bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of antibacterial silicone rubber catheter modified with the biomass carbon dots and its properties
Authors: Liu Jing , Shao Ziyang , Liu Liyuan , Zhao Qinke , Yang Yongzhen , Niu Mei , Zhang Li , Xue Baoxia
Published: December 2024

To endow silicone rubber (SR) catheter with antibacterial property, the SR catheter was modified with a new kind of biomass carbon dots (CDs) by the bulk modification to obtabin the SR/CDs catheter. The antimicrobial behavior and biocompatibility of SR/CDs catheter were analyzed by plate counting method, cytotoxicity test and in vivo animal experiments. The results showed that, SR/CDs catheter possessed antimicrobial properties, and the minimum inhibitory concentration of SR/CDs catheter was 20 mg ml−1 against Escherichia coli and Staphylococcus aureus. The antimicrobial mechanism of SR/CDs was further investigated, and it was found that the SR/CDs induced the production of reactive oxygen species in bacterial cells by disrupting the bacterial membrane through adsorption. In addition, in vivo experiments have shown that SR/CDs catheter owns good biosafety profile and reduces the risk of catheter-associated urinary tract infections by modulating inflammatory factors. Meanwhile, SR/CDs catheter can be produced in a simple production process using an extruder, which is expected to be used as a novelty type of catheter in the clinic.
A Dean–Kawasaki equation for reaction diffusion systems driven by Poisson noise
Authors: Spinney Richard E , Morris Richard G
Published: December 2024

We derive a stochastic partial differential equation that describes the fluctuating behaviour of reaction-diffusion systems of N particles, undergoing Markovian, unary reactions. This generalises the work of Dean (1996 J. Phys. A: Math. and Gen.
29 L613) through the inclusion of random Poisson fields. Our approach is based on weak interactions, which has the dual benefit that the resulting equations asymptotically converge (in the

N
→
∞

limit) on a variation of a McKean–Vlasov diffusion, whilst still being related to the case of Dean-like strong interactions via a trivial rescaling. Various examples are presented, alongside a discussion of possible extensions to more complicated reaction schemes.
Hamiltonian approach to delay ordinary differential equations
Authors: Dorodnitsyn Vladimir , Kozlov Roman , Meleshko Sergey
Published: December 2024

The paper suggests a Hamiltonian formulation for delay ordinary differential equations (DODEs). Such equations are related to DODEs with a Lagrangian formulation via a delay analog of the Legendre transformation. The Hamiltonian delay operator identity is established. It states the relationship for the invariance of a delay Hamiltonian functional, appropriate delay variational equations, and their conserved quantities. The identity is used to formulate a Noether-type theorem, which provides first integrals for Hamiltonian DODEs with symmetries. The relationship between the invariance of the delay Hamiltonian functional and the invariance of the delay variational equations is also examined. Several examples illustrate the theoretical results.
Growth, Spawning, and Management Patterns of Female Euthynnus affinis (Cantor, 1849)) Landed in Kedonganan, Bali
Authors: Arnenda Gussasta Levi , Wiratmini Ngurah Intan , Made Sara Wijana I
Published: December 2024

Euthynnus affinis (Cantor, 1849)) is a type of tuna that lives in a tropical environment, with a neritic climate spread across the Indian and Pacific Oceans, and has exceeded the catch limit. So, the spawning time must be known to show a quantitative index that shows the condition of fish gonad maturity, often called the gonad maturity index. The spawning season or time occurs when gonad maturity index values in both sexes reach the highest level. A total of 235 fish samples were used, with 73 samples of male gonads and 109 samples of female gonads, and 53 samples were not identified by sex. The biological sampling protocol used a sub-sampling system with a purposive stratified sampling method. Data was collected by direct observation at the Kedonganan Fish Landing Base in 2020. The results showed that the female komo tuna’s growth pattern has a positive allometric pattern with a 26.5-58 cm FL length distribution. The sex ratio is balanced at gonad maturity. The spawning of komo tuna has an asynchronous type and partial spawner nature, spawning at night and spawning throughout the year with peak spawning seasons in August and December.
Preparation of Nanoparticle Using Mangosteen Extract (Garcinia mangostana Linn) and Its Application Toward Pickle Goat Skin
Authors: Rohaeti Eli , Ariyanti Nur Aeni , Budiasih Kun Sri , Saputri Febri , Permatasari Asri
Published: December 2024

Silver nanoparticles become one of the fields of science that are widely used in everyday life. Silver nanoparticles are also widely used in leather modifications to produce leather with superior quality. The purpose of this study was to determine the effect of silver nanoparticle prepared with mangosteen peel extract (Garcinia mangostana Linn) and MTMS solution on antimicrobial activity, hydrophobicity, and mechanical properties of composite of pickle goat skin – silver nanoparticles. Characterization of silver nanoparticles by UV Vis and PSA. Based on the results of UV Vis characterization, nanoparticles were formed at a wavelength of 439.6 nm. Based on characterization by PSA, the diameter of silver nanoparticles was about 98.10 nm. The pickle goat skin after modification with nanoparticle in situ had good inhibition against E. coli and S. epidermidis. However, the goat skin before modification had good inhibition against C. albicans at the 24th hour measurement.
Investigating the spatiotemporal distribution of refractivity structure index in low-speed flow field by moiré deflectometry
Authors: Jiang Jian , Chen Yunyun , Cheng Weihao , Cao Zimu , Yang Cuihong
Published: December 2024

Atmospheric optical turbulence is a significant restraint on free-space optical communication and atmospheric laser transmission, whose intensity is generally quantified by the refractivity structure index. In this paper, moiré deflectometry is applied to study the combined effects of thermal disturbance and flow speed on the spatiotemporal distribution of the refractivity structure index. Firstly, three sets of comparative experiments are conducted, and 3600 frames of moiré fringes are captured within an hour in each set. Then, the spatiotemporal distribution is obtained based on the theoretical relationship between the refractivity structure index and the phase information recorded by moiré fringes. Finally, its regularities under different experimental conditions are comparatively analyzed. The findings reveal that both thermal disturbance and flow speed will change the intensity of optical turbulence. And, although the latter has a much weaker impact than the former, their combined effects will increase the intensity by three orders of magnitude. In this process, the contribution of thermal disturbance accounts for more than 75%. In short, the related results could provide some insights for research on atmospheric optical detection and communication.
Cyclic solid-state quantum battery: thermodynamic characterization and quantum hardware simulation
Authors: Razzoli Luca , Gemme Giulia , Khomchenko Ilia , Sassetti Maura , Ouerdane Henni , Ferraro Dario , Benenti Giuliano
Published: December 2024

We introduce a cyclic quantum battery QB model, based on an interacting bipartite system, weakly coupled to a thermal bath. The working cycle of the battery consists of four strokes: system thermalization, disconnection of subsystems, ergotropy extraction, and reconnection. The thermal bath acts as a charger in the thermalization stroke, while ergotropy extraction is possible because the ensuing thermal state is no longer passive after the disconnection stroke. Focusing on the case of two interacting qubits, we show that phase coherence, in the presence of non-trivial correlations between the qubits, can be exploited to reach working regimes with efficiency higher than 50% while providing finite ergotropy. Our protocol is illustrated through a simple and feasible circuit model of a cyclic superconducting QB. Furthermore, we simulate the considered cycle on superconducting IBM quantum machines. The good agreement between the theoretical and simulated results strongly suggests that our scheme for cyclic QBs can be successfully realized in superconducting quantum hardware.
Predicting transient response using data-driven models for ball-impact simulations
Authors: Pivovar Ross , Chen Fei , Katragadda Raghunath , Ananthan Vidyasagar
Published: December 2024

This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. We focus on the canonical problem of ball impact on laminated structures, which captures essential physics while maintaining computational tractability. Novel contributions include: (1) development of a temporal multi-resolution strategy for stable long-time predictions, (2) systematic comparison of U-Nets and Fourier Neural Operators as spatial ML kernels, and (3) demonstration of accurate non-local metric predictions across full time-horizons. Using a synthetic dataset of 6500 impact scenarios, we achieve 3.5–8% prediction accuracy while providing 10,000x speedup compared to traditional FEM simulations. The proposed methodology enables rapid virtual prototyping for impact-resistant design optimization.
Investigating resource productivity: the ecological potential of printed electronics as alternative to PCB
Authors: van Diemen Marieke , Brusselaers Jan , Rentrop Corné
Published: December 2024

Printed electronics are gaining momentum as an alternative to etched electronics. Production of printed electronics requires less energy, water and raw materials and its flexible, transparent, stretchable and breathable potential allows for numerous new applications. While still in its infancy, printed electronics have the potential to become a permanent low-cost and efficient substitute for conventional production methods. However, despite promising assets of the printed electronics technology, few quantitative environmental analysis have been performed comparing printed electronics to the conventional printed circuit board (PCB). To fill this gap in the literature, this research evaluates and compares PCB circuitry production to that of a common printing technology; screen printing. A combination of a resource productivity and pollution intensity evaluation and a material input and output analysis is performed. Performance is evaluated by gathering data from existing literature, expert consultancy and own calculations on production data and product weight. Printed electronics were evaluated according to a worst-case scenario and PCB to a best-case scenario in terms of resource consumption. The findings reveal substantial disparities in resource productivity and pollution intensity between printed electronics and PCB. Printed electronics demonstrate superiority across all indicator ratios in comparison to PCB as a consequence of - for example - a zero-water production process, or a printed solution which is 16.14 times lighter in terms of weight. This research thereby contributes to informing the electronics sector about a promising alternative to PCB from the perspective of resource efficiency and pollution intensity. As such, our research results provide a lever for future research investigating the scalability and profitability of the implementation of this technology.
Post-planting management enhanced the survival and growth of Acacia decurrens in the northern Ethiopia drylands
Authors: Hagazi Niguse , Brhan Abrha , Birhane Emiru , Gebrekirstos Aster , Bräuning Achim
Published: December 2024

The survival and growth performance of tree seedlings in degraded drylands can be improved by applying moisture and fertility-enhancing management interventions. This study evaluated the survival and growth responses of Acacia decurrens seedlings to different management practices, pit sizes, and niches in Tigray, Ethiopia. A factorial experiment was conducted with Watering (W), Watering + Mulching (WM), and Watering + Mulching + Compost (WMC) treatments planted in 30*30 cm and 50*50 cm planting pit sizes arranged in a randomized complete block design with three replications. We measured the survival count, diameter, and height of planted seedlings on a monthly basis for two years. Root parameters, biomass, and relative growth rate measurements were undertaken using destructive sampling after two years of planting. Planting A. decurrens under WMC treatment and in a 50*50 cm pit size revealed better survival and growth performance of planted seedlings. A. decurrens seedlings planted on farmland had significantly higher survival rates compared to planting on a nearby sloppy land (ch2 = 8.33, p = 0.004). The WMC treatment yielded impressive results after two years, with seedlings reaching 7.6 m in height, 7.6 cm in diameter, 17.2 kg in dry biomass, and 1.5 m in root depth. The height relative growth rate of A. decurrens under the WMC treatment increased by 19% compared to the other treatments, with WM showed a 7% increase, and both W and C revealed a 5% increase each. Planting seedlings on farmland and adding WMC makes them grow faster and yields higher biomass. Therefore, applying moisture, mulching, and compost during and after planting ensures optimal survival and growth rates of A. decurrens and other trees in drylands.
Realizing linear synaptic plasticity in electric double layer-gated transistors for improved predictive accuracy and efficiency in neuromorphic computing
Authors: Manimaran Nithil Harris , Sutton Cori Lee Mathew , Streamer Jake W , Merkel Cory , Xu Ke
Published: December 2024

Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (i.e. weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear weight updates observed experimentally in previous studies. By solving modified Poisson–Nernst–Planck equations, we examined ion dynamics within an EDL capacitor and their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation and increased dissipation rates, leading to nonlinear weight updates. For a pulse magnitude of 1 V, both 1 ms 500 Hz and 5 ms 100 Hz pulse inputs saturated at less than half of the steady state EDL concentration, limiting the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the Modified National Institute of Standards and Technology classification task. The ANN with LIWUS-provided linear weight updates required 19% fewer (i.e. 5) epochs in both training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5–4.2% higher than with nonlinear updates, and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications, and the performance with linear weight updates is expected to improve for complex networks with multiple hidden layers.
Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska
Authors: Yang Dedi , Hantson Wouter , Hayes Daniel J , Wu Jin , Serbin Shawn P
Published: December 2024

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (∼7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.
Organized Crime and Illicit Firearms in Europe
Authors: Grip Lina
Published: December 2024

Criminal groups are widely recognized as the primary force behind the illicit firearms market in Europe. Firearms are used to protect and advance the interests of organized criminal groups (OCGs), particularly in the drug trade. Based on open-source data, this chapter provides an overview of the current state of knowledge regarding the nexus of organized crime and illicit firearms in Europe. It finds that there are large differences in the effects of the firearms and OCGs’ nexus, with most countries having experienced positive developments—a reduction of violence—since the 1990s. The chapter details the types of firearms and trafficking modus operandi used by OCGs. Previously stable for twenty years, due to weapons from the Balkans and former communist countries, there has been a shift in recent years to modified pistols and a rapid emergence of 3D-printed weapons. This shift is lowering the bar for accessing weapons, resulting in increased gang violence, most notably in Sweden.
Illicit Firearms, Organized Crime, and Illegal Economies in South-east Asia
Authors: Picard Michael E
Published: December 2024

South-east Asia is host to a broad range of illicit economies as well as a dynamic and varied stockpile of illicit firearms. Yet little research has been carried out examining how illicit firearms enable organized crime and associated illicit economies. This chapter examines a set of recent firearms seizures and trafficking incidents as well as existing literature on illicit economies in South-east Asia to illuminate this interplay. It provides ‘snapshots’ of illicit firearms in the region’s ten states based on the case data. It also provides a brief scoping of the basic elements—the who, what, when, where, and how—of illicit firearms in South-east Asia, with a specific focus on organized crime. These basic elements help frame a deeper analysis of the interplay of illicit firearms, organized crime, and illicit economies in the region.
Illicit Firearms Markets and Organized Crime in Eastern Europe
Authors: Chlebowicz Piotr
Published: December 2024

The chapter attempts to identify the most important issues relating to illicit firearms trade in Eastern Europe. Phenomena such as drug-related crime, human trafficking, money laundering, and illicit firearms trade were new to this part of Europe, expanding in the 1990s. The mechanisms of arms trafficking in Eastern Europe in the 1990s were based on fierce competition in the criminal world, which rapidly underwent the processes of professionalization and brutalization. Organized criminal groups created a demand for weapons and ensured supply by organizing smuggling, among other activities. Nonetheless, illicit firearms trafficking is not the main focus of organized criminal groups because the trade in illicit firearms does not generate high income, especially when compared to drug or human trafficking. The scarce available data allowed us to conclude that organized criminal groups have firearms at their disposal but have not created illegal arsenals. In Eastern Europe, excavation of World War I and World War II battlefields is a specific source of illicit weapons. The chapter also deals with the war in Ukraine, a by-product of which is a potentially large firearms black market. In the future, it may constitute a factor that will continue to destabilize public order and security in the region. It will be necessary to develop strategies to fight the illicit trade in firearms originating in Ukraine, with a particular focus on combatting the smuggling of illicit firearms.
Illicit Firearms Markets Accessible Through the Dark Web
Authors: Décary-Hétu David , Chainé Arielle , Blais Étienne
Published: December 2024

This chapter explores the illicit firearms market on the dark web. It begins with an overview of firearms categorization, emphasizing the importance of components like barrels and triggers in firearms identification. The chapter also reviews the literature on illicit firearms acquisition, noting the shift towards online channels during the Covid-19 pandemic. The dark web’s anonymity and its inherent challenges to regulation are highlighted as factors that enable international firearms trafficking. The chapter presents a study that indexes firearms markets on the dark web, revealing a variety of products, including handguns, long guns, and ammunition. The findings suggest that the dark web firearms market is small and stagnant, with prices and availability remaining relatively unchanged over time. The dark web is presented as a niche platform for illicit firearms transactions, with parcel mail being a common method of delivery. The chapter concludes with policy implications, suggesting that the dark web may not be the primary focus for enforcement agencies, and emphasizes the need for more parcel inspections to intercept illegal shipments.
Introduction
Authors: Parasidis Efthimios
Published: December 2024

This chapter describes how the quest for military superiority is often intertwined with a drive for scientific superiority. It explains how war alters the moral landscape of how we think about science, ethics, law, and society. Just as war is an engine of innovation that propels scientific advancements, war also transcends conventional boundaries, blurring the line between moral and immoral, ethical and unethical, legal and illegal. The chapter reflects on the history of war and science and offers proposals that seek to better harmonize contemporary scientific and national security concerns with fundamental principles of justice, fairness, and human dignity.
Recalibrating Regulatory Review and Strengthening the Medical Autonomy of Service Members
Authors: Parasidis Efthimios
Published: December 2024

This chapter applies the doctrine of jus in praeparatione bellum—introduced in the previous chapter—and proposes amendments to the regulatory regime governing medical countermeasure. It advocates for bolstering data requirements within the FDA’s Emergency Use Authorization and Animal Rule, and recalibrating legal shields available to pharmaceutical companies to permit lawsuits if a company fails to exercise reasonable care during countermeasure research and development. It proposes that products approved via the FDA’s expedited mechanisms be deemed “conditionally approved” until the FDA’s standard protocols for safety and efficacy are satisfied. For conditionally approved products, the chapter contends that service members should maintain the autonomy to refuse administration, and this decision should be private health information that does not impact a service member’s career or duty assignments. The chapter further advocates for revocation of the informed consent waiver rule, which allows the president to waive informed consent in military exigencies.
Associations of nonoccupational sedentary behaviors with cardiometabolic outcomes: coronary artery risk development in young adults (CARDIA)
Authors: Zieff Gabriel , Bancks Michael P , Gabriel Kelley Pettee , Barone Gibbs Bethany , Moore Justin B , Reis Jared P , Stone Keeron , Stoner Lee
Published: December 2024

Background

The association between sedentary behavior (SB) and cardiometabolic risk may differ by SB domain and context. Nonoccupational SB is particularly important because it is discretionary and more amenable to change. This study estimated associations of nonoccupational SB contexts with hypertension (HTN) and diabetes mellitus (DM).

Methods

A total of 3370 middle-aged adults (50.1 ± 3.6 years; 56% F) from the Coronary Artery Risk Development in Young Adults (CARDIA) study were included. Cross-sectional and 5-year prospective associations between self-report total SB and 6 context-specific SBs (television—TV, computer, transportation, phone, music, and paperwork) with HTN and DM were tested using logistic regression. Fully adjusted models controlled for sociodemographic variables, body mass index, and self-report moderate-vigorous intensity physical activity.

Results

Prevalences of HTN and DM at baseline were 48% (1618 cases) and 10% (320 cases), respectively. Each hour per day of total-SB was cross-sectionally associated with HTN (OR: 1.03, 95% CI, 1.01-1.05) but not DM, with nonsignificant prospective associations for HTN and DM. Of the context-specific SBs, only TV-SB was significantly associated with HTN or DM. Each hour of TV-SB was cross-sectionally associated with HTN (OR: 1.09, 95% CI, 1.03-1.15) and DM (OR: 1.18, 95% CI, 1.09-1.29), and prospectively with HTN (OR: 1.14, 95% CI, 1.04-1.26) but not DM.

Conclusion

When comparing total-SB and the 6 context-specific SBs, TV-SB was most robustly associated with HTN. The findings were less clear for DM. Behavior change strategies that target TV-SB reduction may be effective at reducing HTN risk in middle-aged adults.
Assessing alcohol consumption across phosphatidylethanol levels using HDL-cholesterol as a predictor
Authors: Årving Alexander , Hilberg Thor , Vigerust Elisabeth Wiik , Jørgenrud Benedicte , Bogstrand Stig Tore , Mørland Jørg , Høiseth Gudrun
Published: December 2024

Aims

Prior research has established a correlation between increases of High-Density Lipoprotein Cholesterol (HDL-C) levels and alcohol consumption. This study aimed to explore the association between phosphatidylethanol (PEth) levels and the amount of consumed ethanol, utilizing HDL-C as a surrogate marker on a population level. This endeavor offers an adjunct to other studies.

Methods

PEth and HDL-C levels in 50 751 samples from 29 899 patients in Norway were measured simultaneously in whole blood and serum, respectively. Linear mixed model analyses were employed to assess HDL-C levels within different PEth intervals. Drawing on previous research indicating an increase of .0035 mmol/L in HDL-C per gram of pure ethanol consumed per day, and assuming no alcohol intake in the zero PEth group, we estimated mean daily ethanol intake at the group level for males in each PEth interval.

Results

Results revealed a significant correlation between PEth and HDL-C levels (Spearman’s rho = .385 for women, .420 for men, P < .001). Estimated mean HDL-C levels indicated higher alcohol consumption with increasing PEth. Specifically, men with PEth values in the .031–0.100 μmol/L (22–70 ng/ml) interval were estimated to consume approximately mean 20 grams of ethanol daily, while those in the .301–0.500 μmol/L (212–351 ng/ml) PEth interval had an estimated mean daily ethanol intake of 51 grams.

Conclusions

The results from this study suggest an approximate estimation of mean daily amounts of consumed ethanol at group levels in different PEth intervals, based on previously shown correlation of ethanol consumption and HDL-C increase.
The Ubiquity of Cognitive Impairment in Human Illness: a Systematic Review of Meta-Analyses
Authors: Schretlen David J , Finley John-Christopher A , Del Bene Victor A , Varvaris Mark
Published: December 2024

Objective

Cognitive dysfunction occurs in many neurological, psychiatric, and other health conditions. This review aimed to characterize the breadth and degree of cognitive morbidity associated with varied health conditions.

Method

We systematically reviewed Medline, EMBASE, and Cochrane databases for meta-analyses of cognitive dysfunction associated with any health condition. Meta-analyses were eligible if they reviewed studies that compared patients with health conditions to healthy controls on cognitive testing and provided effect sizes.

Results

We found 91 meta-analyses for 94 health conditions. Among >800,297 participants, healthy controls out-performed clinical participants in every condition on cognitive testing. Mean effect sizes ranged from −2.02 to −0.00 across conditions and were ≤ −0.5 on average, denoting moderate to very severe dysfunction for 41% of them.

Conclusions

Cognitive dysfunction is ubiquitous in medicine. Both primary care and specialist physicians likely treat patients with cognitive dysfunction. Depending on its severity, cognitive dysfunction can affect treatment adherence, everyday functioning, quality of life, and the capacity to provide informed consent for treatment. These findings highlight the transdiagnostic nature of cognitive symptoms and the potential value of establishing collaborations between physicians and clinical neuropsychologists to integrate cognitive assessment into patient care. Even brief assessments can identify cognitive deficits that likely affect treatment adherence and functional outcomes.
Detection of germline CNVs from gene panel data: benchmarking the state of the art
Authors: Munté Elisabet , Roca Carla , Del Valle Jesús , Feliubadaló Lidia , Pineda Marta , Gel Bernat , Castellanos Elisabeth , Rivera Barbara , Cordero David , Moreno Víctor , Lázaro Conxi , Moreno-Cabrera José Marcos
Published: December 2024

Germline copy number variants (CNVs) play a significant role in hereditary diseases. However, the accurate detection of CNVs from targeted next-generation sequencing (NGS) gene panel data remains a challenging task. Several tools for calling CNVs within this context have been published to date, but the available benchmarks suffer from limitations, including testing on simulated data, testing on small datasets, and testing a small subset of published tools. In this work, we conducted a comprehensive benchmarking of 12 tools (Atlas-CNV, ClearCNV, ClinCNV, CNVkit, Cobalt, CODEX2, CoNVaDING, DECoN, ExomeDepth, GATK-gCNV, panelcn.MOPS, VisCap) on four validated gene panel datasets using their default parameters. We also assessed the impact of modifying 107 tool parameters and identified 13 parameter values that we suggest using to improve the tool F1 score. A total of 66 tool pair combinations were also evaluated to produce better meta-callers. Furthermore, we developed CNVbenchmarker2, a framework to help users perform their own evaluations. Our results indicated that in terms of F1 score, ClinCNV and GATK-gCNV were the best CNV callers. Regarding sensitivity, GATK-gCNV also exhibited particularly high performance. The results presented here provide an evaluation of the current state of the art in germline CNV detection from gene panel data and can be used as a reference resource when using any of the tools.
Integrating genetic algorithms and language models for enhanced enzyme design
Authors: Nana Teukam Yves Gaetan , Zipoli Federico , Laino Teodoro , Criscuolo Emanuele , Grisoni Francesca , Manica Matteo
Published: December 2024

Enzymes are molecular machines optimized by nature to allow otherwise impossible chemical processes to occur. Their design is a challenging task due to the complexity of the protein space and the intricate relationships between sequence, structure, and function. Recently, large language models (LLMs) have emerged as powerful tools for modeling and analyzing biological sequences, but their application to protein design is limited by the high cardinality of the protein space. This study introduces a framework that combines LLMs with genetic algorithms (GAs) to optimize enzymes. LLMs are trained on a large dataset of protein sequences to learn relationships between amino acid residues linked to structure and function. This knowledge is then leveraged by GAs to efficiently search for sequences with improved catalytic performance. We focused on two optimization tasks: improving the feasibility of biochemical reactions and increasing their turnover rate. Systematic evaluations on 105 biocatalytic reactions demonstrated that the LLM–GA framework generated mutants outperforming the wild-type enzymes in terms of feasibility in 90% of the instances. Further in-depth evaluation of seven reactions reveals the power of this methodology to make “the best of both worlds” and create mutants with structural features and flexibility comparable with the wild types. Our approach advances the state-of-the-art computational design of biocatalysts, ultimately opening opportunities for more sustainable chemical processes.
An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables
Authors: Maleyeff Lara , Golchi Shirin , Moodie Erica E M , Hudson Marie
Published: December 2024

Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.
Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals
Authors: Deng Linsui , He Kejun , Zhang Xianyang
Published: December 2024

In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples include mediation analysis, which simultaneously examines the existence of the exposure-mediator and the mediator-outcome effects, and replicability analysis, aiming to identify simultaneous signals that exhibit statistical significance across multiple independent studies. In this work, we present a new approach called the joint mirror (JM) procedure that effectively detects such features while maintaining false discovery rate (FDR) control in finite samples. The JM procedure employs an iterative method that gradually shrinks the rejection region based on progressively revealed information until a conservative estimate of the false discovery proportion is below the target FDR level. Additionally, we introduce a more stringent error measure known as the composite FDR (cFDR), which assigns weights to each false discovery based on its number of null components. We use the leave-one-out technique to prove that the JM procedure controls the cFDR in finite samples. To implement the JM procedure, we propose an efficient algorithm that can incorporate partial ordering information. Through extensive simulations, we show that our procedure effectively controls the cFDR and enhances statistical power across various scenarios, including the case that test statistics are dependent across the features. Finally, we showcase the utility of our method by applying it to real-world mediation and replicability analyses.
A Bayesian framework for causal analysis of recurrent events with timing misalignment
Authors: Oganisian Arman , Girard Anthony , Steingrimsson Jon A , Moyo Patience
Published: December 2024

Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under 2 treatments within a defined target population over a specified follow-up window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of eligibility criteria, treatment is rarely observed exactly at the time of eligibility. Ad hoc solutions to this timing misalignment can induce bias by incorrectly attributing prior event counts and person-time to treatment. Even if eligibility and treatment are aligned, a terminal event process (eg, death) often stops the recurrent event process of interest. In practice, both processes can be censored so that events are not observed over the entire follow-up window. Our approach addresses misalignment by casting it as a time-varying treatment problem: some patients are on treatment at eligibility while others are off treatment but may switch to treatment at a specified time—if they survive long enough. We define and identify an average causal effect estimand under right-censoring. Estimation is done using a g-computation procedure with a joint semiparametric Bayesian model for the death and recurrent event processes. We apply the method to contrast hospitalization rates among patients with different opioid treatments using Medicare insurance claims data.
Could AI safeguard us from AI?
Authors: Sheybani Laurent
Published: December 2024

Associate Editor Laurent Sheybani discusses how artificial intelligence (AI) could be both a risk and a solution for statistical reliability in scientific research in the context of the European Union AI act, which came into force in the summer of 2024.
Dermal mass with an annular figurate pattern in a patient with leukaemia
Authors: Yoo Li Jie Helena , Hennessy Grace , O’Brien Odharnaith , Sargent Jeremy , Quinn John , Ní Raghallaigh Siona
Published: December 2024

Our patient highlights the cutaneous manifestations and histopathological features of a rare form of leukaemia. To the best of our knowledge, this is the first reported case of a patient with this form of leukaemia presenting with a dermal mass in an annular figurate pattern.
Mediodorsal thalamus nucleus–medial prefrontal cortex circuitry regulates cost–benefit decision-making selections
Authors: Ding Tong-hao , Hu Yu-ying , Li Jia-wen , Sun Chong , Ma Chao-lin
Published: December 2024

Value-based decision-making involves weighing costs and benefits. The activity of the medial prefrontal cortex reflects cost–benefit assessments, and the mediodorsal thalamus, reciprocally connected with the medial prefrontal cortex, has increasingly been recognized as an active partner in decision-making. However, the specific role of the interaction between the mediodorsal thalamus and the medial prefrontal cortex in regulating the neuronal activity underlying how costs and benefits influence decision-making remains largely unexplored. We investigated this by training the rats to perform a self-determined decision-making task, where longer nose poke durations resulted in correspondingly larger rewards. Our results showed that the inactivation of either the medial prefrontal cortex or the mediodorsal thalamus significantly impaired rat to invest more nose poke duration for larger rewards. Moreover, optogenetic stimulation of the mediodorsal thalamus–medial prefrontal cortex pathway enhanced rats’ motivation for larger rewards, whereas inhibition of this pathway resulted in decreased motivation. Notably, we identified a specific population of neurons in the medial prefrontal cortex that exhibited firing patterns correlated with motivation, and these neurons were modulated by the mediodorsal thalamus–medial prefrontal cortex projection. These findings suggest that the motivation during decision-making is encoded primarily by activity of particular neurons in the medial prefrontal cortex and indicate the crucial role of the mediodorsal thalamus–medial prefrontal cortex pathway in maintaining motivation.
Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety
Authors: Chen Kexuan , Ma Yingzi , Yang Rui , Li Fang , Li Wei , Chen Jin , Shao Heng , He Chongjun , Chen Meiling , Luo Yuejia , Cheng Bochao , Wang Jiaojian
Published: December 2024

Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.
The relevance of Marshall’s thought today: from methodological eclecticism to his sociological outlook
Authors: Pietrini Filippo
Published: December 2024

This article leverages the reinterpretations of Marshall emerged from the 1970s onwards to elucidate his philosophical vision, notably in relation to the application of models in economics and to his outlook on ethical progress. The literature showed how Marshall’s cognitive theory can be linked both to his method in studying market equilibria and to his view of social and industrial evolution (Dardi, 2016, 2019; Raffaelli et al., 1994b; Raffaelli, 2003). The focus of the present work is on the elements of his philosophical grounding discernible in both the logical connections mentioned above. The contribution to the literature consists of an interpretation of the lecture on American industry in Whitaker, (1975a) as an insight of a sociologist of knowledge ante litteram. Thus the paper broadens, with respect to Dardi, 2016, the field of Whewell’s influence on Alfred Marshall: from the methodology (Whewell’s idea of the dualistic nature of mathematics linked to the Cambridge mathematical intuitionism) to the causal relationship between industry and character and to Marshall’s keen eye for sociological facets. In many respects a contemporary Marshall comes to the fore, proving particularly valuable for those still grappling with epistemological issues in the social sciences.
Global digital networks
Authors: Zook Matthew , Grote Michael
Published: December 2024

The rapid evolution of digital technologies over the past decades has induced profound economic and social transformations. Economic geography faces the ongoing challenge of assimilating these changes into existing theories that elucidate the dynamics of the global economy. In response, we present the Global Digital Networks (GDN) framework, drawing inspiration from established analytical instruments like Global Production Networks (GPN) and Global Financial Networks (GFN). GDN centres on three key economic materialities—people, things, and places—intertwined with territorially grounded practices of data generation and enhancement. We identify four enhancement types—singularisation, association, centralisation and fractionalisation—driving a cyclical process shaping complex networks across territories. Governance structures, encompassing national regulations, platform systems, and firm governance, play a pivotal role. The GDN cycle, exemplified through diverse territorial scenarios, underscores the intricate interplay of data generation, enhancement and governance structures in delineating global economic networks.