bluuebunny/crossref_metadata_2025 huggingface dataset:
Healthier Food Choices: From Consumer Information to Consumer Empowerment in EU Law
Authors: Gokani N.
Published: April 2024

This article evaluates the European Union’s efforts to regulate consumer food information with the aim of improving consumer nutrition as part of its broader consumer protection agenda. With nearly a million deaths attributed to unhealthy diets annually, the EU’s New Consumer Agenda, its Farm to Fork Strategy, and Europe’s Beating Cancer Plan all highlight the objective of empowering consumers to make informed, healthy food choices. This underscores the EU’s longstanding emphasis on regulating information to protect consumers, an approach which is even stronger with nutrition more specifically. This article shows that the EU food information law is based on two beliefs. First, consumers are given food information that is sufficient, accurate, non-misleading, clear and easy to understand and they are, therefore, well-informed. Second, well-informed consumers are empowered. This article argues that both of these beliefs are misguided. The current food information rules do not truly inform consumers well and do not, on their own, empower consumers to make healthy dietary decisions. Consequently, this article expresses scepticism about the ability of the current rules to promote improved nutrition. In light of the critiques of the current rules, this article offers suggestions for an improved food information paradigm. In addition to rules that would more effectively inform consumers, such a paradigm would acknowledge the limitations of information to empower healthier food choices and, therefore, give greater weight to more substantive interventions. This would align food law with broader trends in consumer protection law and thereby genuinely promote healthier nutrition.
Algebraic properties of the maps $$\chi _n$$
Authors: Schoone Jan , Daemen Joan
Published: April 2024

The Boolean map$$\chi _n :\mathbb {F}_2^n \rightarrow \mathbb {F}_2^n,\ x \mapsto y$$χn:F2n→F2n,x↦ydefined by$$y_i = x_i + (x_{i+1}+1)x_{i+2}$$yi=xi+(xi+1+1)xi+2(where$$i\in \mathbb {Z}/n\mathbb {Z}$$i∈Z/nZ) is used in various permutations that are part of cryptographic schemes, e.g.,Keccak-f (the SHA-3-permutation), ASCON (the winner of the NIST Lightweight competition), Xoodoo, Rasta and Subterranean (2.0). In this paper, we study various algebraic properties of this map. We consider$$\chi _n$$χn(through vectorial isomorphism) as a univariate polynomial. We show that it is a power function if and only if$$n=1,3$$n=1,3. We furthermore compute bounds on the sparsity and degree of these univariate polynomials, and the number of different univariate representations. Secondly, we compute the number of monomials of given degree in the inverse of$$\chi _n$$χn(if it exists). This number coincides with binomial coefficients. Lastly, we consider$$\chi _n$$χnas a polynomial map, to study whether the same rule ($$y_i = x_i + (x_{i+1}+1)x_{i+2}$$yi=xi+(xi+1+1)xi+2) gives a bijection on field extensions of$$\mathbb {F}_2$$F2. We show that this is not the case for extensions whose degree is divisible by two or three. Based on these results, we conjecture that this rule does not give a bijection on any extension field of$$\mathbb {F}_2$$F2.
Identifying and testing drivers of consumers’ attitude towards last-mile delivery modes
Authors: Klink Benjamin Dominique , Schweizer Samuel
Published: April 2024

Many online businesses strive to optimize last-mile delivery cost by implementing delivery modes aimed at cutting costs through automation or collaboration. Convincing consumers to adopt cost-efficient last-mile delivery modes is imperative to successfully achieve this goal. Yet, evidence on consumers’ attitude towards different delivery modes is scarce and causes of differences in attitude are currently not well understood. Based on qualitative interviews with e-food industry experts, we identify potential drivers of consumers’ attitude towards last-mile delivery modes. We test these drivers experimentally, finding privacy concerns to fully mediate differences in attitude of in-home and in-garage delivery when compared to timed in-person delivery. In addition, we find evidence indicating the presence of privacy calculus in delivery mode choice. Next to reducing customer risk, providing added customer value could therefore be a viable approach to encourage consumer adoption of more cost-efficient last-mile delivery.
Evaluation of Siblings’ Perceived Relationship Outcomes with Their Parents in an Open Trial of the SIBS Intervention for Children with Chronic Disorders
Authors: Fredriksen Trude , Vatne Torun M. , Haukeland Yngvild B. , Czajkowski Nikolai O. , Wakefield Claire E. , Fjermestad Krister W.
Published: April 2024

The SIBS intervention aims to prevent mental health problems for siblings of children with chronic disorders and has shown promising results. The mechanisms behind these changes have not been examined. Parent-child relationship quality is a predictor of mental health in siblings. Our first objective was to investigate whether sibling-perceived relationship quality between siblings and parents changes over time after participating in SIBS. Second, as the intervention requires participation of only one parent, we examined with a dual scale (anxiety and avoidance attachment style) whether relationship quality was associated with which parent participated. Third, as SIBS aims to promote parents‘ communication skills, we examined whether sibling-perceived communication quality with parents predicted relationship quality over time. The sample comprised 99 siblings (M = 11.5 years, SD = 2.0; range 8–16; 54% girls, 46% boys) of children with chronic disorders and one parent per sibling (63% mothers). We administered the questionnaires at pre-intervention, at three- and 6-months post-intervention. We applied growth curve models for relationship quality over time with communication quality as a predictor, controlling for parent gender. We found significant improvement in sibling-perceived relationship quality with the participating and the non-participating parent on the relationship anxiety scale over time, with small to medium effect sizes. Higher communication quality significantly predicted improvement of relationship quality in all four relationship outcomes. We conclude that change in relationship quality may be part of the positive outcomes of the SIBS intervention and is mainly explained by communication enhancement. Controlled trials of the SIBS intervention are indicated.
Learning Reliable Dense Pseudo-Labels for Point-Level Weakly-Supervised Action Localization
Authors: Dang Yuanjie , Zheng Guozhu , Chen Peng , Gao Nan , Huan Ruohong , Zhao Dongdong , Liang Ronghua
Published: April 2024

Point-level weakly-supervised temporal action localization aims to accurately recognize and localize action segments in untrimmed videos, using only point-level annotations during training. Current methods primarily focus on mining sparse pseudo-labels and generating dense pseudo-labels. However, due to the sparsity of point-level labels and the impact of scene information on action representations, the reliability of dense pseudo-label methods still remains an issue. In this paper, we propose a point-level weakly-supervised temporal action localization method based on local representation enhancement and global temporal optimization. This method comprises two modules that enhance the representation capacity of action features and improve the reliability of class activation sequence classification, thereby enhancing the reliability of dense pseudo-labels and strengthening the model’s capability for completeness learning. Specifically, we first generate representative features of actions using pseudo-label feature and calculate weights based on the feature similarity between representative features of actions and segments features to adjust class activation sequence. Additionally, we maintain the fixed-length queues for annotated segments and design a action contrastive learning framework between videos. The experimental results demonstrate that our modules indeed enhance the model’s capability for comprehensive learning, particularly achieving state-of-the-art results at high IoU thresholds.
Assessing penmanship of Chinese handwriting: a deep learning-based approach
Authors: Xu Zebo , Mittal Prerit S. , Ahmed Mohd. Mohsin , Adak Chandranath , Cai Zhenguang G.
Published: April 2024

The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.
Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging
Authors: La Salvia Marco , Torti Emanuele , Marenzi Elisa , Danese Giovanni , Leporati Francesco
Published: April 2024

Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.
The impoverishment problem
Authors: Kind Amy
Published: April 2024

Work in philosophy of mind often engages in descriptive phenomenology, i.e., in attempts to characterize the phenomenal character of our experience. Nagel’s famous discussion of what it’s like to be a bat demonstrates the difficulty of this enterprise (1974). But while Nagel located the difficulty in our absence of an objective vocabulary for describing experience, I argue that the problem runs deeper than that: we also lack an adequate subjective vocabulary for describing phenomenology. We struggle to describe our own phenomenal states in terms we ourselves find adequately expressive. This paper aims to flesh out why our phenomenological vocabulary is so impoverished – what I call the impoverishment problem. As I suggest, this problem has both practical and philosophical import. After fleshing out the problem in more detail, I draw some suggestive morals from the discussion in an effort to point the way forward towards a solution.
Time, temperature and media: the three keys to improve the recovery of Campylobacter fetus subsp. venerealis from preputial bull samples
Authors: Polo Coral , García-Seco Teresa , García Nerea , Fernández Víctor , Briones Víctor , Díez-Guerrier Alberto , Álvarez Julio , Domínguez Lucas , Pérez-Sancho Marta
Published: April 2024

The isolation of Campylobacter fetus subsp. venerealis (Cfv) from clinical samples is the gold standard for confirming cases of bovine genital campylobacteriosis, an important cause of infertility in cattle and a potential public health concern. Furthermore, isolation is also necessary for the development of autologous vaccines, characterization of strains for antimicrobial susceptibility patterns, etc. Nevertheless, the sensitivity of culture methods is usually low, and there is no standardized protocol to maximize the recovery of Cfv from clinical samples. The aim of the current study is to design a protocol for the culture of Cfv from preputial samples by evaluating the combination of different transport, enrichment and culture media considering the impact of certain factors (time between collection and enrichment, temperature, and use of filters). The use of modified Lander’s transport medium and storing the sample for 24 h at 21 ± 2 °C led to the highest recovery of Cfv CFUs. In contrast, the storage of the samples during 24–48 h in PBS and Thomann rarely allowed the recovery of Cfv regardless of the temperature. The enrichment medium yielding the best results was Preston (significantly higher recovery than Brucella medium), while Cfv could not be isolated with Bolton. Regarding our diagnostic assay (using Lander as transport medium and Preston as enrichment medium), the best protocol in terms of maximizing Cfv recovery as well as limiting contaminations is to culture the samples in i) solid media Preston or Skirrow, and ii) using 0.65 μm filters and incubating plates at 37 °C in microaerophilic conditions.
The Potential of Stormwater Management Strategies and Artificial Intelligence Modeling Tools to Improve Water Quality: A Review
Authors: Ramovha Ndivhuwo , Chadyiwa Martha , Ntuli Freeman , Sithole Thandiwe
Published: April 2024

Stormwater management modeling tools have been utilized to enhance stormwater operating systems, assess modeling system efficiency, and evaluate the impacts of urban growth on stormwater runoff and water quality. This review explores the potential of stormwater management strategies and Artificial Intelligence modeling tools in enhancing water quality. The study focuses on evaluating stormwater modeling tools for planning and improving stormwater systems, assessing modeling efficiency, and understanding the impacts of new development on stormwater runoff and water quality. Various stormwater modeling tools are compared to aid in water management in urban and rural settings, which is crucial due to increasing storm intensity from climate change. The review debates the advantages and limitations of different modeling tools, particularly in modeling stormwater quantity and quality under different scenarios. It also examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations. The assessment of modeling tools is centred on their support for Green Infrastructure (GI) practices, considering factors like modeling accuracy, data availability, and requirements. The study highlights the importance of these tools in managing water in urban areas and safeguarding water sources during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools are influenced by input data quality, calibration methods, and standardization metrics, with the widely used Stormwater Management Model (SWMM) being a common modeling tool.
Assessment of Climate Change-Induced Water Scarcity Risk by Using a Coupled System Dynamics and Bayesian Network Modeling Approaches
Authors: Dehghani Sara , Massah Bavani Alireza , Roozbahani Abbas , Sahin Oz
Published: April 2024

The water scarcity risk induced by climate change is contributing to a sequence of hydrological and socioeconomic impacts. Certain numbers of related impacts are locked in already and are expected to be much greater in the future. So, there is still a lack of understanding of its dynamics, origin, propagation, and the mutual interaction of its drivers. In recent years, several model-based approaches have been introduced to tackle the complexity, dynamics, and uncertainty of water scarcity specifically. However, the coupled modeling while addressing different aspects of the risk of water scarcity under the climate change scenarios has been rarely done. For bridging this gap, in this research, the combination of complementary System Dynamics modeling and Bayesian Network was applied to Qazvin Plain in Iran with five AOGCM models under two Shared Socioeconomic Pathways (SSP) scenarios (126 and 585). Key findings of this research show: 1) Baseline risk assessment indicates a low probability of water scarcity; however, in the future 30-year time horizon with continuous change in hazard, vulnerability, and exposure for SSP126, the risk fell in the extreme category with an average probability of 41%. Under SSP585, the risk varies between extreme and high categories with an average probability of 47%. 2) Economic development, particularly regional gross domestic product (RGDP) in 2045–2054 in SSP585 can diminish the negative projected consequences of climate change and therefore investments in adaptation policies could offset negative consequences, highlighting the role of economic growth in climate resilience. 3) It is projected that crop yield and income will receive the largest negative effects due to cutting back the agriculture area. 4) Considering the interplay of climate change, economic development, and water extraction policies is essential for the design, operation, and management of water-related activities. The proposed integrated methodology provides a comprehensive framework for understanding climate change-induced water scarcity risks, their drivers, and potential consequences. This approach facilitates adaptive decision-making to address the evolving challenges posed by climate change.
Analysis of Data Generation and Preparation for Porosity Prediction in Cold Spray using Machine Learning
Authors: Eberle Martin , Pinches Samuel , Osborne Max , Qin Kai , Ang Andrew
Published: April 2024

Cold spray is an additive manufacturing and coating process in which powder particles are accelerated to supersonic speeds without melting them and then deposit on a surface to form a layer of a coating. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Machine learning (ML) techniques have been specifically applied in additive manufacturing for tasks such as predicting and characterizing porosity. Machine learning algorithms can learn how a variation in the input spray parameters affects annotated output data, such as experimentally measured part properties. In this work, a dataset was developed from experiments reported in published academic papers, to train ML algorithms for the porosity prediction of cold spray manufactured parts. Data cleaning steps, such as null value replacement and categorical feature handling, were applied to prepare the dataset for the training of different ML models. The dataset was split into training and testing portions, and floating feature selection and hyperparameter optimization were performed using parts of the training set. A final evaluation of all trained models, using the test portion of the dataset, showed that a prediction accuracy with an average deviation of 0-2% porosity of the predicted values compared to the true values can be achieved.

Graphical Abstract
Learning-assisted optimization for transmission switching
Authors: Pineda Salvador , Morales Juan Miguel , Jiménez-Cordero Asunción
Published: April 2024

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.
A field investigation on the soil management practices in a productive vineyard considering C sequestration and water resistance of soil structure
Authors: Šimanský Vladimír , Jonczak Jerzy , Bordoloi Sanandam , Horák Ján
Published: April 2024

Optimal soil structure is linked to soil organic matter (SOM). Different soil management practice alternates SOM but the storing C to soil structure formation in vineyard soils is not sufficiently studied. In addition, understanding the impact of soil management practices in vineyard on changes in SOM and soil structure is crucial for sustainable viticulture. In this study conducted in a productive vineyard near Nitra city, Slovakia, we investigated the dynamics of individual size-fractions of water-stable aggregates (WSA) as results of changes in soil organic matter (SOM) under different soil management practices. Five distinct management practices – Intensive tillage system (T); Intensive tillage system with poultry manure (T + M); Strip grass (G); Fertilization in lower rates of NPK (NPK 1) to grass strips; and Fertilization in higher level of NPK (NPK 2) to grass strips were adopted to to be tested. The results have shown that the highest content of soil organic carbon (SOC) 20.3 ± 3.68 g kg−1, labile carbon (CL) 2.71 ± 0.78 g kg−1, and the most favor soil structure were identified in NPK 1, however, the most effectively improved soil structure and C sequestration (SOC: 19.9 ± 3.88 g kg−1; CL: 2.53 ± 0.83 g kg−1) was under G treatment. SOC had a more significant influence on the content of water-stable aggregates than CL. Intense changes in SOM took place in larger size-fractions of water-stable macroaggregates (WSAma) above 2 mm, while smaller WSAma below 2 mm and water-stable microaggregates were more stable.
Acute, Chronic, and Everyday Physical Pain in Borderline Personality Disorder
Authors: Nance Melissa , Stetsiv Khrystyna , McNamara Ian A. , Carpenter Ryan W. , Hepp Johanna
Published: April 2024

Purpose of Review

Physical pain is an underrecognized area of dysregulation among those with borderline personality disorder (BPD). Disturbances are observed within the experience of acute, chronic, and everyday physical pain experiences for people with BPD. We aimed to synthesize research findings on multiple areas of dysregulation in BPD in order to highlight potential mechanisms underlying the association between BPD and physical pain dysregulation.

Recent Findings

Potential biological mechanisms include altered neural responses to painful stimuli within cognitive-affective regions of the brain, as well as potentially low basal levels of endogenous opioids. Emotion dysregulation broadly mediates dysregulation of physical pain. Certain psychological experiences may attenuate acute physical pain, such as dissociation, whereas others, such as negative affect, may exacerbate it. Social challenges between patients with BPD and healthcare providers may hinder appropriate treatment of chronic pain.

Dysregulated physical pain is common in BPD and important in shaping health outcomes including elevated BPD symptoms, chronic pain conditions, and risk for problematic substance use.
Shading solutions for sustainable water management: impact of colors and intensities on evaporation and water quality
Authors: Zeadeh Doha , Albalasmeh Ammar , Mohawesh Osama , Unami Koichi
Published: April 2024

This study investigated the effectiveness of suspended shade covers in reducing evaporation rates and improving evaporation suppression efficiency. The experiment tested different shading colors (black, white, and green) and intensities (50, 70, and 90%) while considering ecological and economical aspects. Evaporation was determined using class-A evaporation pans, and various microclimatic variables were measured below the shade covers. Additionally, water quality parameters such as EC, phosphate, nitrate, and chloride concentrations were monitored. The results showed that black covers had the highest evaporation suppression efficiency with values of 56.8, 53.6, and 51.7% observed under 90% shading intensity for the black, green, and white covers, respectively. Despite variations in water quality parameters which all met Jordanian and FAO standards for irrigation water, the economic feasibility of installing these covers was found to be viable due to the resulting benefits in water conservation and crop production. However, selecting the best shading cover should consider the multipurpose use of agricultural reservoirs, including aquaculture, and further studies are recommended to investigate other overlapping aspects on a reservoir scale.
Groundwater quality assessment in the La Mojana region of northern Colombia: implications for consumption, irrigation, and human health risks
Authors: Marrugo-Negrete José , Paternina-Uribe Roberth , Enamorado-Montes German , Herrera-Arango Jairo , Rosso-Pinto Mauricio , Ospino-Contreras Juan , Pinedo-Hernández José
Published: April 2024

Water quality is one of the most relevant issues related to water management, with water pollution and access to clean water for drinking and irrigation being common issues in developing countries. Groundwater is an available water source, especially where surface sources are scarce or unsuitable. In this sense, studies to improve knowledge of aquifers should be undertaken where information on groundwater resources is not available. In the current study, 50 groundwater samples were collected from rural and urban wells used by local communities for human consumption and to irrigate local crops. Several indices for drinking water and irrigation use have been used for the assessment of water quality. Conversely, the carcinogenic and noncarcinogenic human health risks due to exposure to heavy metals in the groundwater were estimated, as were the predominant hydrochemical characteristics. The groundwater water quality index (GWQI) shows values < 100 for all the samples; groundwater is recommended for drinking after disinfection. By contrast, the irrigation water quality index (IWQI) shows that 80% of groundwater samples pose a potential threat to irrigation, with restriction categories ranging from high to severe. The individual hazard quotients were < 1. However, the multielement and multipathway hazard index (HI) for children and adults were > 1, whereas As and Mn had a higher contribution to the HI. The Piper trilinear diagram shows that hydrochemical facies of Ca2+ and HCO3− are significantly dominant. By contrast, weathering of rocks and atmospheric precipitation is the dominant factors regulating the chemistry of groundwater in the aquifer system of La Mojana. Our findings indicate that there are significant potential noncarcinogenic health risks for local populations exposed to groundwater.
Modeling HIV-1 infection and NeuroHIV in hiPSCs-derived cerebral organoid cultures
Authors: Donadoni Martina , Cakir Senem , Bellizzi Anna , Swingler Michael , Sariyer Ilker K.
Published: April 2024

The human immunodeficiency virus (HIV) epidemic is an ongoing global health problem affecting 38 million people worldwide with nearly 1.6 million new infections every year. Despite the advent of combined antiretroviral therapy (cART), a large percentage of people with HIV (PWH) still develop neurological deficits, grouped into the term of HIV-associated neurocognitive disorders (HAND). Investigating the neuropathology of HIV is important for understanding mechanisms associated with cognitive impairment seen in PWH. The major obstacle for studying neuroHIV is the lack of suitable in vitro human culture models that could shed light into the HIV-CNS interactions. Recent advances in induced pluripotent stem cell (iPSC) culture and 3D brain organoid systems have allowed the generation of 2D and 3D culture methods that possess a potential to serve as a model of neurotropic viral diseases, including HIV. In this study, we first generated and characterized several hiPSC lines from healthy human donor skin fibroblast cells. hiPSCs were then used for the generation of microglia-containing human cerebral organoids (hCOs). Once fully characterized, hCOs were infected with HIV-1 in the presence and absence of cART regimens and viral infection was studied by cellular, molecular/biochemical, and virological assays. Our results revealed that hCOs were productively infected with HIV-1 as evident by viral p24-ELISA in culture media, RT-qPCR and RNAscope analysis of viral RNA, as well as ddPCR analysis of proviral HIV-1 in genomic DNA samples. More interestingly, replication and gene expression of HIV-1 were also greatly suppressed by cART in hCOs as early as 7 days post-infections. Our results suggest that hCOs derived from hiPSCs support HIV-1 replication and gene expression and may serve as a unique platform to better understand neuropathology of HIV infection in the brain.
Analysis of breathing patterns to stabilize cardiovascular changes in physical stress environments : inspiration responds to rapid changes in blood pressure
Authors: Kim Daechang , Baek Seungbin , Kim Seunghui , Im Sanghee , Kim Sungmin
Published: April 2024

The thoracic nerves form a complex neural network that coordinates involuntary muscles such as breathing and the heart. Breathing has various patterns to maintain homeostasis in the human body. This study analyzes changes in the cardiovascular system and breathing patterns induced by stress caused by various mechanical movements performed in daily life and ultimately, the goal is to propose effective breathing patterns and breathing control methods to maintain cardiovascular homeostasis. The participants’ age was 26.97 ± 3.93 years, height was 170.24 ± 8.61 cm, and weight was 65.69 ± 13.55 Kg, and there were 62 men and 38 women. Breathing and electrocardiogram were obtained using HiCard+, a biometric monitoring device. The measured electrocardiogram was analyzed for heartbeat interval, which indicates changes in the cardiovascular system, and standard deviation of normal to normal interval (SDNN) and root mean square of the successive differences (rMSSD), which indicate the activity of the autonomic and parasympathetic nervous systems. For respiration, time changes were analyzed as patterns by calculating inspiration and exhalation times. As a result of this study, rapid changes in blood pressure increased SDNN and rMSSD from 0.053 ± 0.06 and 0.056 ± 0.087 to 0.109 ± 0.114 and 0.125 ± 0.170 s, and induced an increase in spontaneous inspiratory time from 1.46 to 1.51 s (p < 0.05). Ultimately, we hope that the results of this study will be used as a breathing control training technique to prevent and manage rapid cardiovascular changes.
Shear strength of soil by using rice husk ash waste for sustainable ground improvement
Authors: Shehata Abdelmageed Atef Abdelmageed , Owino Alex Otieno , Islam Md. Yachin , Hossain Zakaria
Published: April 2024

In the global construction industry, areas characterized by weak and expansive soils are on the rise, necessitating effective solutions for strength enhancement. Addressing this concern, sustainable soil amendments have gained attention, with rice husk ash (RHA) from rice milling industries being a notable focus. Our experimental study aimed to assess the shear strength of this innovative construction material, introducing a unique approach that considers subgrade layers with minimal cement dosage, including upper, bottom, and double layers a novel contribution yet unexplored in existing literature. In addition to conventional mechanical testing, we employed SEM (Scanning Electron Microscopy) and EDS (Energy-Dispersive X-ray Spectroscopy) analyses to comprehensively explore the treated soils' microstructural and elemental composition aspects. Examining sixteen specimen combinations of weak expansive soil-RHA-cement, varying proportions of RHA (2%, 4%, 6%) and cement (2%, 4%, 6%) were mixed to understand their effects on shear strength parameters. Our findings revealed significant shear strength improvement in each subgrade layer, with specimen 6%RHA6%C in the lower subgrade layer exhibiting the highest cohesive strength at 143 kN/m2. Notably, the double layer configuration, specimen 2%RHA6%C, achieved maximum deviatoric stresses of 383 kN/m2. This novel construction material contributes to effective waste management and presents an innovative engineering solution for sustainable ground improvement, offering promising prospects for future geotechnical advancements.
A Comparison of Suicides in Public Safety Personnel With Suicides in the General Population in Ontario, 2014 to 2018
Authors: Hatcher Simon , Sinyor Mark , Edgar Nicole E. , Schaffer Ayal , MacLean Sarah E. , Carleton R. Nicholas , Colman Ian , Jayakumar Navitha , Ward Brooklyn , Zaheer Rabia
Published: April 2024

Abstract: Background: There is conflicting evidence on the suicide rates of different public safety personnel (PSP). There have been few studies that compare suicides in PSP with the general population and none that have used a detailed comparison of coroner records. Aims: The current study estimates suicide rates among different PSP and compares PSP suicides with the general population. Method: We identified coroner records of PSP suicides from January 2014 to December 2018 and compared each one to two matched general population controls. Results: We identified 36 PSP suicides and 72 general population controls. Police had a higher suicide rate than other PSP groups. PSP were more likely to die by firearm, be separated/divorced or married, die in a motor vehicle, have problems at work, and have a PTSD diagnosis. PSP were less likely to die by jumping. Limitations: The study may have not identified all PSP suicides. Apart from the cause of death, data in coroner records are not systematically collected, so information may be incomplete. Conclusion: PSP suicides appear different than the general population. Death records need to have an occupation identifier to enable monitoring of trends in occupational groups, such as PSP.
Hydrological Projections in the Third Pole Using Artificial Intelligence and an Observation‐Constrained Cryosphere‐Hydrology Model
Authors: Long Junshui , Wang Lei , Chen Deliang , Li Ning , Zhou Jing , Li Xiuping , Guo Xiaoyu , Liu Hu , Chai Chenhao , Fan Xinfeng
Published: April 2024

The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact the water and food security of millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged the long short‐term memory model (LSTM) to craft a grid‐scale artificial intelligence (AI) model named LSTM‐grid. This model has enabled the production of hydrological projections for the seven major river basins of TP. The LSTM‐grid model integrates monthly precipitation, air temperature, and total glacier mass changes (total_GMC) data at a 0.25‐degree model grid. Training the LSTM‐grid model employed gridded historical monthly runoff and evapotranspiration data sets generated by an observation‐constrained cryosphere‐hydrology model at the headwaters of seven TP river basins during 2000–2017. Our results demonstrate the LSTM grid's effectiveness and usefulness, exhibiting a Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 during the verification periods (2013–2017). Moreover, river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies region. Intra‐annual projections indicated notable increases in spring runoff, especially in basins where glacier meltwater significantly contributes to runoff. Additionally, the LSTM‐grid model aptly captures the runoff changes before and after the turning points of glacier melting, highlighting the growing influence of precipitation on runoff after reaching the maximum total_GMC. Therefore, the LSTM‐grid model offers a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions by tapping into AI's potential to drive scientific discovery and provide reliable data.
Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model
Authors: Gowtam V. Sai , Connor Hyunju , Kunduri Bharat S. R. , Raeder Joachim , Laundal Karl M. , Tulasi Ram S. , Ozturk Dogacan S. , Hampton Donald , Chakraborty Shibaji , Owolabi Charles , Keesee Amy
Published: April 2024

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML‐AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
Metasurface array for single-shot spectroscopic ellipsometry
Authors: Wen Shun , Xue Xinyuan , Wang Shuai , Ni Yibo , Sun Liqun , Yang Yuanmu
Published: April 2024

Spectroscopic ellipsometry is a potent method that is widely adopted for the measurement of thin film thickness and refractive index. Most conventional ellipsometers utilize mechanically rotating polarizers and grating-based spectrometers for spectropolarimetric detection. Here, we demonstrated a compact metasurface array-based spectroscopic ellipsometry system that allows single-shot spectropolarimetric detection and accurate determination of thin film properties without any mechanical movement. The silicon-based metasurface array with a highly anisotropic and diverse spectral response is combined with iterative optimization to reconstruct the full Stokes polarization spectrum of the light reflected by the thin film with high fidelity. Subsequently, the film thickness and refractive index can be determined by fitting the measurement results to a proper material model with high accuracy. Our approach opens up a new pathway towards a compact and robust spectroscopic ellipsometry system for the high throughput measurement of thin film properties.
Exploring the genetic etiology across the continuum of the general psychopathology factor: a Swedish population-based family and twin study
Authors: Liu Yangjun , Lichtenstein Paul , Kotov Roman , Larsson Henrik , D’Onofrio Brian M. , Pettersson Erik
Published: April 2024

Psychiatric comorbidity can be accounted for by a latent general psychopathology factor (p factor), which quantifies the variance that is shared to varying degrees by every dimension of psychopathology. It is unclear whether the entire continuum of the p factor shares the same genetic origin. We investigated whether mild, moderate, and extreme elevations on the p factor shared the same genetic etiology by, first, examining the linearity of the association between p factors across siblings (N = 580,891 pairs). Second, we estimated the group heritability in a twin sample (N = 17,170 pairs), which involves testing whether the same genetic variants influence both extreme and normal variations in the p factor. In both samples, the p factor was based on 10 register-based psychiatric diagnoses. Results showed that the association between siblings’ p factors appeared linear, even into the extreme range. Likewise, the twin group heritabilities ranged from 0.42 to 0.45 (95% CI: 0.33–0.57) depending on the thresholds defining the probands (2–3.33 SD beyond the mean; >2 SD beyond the mean; >4.33 SD beyond the mean; and >5.33 SD beyond the mean), and these estimates were highly similar to the estimated individual differences heritability (0.41, 95% CI: 0.39–0.43), indicating that scores above and below these thresholds shared a common genetic origin. Together, these results suggest that the entire continuum of the p factor shares the same genetic origin, with common genetic variants likely playing an important role. This implies, first, genetic risk factors for the aspect that is shared between all forms of psychopathology (i.e., genetic risk factors for the p factor) might be generalizable between population-based cohorts with a higher prevalence of milder cases, and clinical samples with a preponderance of more severe cases. Second, prioritizing low-cost genome-wide association studies capable of identifying common genetic variants, rather than expensive whole genome sequencing that can identify rare variants, may increase the efficiency when studying the genetic architecture of the p factor.
Sorafenib inhibits ossification of the posterior longitudinal ligament by blocking LOXL2-mediated vascularization
Authors: Wang Longqing , Jiang Wenhao , Zhao Siyuan , Xie Dong , Chen Qing , Zhao Qi , Wu Hao , Luo Jian , Yang Lili
Published: April 2024

Ossification of the Posterior Longitudinal Ligament (OPLL) is a degenerative hyperostosis disease characterized by the transformation of the soft and elastic vertebral ligament into bone, resulting in limited spinal mobility and nerve compression. Employing both bulk and single-cell RNA sequencing, we elucidate the molecular characteristics, cellular components, and their evolution during the OPLL process at a single-cell resolution, and validate these findings in clinical samples. This study also uncovers the capability of ligament stem cells to exhibit endothelial cell-like phenotypes in vitro and in vivo. Notably, our study identifies LOXL2 as a key regulator in this process. Through gain-and loss-of-function studies, we elucidate the role of LOXL2 in the endothelial-like differentiation of ligament cells. It acts via the HIF1A pathway, promoting the secretion of downstream VEGFA and PDGF-BB. This function is not related to the enzymatic activity of LOXL2. Furthermore, we identify sorafenib, a broad-spectrum tyrosine kinase inhibitor, as an effective suppressor of LOXL2-mediated vascular morphogenesis. By disrupting the coupling between vascularization and osteogenesis, sorafenib demonstrates significant inhibition of OPLL progression in both BMP-induced and enpp1 deficiency-induced animal models while having no discernible effect on normal bone mass. These findings underscore the potential of sorafenib as a therapeutic intervention for OPLL.
Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer
Authors: Alexander John , Schipper Koen , Nash Sarah , Brough Rachel , Kemp Harriet , Iacovacci Jacopo , Isacke Clare , Natrajan Rachael , Sawyer Elinor , Lord Christopher J. , Haider Syed
Published: April 2024

Background

Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited.

Methods

Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48).

Results and conclusions

mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.