Pieces Of Information Research Articles

A Comprehensive Review of Preclinical Models for Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is a reproductive, metabolic, and endocrine disorder with unclear aetiology. PCOS, the most common cause of female reproductive and metabolic disorders, is known to affect more than one in ten women globally. PCOS and associated clinical manifestations are probably underdiagnosed despite their high occurrence. Objective: Alternative animal models have been employed to investigate the causes of PCOS or assess potential treatments. In light of this piece of information, it is challenging to create an animal model that accurately captures all components of this condition; nonetheless, the resemblance of an animal model's biology and/or biochemical characteristics to the phenotypes of PCOS in humans may boost its applicability. Result: The key characteristics of these models are closer to human situations when compared to women with PCOS, as shown by this comparison. The creation and testing of drugs for the treatment of PCOS are necessary. Conclusion:: The overview of PCOS, current preclinical models, and appropriate models chosen in different studies to mimic various phenotypes in PCOS studies are all covered in this review paper. Additionally, we have outlined the benefits and drawbacks of PCOS animal models.

CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES

Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts. In this case, data mining techniques can identify patterns and relationships in population data. The K-Means algorithm is a clustering technique that divides data into groups or clusters based on similar characteristics. This research aims to apply the K-Means method with various approaches to clustering sub-districts in the Bojonegoro district according to population data. The research method used is a quantitative method with an exploratory study in the application of the K-Means method with a variety of approaches, namely the use of the Kernel K-Means method by utilizing the mapping function to map data to a higher dimension before the clustering process. In addition, the Fast K-Means method is used, which reduces the model training time to improve the cluster-centered recalibration problem as the amount of data increases. The data source used in this research is secondary population data in the form of birth, death, migrant, and moving variables obtained from the Satu Data Bojonegoro website developed by the Bojonegoro Regency Government. It is found that the best K-Means approach is the Kernel K-Means method with a number of clusters of 5. The performance of the cluster method is evaluated by measuring the average distance within the cluster. The data coordinate pattern in the Kernel K-means method clustering shows a smooth initial trend when the value of the number of clusters is 5 so that the clusters formed are obtained clearly. The conclusion from this study's results is that the K-Means method's best approach in grouping sub-districts in Bojonegoro district is the Kernel K-Means approach.

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.

Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Creativity is motivated by novelty. Curiosity is triggered by uncertainty.

Although creativity and curiosity can be similarly construed as knowledge-building processes, their underlying motivation is fundamentally different. Specifically, curiosity drives organisms to seek information that reduces uncertainty so that they can make a better prediction about the world. On the contrary, creative processes aim to connect distant pieces of information, maximizing novelty and utility.

Implications of social network structures on socially influenced decision-making

AbstractIndividuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

Intestinal Helminth Infections among Primary School Aged Pupils in Awka North Local Government Area, Anambra State, Nigeria

This study was conducted to determine the prevalence of helminthiasis in primary school children in Awka North Local Government Area of Anambra State, Nigeria. A total of 172 faecal samples were collected and analyzed using Kato-Katz technique. Other pieces of information concerning demography were obtained through structured questionnaires. Data was analyzed using chi-square test and P˂0.05 were considered significant. Overall, 45 (26.16 %) of the samples were infected while 127 (73.84 %) were negative. Among the schools sampled, Community Primary School, Amowere recorded the highest infection prevalence of 58.18% while no infection was recorded in Umuoji Primary School, Ebenebe (0.00 %). In relation to class, pupils in class four were the most infected 12 (31.58 %) while class one children had the least prevalence 5 (12.19 %). Gender-wise, males recorded the highest prevalence of 28.43 % with female children having a lower prevalence of 22.86 %. In terms of age, children between the age bracket of 7–9 years were most infected 31 (30.09 %) with the least prevalence occurring in the 4–6 years age group. In total, four helminth parasites were found in the stool samples of the children, A. lumbricoides 35 (20.59 %), Hookworm 7 (4.16 %), and T. trichiura 5 (2.19 %). Among the predisposing factors studied, school and type of parasite were found to be statistically significant (P˂0.05). Mass deworming of primary school children should be undertaken regularly by health officers as well as enlightenment programs of pupils on proper hygiene practices and sanitation.

A Roman Fleet Soldier’s Gravestone from Scarbantia (TRHR 38)

In his paper the author deals with a funerary verse epitaph from Scarbantia (Sopron) published by him earlier (TRHR 38). Based on his new restoration, the gravestone (stela) was erected to a veteran by his wife who served earlier as fleet soldier. He was most probably called Britto. Several vulgar Latin phenomena can be observed in the inscription that was erected in the second half of the third century or at the very beginning of fourth c. The type of the metrical formula resiste viator et lege can be found in several Pannonian verse inscriptions. Based on the pieces of information given in the problematical parts of lines 2 and 3 that concern the wife, at least one line is missing at the beginning of the text and she erected the grave monument and the funerary epitaph to herself as well.

Integration of computer networks and artificial neural networks for an AI-based network operator

Integration of computer networks and artificial neural networks for an AI-based network operator

Determining the Factors accounting for Reaction Selectivity A Relative Energy Gradient - Interacting Quantum Atoms and Natural Bonding Orbitals Study.

Identifying the main physicochemical properties accounting for the course of a reaction is of utmost importance to rationalize chemical syntheses. To this aim, the relative energy gradient (REG) method is an appealing approach because it is an unbiased and automatic process to extract the most relevant pieces of energy information. Initially formulated within the interacting quantum atoms (IQA) framework for a single reaction, here we extend the REG method to natural bond orbitals (NBO) analysis and to the case of two competitive processes. This development enables the determination of the driving forces of any chemical selectivity. We illustrate the extended REG method on the case study of ring opening in cyclobutenes, which is an important instance of the so-called torquoselectivity.

Scoring Multi-hop Question Decomposition Using Masked Language Models

Question answering (QA) is a sub-field of Natural Language Processing (NLP) that focuses on developing systems capable of answering natural language queries. Within this domain, multi-hop question answering represents an advanced QA task that requires gathering and reasoning over multiple pieces of information from diverse sources or passages. To handle the complexity of multi-hop questions, question decomposition has been proven to be a valuable approach. This technique involves breaking down complex questions into simpler sub-questions, reducing the complexity of the problem. However, it’s worth noting that existing question decomposition methods often rely on training data, which may not always be readily available for low-resource languages or specialized domains. To address this issue, we propose a novel approach that utilizes pre-trained masked language models to score decomposition candidates in a zero-shot manner. The method involves generating decomposition candidates, scoring them using a pseudo-log likelihood estimation, and ranking them based on their scores. To evaluate the efficacy of the decomposition process, we conducted experiments on two datasets annotated on decomposition in two different languages, Arabic and English. Subsequently, we integrated our approach into a complete QA system and conducted a reading comprehension performance evaluation on the HotpotQA dataset. The obtained results emphasize that while the system exhibited a small drop in performance, it still maintained a significant advance compared to the baseline model. The proposed approach highlights the efficiency of the language model scoring technique in complex reasoning tasks such as multi-hop question decomposition.

Adoption of identity theft protection services in social media: A PMT investigation

In the last few years, social media has become a part of our lives. It is not a website anymore. It offers us blogging, watching videos, and connecting with more people. In our social media profiles, we create our virtual identity. We share every piece of information connected to our lives. Social media has become a very important part of our lives. Social media changes itself tremendously; it also makes it possible for criminals to take their work to another level. Many crimes happen on social media. One of them is identity theft. As we create our own identities on social media. There is a high risk of identity theft. For this reason, many identity theft protection services have been created. With identity theft protection, we are able to protect our identities on social media. Those identity theft production services are my best product because they help us identify identity threats and get rid of them. They give us protection from identity theft in exchange for some monthly fees. But those services are not common in our nation. We don't know if those services will work in our country or not. With this thesis, we will be able to find out the adoption intention of social media identity theft protection services in our country through PMT investigations.

Reviving Static Charts into Live Charts.

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience.

New method for assigning cardinal weights in multi-criteria decision-making: the constant weight ratio method

A new method is proposed to convert ordinal ranking of a number of criteria and an additional piece of information into numerical weights. A literature review of methods for assigning cardinal weights based on ordinal ranking is performed, as well as an analysis of their behaviour. The new method, called ‘constant weight ratio’ (CWR), enables better adjustment to the decision-maker’s preferences than purely ordinal ranking methods. It also solves the problem of the excessive decrease in the weight of the most important criterion (or criteria) when the total number of criteria is large and the weight of the most important criterion (or criteria) must be high. It is achieved via three simple steps and flexible input data. The additional piece of information may be: (i) the relative importance of the criteria, i.e., the weight ratio, (ii) the total weight of the most important set of criteria, or (iii) the weight of the most important criterion. The proposed method is applied to two case studies in the cultural sector to illustrate that the resulting weights are equivalent to other methods requiring more input data from the decision maker.

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

Nativeness is a binary concept —Invasiveness and its management are not

In interdisciplinary fields such as biodiversity conservation or invasion science —where multiple perspectives from diverse disciplines often need to converge for effective environmental management, it is crucial to minimise terminological confusion in order to understand and transmit concepts accurately. The diversity of perspectives can exert a substantial influence on defining key terms in those interdisciplinary fields, potentially resulting in confusion. A lively topic within invasion science concerns the definitions of nativeness, non-nativeness, and invasiveness. While some academics dismiss the nativeness concept because it cannot be objectively defined, others advocate for its categorization, and a third perspective posits it as a binary term. Here we argue the inherent binary nature of nativeness, even when our capacity to observe is challenging. Nativeness (and consequently, non-nativeness) is an intrinsic and binary property of a species (i.e. the set of populations of a species) in a place, which should remain a central piece of information in biodiversity conservation and ecosystem restoration. In contrast, invasiveness, which relies on quantitative metrics (including abundance, spread, or impacts), should not be defined on binary terms. This underscores the importance of offering diverse, context-specific management strategies to deal with it. We illustrate the consistency of nativeness' binary nature and the need to rely on diverse management options to address different invasion scenarios with the example of the freshwater crayfish in the Iberian Peninsula.

Enhancing bibliographic reference parsing with contrastive learning and prompt learning

Bibliographic references, typically comprising author names, journal titles, paper titles, and publication dates, play a vital role in academic research. Accurately identifying these structured pieces of information from references is a crucial step in developing intelligent bibliographic management systems. However, existing methods often rely on extensive high-quality training data. To mitigate the reliance on extensive training data, we propose a method that integrates prompt learning and contrastive learning for extracting structured information from bibliographic references, named CONT_Prompt_ParseRef. This approach aims to utilize contrastive learning to deepen the understanding of different metadata label types and employ prompt learning to provide specific guidelines for processing and recognition. We constructed a dataset comprising 12,000 samples, available in both Chinese and English versions. The experimental results on this bilingual dataset demonstrate the model's superior performance over existing techniques. Notably, CONT_Prompt_ParseRef shows remarkable robustness in low-resource environments, particularly in scenarios with limited training data, both contrastive and prompt learning play pivotal roles in label extraction from bibliographic references. The ablation study illustrates that omitting either component leads to a decline in performance, with contrastive learning being slightly more influential.

ACcurate COnsensus Reporting Document (ACCORD) explanation and elaboration: Guidance and examples to support reporting consensus methods.

When research evidence is limited, inconsistent, or absent, healthcare decisions and policies need to be based on consensus among interested stakeholders. In these processes, the knowledge, experience, and expertise of health professionals, researchers, policymakers, and the public are systematically collected and synthesised to reach agreed clinical recommendations and/or priorities. However, despite the influence of consensus exercises, the methods used to achieve agreement are often poorly reported. The ACCORD (ACcurate COnsensus Reporting Document) guideline was developed to help report any consensus methods used in biomedical research, regardless of the health field, techniques used, or application. This explanatory document facilitates the use of the ACCORD checklist. This paper was built collaboratively based on classic and contemporary literature on consensus methods and publications reporting their use. For each ACCORD checklist item, this explanation and elaboration document unpacks the pieces of information that should be reported and provides a rationale on why it is essential to describe them in detail. Furthermore, this document offers a glossary of terms used in consensus exercises to clarify the meaning of common terms used across consensus methods, to promote uniformity, and to support understanding for consumers who read consensus statements, position statements, or clinical practice guidelines (CPGs). The items are followed by examples of reporting items from the ACCORD guideline, in text, tables, and figures. The ACCORD materials-including the reporting guideline and this explanation and elaboration document-can be used by anyone reporting a consensus exercise used in the context of health research. As a reporting guideline, ACCORD helps researchers to be transparent about the materials, resources (both human and financial), and procedures used in their investigations so readers can judge the trustworthiness and applicability of their results/recommendations.

A study of blood glucose determination by means of an interlaboratory test

Through inter-laboratory comparison, based on the measurement process and measuring equipment conducted according to predetermined criteria, it is possible to evaluate the performance of the participants, which is known as qualification testing and is regulated in accordance with ISO 17043:2017/2018.In the medical field, the term “external quality assessment” is used for qualification testing schemes or wider programs where samples (standard, certified, attested), products, artefacts, pieces of equipment, measurement standards, data sets or other information are used for qualification testing. The processing of measurement data, which in many cases includes a random error detector along with a systematic error detector, is carried out using mathematical statistics methods, including statistical methods that are less sensitive to small deviations from the basic assumptions surrounding the probabilistic model, which are known as robust methods. Robust methods are a powerful tool for demonstrating competence in performing tests and measurements related to qualification testing schemes.

A modified neural circuit framework for semantic memory retrieval with implications for circuit modulation to treat verbal retrieval deficits.

Word finding difficulty is a frequent complaint in older age and disease states, but treatment options are lacking for such verbal retrieval deficits. Better understanding of the neurophysiological and neuroanatomical basis of verbal retrieval function may inform effective interventions. In this article, we review the current evidence of a neural retrieval circuit central to verbal production, including words and semantic memory, that involves the pre-supplementary motor area (pre-SMA), striatum (particularly caudate nucleus), and thalamus. We aim to offer a modified neural circuit framework expanded upon a memory retrieval model proposed in 2013 by Hart etal., as evidence from electrophysiological, functional brain imaging, and noninvasive electrical brain stimulation studies have provided additional pieces of information that converge on a shared neural circuit for retrieval of memory and words. We propose that both the left inferior frontal gyrus and fronto-polar regions should be included in the expanded circuit. All these regions have their respective functional roles during verbal retrieval, such as selection and inhibition during search, initiation and termination of search, maintenance of co-activation across cortical regions, as well as final activation of the retrieved information. We will also highlight the structural connectivity from and to the pre-SMA (e.g., frontal aslant tract and fronto-striatal tract) that facilitates communication between the regions within this circuit. Finally, we will discuss how this circuit and its correlated activity may be affected by disease states and how this circuit may serve as a novel target engagement for neuromodulatory treatment of verbal retrieval deficits.