixture-of-Experts Models

Scaling models to large sizes to improve performance has led a trend in deep learning, and sparsely activated Mixture-of-Expert (MoE) is a promising architecture to scale models. However, training MoE models in existing systems is expensive, mainly due to the All-to-All communication between layers. All-to-All communication originates from expert-centric paradigm: keeping experts in-place and exchanging intermediate data to feed experts. We propose the novel data-centric paradigm: keeping data in-place and moving experts between GPUs. Since experts' size can be smaller than the size of data, data-centric paradigm can reduce communication workload. Based on this insight, we develop Janus. First, Janus supports fine-grained asynchronous communication, which can overlap computation and communication. Janus implements a hierarchical communication to further reduce cross-node traffic by sharing the fetched experts in the same machine. Second, when scheduling the "fetching expert" requests, Janus implements a topology-aware priority strategy to utilize intra-node and inter-node links efficiently. Finally, Janus allows experts to be prefetched, which allows the downstream computation to start immediately once the previous step completes. Evaluated on a 32-A100 cluster, Janus can reduce the traffic up to 16× and achieves up to 2.06× speedup compared with current MoE training system.
Poster: Learning distributions to detect anomalies using all the network traffic

Anomaly detection is an essential building block of many applications, including DDoS detection, root cause analysis, traffic estimation, and change detection. A vital part of detecting anomalies is establishing a sense of normality, e.g., by learning distributions for various features from benign traffic. Learning these distributions in the control plane requires coping with the limited visibility of sampling; learning distributions in the data plane requires relying on simplistic techniques because of hardware constraints. We propose a novel data- and control-plane co-design for learning distributions: in the control plane, we search for candidate distributions with Bayesian optimization; in the data plane, we evaluate how well each distribution matches all observed traffic, without missing rare events. The aggregated evaluation results are fed back to the control plane to guide the optimization and learn accurate distributions. Our key insight is that while learning and optimization are infeasible in the data plane, evaluating distributions is feasible and leverages data plane strengths. We confirm the feasibility of our approach with a preliminary evaluation.
New Subspace Method for Unconstrained Derivative-Free Optimization

This article defines an efficient subspace method, called SSDFO, for unconstrained derivative-free optimization problems where the gradients of the objective function are Lipschitz continuous but only exact function values are available. SSDFO employs line searches along directions constructed on the basis of quadratic models. These approximate the objective function in a subspace spanned by some previous search directions. A worst-case complexity bound on the number of iterations and function evaluations is derived for a basic algorithm using this technique. Numerical results for a practical variant with additional heuristic features show that, on the unconstrained CUTEst test problems, SSDFO has superior performance compared to the best solvers from the literature.
Beyond a Centralized Verifier: Scaling Data Plane Checking via Distributed, On-Device Verification

Centralized data plane verification (DPV) faces significant scalability issues in large networks (i.e., the verifier being a performance bottleneck and single point of failure and requiring a reliable management network). We tackle this scalability challenge by introducing Tulkun, a distributed, on-device DPV framework. Our key insight is that DPV can be transformed into a counting problem on a directed acyclic graph, which can be naturally decomposed into lightweight tasks executed at network devices, enabling fast data plane checking in networks of various scales and types. With this insight, Tulkun consists of (1) a declarative invariant specification language, (2) a planner that employs a novel data structure DPVNet to systematically decompose global verification into on-device counting tasks, (3) a distributed verification messaging (DVM) protocol that specifies how on-device verifiers efficiently communicate task results to jointly verify the invariants, and (4) a mechanism to verify invariant fault-tolerance with minimal involvement of the planner. Extensive experiments with real-world datasets (WAN/LAN/DC) show that Tulkun verifies a real, large DC in 41 seconds while others tools need minutes or up to tens of hours, and shows an up to 2355× speed up on 80% quantile of incremental verification with small overhead on commodity network devices.