bluuebunny/crossref_metadata_2025 huggingface dataset:
Biodegradable and healable strain gauge based on Ti3C2TX MXene-gelatin hydrogel composite for eco-friendly applications
Authors: Han Hyun-Gu , Lee Dong-Geon , Kim Ji-Yeop , Jin Mi-Jin , Park Seungyoung , Um Doo-Seung , Kim Chang-Il
Published: January 2025

Here, we report a novel MXene–gelatin hydrogel composite (MGHC) strain gauge with healable and biodegradable properties, aimed at bio-comfortable and environmentally sustainable applications. The performance of the MGHC gauge was demonstrated through resistance modulation at different strain levels, cyclic endurance, stretching speed sensitivity, and hysteresis properties. The gauge factor was close to unity, approximately 1.0, indicating a linear relationship between strain and resistance change and ensuring reliable measurements. The sensor exhibited cyclic endurance over 300 cycles, resulting in approximately a 3% increase in resistance, and sensitivity to stretching speeds was observed. Moreover, diverse applications were explored, including weight detection, joint angle measurement, and torsion angle sensing. The healable ability was investigated under varied temperature conditions to identify optimal healing conditions. Furthermore, the biodegradability of the sensor was assessed by observing complete degradation in phosphate-buffered saline and diluted hydrogen peroxide mixed solution, evaluating eco-friendly by-products. Overall, this study highlights the potential of the MGHC strain sensor for sustainable and biocompatible applications.
Simulation and validation of the effect of hydrostatic and non-hydrostatic pressure on contact pressure in a resilient seat butterfly valve
Authors: Yuvaraj K , Arunkumar G
Published: January 2025

This study investigates the impact of hydrostatic and non-hydrostatic pressures on the contact pressure distribution in resilient-seated butterfly valves, a critical component in fluid control systems. Reliable sealing in such valves is essential to prevent leakage under varying pressure conditions. A 3-inch valve was analyzed under fully closed conditions with and without internal pressure. Finite Element Analysis simulated the contact pressure distribution at the disc-seat interface, revealing pressures ranging between 6.2 MPa and 12.9 MPa, which exceed the applied hydrostatic pressure, ensuring effective containment. Experimental validation included hydrostatic testing per API 598 standards and non-hydrostatic testing using prescale contact pressure films. Hydrostatic tests confirmed a leakage rate of 0 ml min−1 under low-pressure conditions, complying with API 598 requirements. Non-hydrostatic tests, performed with prescale films, recorded consistent pressure distributions in the range of 6 MPa to 10.5 MPa, verifying the EPDM seat’s effectiveness and uniform contact. The results confirm the valve’s ability to maintain leak-proof operation under both hydrostatic and non-hydrostatic pressures, validating FEA predictions and reducing reliance on time-intensive physical testing. These findings emphasize the robustness of resilient-seated butterfly valve design and its optimization for industrial applications, improving operational reliability and enhancing simulation accuracy in fluid control systems.
Evaluation of dose calculation method with a combination of Monte Carlo method and removal-diffusion equation in heterogeneous geometry for boron neutron capture therapy
Authors: Nojiri Mai , Takata Takushi , Sasaki Akinori , Tamari Yuki , Matsubayashi Nishiki , Hu Naonori , Sakurai Yoshinori , Suzuki Minoru , Tanaka Hiroki
Published: January 2025

Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.
Willingness to pay for blue-green space and koala habitat protection following a Covid-19 versus bushfire experimental prime
Authors: Leviston Zoe , Cohen Rubijayne , Gorman Stephanie , Becvarik Zoe , Lal Aparna
Published: January 2025

Community-supported investment in blue-green space and fauna habitat protection is important, yet the drivers of willingness to pay (WTP) are poorly understood. Moreover, research on WTP for environmental services rarely considers the role of context in determining which services donors prefer. We address these gaps with a novel experimental approach (N = 360), randomly assigning participants to a condition priming either Covid19, the Australian 2019–2020 bushfires, or to one of two control conditions. We measured subsequent WTP for blue-green space and koala habitat, finding mostly non-significant effects for experimental condition on WTP, preparedness to donate, or the importance placed on investment. Both WTP for blue-green space and koala habitat was significantly associated with experience with bushfire, left-leaning political orientation, and higher blue-green space usage. Assessment of WTP for blue-green space in Australian cities indicates a positive attitude, especially among active users, although people are still willing to contribute to koala protection.
Weighing temporal fluctuations of drought features in Rwanda through the implementation of the standardized precipitation evapotranspiration index and standardized precipitation index
Authors: Jonah Kazora , Weijun Zhu , Oo Than Kyaw , Asfaw Temesgen Gebremariam , Gyamfi Kedjanyi Emmanuel Adu , Okrah Abraham , Diane Akimana , Mugunga Mathieu Mbati
Published: January 2025

Climate change poses significant challenges globally, affecting weather patterns and leading to more frequent and severe extreme events such as droughts. In Rwanda, a country highly reliant on rainfed agriculture, understanding the changing nature of droughts is crucial for effective climate adaptation and resilience-building efforts. This study investigates temporal variations in drought characteristics across Rwanda using the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). Through a comprehensive analysis spanning multiple time scales (3-month and 12-month periods) and spatial extents, the research assesses drought occurrence, frequency, intensity, and severity from 1983 to 2020. Utilizing the homogeneity test, Mann-Kendall trend test, and Sen’s slope method, the study identifies significant trends in precipitation, SPEI, SPI, potential evapotranspiration (PET), and trends in PET changes over time. Results reveal substantial spatial and temporal variability in drought patterns, with distinct interdecadal shifts observed. While certain regions experienced increased drought frequency and severity, others exhibited wetter conditions. Notably, the Eastern and Southern provinces faced intensified drought impacts, with prolonged dry spells lasting over 3 to 6 months. The research emphasizes the necessity of comprehending the temporal and spatial fluctuations in drought attributes to enhance agricultural practices and adaptation measures. Through the utilization of complementary drought indices and robust statistical approaches, this study offers valuable insights into Rwanda’s susceptibility to climate variability and its impacts on socio-economic advancement. The findings offer essential guidance for policymakers, stakeholders, and communities in developing targeted interventions to mitigate the adverse impacts of droughts and enhance resilience to climate change.
A comprehensive review of computational diagnostic techniques for lymphedema
Authors: K R Jayasree , Vijayakumar D K , Sugumaran Vijayan , Pathinarupothi Rahul Krishnan
Published: January 2025

Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2–5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema. This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning (ML) approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies bioimpedance spectroscopy (BIS), Kinect sensor and ML integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas ML and artificial neural network achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. ML and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.
3D laser structuring of supermetalphobic microstructures inside elastomer for multilayer high-density interconnect soft electronics
Authors: Zhang Chengjun , Yang Qing , Li Haoyu , Luo Zexiang , Lu Yu , Zhang Jialiang , Li Cheng , Chen Feng
Published: January 2025

High-density interconnect (HDI) soft electronics that can integrate multiple individual functions into one miniaturized monolithic system is promising for applications related to smart healthcare, soft robotics, and human-machine interactions. However, despite the recent advances, the development of three-dimensional (3D) soft electronics with both high resolution and high integration is still challenging because of the lack of efficient manufacturing methods to guarantee interlayer alignment of the high-density vias and reliable interlayer electrical conductivity. Here, an advanced 3D laser printing pathway, based on femtosecond laser direct writing (FLDW), is demonstrated for preparing liquid metal (LM)-based any layer HDI soft electronics. FLDW technology, with the characteristics of high spatial resolution and high precision, allows the maskless fabrication of high-resolution embedded LM microchannels and high-density vertical interconnect accesses for 3D integrated circuits. High-aspect-ratio blind/through LM microstructures are formed inside the elastomer due to the supermetalphobicity induced during laser ablation. The LM-based HDI circuit featuring high resolution (∼1.5 μm) and high integration (10-layer electrical interconnection) is achieved for customized soft electronics, including various customized multilayer passive electric components, soft multilayer circuit, and cross-scale multimode sensors. The 3D laser printing method provides a versatile approach for developing chip-level soft electronics.
Construction of complex three-dimensional vascularized liver tissue model in vitro based on a biphasic cell-laden embedding medium
Authors: Lv Weikang , Yu Haoran , Aazmi Abdellah , Naren Tuya , Cheng Wanli , Yu Mengfei , Wang Zhen , Xu Xiaobin , Yang Huayong , Ma Liang
Published: January 2025

Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation, mimicking physiological heterogeneous structures, and recreating the cellular microenvironment. However, the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink, making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials. To overcome this limitation, we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel. This medium possesses yield stress and self-healing properties, facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it. By adjusting the printing speed, we controlled the filament diameter, achieving a range from 250 μm to 1000 μm, and ensuring precise control over ink deposition locations and filament shapes. Using the in situ endothelialization method, we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells. In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue. We also investigated the impact of varying vascular densities on liver tissue function. Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways. In summary, this method is adaptable to various materials, allowing the rheological properties of the supporting bath and the tissue’s porosity to be modified using microgels, thus enabling precise regulation of the liver tissue microenvironment. Additionally, it facilitates the rapid construction of three-dimensional vascular structures within liver tissue. The resulting vascularized liver tissue model exhibits enhanced biological functionality, opening new opportunities for biomedical applications.
Self-propelled ferroptosis nanoinducer for enhanced cancer therapy
Authors: Xu Wenxin , Tian Hao , Song Yanzhen , Qin Hanfeng , Gao Junbin , Chen Yichi , Huang Weichang , Lin Lin , Tan Haixin , Ye Yicheng , Zhang Xiaoting , Wilson Daniela A , Yang Guang , Peng Fei , Tu Yingfeng
Published: January 2025

Ferroptosis is a newly proposed type of programmed cell death, which has been associated with a variety of diseases including tumors. Researchers have thereby presented nanoplatforms to mediate ferroptosis for anti-cancer therapy. However, the development of ferroptosis-based nanotherapeutics is generally hindered by the limited penetration depth in tumors, poor active pharmaceutical ingredient (API) loading content and the systemic toxicity. Herein, self-propelled ferroptosis nanoinducers composed of two endogenous proteins, glucose oxidase and ferritin, are presented to show enhanced tumor inhibition via ferroptosis while maintaining high API and biocompatibility. The accumulation of our proteomotors at tumor regions is facilitated by the active tumor-targeting effect of ferritin. The enhanced diffusion of proteomotors is then actuated by efficiently decomposing glucose into gluconic acid and H2O2, leading to deeper penetration and enhanced uptake into tumors. Under the synergistic effect of glucose oxidase and ferritin, the equilibrium between reactive oxygen species and GSH is damaged, leading to lipid peroxidation. As a result, by inducing ferroptosis, our self-propelled ferroptosis nanoinducers exhibit enhanced tumor inhibitory effects. This work paves a way for the construction of a biocompatible anticancer platform with enhanced diffusion utilizing only two endogenous proteins, centered around the concept of ferroptosis.
Micropattern of core-shell Ag@MCS/PEGDA nanoparticles fabricated by femtosecond laser maskless optical projection lithography
Authors: Bin Fan-Chun , Wu Xin-Yi , Liu Jie , Dong Xian-Zi , Li Teng , Duan Qi , Zhang Jian-Miao , Fujita Katsumasa , Zheng Mei-Ling
Published: January 2025

Chitosan (CS)-based nanocomposites have been studied in various fields, requiring a more facile and efficient technique to fabricate nanoparticles with customized structures. In this study, Ag@methacrylamide CS/poly(ethylene glycol) diacrylate (Ag@MP) micropatterns are successfully fabricated by femtosecond laser maskless optical projection lithography (Fs-MOPL) for the first time. The formation mechanism of core-shell nanomaterial is demonstrated by the local surface plasmon resonances and the nucleation and growth theory. Amino and hydroxyl groups greatly affect the number of Ag@MP nanocomposites, which is further verified by replacing MCS with methacrylated bovine serum albumin and hyaluronic acid methacryloyl, respectively. Besides, the performance of the surface-enhanced Raman scattering, cytotoxicity, cell proliferation, and antibacterial was investigated on Ag@MP micropatterns. Therefore, the proposed protocol to prepare hydrogel core-shell micropattern by the home-built Fs-MOPL technique is prospective for potential applications in the biomedical and biotechnological fields, such as biosensors, cell imaging, and antimicrobial.
Tailoring adhesive bonding strength: the role of mechanical abrasion and cure time in aluminum-stainless steel joints
Authors: Abid Jawad , Ahmad Arslan , Raza Yasir , Ayub Azeem , Yasir Hammad , Raza Hasan , Abid Muhammad , Murtaza Mubashir Ali , Masood Wajeeh , Janjua Ajaz Bashir
Published: January 2025

Adhesive bonding is an efficient strategy for fabricating lightweight structural components owing to its superior toughness and fatigue resistance. Major factors affecting the adhesion of various materials encompass the elimination of impurities, adhesive wettability, and surface roughness. This study examines the effects of surface roughness and curing duration on the adhesive bonding of AL5083 and SS 304 substrates. Impurities are eliminated from the substrates utilizing laboratory-grade acetone, succeeded by pre-treatment with P30, P40, and P60 grades of silicon carbide sheets. Surface roughness is quantified using a profilometer Surftest (SJ-210). Morphologies were analyzed by scanning electron microscopy (SEM) and x-ray diffractometry (XRD) were employed to validate the specimen fabrication. The adhesively bonded specimens, exhibiting different surface roughness, undergo lap shear tensile tests utilizing a Universal Testing Machine (UTM), and tensile strength is determined. The results demonstrate that the surface treated with silicon carbide P30 and cured for 48 h attained the maximum tensile strength for both adherend materials: 129.00 MPa for SS 304 with a surface roughness of Ra = 3.20 μm, and 44.65 MPa for AL5083 with a surface roughness of Ra = 5.00 μm. This research will pave the way to optimize the surface preparation for bonded joints.
Temporal convolution long short-term memory network with multiple attention for remaining useful life prediction of rolling bearings
Authors: Zhang Jiashuo , He Deqiang , Wu Jinxin , Jin Zhenzhen , Xiang Weibin , Shan Sheng
Published: January 2025

Remaining useful life (RUL) prediction of rolling bearings prevents sudden mechanical failures and reduces equipment maintenance costs. Due to the strong performance in time series forecasting tasks, the temporal convolutional network (TCN) has become a mainstream model for RUL prediction. However, existing TCN-based prediction models struggle to fully capture both long-term and global dependencies in complex data. To address these issues, a temporal convolutional long short-term memory network integrated with multi-head attention mechanism (TCLSTM-MA) is proposed to predict the rolling bearings’ RUL. Firstly, the time-domain and frequency-domain features are extracted from the acquired raw vibration signals to form a complete degradation feature. Secondly, we enhance the traditional TCN by combining it with LSTM and introducing a multi-head attention mechanism. This integration allows the model to effectively capture both global degradation information and local context information. Additionally, a time-weighted t-MSE loss function is employed throughout training to make the model focus more on data close to failure points. Finally, the trained TCLSTM-MA model is used for RUL prediction. Extensive experiments were conducted on two authoritative rolling bearing datasets and compared with other methods. The experimental results demonstrate that the proposed method exhibits good accuracy and generalization capability.
Full space scanning leaky wave antenna with wide angle beam steering based on SSPPs
Authors: Rajaparthiban Mani , Karthikeyan Madurakavi
Published: January 2025

We proposed a leaky wave antenna based on spoof surface plasmon polariton (SSPP) transmission line (TL) with unit cell of X-Shaped orthogonal metal patches. These patches are arranged in a couple of metallic patches along with semi-elliptical patches on both sides of the slot to attain the full space scanning (−90° to +90°) between the frequency range of 2.53 GHz and 5 GHz. The designed LWA shows that the continuous beam scanning range from −94° to + 2° (96°), with an average gain of 8.70 dBi, a radiation efficiency of 79.09%, and a scanning rate of 2.44°/% has been achieved over the frequency range of 5 GHz to 7.45 GHz. Furthermore, the antenna achieves a 174° scanning range from −172 to +2° between the frequency range of 100 MHz and 7.45 GHz, with an average gain of 8.33dBi, a radiation efficiency of 69.28%, a scanning rate of 0.88°/%, and obtains a lower side lobe level of less than −10 dB. The proposed antenna has a beam coverage range of 357° over the frequency range of 5 MHz-3.1 GHz. The antenna was fabricated using Rogger RT 5880 substrate with εr = 2.2. The total size of the structure is 180 × 30 × 0.508. With these features, this antenna is suitable for continuous beam scanning and full space scanning applications in mobile/wireless communication and microwave radar communication.
Satellite image classification with neural quantum kernels
Authors: Rodriguez-Grasa Pablo , Farzan-Rodriguez Robert , Novelli Gabriele , Ban Yue , Sanz Mikel
Published: January 2025

Achieving practical applications of quantum machine learning (QML) for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation industry, using QML techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels-quantum kernels derived from trained quantum neural networks-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.
Nanonization of ethanolic tulsi leaf extract and its major compound eugenol and evaluation of their inhibitory potential against calcium oxalate crystal (a kidney stone component)
Authors: Ghosh Sourav , Basu Tarakdas
Published: January 2025

The aim of this study was a) to synthesize nanoforms of the whole leaf extract of the flowering herb ‘Tulsi—Ocimum sanctum’ (regarded as the ‘queen of herbs’ having diverse healing and health-improving properties) and its major constituent eugenol, b) to characterize some of their physico-chemical properties and c) to explore their inhibitory role on formation and disintegration of the kidney stone-former calcium oxalate crystals, through in vitro investigation. Colloidal suspensions of Tulsi-leaf-extract nanoparticles (TLE-NPs) and eugenol nanoparticles (ENPs) were synthesized separately by ultrasonication method through emulsification of hydrophobic TLE/eugenol into hydrophilic protein gelatin. Due to nanonization, water-insoluble TLE/eugenol became water-soluble and thus bioavailable. The average size of the particles was about 35nm for TLE-NPs and 25 nm for ENPs, entrapment efficiency of TLE and eugenol within gelatin was about 20 and 80% respectively, and the release rate of TLE/eugenol from gelatin cap was slow and sustained throughout 72 h for TLE-NPs and 120 h for ENPs. When spectroscopic and microscopic studies were performed on the formation and disintegration of calcium oxalate crystals in presence of TLE-NP and ENP, both types of particles depicted significant inhibitory potential on crystal formation and disintegrating potential on pre-formed crystals, compared to the same doses of their bulk forms i.e., bare TLE and eugenol; ENP was more potent than TLE-NP with respect to crystallization inhibitory activity, whereas they were comparable in crystal disintegration potency. Future in vivo pharmacological and toxicological studies will surely lead to the development of the TLE-NPs and ENPs as potential therapeutic agents against calcium oxalate pathogenesis.
Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware
Authors: Cotteret Madison , Greatorex Hugh , Renner Alpha , Chen Junren , Neftci Emre , Wu Huaqiang , Indiveri Giacomo , Ziegler Martin , Chicca Elisabetta
Published: January 2025

Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly nonideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. Moreover, it demonstrates that distributed symbolic representations serve as a highly capable representation-invariant language for cognitive algorithms in neuromorphic hardware.
Asymptotic behavior of an integral equation of Allen-Cahn type
Authors: Lei Yutian
Published: January 2025

In this paper, we are concerned with the asymptotic behavior of solutions of an integral equation of the Allen-Cahn type
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="u left-parenthesis x right-parenthesis equals ModifyingAbove l With right-arrow plus upper K left-parenthesis x right-parenthesis upper W Subscript beta comma p Baseline left-bracket u StartAbsoluteValue u StartAbsoluteValue left-parenthesis 1 minus StartAbsoluteValue u EndAbsoluteValue Superscript q 1 Baseline right-parenthesis StartAbsoluteValue 1 minus EndAbsoluteValue u EndAbsoluteValue Superscript q 1 minus 2 Baseline Superscript q 1 Baseline EndAbsoluteValue Superscript q 2 minus 2 Baseline right-bracket left-parenthesis x right-parenthesis">

u
(
x
)
=

l
→

+
K
(
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)

W

β
,
p

[
u

|

u

|

q
1

−
2

(
1
−

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q
1

)

|

1
−

|

u

|

q
1

|

q
2

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(
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\begin{equation*} u(x)=\overrightarrow {l} +K(x)W_{\beta ,p}[u|u|^{q_1-2}(1-|u|^{q_1})|1-|u|^{q_1}|^{q_2-2}](x) \end{equation*}

when

|

x

|

→
∞

|x| \to \infty

. Here

u
:

R

n

→

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k

u:{\mathbb {R}}^n\rightarrow {\mathbb {R}}^k

is uniformly continuous, and

k
≥
1

k \geq 1

,

n
≥
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n \geq 2

,

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>
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,

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>
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,

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, and

min
{

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. In addition,

l
→

∈

R

k

\overrightarrow {l} \in {\mathbb {R}}^k

is a constant vector and

K
(
x
)

K(x)

is a nonnegative bounded function. We prove that if

u
u

or

1
−

|

u

|

q
1

1-|u|^{q_1}

has some integrability, then

|

l
→

|

∈
{
0
,
1
}

|\overrightarrow {l}| \in \{0,1\}

and

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→

l
→

u \to \overrightarrow {l}

when

|

x

|

→
∞

|x| \to \infty

. Moreover, if

u
u

or

1
−

|

u

|

q
1

1-|u|^{q_1}

has some better integrability, then
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|

|

u
(
x
)

|

−

|

l
→

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=
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(

|

x

|

(
p
β
−
n
)

/

(
p
−
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)

)

| |u(x)|-|\overrightarrow {l}| |=O(|x|^{(p\beta -n)/(p-1)})

when

|

x

|

→
∞

|x| \to \infty

. Here a regularity lifting lemma comes into play in the proof.
Cul3 substrate adaptor SPOP targets Nup153 for degradation
Authors: Ong Joseph Y. , Abdusamad Mai , Ramirez Ivan , Gholkar Ankur , Zhang Xiaoxuan , Gimeno Thomas V. , Torres Jorge Z.
Published: January 2025

SPOP is a Cul3 substrate adaptor responsible for the degradation of many proteins related to cell growth and proliferation. Because mutation or misregulation of SPOP drives cancer progression, understanding the suite of SPOP substrates is important to understanding the regulation of cell proliferation. Here, we identify Nup153, a component of the nuclear basket of the nuclear pore complex, as a novel substrate of SPOP. SPOP and Nup153 bind to each other and colocalize at the nuclear envelope and some nuclear foci in cells. The binding interaction between SPOP and Nup153 is complex and multivalent. Nup153 is ubiquitylated and degraded upon expression of SPOPWT but not its substrate binding-deficient mutant SPOPF102C. Depletion of SPOP via RNAi leads to Nup153 stabilization. Upon loss of SPOP activity, the nuclear envelope localization of spindle assembly checkpoint protein Mad1, which is tethered to the nuclear envelope by Nup153, is stronger. Altogether, our results demonstrate that SPOP regulates Nup153 levels and expands our understanding of the role of SPOP in protein and cellular homeostasis.
A Stepwise Approach to Surgical Decision-making for Lower Eyelid Ectropion: Insights from a Retrospective Study
Authors: Wang Yinmin , Yang Jun , Yu Shun , Jin Rui , Gu Chuan , Lu Lin
Published: January 2025

Background

Cicatricial lower eyelid ectropion is a serious complication resulting from undesired lower lid blepharoplasty or impaired wound healing. Surgical treatment for ectropion is challenging for oculoplastic surgeons due to the unpredictability of surgical outcome and the difficulty of surgical design.

Objectives

The authors aimed to fully describe the surgical decision-making strategy for cicatricial ectropion, and to potentially enhance treatment outcomes.

Methods

In this study, we conducted a retrospective review of the treatment of 26 patients (30 eyelids) with cicatricial ectropion between 2017 and 2021. We summarized the corresponding decision-making processes before and during surgery. Treatment involved scar tissue release combined with lateral canthal procedures and repair of the skin defect.

Results

During the early postoperative stage, no flap necrosis was observed, and complete surgical healing was achieved within 2 weeks. The ectropion grading scale showed a significant decrease after surgical treatment based on our approach, and most patient symptoms were alleviated. Additionally, the majority of the patients expressed high satisfaction with the aesthetic outcome of the procedure.

Conclusions

This treatment approach significantly enhanced both the appearance and function of the lower lid, which holds considerable promise as an ideal surgical design for cicatricial ectropion.

Level of Evidence: 3 (Therapeutic)
Transcription factor prediction using protein 3D secondary structures
Authors: Liebold Jeanine , Neuhaus Fabian , Geiser Janina , Kurtz Stefan , Baumbach Jan , Newaz Khalique
Published: January 2025

Motivation

Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs.

Results

We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on ∼525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method’s performance. We find that StrucTFactor significantly (P-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient.

Availability and implementation

Data and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor
nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
Authors: Mattessich Max , Reyna Joaquin , Aron Edel , Ay Ferhat , Kilmer Misha , Kleinstein Steven H , Konstorum Anna
Published: January 2025

With the increased reliance on multi-omics data for bulk and single-cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Nonlinear Iterative Partial Least Squares. We applied nipalsMCIA to both bulk and single-cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.

Availability and implementation

nipalsMCIA is available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes.
Molecular dynamics simulations of putative primitive proteins including β-aspartic acid residues
Authors: Kato Koichi , Nakayoshi Tomoki , Mizuno Ayato , Yabu Mayuka , Kurimoto Eiji , Oda Akifumi
Published: January 2025

Aspartic acid (Asp) has 2 carboxyl groups, either of which can form an amide bond. However, among the present proteins, only one is involved in a peptide bond, whereas the other is regarded as “side chain,” with only α-Asp identified as a gene-encoded amino acid residue. In this study, molecular dynamics simulations of random peptides were performed to investigate why β-Asp, which may have been present in primitive proteins in the origin of proteins, was excluded from proteinogenic amino acids. [GADV]-peptides, constructed from Gly, Ala, Asp, and Val, were used as putative primitive proteins. Molecular dynamics simulations were performed when β-Asp was used instead of Asp in the [GADV]-peptides and when β-Asp was added to the [GADV]-peptide. The secondary structures of peptides containing β-Asp were less than [GADV]-peptides, and their ability to form a protein-like structure was inferior to that of peptides excluding β-Asp. Thus, the ability to form protein-like structures may have acted as a selection pressure, leading to the elimination of protein synthesis systems utilizing β-Asp from primitive life. When the [GADVB]-peptide, i.e. a β-amino acid β-Ala, was added to the [GADV] amino acid set, the ability to form secondary structures reduced. Therefore, β-amino acids that altered the main chain length were excluded from primitive life, despite β-Ala being relatively abundant on primitive Earth.
Effect of Mg–Nb oxides on hydrogen sorption kinetics of ball-milled MgH2
Authors: Rahman Md Wasikur , Siddiquee Muhammad Nurunnabi , Hossain Mohammad M , Uddin M Jasim
Published: January 2025

Addition of Mg–Nb oxides (e.g. MgNb2O6, Mg4Nb2O9, and Mg3Nb6O11) ameliorates H2 absorption/desorption kinetics of MgH2 as demonstrated in the current article. H2 desorption and absorption rates of the ball-milled MgH2 are evidently temperature-dependent, which points out that the prior rate increases with increasing temperature (593–673 K) and vice versa. Among the tested samples, MgH2 with Mg3Nb6O11 nanoparticles showed superior performance. The Johnson–Mehl–Avrami equation was employed to construct H2 desorption curves as well as find out reaction rate constants at different temperatures. The Arrhenius equation was fitted in the context to estimate the activation energy of the ball-milled MgH2 and MgH2/Mg3Nb6O11 mixtures; for example, the values obtained were 127 and 88 kJ·mol−1, respectively. In addition, a novel experimental setup combining a hydrogen detector with a differential scanning calorimeter was used to confirm the H2 desorption properties of the ball-milled nanoparticles discussed based on the kinetic argument.
Impact of Ag Doping on the Structural, Optical, and Photovoltaic Properties of MAPbI2Br Thin Films
Authors: Hussain Saddam , Khan M I , Atif M , Rodríguez-Rodríguez Margarita , Pellegrini-Cervantes Manuel J
Published: January 2025

Using the spray pyrolysis process, the work shows the production of pristine and 6% Ag-doped methylammonium lead iodide bromide (MAPbI2Br) film. Through X-ray diffraction analysis, it was found that Ag doping led to a significant increase in grain size to 29.64 nm, alongside reductions in dislocation line density to 5.39 x1014 m-2 and d-spacing to 3.18 Å, while maintaining the native cubic crystal structure of MAPbI2Br. This research demonstrates a reduction in deep-level trap states with Ag doping, along with a significant narrowing of the band gap to 1.91 eV in the 6% Ag-doped MAPbI2Br. Moreover, the refractive index and extinction coefficient increased to 2.54 and 2.13, respectively. Regarding solar cell performance, all cells demonstrated encouraging outcomes; still, the 6% Ag-doped cell distinguished itself with a fill factor of 0.82, an open-circuit voltage of 1.07 V, an outstanding short-circuit current density of 11.31 mA/cm², and an efficiency of 10.03%. These results highlight the effectiveness of Ag doping in improving perovskite solar cell technology, marking a notable progress in this field.
Multiple actinic keratoses treated with oral capecitabine
Authors: Yoong Nicole K M , Triwongwaranat Daraporn , Ghiya Ragini , Eisman Samantha
Published: January 2025

Actinic keratoses are cutaneous lesions with epidermal dysplasia secondary to chronic sun exposure. Their natural history is to persist, regress spontaneously or evolve into squamous cell carcinomas. We present the case of a patient who had multiple actinic keratoses treated with the chemopreventative agent, oral capecitabine.
Brain functional differences during irony comprehension in adolescents with ASD
Authors: Xu Shilin , Wang Xin , Shen Linling , Yan Xiaohui , Feng Guoyan , Cao Fan
Published: January 2025

Irony comprehension is challenging for both individuals with ASD and poor comprehenders (PCs). We aimed to examine the common and specific mechanisms underlying irony comprehension difficulty in the two populations. Both adolescents with ASD and PC showed lower performance in irony comprehension than an age-matched typical control group (TD). The ASD group also showed deficits in theory of mind (ToM), while the PC group showed impairments in structural language skills. In the brain, the ASD group showed reduced brain activation in the left inferior frontal gyrus (IFG) compared to both the TD and the PC group, suggesting ASD-specific differences, which was further found to be correlated with ToM deficits in ASD. Both the TD and the PC group showed greater activation for the ironic than the literal condition in the bilateral medial prefrontal cortex (mPFC), but the ASD group did not, suggesting ASD-specific difference in irony comprehension. The PC group showed reduced activation in the right cuneus compared to the TD, which was correlated with the language comprehension score, suggesting different mechanisms than ASD. Our findings provide insights about the neurocognitive mechanisms underlying impaired irony comprehension in ASD and PC.
Hayek and Schmitt on the ‘depoliticization’ of the economy
Authors: Nientiedt Daniel
Published: January 2025

Both Friedrich Hayek and Carl Schmitt criticize the role played by economic interest groups in modern democracies. This paper points out that their descriptions of how such groups attempt to obtain favourable treatment from the state (‘rent seeking’) are highly similar. The paper then discusses their proposed solutions. Schmitt’s solution, referred to as ‘depoliticization’, involves the establishment of an authoritarian state together with a corporatist economic system. Hayek, by contrast, argues in favour of constitutionally limiting the ability of politicians to grant privileges to economic groups. The paper further asks whether, as claimed in the literature, Hayek’s solution is similar to Schmitt’s in that both attempt to shield the free market economy from democratic oversight. It is reasoned that Hayek’s solution is not undemocratic (unless one defines democracy as pure majoritarianism), while Schmitt’s solution is not conducive to free markets.
Deconditioning in quiescent Crohn’s disease patients with heightened fatigue perception
Authors: McGing Jordan J , Serres Sébastien , Nicholas Rosemary , Gupta Ayushman , Radford Shellie J , Nixon Aline V , Mallinson Joanne , Bradley Christopher , Bawden Stephen , Francis Susan T , Greenhaff Paul L , Moran Gordon W
Published: January 2025

Background and Objective

Inflammatory bowel disease (IBD) fatigue aetiology is poorly understood. This study quantified body composition and physical function alongside proton magnetic resonance imaging (1H MRI) and spectroscopy (31P MRS) measures of organ structure and function in quiescent Crohn’s disease patients (CD) and healthy volunteers (HVs), to identify a physiological basis for IBD fatigue.

Methods

Body composition was determined using dual-energy X-ray absorptiometry and 1H MRI. Knee extensor isometric strength and isokinetic fatigue were measured using dynamometry. 1H MRI was used to quantify cardiac output, cerebral blood flow (gmCBF), and brain oxygen extraction fraction (OEF) at rest, and during supine, steady-state exercise, and recovery. 31P MRS was used to quantify post-exercise muscle phosphocreatine (PCr) resynthesis.

Results

Sixteen CD and 12 HV (age, sex, and BMI matched) were recruited. Fatigue perception was greater (13.9 ± 1 vs 8.3 ± 0.9, P = .001), and daily step count was less (5482 ± 684 vs 8168 ± 1123, P = .04) in CD. During steady-state exercise, gmCBF was less in CD (653 ± 30 vs 823 ± 40 mL/min, P = .003). Cardiac output and brain OEF were no different. Post-exercise PCr resynthesis was less in CD (17.2 ± 2.0 vs 25.3 ± 2.4 mM·min−1, P = .02). Body composition, isometric strength, and isokinetic fatigability were no different.

Conclusions

CD self-reported increased fatigue perception and exhibited a slower rate of post-exercise PCr resynthesis compared to HV. This occurred independently of changes in body composition, muscle strength, and fatigability. IBD fatigue may be linked to peripheral muscle deconditioning and lower gmCBF during submaximal exercise.
A Binary IV Model for Persuasion: Profiling Persuasion Types among Compliers
Authors: Yu Zeyang
Published: January 2025

In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens-Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilised voters and their statistical characteristic defined by the moments of the joint distribution of treatment and covariates. Specifically, we develop a method that enables researchers to identify the statistical characteristic of persuasion types: always-voters, never-voters, and mobilised voters among compliers. These findings extend the kappa weighting results in Abadie (2003). We also provide a sharp test for the two sets of identification assumptions. The test boils down to testing whether there exists a nonnegative solution to a possibly under-determined system of linear equations with known coefficients. An application based on on Green et al. (2003) is provided.
Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room
Authors: Moon Jose , Kim Jong-Ho , Hong Soon Jun , Yu Cheol Woong , Kim Yong Hyun , Kim Eung Ju , Cha Jung-Joon , Joo Hyung Joon
Published: January 2025

Aims

Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.

Methods and results

In this retrospective cohort study, we analysed the ECG data of 19 285 patients who visited ERs of three hospitals between 2016 and 2020; 9119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study. We extracted morphological and clinical parameters from ECG data to train and validate four machine learning models: baseline linear regression and more advanced models including XGBoost, Light GBM, and CatBoost. The CatBoost algorithm outperformed other models, showing superior area under the receiver operating characteristic and area under the precision–recall curve diagnostic accuracy across both internal (0.89 ± 0.01 and 0.89 ± 0.01) and external (0.90 and 0.89) validation data sets, respectively. The model demonstrated high accuracy, precision, recall, and f1 score, indicating robust performance in AHF identification.

Conclusion

The developed machine learning model significantly enhanced AHF detection in the ER using conventional 12-lead ECGs combined with clinical data. These findings suggest that ECGs, a common tool in the ER, can effectively help screen for AHF.