The Clash of Microbiomes: From the Food Matrix to the Host Gut

Food fermentation has led to the improvement of the safety characteristics of raw materials and the production of new foodstuffs with elevated organoleptic characteristics. The empirical observation that these products could have a potential health benefit has garnered the attention of the scientific community. Therefore, several studies have been conducted in animal and human hosts to decipher which of these products may have a beneficial outcome against specific ailments. However, despite the accumulating literature, a relatively small number of products have been authorized as ‘functional foods’ by regulatory bodies. Data inconsistency and lack of in-depth preclinical characterization of functional products could heavily contribute to this issue. Today, the increased availability of omics platforms and bioinformatic algorithms for comprehensive data analysis can aid in the systematic characterization of microbe–microbe, microbe–matrix, and microbe–host interactions, providing useful insights about the maximization of their beneficial effects. The incorporation of these platforms in food science remains a challenge; however, coordinated efforts and interdisciplinary collaboration could push the field toward the dawn of a new era.

EXPRESS: Extremity Bias in Online Reviews: The Role of Attrition

In a range of studies across platforms, online ratings have been shown to be characterized by distributions with disproportionately-heavy tails. We focus on understanding the underlying process that yields such “j-shaped” or “extreme” distributions. We propose a novel theoretical mechanism behind the emergence of “j-shaped” distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. We present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. We show that while all three mechanisms can give rise to extreme distributions, only the utility-based and the attrition-based mechanisms can explain our empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility.

Dis-Cover AI Minds to Preserve Human Knowledge

Modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the intellectual solution is realized—and the search for strategic reference points evoke important issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped ahead with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we would investigate the syntax as possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge.

Language as an Object of Empirical Study in the Dutch Republic

This article takes a dictionary by Joos Lambrecht, dating from 1546, as its point of departure. It argues that this dictionary, as well as other dictionaries and treatises produced in the wake of Lambrecht’s, did more than teach their young audience in the Dutch Republic the meaning of existing words and thus transfer cultural and linguistic knowledge as was already understood. They also taught youngsters how to obtain (new) knowledge from their own empirical observations. The Dutch books on morphology, orthography, phonology, and grammar – produced in large numbers – offered their readers the opportunity to use their own language as an object for empirical study. By charting the dynamics of language, knowledge, and empirical training, it is argued that the Dutch language was, for a short time, treated by writers not merely as a means to express and share knowledge, but also as an object of study in itself. What might have formed an accessible training ground for the development of skills in empirical observation and especially self-reflexive practice, was, however, soon snuffed out by a second wave of tutorial books which emphasised the prescriptive over the explorative.

Ion channel distributions in cortical neurons are optimized for energy-efficient active dendritic computations

The mammalian brain uses more than 20% of the energy consumed by the entire body. This enormous demand for energy is thought to impose strong selective pressure by which neurons evolve in ways that ensure robust function at minimal energy cost. Here we demonstrate that the ion channel expression patterns by which pyramidal tract neurons - the major output cell type of the cerebral cortex - could implement their complex intrinsic physiology is extremely widespread. Surprisingly, this wide spectrum does not reflect morphological variability, but the energy costs for generating dendritic calcium action potentials. We found that energy-efficient calcium action potentials require a low expression of slow inactivating potassium channels in the distal dendrites, an empirical observation whose significance remained unclear for more than a decade. Thus, cortical neurons do not utilize all theoretically possible ways to implement their functions, but instead appear to select those optimized for energy-efficient active dendritic computations.

Energy Analysis via Value Stream Mapping: A case study of an Automotive Weld Plant

Over the past few decades, Lean Manufacturing (LM) has been the pinnacle of strategies applied for cost and waste reduction. However as the search for competitive advantage and production growth continues, there is a growing consciousness towards environmental preservation. With this consideration in mind this research investigates and applies Value Stream Mapping (VSM) techniques to aid in reducing environmental impacts of manufacturing companies. The research is based on empirical observation within the Chassis weld plant of Company X. The observation focuses on the weld operations and utilizes the cross member line of Auxiliary Cross as a point of study. Using various measuring instruments to capture the emissions emitted by the weld and service equipment, data is collected. The data is thereafter visualised via an Environmental Value Stream Map (EVSM) using a 7-step method. It was found that the total lead-time to build an Auxiliary Cross equates to 16.70 minutes and during this process is emitted. It was additionally found that the UPR x LWR stage of the process indicated both the highest cycle time and carbon emissions emitted and provides a starting point for investigation on emission reduction activity. The EVSM aids in the development of a method that allows quick and comprehensive analysis of energy and material flows. The results of this research are important to practitioners and academics as it provides an extension and further capability of Lean Manufacturing tools. Additionally, the EVSM provides a gateway into realising environmental benefits and sustainable manufacturing through Lean Manufacturing.

Dynamic restructuring of supported metal nanoparticles and its implications for structure insensitive catalysis

Some fundamental concepts of catalysis are not fully explained but are of paramount importance for the development of improved catalysts. An example is the concept of structure insensitive reactions, where surface-normalized activity does not change with catalyst metal particle size. Here we explore this concept and its relation to surface reconstruction on a set of silica-supported Ni metal nanoparticles (mean particle sizes 1–6 nm) by spectroscopically discerning a structure sensitive (CO2 hydrogenation) from a structure insensitive (ethene hydrogenation) reaction. Using state-of-the-art techniques, inter alia in-situ STEM, and quick-X-ray absorption spectroscopy with sub-second time resolution, we have observed particle-size-dependent effects like restructuring which increases with increasing particle size, and faster restructuring for larger particle sizes during ethene hydrogenation while for CO2 no such restructuring effects were observed. Furthermore, a degree of restructuring is irreversible, and we also show that the rate of carbon diffusion on, and into nanoparticles increases with particle size. We finally show that these particle size-dependent effects induced by ethene hydrogenation, can make a structure sensitive reaction (CO2 hydrogenation), structure insensitive. We thus postulate that structure insensitive reactions are actually apparently structure insensitive, which changes our fundamental understanding of the empirical observation of structure insensitivity.

PSYCHO-EMOTIONAL STATE OF STUDENTS UNDER DISTANCE LEARNING

The purpose of the article is to theoretically substantiate and empirically investigate the psycho-emotional state of students under distance learning in higher education institutions.Methods. A set of research methods are used in order to achieve the goal: a) theoretical – analysis, synthesis, comparison, generalization and systematization of obtained results; b) empirical – observation, conversation, psychodiagnostic techniques: a technique of “Self-assessment of mental states” by H. Eysenck – to determine the indicators of mental states of students (anxiety, frustration, aggression, rigidity); a research technique of emotional intelligence by G. Hall – to determine the indicators and level of development of emotional intelligence in students; test “Pre-examination well-being” (modification of the technique of S.I. Boltivets) – to study the psychological readiness for passing exams and pre-examination well-being of students; c) mathematical and statistical data processing – descriptive statistics (finding the levels and frequency of expression of the trait in percent), linear correlation analysis by Pearson.Results. The study involved 80 students enrolled in 1st–2nd yearof Master’sdegree program at Ternopil Volodymyr Hnatiuk National Pedagogical University. The age of the respondents is 21–25 years. The study was carried out during 2020–2021.It was found out that under distance learning, a third part of students have a high level of anxiety, frustration, aggression. They experience anxiety, confusion, worrying, disillusion associated with prolonged social isolation, online learning, virtual communication with teachers and peers. More than half of students have an average level of the development of emotional intelligence therefore they are not always able to correctly recognize, understand and deal with emotions. Under distance learning half of the students showed an average level of psychological readiness for the exam and not entirely good pre-examination well-being. They are emotionally tense before the exam, often feel fear, lack of self-confidence in themselves and their knowledge.Conclusions. It was found out that under distance learning psycho-emotional state of students significantly deteriorates. Thereby, teachers need to create favorable conditions for students to study, taking into consideration their individual psychological characteristics, to provide them with psychological support.Key words: mental state, emotional state, emotional intelligence, distance learning, students, higher education institutions. Мета статті – теоретично обґрунтувати та емпірично дослідити психоемоційний стан студентів в умовах дистанційного навчання в закладах вищої освіти.Методи. Для реалізації поставленої мети використано комплекс методів дослідження: а) теоретичні–аналіз, синтез, порівняння, узагальнення та систематизація отриманих результатів; б) емпіричні – спостереження, бесіда, психодіагностичні методики: методика «Самооцінка психічних станів» Г. Айзенка – для визначення показників психічних станів студентів (тривожність, фрустрація, агресивність, ригідність); методика дослідження емоційного інтелекту Н. Холла – для визначення показників та рівня розвитку емоційного інтелекту в студентів; тест «Передекзаменаційне самопочуття» (модифікація методики С.І. Болтівця) – для дослідження психологічної готовності до складання іспитів та передекзаменаційного самопочуття студентів; в) методи математично-статичної обробки даних – описова статистика (знаходження рівнів і частоти вираженості ознаки у відсотках), лінійний кореляційний аналіз Пірсона.Результати. У дослідженні взяли участь 80 студентів, які навчаються на 1–2 курсах магістратури в Тернопільському національному педагогічному університеті імені Володимира Гнатюка. Вік респон-дентів становить 21–25 років. Дослідження здійснювалося впродовж 2020–2021 років.Виявлено, що за умов дистанційного навчання третина студентів мають високий рівень тривож-ності, фрустрації, агресивності. Вони відчувають тривогу, хвилювання, неспокій, розчарування, які пов’язані з тривалою соціальною ізоляцією, навчанням у режимі онлайн, віртуальним спілкуванням з викладачами й однолітками. Більше половини студентів мають середній рівень розвитку емоційного інтелекту, тому не завжди здатні правильно розпізнавати, розуміти та управляти емоціями. В умовах дистанційного навчання у половини студентів виявлено середній рівень психологічної готовності до іспиту та не зовсім хороше передекзаменаційне самопочуття. Вони емоційно напружені перед іспитом, часто відчувають страх, невпевненість у собі та своїх знаннях.Висновки. З’ясовано, що в умовах дистанційного навчання психоемоційний стан студентів суттєво погіршується. У зв’язку з цим викладачам необхідно створити сприятливі умови для навчання студентів з урахуванням їхніх індивідуально-психологічних особливостей, забезпечити надання їм психологічної підтримки. Ключові слова: психічний стан, емоційний стан, емоційний інтелект, дистанційне навчання, студенти, заклади вищої освіти.

A geographical assessment of Chariganga and Arpara Beel (wetlands) of Nadia, West Bengal as a habitat of wetland birds

The present study attempts to assess the impact of human intervention on the population, distribution, and habitat perspectives of the water birds found in and around Chariganga and Arpara ‘Beel’ wetlands, leftover channels of the River Bhagirathi. The point count method was adopted during field surveys conducted from April 2019 to March 2020. These wetlands are the natural habitats for 37 species of wetland birds belonging to 18 families and 11 orders, of which 26 species are residents, three are summer migrants, and eight are winter immigrants. The wetlands also harbour 10 bird species whose population is globally declining over the last few decades. Relative Diversity index unveils that among waterfowls Ardeidae is the dominant family. Species richness reaches its peak in winter, and is least during the monsoon. Empirical observation documented one Vulnerable (Greater Adjutant) and one Near Threatened (Black-Headed Ibis) species residing on the banks and adjoining paddy fields. Indiscriminate extraction of wetland products by local people, along with agricultural expansion towards the waterfront of the wetlands, has deteriorated the health of those wetlands and threatened the existence of waterbirds, especially shorebirds. Populations of 22 species living in water edge areas has changed conspicuously owing to cultural and economic activities of neighboring human groups. We suggest improving the ecological balance of the wetlands and restraining further degradation through proper management to preserve avian diversity.

A data transformation process for using Benford’s Law with bounded data

Benford’s Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford’s Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford’s Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford’s Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford’s Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.