Month: October 2024

Structure and functional implications of photoreceptive systems in non-marine ostracods: a review

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Abstract

Ostracods are small, bivalved crustaceans living in all types of aquatic habitats. All non-marine species belong to the order Podocopida. They usually possess a simple optical system composed of three eyecups called naupliar eye. Phylogenetic data and morphological characteristics support the homology of naupliar eyes between ostracods and other crustacean groups. The photoreceptive system is formed by several specialised cells and can be approximated by a lens-mirror optical apparatus. In some cases, a transparent area of the calcitic carapace can form an additional lens. The visual stimuli are processed by the protocerebrum, possibly via monoaminergic neurons. The presence or absence of one or more specialised cells influence the function of the eyes, and, most likely, their evolution or loss are associated with the adaptation to different habitats. Podocopid ostracods may express long wavelength-sensitive rhabdomeric opsins and may possess nonvisual opsins. The few ethological experiments so far conducted demonstrate that non-marine ostracods might be capable of associative learning when trained with light or specific light wavelengths. This work will provide an overview of what is known and what remains to be further investigated about vision and how light cues affect the behaviour in non-marine ostracods.

NASH triggers cardiometabolic HFpEF in aging mice

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Abstract

Both heart failure with preserved ejection fraction (HFpEF) and non-alcoholic fatty liver disease (NAFLD) develop due to metabolic dysregulation, has similar risk factors (e.g., insulin resistance, systemic inflammation) and are unresolved clinical challenges. Therefore, the potential link between the two disease is important to study. We aimed to evaluate whether NASH is an independent factor of cardiac dysfunction and to investigate the age dependent effects of NASH on cardiac function. C57Bl/6 J middle aged (10 months old) and aged mice (24 months old) were fed either control or choline deficient (CDAA) diet for 8 weeks. Before termination, echocardiography was performed. Upon termination, organ samples were isolated for histological and molecular analysis. CDAA diet led to the development of NASH in both age groups, without inducing weight gain, allowing to study the direct effect of NASH on cardiac function. Mice with NASH developed hepatomegaly, fibrosis, and inflammation. Aged animals had increased heart weight. Conventional echocardiography revealed normal systolic function in all cohorts, while increased left ventricular volumes in aged mice. Two-dimensional speckle tracking echocardiography showed subtle systolic and diastolic deterioration in aged mice with NASH. Histologic analyses of cardiac samples showed increased cross-sectional area, pronounced fibrosis and Col1a1 gene expression, and elevated intracardiac CD68+ macrophage count with increased Il1b expression. Conventional echocardiography failed to reveal subtle change in myocardial function; however, 2D speckle tracking echocardiography was able to identify diastolic deterioration. NASH had greater impact on aged animals resulting in cardiac hypertrophy, fibrosis, and inflammation.

Are impacts of the invasive alien plant Crassula helmsii mediated by detritus? A litter experiment in a temperate pond

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Abstract

Because of the high growth rates often achieved by invasive alien macrophytes, their establishment in recipient ecosystems may alter the abundance and composition of litter entering detrital pathways, representing a significant—but often overlooked—ecological effect of these invasions. Crassula helmsii (Kirk) Cockayne (New Zealand pygmyweed) is an invasive alien macrophyte, notorious for its profuse growth in invaded waterbodies. C. helmsii is perennial and often forms dense stands, producing abundant detritus. To investigate whether some of C. helmsii’s impacts are mediated by this detritus, we conducted an 85-day litterbag experiment comparing decomposition of C. helmsii with that of Callitriche stagnalis Scop. (water-starwort), a commonly co-occurring native macrophyte. Macroinvertebrate assemblage composition was comparable between macrophyte species throughout the experiment, but shifted as plants decayed. Litterbags were initially dominated by the invasive shredder Crangonyx pseudogracilis Bousfield, 1958 and later by Euglesa casertana (Poli, 1791), an interstitial suspension feeder. C. helmsii litter decomposed more slowly, with proportionally less invertebrate-mediated breakdown, but was ultimately colonised by more abundant macroinvertebrates, including more C. pseudogracilis. Decomposition may be slowed by C. helmsii’s high carbon: nitrogen ratio. These results suggest that C. helmsii invasion may impact macroinvertebrate assemblages via the production of long-lasting and relatively unpalatable detritus.

A Study on the Effects of Using the 6E Model and a Robot Teaching Assistant on Junior High School Students’ STEM Knowledge, Learning Motivation, and Hands-on Performance

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Abstract

The integration of education and robotics has emerged as a crucial development in the technological landscape. This study focuses on the use of a robot teaching assistant to enhance the learning efficiency of 8th-grade students in hands-on STEM activities centered around the theme of “Smart City.” It explores the impact of educational robots on students' learning outcomes and their development of hands-on skills through diverse learning methods. Conducted over 12 weeks with 103 participants, the study employed a quasi-experimental design. Students were split into two groups: The Experimental Group (EG), using the 6E model with robot teaching assistants, and the Control Group (CG), using only the 6E model. The analysis of covariance revealed that the EG exhibited superior performance in STEM knowledge, motivation, and hands-on skills compared to the CG. Further analysis indicated that learning motivation significantly influenced hands-on performance in the EG, particularly in high-scoring subgroups. The findings suggest that combining the 6E model with educational robots effectively enhances STEM learning and student engagement. Educational robots as teaching assistants not only aid in knowledge acquisition but also significantly boost students' motivation and hands-on skill development. This implies a promising direction for integrating advanced technology in educational practices to foster more effective learning environments.

Implementation and Outcomes of the Trauma Ambassadors Program: A Case Study of Trauma-Informed Youth Leadership Development

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Abstract

Community-based programs serve a critical need for vulnerable youth and families. In recent years, researchers and practitioners have urged programs to adopt a trauma-informed care (TIC) approach to address adversity in young people’s lives. The purpose of this article is to describe the implementation and outcomes of the Trauma Ambassador (TA) Program, a pilot youth leadership program guided by a community-university partnership that utilized a TIC approach in an underserved East North Philadelphia neighborhood. Fourteen youth engaged in interactive trainings to build their understanding of trauma and develop practical tools to support encounters with individuals with trauma histories. Focus groups and individual interviews were conducted to better understand program implementation and outcomes. Rich data emerged that identifies a myriad of ways that youth and their community might benefit from a program like the one described. The program successfully impacted participants, as TAs recognized their own trauma and were motivated to help others who may have trauma histories. This program provided quality youth development experiences, particularly with respect to trauma-informed care, and results support taking a holistic, healing-centered approach to foster well-being for youth and adult mentors.

Taking AI risks seriously: a new assessment model for the AI Act

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Abstract

The EU Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This integrated model enables the estimation of AI risk magnitude  by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We illustrate this model using large language models (LLMs) as an example.

Enhancing 3D localization in wireless sensor network: a differential evolution method for the DV-Hop algorithm

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Abstract

Wireless sensor network is large-scale, self-organizing and reliable. It is widely used in the military, disaster management, environmental monitoring, and other fields. Algorithms for localization can be classified as range-based or range-free based on their ability to achieve effective localization. Range-based algorithms require hardware support, which increases deployment costs and complexity. Instead of measuring distance directly, range-free algorithms estimate the position based on hop counts between nodes. While simpler in terms of hardware requirements, this algorithm suffers from large localization errors. To address this problem, this paper proposes an improved 3D DV-Hop localization algorithm (3D DEHDV-Hop) using a differential evolutionary algorithm. First of all, theoretical analysis shows a correlation between the volume of the intersection area containing the communication range between neighbors and the number of shared single-hop nodes. Then, using the number of shared single-hop nodes between nodes, the number of hops is converted from a discrete value to an exact continuous value. Finally, the localization problem is transformed into a minimum optimization problem by incorporating a differential evolutionary algorithm. As compared to the other four algorithms compared, 3D DEHDV-Hop improves localization accuracy by an average of 10.3% under different anchor node densities, 13.7% under different communication radiuses, and 12.1% under different anchor node numbers.

An improved SMOTE based on center offset factor and synthesis strategy for imbalanced data classification

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Abstract

It is an enormous challenge for imbalanced data learning in the field of machine learning. To construct balanced datasets, oversampling techniques have been studied extensively. However, many oversampling methods suffer from introducing noisy samples and blurring classification boundaries, leading to overfitting. To solve this problem, this paper proposes a new oversampling method, namely CS-SMOTE, for synthesizing minority class samples by three-point interpolation. CS-SMOTE is mainly based on the center offset factor and a synthesis strategy. First, the CS-SMOTE method removes noise samples, calculates the center offset factor, and selects sparsely distributed minority class samples by using the K-distance graph technique. Next, new samples are generated based on sparse minority samples, random minority samples, and sub-cluster centers located in the same sub-cluster samples. Finally, multiple comparative experiments on 18 well-known datasets demonstrate the effectiveness and general applicability of the proposed CS-SMOTE method for the imbalanced data classification. The experiments show that CS-SMOTE outperforms other competitors in terms of classification accuracy, while avoiding the issue of overfitting.

Self-Change from Alcohol Problems among Racially and Ethnically Minoritized Adults: A Systematic Review

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Abstract

Purpose of the review

Many individuals recover from alcohol problems without formal treatment (referred to here as self-change). However, self-change is understudied, especially among racially and ethnically minoritized (REM) populations. The present paper is a systematic literature review on self-change from alcohol problems among REM adults in the U.S.

Recent findings

Fifteen articles met criteria for inclusion. Of these, the majority (9) described the process of self-change among American Indian and Alaska Native communities and traditional healing strategies (e.g., meeting with elders or traditional healers) were commonly used. Fewer studies described self-change among Black and Latine groups, and no studies provided data on Asian, Native Hawaiian, Pacific Islander, or Multiracial groups.

Summary

Self-change among REM groups has been studied most often among American Indian and Alaska Native groups. Additional research is needed to better understand self-change among REM groups, including the influence of relevant constructs like racial identity.

Response of the shallow groundwater level to the changing environment in Zhongmu County, China

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Abstract

The analysis of the influence of human activities and climate change on groundwater is an important basis for formulating groundwater management policies. However, the relationship between climate change, human activities and groundwater system is complex, and the research on the response of groundwater to changing environment is in the initial stage. In this paper, the interactions between groundwater water cycle and climate change and human activities are analyzed, based on climate change data and hydrogeological information from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The MODFLOW model was used to develop a numerical model of shallow groundwater movement in Zhongmu County, Henan province, and to predict the response of groundwater levels to climate change and human activities in three cases from 2016 to 2050. The results show that under the current scenario, the groundwater level will decrease at an average annual rate of 4.24 cm/A from 2016 to 2050. Under the climate change scenario, the precipitation increased by an average of 5.01%, the annual evaporation increased by an average of 17.84% and the annual temperature increased by an average of 1.29 °C from 2016 to 2050 under the three emission cases of RCP2.6, RCP4.5 and RCP8.5, under the climate change–autonomous human activities scenario, when water conservation and South–North Water Transfer Project water supply are implemented simultaneously, the water table will decrease by an average of 5.58 CMA per year under the direct impact scenario and by an average of 4.44 CMA per year under the indirect impact scenario, the water table dropped by 3.21 cm/A. The changing environment will have an important effect on groundwater circulation, and appropriate measures must be taken to deal with the continuous decline of groundwater level.