bluuebunny/crossref_metadata_2025 huggingface dataset:
Vers une étude du temps dérivé en psychologie et psychopathologie
Authors: Barthélémy Jean-Marie
Published: October 2024

Les entrelacs d’un trajet personnel de praticien, chercheur et enseignant en psychopathologie et psychologie clinique, avec une réflexion prolongée sur les divers registres d’inscription d’une temporalité vécue, qui prirent source aux œuvres et destins croisés d’Eugène Minkowski et Françoise Minkowska, créent l’opportunité d’un regard à la fois introspectif, rétrospectif et prospectif sur les dimensions dialectiques de la méthode phénoméno-structurale dont la dénomination dédoublée porte la marque. La notion de « temps dérivé » que nous proposons comme petite pierre contributive en marge, fil rouge et continuité dans cette publication, tente de définir quelques-uns de ses contours et détours auxquels elle nous a conduit jusqu’ici et des potentialités qu’elle nous semble contenir et détenir, prometteuses d’un essor où des perspectives d’avenir pour la psychiatrie et de la psychopathologie pourraient trouver place respectable en redéfinissant leurs rôles et aspirations.
Quantile via Moments Panel Data Analysis: Dissecting Political Regimes and the Spectrum of Internal Conflicts
Authors: Parsons Brandon , Marie Joseph St. , Pauly Robert
Published: October 2024

This study investigates whether the political regime affects internal conflicts and specific types (e.g., civil war, political violence, and civil disorder). The study reveals that even incremental movements toward democracy are associated with reduced internal conflict, challenging some elements of the inverted U hypothesis. Moreover, factors such as government stability are better indicators of smaller-scale forms of internal conflict than the political regime type. Finally, scenarios of acute internal conflict, cultural tension, and government instability serve as more reliable indicators, implying that these elements eclipse the stabilizing influence of a regime's progression toward a more inclusive government.
In-plane thermal conductivity measurements of Si μ-cantilevers using the suspended thermo-reflectance (STR) technique
Authors: Sarkar Dipta , Singh Gurpreet , Yilbas Bekir S. , Mansoor Saad B. , Al-Qahtani Hussain , Leseman Zayd C.
Published: October 2024

The Suspended Thermoreflectance (STR) technique is described in this paper. This optoelectronic measurement tool performs thermal characterization of freestanding micro-/nanoscale materials. STR performs thermal mapping at the submicron level and produces unconstrained thermal conductivity unlike other optical measurement techniques where independent conductivity measurement is not possible due to their reliance on heat capacity. STR works by changing the temperature of a material and collecting the associated change in light reflection from multiple points on the sample surface. Reflection is a function of the material being tested, the wavelength of the probe light, geometry, and the composition of the specimen for transparent and quasi-transparent materials. In this article, Si μ-cantilevers are studied. In addition, a thermal analytical model is developed and incorporated with optical equations to characterize the conductivity of the Si μ-cantilevers. The analytical model is compared with a finite element model to check its applicability in the STR experiment and data analysis. To validate the technique, the thermal conductivity of 2 and 3 µm thick Si μ-cantilevers was determined using STR at a temperature range of 20–350 K and compared to simulations using the equation of phonon radiative transfer and literature values.
On acoustic solitary waves in a multispecies degenerate relativistic magnetized plasma using physics informed neural networks
Authors: Abdikian Alireza
Published: October 2024

In this paper, we investigate the nonlinear electrostatic wave propagation in a two-dimensional magnetized plasma. The plasma consists of electron and positron components with relativistic degeneracy and stationary ions for neutralizing its background. Using the basic equations for this type of plasma in combination with the reductive perturbation method, we derived the Zakharov–Kuznetsov equation using the Lorentz transformation stretching method (LT). For the first time, we compared the results of the Galilean transformation stretching method (GT) and the LT method to investigate the effect of plasma parameters, such as the relativistic degeneracy parameter of electron particles (re0), the density ratio of ion to electrons (δ), and the normalized electron cyclotron (Ωe), on the amplitude and width of the wave solutions. The plasma parameters used in this research are representative of compact astrophysical objects. Numerical results showed that the amplitude of wave solutions obtained by the LT method is smaller than the GT method, but the width is greater. We provide a physical explanation for these differences. Furthermore, we present a physics-informed neural network (PINN) approach to directly recover the intrinsic nonlinear dynamics from spatiotemporal data. The PINN model uses a deep neural network constrained by the governing equations to learn the optimal parameters, with the aim of enhancing the predictive capabilities of the system. The results of this study provide valuable insight into the propagation of nonlinear waves in white dwarfs, where relativistic effects are significant. These findings could substantially advance the development of emerging machine learning applications in astrophysics.
Shear-driven stability of a rigid particle in yield stress fluids
Authors: Alrashdan Rakan , Khabaz Fardin
Published: October 2024

The stability of a rigid particle in yield stress fluids, comprised of soft particle glasses (SPGs), is investigated in shear flow under an applied external force, such as weight, using particle dynamics simulations. Results provide the critical force threshold, in terms of the dynamic yield stress and the flow strength, required to initiate sedimentation of the rigid particle over a wide range of shear rates and volume fractions. The streamlines of the SPGs show local disturbances when the rigid particle settles. The form of these disturbances is consistent with the microdynamics and microstructure response of the neighboring soft particles of the sedimenting rigid particle. Sedimenting particle induces non-affine displacement to the suspensions at low shear rates and high applied forces, while these dynamical events are localized and suppressed at high shear rates. Stability diagrams, which provide the conditions of the sedimentation of the rigid particle, are presented in terms of the applied force and the shear rate. These individual stability diagrams at each volume fraction map onto a universal stability diagram when the external force is scaled by the dynamic yield stress and shear rate with a ratio of the solvent viscosity to the low-frequency modulus of the SPGs.
Generating sub-diffraction microwave needle beam for nondestructive testing by multifunctional transmissive metasurfaces
Authors: Feng Jialin , Shi Hongyu , Jiao Yunhao , Yi Jianjia , Chen Juan , Chen Xiaoming , Zhang Anxue , Xu Zhuo
Published: October 2024

Sub-diffraction needle beams with high intensity, sub-diffraction focal size, and long depth of focus (DOF) have attracted many researchers’ attention. However, the traditional methods for needle beam generation typically require many devices, such as phase elements, amplitude filters, and lens, which leads to a complex and bulky system and unfavorable for their integration. To address these challenges, we use a single multifunctional transmissive metasurface to convert a linearly polarized plane wave into a needle beam in the microwave range. The guided wave inspired unit cells of the proposed metasurface is designed to simultaneously and independently modulate the polarization and phase of transmitted waves. By imposing the desired polarization and phase distributions on the metasurface, the proposed multifunctional transmissive metasurface can efficiently generate a needle beam with subdiffraction size and extended DOF at 10 GHz when it is illuminated by an x-polarized wave. The proposed metasurface is fabricated, and a sub-diffraction needle beam with good performance is obtained in our measurements. In addition, a proof-of-concept of a high-resolution nondestructive testing experiment based on our designed metasurface is accomplished. Our work is expected to have potential applications in nondestructive testing of materials and structures.
SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences
Authors: Chow Chi Fung Willis , Ghosh Soumyadeep , Hadarovich Anna , Toth-Petroczy Agnes
Published: October 2024

Intrinsically disordered regions (IDRs) are structurally flexible protein segments with regulatory functions in multiple contexts, such as in the assembly of biomolecular condensates. Since IDRs undergo more rapid evolution than ordered regions, identifying homology of such poorly conserved regions remains challenging for state-of-the-art alignment-based methods that rely on position-specific conservation of residues. Thus, systematic functional annotation and evolutionary analysis of IDRs have been limited, despite them comprising ~21% of proteins. To accurately assess homology between unalignable sequences, we developed an alignment-free sequence comparison algorithm, SHARK (Similarity/Homology Assessment by Relating K-mers). We trained SHARK-dive, a machine learning homology classifier, which achieved superior performance to standard alignment-based approaches in assessing evolutionary homology in unalignable sequences. Furthermore, it correctly identified dissimilar but functionally analogous IDRs in IDR-replacement experiments reported in the literature, whereas alignment-based tools were incapable of detecting such functional relationships. SHARK-dive not only predicts functionally similar IDRs at a proteome-wide scale but also identifies cryptic sequence properties and motifs that drive remote homology and analogy, thereby providing interpretable and experimentally verifiable hypotheses of the sequence determinants that underlie such relationships. SHARK-dive acts as an alternative to alignment to facilitate systematic analysis and functional annotation of the unalignable protein universe.
The peptide hormone
Pj
CLE1 stimulates haustorium formation in the parasitic plant
Phtheirospermum japonicum
Authors: Greifenhagen Anne , Ruwe Hannes , Zimmer Victoria , Messerschmidt Jana , Bhukya Durga Prasad Naik , Kenea Hawi Deressa , Schaller Andreas , Spallek Thomas
Published: October 2024

Phtheirospermum japonicum
is a hemiparasitic plant of the Orobanchaceae, the largest family of parasitic plants. It extracts water and nutrients from other plants through haustoria along its roots. Haustoriogenesis, the formation of haustoria, is initiated by host-derived haustorium-inducing factors (HIFs). The first step in haustoriogenesis is the development of parasitically inactive protohaustoria. Here, we report that an endogenous peptide hormone, CLAVATA3/Embryo Surrounding Region 1 (
Pj
CLE1), is sufficient to induce protohaustorium formation.
Pj
CLE1 hyperactivated HIF-responses and caused prolific protohaustoria formation.
PjCLE1
expression and activation by the subtilisin-type protease
Pj
SBT1.2.3 occur in fully developed, mature haustoria, suggesting that
Pj
CLE1 acts as an internal signal produced by mature haustoria to stimulate additional protohaustorium formation for effective extraction of resources from hosts.
Pj
CLE1 is similar in sequence to CLEs regulating nodulation in legumes and part of a regulatory system for haustoria formation in parasitic plants.
CryoSeek: A strategy for bioentity discovery using cryoelectron microscopy
Authors: Wang Tongtong , Li Zhangqiang , Xu Kui , Huang Wenze , Huang Gaoxingyu , Zhang Qiangfeng Cliff , Yan Nieng
Published: October 2024

Structural biology is experiencing a paradigm shift from targeted structural determination to structure-guided discovery of previously uncharacterized bioentities. We employed cryoelectron microscopy (cryo-EM) to analyze filtered water samples collected from the Tsinghua Lotus Pond. Here, we report the structural determination and characterization of two highly similar helical fibrils, named TLP-1a and TLP-1b, each approximately 8 nm in diameter with a 15-Å wide tunnel. These fibrils are assembled from a similar protein protomer, whose structure was conveniently automodeled in CryoNet. The protomer structure does not match any available experimental structures, but shares the same fold as many predicted structures of unknown functions. The amino-terminal β strand of protomer n + 4 inserts into a cleft in protomer n to complete an immunoglobulin (Ig)-like domain. This packing mechanism, known as donor-strand exchange (DSE), has been observed in several bacterial pilus assemblies, wherein the donor is protomer n + 1. Despite distinct shape and thickness, this reminiscence suggests that TLP-1a/b fibrils may represent uncharacterized bacterial pili. Our study demonstrates an emerging paradigm in structural biology, where high-resolution structural determination precedes and drives the identification and characterization of completely unknown objects.
Supplementary comparisons of reference measuring systems of electric capacitance and loss dissipation factor on AC high voltage of power frequency COOMET 604/RU-a/13 (COOMET.EM-S18)
Authors: Kiselev Victor , Leonov Alexey , Dubrovskaya Tatiana , Styblikova Renata , Gelovani Manana , Kikalo Valery
Published: October 2024

Main text

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCEM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Enhanced detection of glyphosate with a Co-MOF integrated opto-electrochemical sensor
Authors: Sankhla Lakshya , Kushwaha Himmat Singh
Published: October 2024

This study presents a new method for detecting the organophosphorus pesticide glyphosate using advanced screen-printed electrodes and enhanced fluorescence. Metal-organic frameworks from cobalt ions were synthesized using a solvothermal method. It is characterized using Raman spectroscopy, FT-IR, and x-ray diffraction techniques. The electrocatalytic behavior of the materials was studied using electrochemical impedance spectroscopy and cyclic voltammetry. Differential pulse voltammetry examined the positive response of plants to glyphosate over a concentration range of 0.55–5.95 mM with a detection limit of 0.334 mM. The fluorescence enhancement ranges from 0.07 mM to 0.67 mM, and the detection limit is 0.0998 mM. Additionally, the selectivity of the proposed opto-electrochemical sensor was evaluated. This selection demonstrates the sensor’s ability to detect glyphosate in complex wastewater matrices. This has important implications for environmental monitoring. By addressing glyphosate contamination, the sensor could significantly advance ecological remediation and monitoring strategies. The selectivity, sensitivity, and ability to operate under harsh conditions represent a significant advance in the development of efficient and reliable glyphosate technology for wastewater treatment and environmental protection. In real-sample matrices, the suggested sensor showed a good recovery of the pesticide that had been spiked.
Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network
Authors: Li 李 Zhi-Jun 志军 , Zhang 张 Jing 晶
Published: October 2024

Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant seven-dimensional (7D) neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum, and two-dimensional (2D) dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.
Design of railway simulation training system based on WebGL
Authors: Han Shibo , Su Hu , Zhao Yuanhang , Ma Chi
Published: October 2024

In order to solve the problems of poor interaction with students and insufficient practicality in the previous railway freight station operation training, this article developed a training system based on WebGL technology that can interact with students and truly display the operation situation of the freight yard. This system is based on the Unity3D engine and WebGL technology. It imports 3D models to build large map scenes, and introduces functions such as cargo bundling and map roaming. The complete designed system is published to the Web through WebGL technology. After experimental analysis, the parking lot training system has solved the problems of poor practicality, little interaction, low efficiency, and narrow coverage in previous teaching. It has promoted the standardization and standardization of training students’ future work, and has effectively improved the professionalism of parking lot managers.
Design and research of high-performance convolutional neural network accelerator based on Chipyard
Authors: Fan Yuxiao
Published: October 2024

Neural network accelerator performs well in the research and verification of neural network models. In this paper, a convolutional neural network accelerator system composed of RISC-V processor core and Gemmini array accelerator is designed in Chisel language within the Chipyard framework, and the acceleration effect of different Gemmini array configurations for different input matrices is further investigated. The result shows that the accelerator system can achieve thousands of times acceleration compared with a single processor for large matrix calculations.
IRDNet: An image-defogging algorithm based on dark channel prior
Authors: Guo Yanjun , Jia Guimin , Lu Pengyu
Published: October 2024

Image-defogging, as an important part of computer vision, has been widely used in intelligent driving, target recognition, satellite detection, underwater exploration and so on. Improving the performance of the defogging algorithm based on deep learning has practical significance for the completion of high-level vision tasks. This paper proposes an IRDNet algorithm by improving the image deep learning algorithm based on the RefineDNet framework and designing a new deep learning network structure. The proposed algorithm combines dark channel prior knowledge and atmospheric degradation model to decompose the input fogged images into dark channel images and degraded images, and then carries out feature extraction and detail enhancement for the two images by convolutional neural network. IRDNet introduces a fully connected convolutional structure, attention mechanism, and a pyramid structure to improve the performance of the overall defogging network. By testing on OTS datasets, we compare the IRDNet algorithm with traditional defogging methods, end-to-end deep learning defogging methods, and prior knowledge-guided deep learning defogging methods. Test results show that the performance of IRDNet is better than other methods, and the defogging network shows richer details of texture and more realistic colors.
Disentangled latent factors for muti-cause treatment effect estimation
Authors: Zhao Zhuoyue , Wang Haotian , Xu Liyang , Wang Qian , Tang Yuhua
Published: October 2024

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation.
Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy
Authors: Lyu Zhitong , Li Jie , Wang Haoming , Wang Hao
Published: October 2024

In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.
Research on extraction method based on mark point projection
Authors: Diao Zhaoheng , Gao Mingliang , Shi Weili , Xing Shuo , Li Fei
Published: October 2024

In order to achieve high-precision positioning of surgical instruments, the accurate position of the mark point must be obtained in real time. The three-dimensional positioning process of mark points is generally divided into four parts: projection extraction of mark points, distortion correction, stereo matching, and three-dimensional reconstruction. Among them, the accuracy of mark point projection extraction directly determines the positioning accuracy of mark points, while the extraction of mark point projections can be divided into three parts: mark point edge detection, mark point contour screening, and mark point center extraction. In view of this, a mark point projection extraction method based on edge features is proposed to achieve high-precision extraction of the mark point center.
Importance of FinTech and green finance to achieve the carbon neutrality targets: a study of australian perspective
Authors: Anwar Humaira , Waheed Rida , Aziz Ghazala
Published: October 2024

Based on Australian data from 2000 to 2022, this study examines how FinTech will influence the nation’s environmental future. The research shows that by directly intervening, FinTech-supported green finance is a chief driver of carbon reductions. This study contributes to existing literature by examine the role of Fintech and green finance in carbon neutrality targets of Australia. The complex web of interrelationships between technological and financial innovations is revealed as a factor that impacts the environment. When applied to Australia, which has its own FinTech industry and commitment to sustainable development, these results become even more eye-catching. This synergy takes global dimensions, with the research demonstrating a need to harmonize technological development and workable financial plans if environmental breakthroughs are to be made. However, the study points out to policymakers that they must foster a nurturing environment for digital financial innovations, develop stronger regulatory mechanisms, and encourage sustainable financial products. In terms of methodology, the research uses regression and mediation analysis to reach its conclusions. This research provides empirical evidence on the critical role of Fintech in advancing environmental sustainability in Australia, highlighting its direct and mediated effects on carbon emission reductions. By demonstrating the synergistic impact of technological and financial innovations, the study offers valuable insights for policymakers to foster a supportive ecosystem for sustainable digital finance.
Impact of urbanization, economic growth, FDI, and trade openness on energy demand in Ireland: an ARDL approach
Authors: Tahrim Farian , Hasan Md Atik , Akter Salma , Das Mihir Kumar , Pattak Dulal Chandra
Published: October 2024

This research study uses an autoregressive distributed lag (ARDL) paradigm to examine the effects of trade openness, urbanization (URBA), economic growth, and foreign direct investment (FDI) on energy demand in Ireland. This thorough theoretical framework anticipates a relationship between energy demand and trade, FDI, and URBA. This study demonstrates both short- and long-run cointegration among the variables employing an ARDL bounds-testing method for the data set period from 1972 to 2021. Additionally, fully modified ordinary least square (OLS), dynamic OLS, and canonical cointegrating regression were utilized in the study to verify the results’ robustness. Nonetheless, this analysis discovers that trade openness and rising gross domestic product per capita increase energy consumption (EC). The results show that for every 1% increase in GDP, EC increases by 0.967%. On the flip side, EC in Ireland is declining due to increased URBA and FDI. The results show that for every 1% increase in URBA, energy usage decreases by 1.738%. The investigation shows that Ireland’s energy usage is strongly impacted by trade liberalization, FDI, URBA, and economic expansion. Findings like these can help policymakers and stakeholders manage and optimize energy usage in the context of URBA, economic expansion, FDI, and global trade. The report recommends sustainable energy planning and policy initiatives to strike a healthy balance between meeting the increasing demand for energy and protecting the environment.
Metric flows with neural networks
Authors: Halverson James , Ruehle Fabian
Published: October 2024

We develop a general theory of flows in the space of Riemannian metrics induced by neural network (NN) gradient descent. This is motivated in part by recent advances in approximating Calabi–Yau metrics with NNs and is enabled by recent advances in understanding flows in the space of NNs. We derive the corresponding metric flow equations, which are governed by a metric neural tangent kernel (NTK), a complicated, non-local object that evolves in time. However, many architectures admit an infinite-width limit in which the kernel becomes fixed and the dynamics simplify. Additional assumptions can induce locality in the flow, which allows for the realization of Perelman’s formulation of Ricci flow that was used to resolve the 3d Poincaré conjecture. We demonstrate that such fixed kernel regimes lead to poor learning of numerical Calabi–Yau metrics, as is expected since the associated NNs do not learn features. Conversely, we demonstrate that well-learned numerical metrics at finite-width exhibit an evolving metric-NTK, associated with feature learning. Our theory of NN metric flows therefore explains why NNs are better at learning Calabi–Yau metrics than fixed kernel methods, such as the Ricci flow.
Humanity’s Debt to Science
Authors: Powell James Lawrence
Published: October 2024

Since the Enlightenment, science has brought about a dramatic improvement in both the length and the quality of human life. Yet individuals, and entire nations, reject science whenever it violates their ideology. Examples from history include Lysenkoism in the USSR, Mao’s failed agricultural policies, Nazi eugenics, so-called German physics, Mbeki’s AIDS denial, COVID-19, and manmade global warming. With global warming, the ultimate cost of state science denial has become frighteningly clear. Nations and their leaders today have a choice: either learn from history, trust scientists and act on their advice, or cripple the lives of coming generations.
Lysenko and the Origins of Soviet Pseudoscience
Authors: Powell James Lawrence
Published: October 2024

As the twentieth century began, Russian scientists were among the world’s best. By the late 1920s, however, the denial of genetics under Stalin and his agent, the pseudoscientist Trofim Lysenko, was on its way to becoming state policy. The leading Soviet geneticist, Nikolai Vavilov, tried to conciliate Lysenko, but that proved to be impossible. With Stalin’s approval, the USSR began to adopt Lysenko’s method of vernalization, in which plants were supposed to easily be changed to a different species, something that genetics showed was impossible. Those who rejected Lysenko and his pseudoscience began to be fired, persecuted, and even executed.
AIDS
Authors: Powell James Lawrence
Published: October 2024

No sooner was the virus that causes AIDS identified in 1984 than a denialist movement took root. It found its champion in a Nobel prize-winning biologist named Peter Duesberg, who, for example, published an article titled “HIV Is Not the Cause of AIDS.” One who was persuaded by Duesberg and the other denialists was President Thabo Mbeki of South Africa. He did his own internet research and concluded that HIV did not cause AIDS and that retroviral drugs did more harm than good. He appointed a denialist as his health minister, who recommended folk remedies such as garlic, beetroot, and potatoes. One study attributed 330,000 unnecessary deaths in South Africa to Mbeki’s AIDS denial.
Global Warming: The Ultimate Triumph of Science Denial?
Authors: Powell James Lawrence
Published: October 2024

Over the second half of the twentieth century and continuing today, the theory of manmade global warming has been rejected by growing numbers of the public and right-wing politicians. In 2008, Republican presidential candidate John McCain could still openly endorse the theory, but soon nearly every elected Republican had signed a pledge promising to do nothing about it. President Trump was on record as saying that global warming was a “Chinese hoax” and early in his presidency withdrew the United States from the Paris Agreement on climate change. An exposé showed that Exxon’s own scientists had told the company in the 1970s that global warming was caused by fossil fuels and was dangerous. But the company hid this finding and funded denialist organizations. In spite of the Paris Agreement and many noble sentiments, fossil fuel emissions, atmospheric carbon dioxide, and global temperatures have continued to rise. On this trajectory, science denial will achieve its ultimate triumph, at the cost of many if not most of the benefits science has brought humanity, possibly even human civilization itself.
Roadmap to Doomsday
Authors: Powell James Lawrence
Published: October 2024

Science is fundamentally a search for the truth about the natural world. Therefore, science is profoundly incompatible with science denial, which is often rooted in misconceptions or even lies. Truth is essential in all human affairs at all levels, from personal relations to international ones. To let lies replace truth is to abandon reason and common sense and, facing the existential threat of global warming, is apt to prove fatal. The Republican Party has planned to double-down on global warming denial and inaction if former President Trump wins a second term. For the sake of our children and grandchildren, Americans must elect politicians who trust in science.
Analyzing structural racism and its health impacts: it’s about time
Authors: Krieger Nancy
Published: October 2024

It’s about time. Since 2020, new work on structural racism and health has erupted in the United States, with 93% of the 1655 PubMed articles indexed by “structural racism” as of April 4, 2024 having been published during or since 2020. Among the 310 review articles included, most have focused either on improving conceptual, substantive, and methodologic clarity and precision about measuring and modeling structural racism, or reviewing evidence about its impacts on specific exposures or outcomes. However, only 2.5% of these 1655 articles are also indexed by the term “lifecourse” and its variants, and among the reviews considered here, none explicitly discuss issues involving etiologic period. Informed by ecosocial theory’s temporal theorizing about pathways of embodiment, and also Latin American social medicine-collective health framings of “health-illness-disease processes,” lifecourse models, and the construct of the “cancer control continuum,” in this brief commentary I consider how time matters in relation to concrete examples involving structural racism and cancer, and also inconsistent results reported by several studies using latent measures of structural racism. When it comes to structural racism and health, it truly is about time – and it is time for this work to tackle issues of time.
Blood Pressure Cuff Sizes for Pregnant Women in the United States: Findings From the National Health and Nutrition Examination Survey
Authors: Shahi Shamim , Streeter Taylor E , Wall Hilary K , Zhou Wen , Kuklina Elena V , Jackson Sandra L
Published: October 2024

BACKGROUND

The use of correctly sized blood pressure (BP) cuffs is important to ensure accurate measurement and effective management of hypertension. The goals of this study were to determine the proportions of pregnant women that would require small, adult, large, and extra-large (XL) cuff sizes, and to examine the demographic characteristics associated with need for a large or XL cuff.

METHODS

This cross-sectional study analyzed 1,176 pregnant women (≥18 years) included in the National Health and Nutrition Examination Survey (NHANES) 1999–2006 cycles. Recommended BP cuff sizes, based on American Heart Association recommendations, were categorized by mid-arm circumference: small adult (≤26 cm), adult (>26 to ≤34 cm), large (>34 to ≤44 cm), and extra-large (XL) (>44 cm).

RESULTS

Among US pregnant women, recommended cuff sizes were: 17.9% small adult, 57.0% adult, and 25.1% for large or XL. About 38.5% of non-Hispanic Black, 21.6% of Mexican American and 21.0% of non-Hispanic White pregnant women required a large or XL cuff. About 81.8% of women in the highest quartile for BMI required large or XL cuffs, which was significantly higher than women in other quartiles.

CONCLUSION

Roughly one out of every four pregnant women required large or XL BP cuffs. The requirement for large or XL cuffs was highest among non-Hispanic Black women and women with the highest BMI. For pregnant women, measuring the arm circumference and selecting an appropriately sized cuff is important to facilitate accurate blood pressure monitoring and hypertension management.
Responses of oak seedlings to increased herbivory and drought: a possible trade-off?
Authors: Peláez Marta , López-Sánchez Aida , Fernandes Geraldo Wilson , Dirzo Rodolfo , Rodríguez-Calcerrada Jesús , Perea Ramón
Published: October 2024

Background and Aims

Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, could herbivory compensate for the adverse impact of drought by reducing evapotranspiration? Thus, we investigated the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance.

Methods

California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n = 387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a glasshouse to three to four clipping levels simulating herbivory and three to four watering levels, depending on the species. We measured survival, growth and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May–September) and harvested above- and below-ground biomass at the end of the growing season.

Key Results

For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species benefited from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory – although water stress reduced differences in anthocyanins due to herbivory.

Conclusions

Herbivory seems to allow Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.
Social and seasonal variation in dwarf mongoose home-range size, daily movements, and burrow use
Authors: Arbon Josh J , Morris-Drake Amy , Kern Julie M , Giuggioli Luca , Radford Andrew N
Published: October 2024

When making decisions about resource use, social species must integrate not only environmental factors but also the influence of opportunities and costs associated with group living. Bigger groups are expected to move further and to need access to larger areas for adequate food acquisition, but the relationships with group size can vary seasonally and with reproductive stage. Shelters are often more consistent in availability than food, but their use relates to factors such as predator defense and parasite transmission that are themselves influenced by group size and seasonality. Here, we used long-term data to investigate resource use and associated movement in a wild population of dwarf mongooses (Helogale parvula). We found that bigger groups occupied larger home ranges, moved larger daily distances and covered more daily area than smaller ones, while environmental greenness (measured by normalized difference vegetation index [NDVI]) influenced daily movements in the breeding season but not the non-breeding season. Both assessed axes of seasonality also had pronounced effects on shelter use: mongoose groups used more unique sleeping burrows, and switched between burrows more often, in the breeding season, but also switched more when environmental greenness was higher. By investigating specific periods within the breeding season, we revealed the constraints that vulnerable, poorly mobile offspring impose on both group movements and burrow use, highlighting a potentially overlooked cost of reproduction. Our results show how both social and environmental factors can affect key resource-use decisions, demonstrating potential costs and benefits to group living within distinctly seasonal geographic areas.
Surgical complications after immediate implant-based breast reconstruction for breast cancer in women over 65 years
Authors: Liu Yihang , Johansson Anna L V , Oikonomou Ira , Frisell Axel , Adam Hannah C , Ansarei Dhirar , Halle Martin , Sackey Helena , de Boniface Jana
Published: October 2024

Background

While immediate breast reconstruction rates in breast cancer are increasing, they remain low in women over 65 years old. The aim was to investigate surgical outcomes in women older than 65 years receiving implant-based immediate breast reconstruction.

Method

The population-based Stockholm Breast Reconstruction Database includes all adult women with breast cancer receiving an implant-based immediate breast reconstruction in Stockholm, Sweden, 2005–2015. Primary outcomes within 30 days from immediate breast reconstruction were: infection requiring antibiotics and reoperation on. Implant removal was a secondary outcome. Women more than 65 years were compared with younger age groups. Chi-square tests and multivariable logistic regression were applied for the primary outcomes, and Kaplan–Meier analysis for the secondary outcome.

Results

Among 1749 cases of immediate breast reconstruction, 140 (8.0%) were in women more than 65 years. Median follow-up was 74 months (1–198). Postoperative infection was not more common in women older than 65 years old (22 of 140, 15.7%) than in women under 65 years old (303 of 1609, 18.8%; P = 0.221). Reoperation on was more frequent in women older than 65 years than in other age groups (more than 65: 8.6%; 50–64: 6.5%; 40–49: 3.5%; less than 40: 1.6%; P < 0.001), however, age older than 65 years was not an independent risk factor in the multivariable analysis (OR 1.00, 95% c.i. 0.44 to 2.28). Overall, 6-year probability of implant removal was 11.4%, (8.1% due to complications and 3.3% due to patient preference). There was no statistically significant difference between age groups for either reason (P = 0.085 and P = 0.794 respectively).

Conclusion

Older age alone was not associated with worse surgical outcomes after implant-based immediate breast reconstruction in highly selected patients older than 65 years when compared with their younger counterparts.
Dynactin-1 mediates rescue of impaired axonal transport due to reduced mitochondrial bioenergetics in amyotrophic lateral sclerosis motor neurons
Authors: Dafinca Ruxandra , Tosat-Bitrian Carlota , Carroll Emily , Vahsen Björn F , Gilbert-Jaramillo Javier , Scaber Jakub , Feneberg Emily , Johnson Errin , Talbot Kevin
Published: October 2024

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the motor system with complex determinants, including genetic and non-genetic factors. A key pathological signature of ALS is the cytoplasmic mislocalization and aggregation of TDP-43 in affected motor neurons, which is found in 97% of cases. Recent reports have shown that mitochondrial dysfunction plays a significant role in motor neuron degeneration in ALS, and TDP-43 modulates several mitochondrial transcripts. In this study, we used induced pluripotent stem cell-derived motor neurons from ALS patients with TDP-43 mutations and a transgenic TDP-43M337V mouse model to determine how TDP-43 mutations alter mitochondrial function and axonal transport. We detected significantly reduced mitochondrial respiration and ATP production in patient induced pluripotent stem cell-derived motor neurons, linked to an interaction between TDP-43M337V with ATPB and COX5A. A downstream reduction in speed of retrograde axonal transport in patient induced pluripotent stem cell-derived motor neurons was detected, which correlated with downregulation of the motor protein complex, DCTN1/dynein. Overexpression of DCTN1 in patient induced pluripotent stem cell-derived motor neurons significantly increased the percentage of retrograde travelling mitochondria and reduced the percentage of stationary mitochondria. This study shows that ALS induced pluripotent stem cell-derived motor neurons with mutations in TDP-43 have deficiencies in essential mitochondrial functions with downstream effects on retrograde axonal transport, which can be partially rescued by DCTN1 overexpression.
A circuit from lateral hypothalamic to dorsal hippocampal dentate gyrus modulates behavioral despair in mice
Authors: Huang Xin , Hu Shan-Shan , Zhang Qi-Lu , Han Xiao-Meng , Chen Zhi-Gang , Nie Rui-Zhe , Cao Xian , Yuan Dan-Hua , Long Yan , Hong Hao , Tang Su-Su
Published: October 2024

Behavioral despair is one of the clinical manifestations of major depressive disorder and an important cause of disability and death. However, the neural circuit mechanisms underlying behavioral despair are poorly understood. In a well-established chronic behavioral despair (CBD) mouse model, using a combination of viral tracing, in vivo fiber photometry, chemogenetic and optogenetic manipulations, in vitro electrophysiology, pharmacological profiling techniques, and behavioral tests, we investigated the neural circuit mechanisms in regulating behavioral despair. Here, we found that CBD enhanced CaMKIIα neuronal excitability in the dorsal dentate gyrus (dDG) and dDGCaMKIIα neurons involved in regulating behavioral despair in CBD mice. Besides, dDGCaMKIIα neurons received 5-HT inputs from median raphe nucleus (MRN) and were mediated by 5-HT1A receptors, whereas MRN5-HT neurons received CaMKIIα inputs from lateral hypothalamic (LH) and were mediated by AMPA receptors to regulate behavioral despair. Furthermore, fluvoxamine exerted its role in resisting behavioral despair through the LH-MRN-dDG circuit. These findings suggest that a previously unidentified circuit of LHCaMKIIα-MRN5-HT-dDGCaMKIIα mediates behavioral despair induced by CBD. Furthermore, these support the important role of AMPA receptors in MRN and 5-HT1A receptors in dDG that might be the potential targets for treatment of behavioral despair, and explain the neural circuit mechanism of fluvoxamine-resistant behavioral despair.