bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Robustness of Arctic Sea Ice Bi‐Stability in the Presence of Atmospheric Feedbacks
Authors: Hankel Camille , Tziperman Eli
Published: November 2023

Arctic sea‐ice loss is influenced by multiple positive feedbacks, sparking concerns of accelerated loss in the coming years or even a tipping point, where a sea‐ice equilibrium disappears at a given CO2 value and sea ice rapidly evolves to a new steady state. Such a tipping point would imply a bi‐stability of the Arctic climate—where multiple steady‐state Arctic climates are possible at the same CO2 value. Previous works have sought to establish the existence of bi‐stability using a range of models, from zero‐dimensional sea ice thermodynamic models to fully coupled global climate models, with conflicting results. Here, we present a new model of the Arctic that includes both sea‐ice thermodynamics and key atmospheric feedbacks in a simple framework. We exploit the model's simplicity to identify physical mechanisms that control the timing and extent of sea‐ice bi‐stability, and the abruptness of ice loss. We show that longwave radiation feedbacks can have a strong influence on Arctic surface climate from atmospheric temperature increases alone, even without major contributions from clear‐sky moisture or convective clouds suggested previously. While winter sea‐ice bi‐stability is robust to changes in uncertain model parameters in this study, summer sea ice is more sensitive. Finally, our model indicates that positive feedbacks may modulate the CO2 threshold of sea‐ice loss and the width of bi‐stability much more strongly than the abruptness of loss. These results lead to a comprehensive understanding of the conditions that favor Arctic sea‐ice bi‐stability, particularly the role of atmospheric feedbacks, in both future and past climates.
Mesospheric Temperature and Circulation Response to the Hunga Tonga‐Hunga‐Ha'apai Volcanic Eruption
Authors: Yu Wandi , Garcia Rolando , Yue Jia , Smith Anne , Wang Xinyue , Randel William , Qiao Zishun , Zhu Yunqian , Harvey V. Lynn , Tilmes Simone , Mlynczak Martin
Published: November 2023

The Hunga Tonga Hunga‐Ha'apai (HTHH) volcanic eruption on 15 January 2022 injected water vapor and SO2 into the stratosphere. Several months after the eruption, significantly stronger westerlies, and a weaker Brewer‐Dobson circulation developed in the stratosphere of the Southern Hemisphere and were accompanied by unprecedented temperature anomalies in the stratosphere and mesosphere. In August 2022, the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument observed record‐breaking temperature anomalies in the stratosphere and mesosphere that alternate signs with altitude. Ensemble simulations carried out with the Whole Atmosphere Community Climate Model (WACCM6) indicate that the strengthening of the stratospheric westerlies explains the mesospheric temperature changes. The stronger westerlies cause stronger westward gravity wave drag in the mesosphere. Although the enhanced gravity wave drag is partly balanced by a weakening of planetary wave forcing, the net result is an acceleration of the mesospheric mean meridional circulation. The stronger mesospheric circulation, in turn, plays a dominant role in driving the changes in mesospheric temperatures. This study highlights the impact of large volcanic eruptions on middle atmospheric dynamics and provides insight into their long‐term effects in the mesosphere. On the other hand, we could not discern a clear mechanism for the observed changes in stratospheric circulation. In fact, an examination of the WACCM ensemble reveals that not every member reproduces the large changes observed by SABER. We conclude that there is a stochastic component to the stratospheric response to the HTHH eruption.
Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization
Authors: Yasuda Yuki , Onishi Ryo
Published: November 2023

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational costs. Super‐resolution is one such technique for high‐resolution inference from low‐resolution data. This paper proposes a new scheme, called four‐dimensional super‐resolution data assimilation (4D‐SRDA). This framework calculates the time evolution of a system from low‐resolution simulations using a physics‐based model, while a trained neural network simultaneously performs data assimilation and spatio‐temporal super‐resolution. The use of low‐resolution simulations without ensemble members reduces the computational cost of obtaining inferences at high spatio‐temporal resolution. In 4D‐SRDA, physics‐based simulations and neural‐network inferences are performed alternately, possibly causing a domain shift, that is, a statistical difference between the training and test data, especially in offline training. Domain shifts can reduce the accuracy of inference. To mitigate this risk, we developed super‐resolution mixup (SR‐mixup)–a data augmentation method for domain generalization. SR‐mixup creates a linear combination of randomly sampled inputs, resulting in synthetic data with a different distribution from the original data. The proposed methods were validated using an idealized barotropic ocean jet with supervised learning. The results suggest that the combination of 4D‐SRDA and SR‐mixup is effective for robust inference cycles. This study highlights the potential of super‐resolution and domain‐generalization techniques, in the field of data assimilation, especially for the integration of physics‐based and data‐driven models.
Estimating Plio‐Pleistocene North African Monsoon Runoff Into the Mediterranean Sea and Temperature Impacts
Authors: Heslop D. , Amarathunga U. , Rohling E. J.
Published: November 2023

Sapropels are dark, organic‐rich layers found in Mediterranean sediments that formed during periods of bottom water anoxia. While various mechanisms have been proposed to have caused anoxic conditions, a primary factor is considered to be water column stratification induced by freshwater runoff related to intensified North African monsoon precipitation during precession minima. Monsoon intensification also induced Green Sahara Periods that may have impacted North African hominin dispersal. In this study, we present a novel regression‐based deconvolution of a high‐resolution planktonic foraminiferal oxygen isotope record to estimate the combination of freshwater runoff reaching the eastern Mediterranean and associated surface warming of the water column over the past 5 million years. Sapropels are known to occur in clusters associated with periods of high orbital eccentricity. Our analysis reveals a consistent influence of orbital eccentricity in modulating the North African monsoon, and a possible shift in runoff source area induced by the initiation of Northern Hemisphere ice sheets. Our findings provide important insights into the role of the North African monsoon in shaping Mediterranean environmental changes over the past 5 million years.
Structural adaptation of fungal cell wall in hypersaline environment
Authors: Fernando Liyanage D. , Pérez-Llano Yordanis , Dickwella Widanage Malitha C. , Jacob Anand , Martínez-Ávila Liliana , Lipton Andrew S. , Gunde-Cimerman Nina , Latgé Jean-Paul , Batista-García Ramón Alberto , Wang Tuo
Published: November 2023

Halophilic fungi thrive in hypersaline habitats and face a range of extreme conditions. These fungal species have gained considerable attention due to their potential applications in harsh industrial processes, such as bioremediation and fermentation under unfavorable conditions of hypersalinity, low water activity, and extreme pH. However, the role of the cell wall in surviving these environmental conditions remains unclear. Here we employ solid-state NMR spectroscopy to compare the cell wall architecture of Aspergillus sydowii across salinity gradients. Analyses of intact cells reveal that A. sydowii cell walls contain a rigid core comprising chitin, β-glucan, and chitosan, shielded by a surface shell composed of galactomannan and galactosaminogalactan. When exposed to hypersaline conditions, A. sydowii enhances chitin biosynthesis and incorporates α-glucan to create thick, stiff, and hydrophobic cell walls. Such structural rearrangements enable the fungus to adapt to both hypersaline and salt-deprived conditions, providing a robust mechanism for withstanding external stress. These molecular principles can aid in the optimization of halophilic strains for biotechnology applications.
Daily rhythm in cortical chloride homeostasis underpins functional changes in visual cortex excitability
Authors: Pracucci Enrico , Graham Robert T. , Alberio Laura , Nardi Gabriele , Cozzolino Olga , Pillai Vinoshene , Pasquini Giacomo , Saieva Luciano , Walsh Darren , Landi Silvia , Zhang Jinwei , Trevelyan Andrew J. , Ratto Gian-Michele
Published: November 2023

Cortical activity patterns are strongly modulated by fast synaptic inhibition mediated through ionotropic, chloride-conducting receptors. Consequently, chloride homeostasis is ideally placed to regulate activity. We therefore investigated the stability of baseline [Cl-]i in adult mouse neocortex, using in vivo two-photon imaging. We found a two-fold increase in baseline [Cl-]i in layer 2/3 pyramidal neurons, from day to night, with marked effects upon both physiological cortical processing and seizure susceptibility. Importantly, the night-time activity can be converted to the day-time pattern by local inhibition of NKCC1, while inhibition of KCC2 converts day-time [Cl-]i towards night-time levels. Changes in the surface expression and phosphorylation of the cation-chloride cotransporters, NKCC1 and KCC2, matched these pharmacological effects. When we extended the dark period by 4 h, mice remained active, but [Cl-]i was modulated as for animals in normal light cycles. Our data thus demonstrate a daily [Cl-]i modulation with complex effects on cortical excitability.
Rational design of a JAK1-selective siRNA inhibitor for the modulation of autoimmunity in the skin
Authors: Tang Qi , Fakih Hassan H. , Zain UI Abideen Mohammad , Hildebrand Samuel R. , Afshari Khashayar , Gross Katherine Y. , Sousa Jacquelyn , Maebius Allison S. , Bartholdy Christina , Søgaard Pia Pernille , Jackerott Malene , Hariharan Vignesh , Summers Ashley , Fan Xueli , Okamura Ken , Monopoli Kathryn R. , Cooper David A. , Echeverria Dimas , Bramato Brianna , McHugh Nicholas , Furgal Raymond C. , Dresser Karen , Winter Sarah J. , Biscans Annabelle , Chuprin Jane , Haddadi Nazgol-Sadat , Sherman Shany , Yıldız-Altay Ümmügülsüm , Rashighi Mehdi , Richmond Jillian M. , Bouix-Peter Claire , Blanchard Carine , Clauss Adam , Alterman Julia F. , Khvorova Anastasia , Harris John E.
Published: November 2023

Inhibition of Janus kinase (JAK) family enzymes is a popular strategy for treating inflammatory and autoimmune skin diseases. In the clinic, small molecule JAK inhibitors show distinct efficacy and safety profiles, likely reflecting variable selectivity for JAK subtypes. Absolute JAK subtype selectivity has not yet been achieved. Here, we rationally design small interfering RNAs (siRNAs) that offer sequence-specific gene silencing of JAK1, narrowing the spectrum of action on JAK-dependent cytokine signaling to maintain efficacy and improve safety. Our fully chemically modified siRNA supports efficient silencing of JAK1 expression in human skin explant and modulation of JAK1-dependent inflammatory signaling. A single injection into mouse skin enables five weeks of duration of effect. In a mouse model of vitiligo, local administration of the JAK1 siRNA significantly reduces skin infiltration of autoreactive CD8+ T cells and prevents epidermal depigmentation. This work establishes a path toward siRNA treatments as a new class of therapeutic modality for inflammatory and autoimmune skin diseases.
Systemic and intrinsic functions of ATRX in glial cell fate and CNS myelination in male mice
Authors: Rowland Megan E. , Jiang Yan , Shafiq Sarfraz , Ghahramani Alireza , Pena-Ortiz Miguel A. , Dumeaux Vanessa , Bérubé Nathalie G.
Published: November 2023

Myelin, an extension of the oligodendrocyte plasma membrane, wraps around axons to facilitate nerve conduction. Myelination is compromised in ATR-X intellectual disability syndrome patients, but the causes are unknown. We show that loss ofATRXleads to myelination deficits in male mice that are partially rectified upon systemic thyroxine administration. Targeted ATRX inactivation in either neurons or oligodendrocyte progenitor cells (OPCs) reveals OPC-intrinsic effects on myelination. OPCs lacking ATRX fail to differentiate along the oligodendrocyte lineage and acquire a more plastic state that favors astrocytic differentiation in vitro and in vivo. ATRX chromatin occupancy in OPCs greatly overlaps with that of the chromatin remodelers CHD7 and CHD8 as well as H3K27Ac, a mark of active enhancers. Overall, our data indicate that ATRX regulates the onset of myelination systemically via thyroxine, and by promoting OPC differentiation and suppressing astrogliogenesis. These functions of ATRX identified in mice could explain white matter pathogenesis observed in ATR-X syndrome patients.
Heat current-driven topological spin texture transformations and helical q-vector switching
Authors: Yasin Fehmi Sami , Masell Jan , Karube Kosuke , Shindo Daisuke , Taguchi Yasujiro , Tokura Yoshinori , Yu Xiuzhen
Published: November 2023

The use of magnetic states in memory devices has a history dating back decades, and the experimental discovery of magnetic skyrmions and subsequent demonstrations of their control via magnetic fields, heat, and electric/thermal currents have ushered in a new era for spintronics research and development. Recent studies have experimentally discovered the antiskyrmion, the skyrmion’s antiparticle, and while several host materials have been identified, control via thermal current remains elusive. In this work, we use thermal current to drive the transformation between skyrmions, antiskyrmions and non-topological bubbles, as well as the switching of helical states in the antiskyrmion-hosting ferromagnet (Fe0.63Ni0.3Pd0.07)3P at room temperature. We discover that a temperature gradient $${{{{{\boldsymbol{\nabla }}}}}}T$$
∇
T
drives a transformation from antiskyrmions to non-topological bubbles to skyrmions while under a magnetic field and observe the opposite, unidirectional transformation from skyrmions to antiskyrmions at zero-field, suggesting that the antiskyrmion, more so than the skyrmion, is robustly metastable at zero field.
Nucleophilic aromatization of monoterpenes from isoprene under nickel/iodine cascade catalysis
Authors: Zhang Wei-Song , Ji Ding-Wei , Yang Yang , Song Ting-Ting , Zhang Gong , Wang Xiao-Yu , Chen Qing-An
Published: November 2023

As a large number of organic compounds possessing two isoprene units, monoterpenes and monoterpenoids play important roles in pharmaceutical, cosmetic, agricultural, and food industries. In nature, monoterpenes are constructed from geranyl pyrophosphate (C10) via various transformations. Herein, the bulk C5 chemical—isoprene, is used for the creation of various monoterpenoids via a nucleophilic aromatization of monoterpenes under cascade catalysis of nickel and iodine. Drugs and oil mixtures from conifer and lemon can be convergently transformed to the desired monoterpenoid. Preliminary mechanistic studies are conducted to get insights about reaction pathway. Two types of cyclic monoterpenes can be respectively introduced onto two similar heterocycles via orthogonal C–H functionalization. And various hybrid terpenyl indoles are programmatically assembled from abundant C5 or C10 blocks. This work not only contributes a high chemo-, regio-, and redox-selective transformation of isoprene, but also provides a complementary approach for the creation of unnatural monoterpenoids.
Bioinspired soft robots for deep-sea exploration
Authors: Li Guorui , Wong Tuck-Whye , Shih Benjamin , Guo Chunyu , Wang Luwen , Liu Jiaqi , Wang Tao , Liu Xiaobo , Yan Jiayao , Wu Baosheng , Yu Fajun , Chen Yunsai , Liang Yiming , Xue Yaoting , Wang Chengjun , He Shunping , Wen Li , Tolley Michael T. , Zhang A-Man , Laschi Cecilia , Li Tiefeng
Published: November 2023

The deep ocean, Earth’s untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines. This perspective addresses challenges, recent strides, and design strategies for bioinspired deep-sea soft robots. Drawing from abyssal life, it explores the actuation, sensing, power, and pressure resilience of multifunctional deep-sea soft robots, offering game-changing solutions for profound exploration and operation in harsh conditions.
Tailoring grain boundary stability of zinc-titanium alloy for long-lasting aqueous zinc batteries
Authors: Zhao Yunxiang , Guo Shan , Chen Manjing , Lu Bingan , Zhang Xiaotan , Liang Shuquan , Zhou Jiang
Published: November 2023

The detrimental parasitic reactions and uncontrolled deposition behavior derived from inherently unstable interface have largely impeded the practical application of aqueous zinc batteries. So far, tremendous efforts have been devoted to tailoring interfaces, while stabilization of grain boundaries has received less attention. Here, we demonstrate that preferential distribution of intermetallic compounds at grain boundaries via an alloying strategy can substantially suppress intergranular corrosion. In-depth morphology analysis reveals their thermodynamic stability, ensuring sustainable potency. Furthermore, the hybrid nucleation and growth mode resulting from reduced Gibbs free energy contributes to the spatially uniform distribution of Zn nuclei, promoting the dense Zn deposition. These integrated merits enable a high Zn reversibility of 99.85% for over 4000 cycles, steady charge-discharge at 10 mA cm−2, and impressive cyclability for roughly 3500 cycles in Zn-Ti//NH4V4O10 full cell. Notably, the multi-layer pouch cell of 34 mAh maintains stable cycling for 500 cycles. This work highlights a fundamental understanding of microstructure and motivates the precise tuning of grain boundary characteristics to achieve highly reversible Zn anodes.
IL-21R-STAT3 signalling initiates a differentiation program in uterine tissue-resident NK cells to support pregnancy
Authors: Han Mengwei , Hu Luni , Wu Di , Zhang Yime , Li Peng , Zhao Xingyu , Zeng Yanyu , Ren Guanqun , Hou Zhiyuan , Pang Yanli , Zhao Tongbiao , Zhong Chao
Published: November 2023

Tissue-resident Natural Killer (trNK) cells are crucial components of local immunity that activate rapidly upon infection. However, under steady state conditions, their responses are tightly controlled to prevent unwanted tissue damage. The mechanisms governing their differentiation and activation are not fully understood. Here, we characterise uterine trNK cells longitudinally during pregnancy by single cell RNA sequencing and find that the combined expression pattern of 4-1BB and CD55 defines their three distinct stages of differentiation in mice. Mechanistically, an IL-21R-STAT3 axis is essential for initiating the trNK cell differentiation. The fully differentiated trNK cells demonstrate enhanced functionality, which is necessary for remodelling spiral arteries in the decidua. We identify an apoptotic program that is specific to the terminal differentiation stage, which may preclude tissue damage by these highly activated trNK cells. In summary, uterine trNK cells become intensely active and effective during pregnancy, but tightly controlled via a differentiation program that also limits potential harm, suggesting an intricate mechanism for harnessing trNK cells in maintaining pregnancy.
Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome
Authors: Yavuz Bengi Ruken , Arici M. Kaan , Demirel Habibe Cansu , Tsai Chung-Jung , Jang Hyunbum , Nussinov Ruth , Tuncbag Nurcan
Published: November 2023

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.
Chromosome-level genome assembly of the northern Pacific seastar Asterias amurensis
Authors: Wang Yanlin , Wang Yixin , Yang Yujia , Ni Gang , Li Yulong , Chen Muyan
Published: November 2023

Asterias amurensis has attracted widespread concern because of its population outbreaks, which has impacted fisheries and aquaculture, as well as disrupting local ecosystems. A high-quality reference genome is necessary to better investigate mechanisms of outbreak and adaptive changes. Combining PacBio HiFi and Hi-C sequencing data, we generated a chromosome-level A. amurensis genome with a size of 491.53 Mb. The contig N50 and scaffold N50 were 8.05 and 23.75 Mb, respectively. The result of BUSCO analysis revealed a completeness score of 98.85%. A total of 16,531 protein-coding genes were predicted in the genome, of which 94.63% were functionally annotated. The high-quality genome assembly resulting from this study will provide a valuable genetic resource for future research on the mechanism of population outbreaks and invasion ecology.
Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework
Authors: Stebani Jannik , Blaimer Martin , Zabler Simon , Neun Tilmann , Pelt Daniël M. , Rak Kristen
Published: November 2023

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ($$N=43$$

N
=
43

) and clinical practice ($$N=9$$

N
=
9

). The model robustness was further evaluated on three independent open-source datasets ($$N = 23{} + 7{} + 17$$

N
=
23

+
7

+
17

scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of $$\text{0.97 and 0.94}$$

0.97 and 0.94

, intersection-over-union scores of $$\text{0.94 and 0.89}$$

0.94 and 0.89

and average Hausdorff distances of $$0.065{}$$

0.065

and $$0.14{}$$

0.14

voxel units were achieved. The landmark localization task was performed automatically with an average localization error of $$\text{3.3 and 5.2}$$

3.3 and 5.2

voxel units. A robust, albeit reduced performance could be attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance benefit of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.
Psychiatric comorbidities and concurrent substance use among people who inject drugs: a single-centre hospital-based study
Authors: Kar Hadiya , Gania Abdul Majid , Bandy Altaf , ud din Dar Nizam , Rafiq Farhana
Published: November 2023

The management of people who inject drugs (PWID) is compounded by the presence of psychiatric comorbidities leading to frequent relapses and poor treatment outcomes. Early identification and treatment of psychiatric comorbidities should be included in the management to enhance treatment outcomes. The objective of this study was to estimate the prevalence of psychiatric comorbidities and concurrent substance use among opioid injectors. This hospital-based, cross-sectional study was conducted from March 2021 to August 2022. This study included opioid injectors of all ages and both sexes. The Mini International Neuropsychiatric Interview-7 (MINI-7) and WHO-ASSIST were used to determine psychiatric comorbidities and concurrent substance use, respectively. Both crude and adjusted odds ratios were calculated to assess associations among demographic variables, concurrent substance use and psychiatric comorbidities. Among the 328 opioid injectors, the overall prevalence of psychiatric comorbidities was 88.1%, with the majority (68.6%) having more than one comorbidity. The most common psychiatric comorbidities were panic disorder (41.2%), social anxiety disorder (40.5%), and antisocial personality disorder (39.3%). Concurrent use of alcoholic beverages doubled the risk of ASPD (odds ratio 2.14 (1.24–3.72)). Cocaine (odds ratio 2.36 (1.10–5.03)) and amphetamines (odds ratio 7.68 (2.21–26.65)) increased the risk of OCD. Daily heroin injections were negatively associated (odds ratio 0.18 (0.03–0.94)) with psychotic disorders. Younger age (adjusted odds ratio 0.20 (0.79–0.53)) and never married status (adjusted odds ratio 2.62 (1.06–6.47)) were the only significant variables in the regression analysis. In conclusion, opioid injectors had a higher prevalence of numerous psychiatric comorbidities. The most common comorbidity was anxiety disorders. Concurrent use of tobacco, cannabis, cocaine, inhalants, etc., greatly increased the risk of psychiatric comorbidities.
The effects of psychosocial and behavioral interventions on depressive and anxiety symptoms during the COVID-19 pandemic: a systematic review and meta-analysis
Authors: He Jiali , Lin Jingxia , Sun Wen , Cheung Teris , Cao Yuan , Fu Eugene , Chan Sunny H. W. , Tsang Hector W. H.
Published: November 2023

Psychosocial and behavioral interventions have been shown to significantly reduce depressive and anxiety symptoms in different populations. Recent evidence suggests that the mental health of the general population has deteriorated significantly since the start of Coronavirus Disease 2019 (COVID-19) pandemic. We conducted a systematic review and meta-analysis of studies on the therapeutic effects of psychosocial and behavioral interventions on depression and anxiety during the COVID-19 pandemic. We systematically searched six electronic databases between December 2019 and February 2022 including PubMed, PsycINFO, Scopus, Web of Science, CNKI, and Wanfang Data. We included randomized clinical trials of psychosocial and behavioral interventions in individuals with depressive or anxiety symptoms during the COVID-19 outbreak compared to various control conditions. A total of 35 eligible studies with 5457 participants were included. The meta-analysis results showed that psychosocial and behavioral interventions had statistically significant moderate effects on depression [SMD =  − 0.73, 95% CI (− 1.01, − 0.45), I2 = 90%] and large effects on anxiety [SMD =  − 0.90, 95% CI (− 1.19, − 0.60), I2 = 92%], especially in the general population and COVID-19 survivors. Exercise and cognitive behavioral therapy were found to be the most effective treatments with moderate-to-large effect size for depression and anxiety during the outbreak of COVID-19. We also found the internet-based approach could also achieve almost equally significant effects on depression and anxiety compared with face-to-face traditional approach. Our findings suggest that cognitive behavioral therapy and physical exercise intervention are significantly effective for depression and anxiety related to the COVID-19 pandemic regardless of the delivery modes, and gender differences should be taken into consideration for better implementation of interventions in clinical and community practice.
Transformer-based convolutional forgetting knowledge tracking
Authors: Liu Tieyuan , Zhang Meng , Zhu Chuangying , Chang Liang
Published: November 2023

Knowledge tracking is to analyze the mastery of students' knowledge through the learning track. This is very important for online education, since it can determine a learner’s current knowledge level by analyzing the learning history and then make recommendations for future learning. In the past, the commonly used model for knowledge tracking is the convolutional neural network, but it has long-term sequence dependencies. With the invention of Transformer, it has excellent performance in long-sequence modeling by virtue of the attention mechanism, and is gradually introduced into the field of knowledge tracking. However, through our research, some knowledge tracking data sets have a large number of continuous and repetitive training, which will cause Transformer model to ignore the potential connections between some knowledge points. To overcome this problem, we introduce a convolutional attention mechanism to help the model perceive contextual information better. In addition, we simulate the forgetting phenomenon of students during the learning process by calculating the forgetting factor, and fuse it with the weight matrix generated by the model to improve the accuracy of the model. As a result, a Transformer-based Convolutional Forgetting Knowledge Tracking (TCFKT) model is presented in this paper. According to the experimental results conducted on the real world ASSITments2012, ASSISTments2017, KDD a, STATIC datasets, the TCFKT model outperforms other knowledge tracking models.
Electrochemical etching strategy for shaping monolithic 3D structures from 4H-SiC wafers
Authors: Hochreiter André , Groß Fabian , Möller Morris-Niklas , Krieger Michael , Weber Heiko B.
Published: November 2023

Silicon Carbide (SiC) is an outstanding material, not only for electronic applications, but also for projected functionalities in the realm of spin-based quantum technologies, nano-mechanical resonators and photonics-on-a-chip. For shaping 3D structures out of SiC wafers, predominantly dry-etching techniques are used. SiC is nearly inert with respect to wet etching, occasionally photoelectrochemical etching strategies have been applied. Here, we propose an electrochemical etching strategy that solely relies on defining etchable volumina by implantation of p-dopants. Together with the inertness of the n-doped regions, very sharp etching contrasts can be achieved. We present devices as different as monolithic cantilevers, disk-shaped optical resonators and membranes etched out of a single crystal wafer. The high quality of the resulting surfaces can even be enhanced by thermal treatment, with shape-stable devices up to and even beyond 1550°C. The versatility of our approach paves the way for new functionalities on SiC as high-performance multi-functional wafer platform.
Water filtration by endobenthic sandprawns enhances resilience against eutrophication under experimental global change conditions
Authors: Thomas C. M. , de Cerff C. , Maniel G. A. V. , Oyatoye A. E. , Rocke E. , Marco H. G. , Pillay D.
Published: November 2023

Identifying processes that confer resilience against global change is a scientific challenge but is central to managing ecosystem functionality in future. Detecting resilience-enhancing mechanisms is especially relevant in coastal ecosystems, where multi-stressor interactions can drive degradation over time. Here, we quantify the resilience-conferring potential of endobenthic sandprawns against eutrophication, including under high temperatures. We show using a global change mesocosm experiment that sandprawn presence was associated with declines in phytoplankton biomass, particularly under eutrophic conditions, where sandprawns reduced phytoplankton biomass by approximately 74% and prevented a shift to extreme eutrophy. Eutrophic waters were nanophytoplankton-dominated, but sandprawn presence countered this, resulting in even contributions of pico- and nanophytoplankton. Our findings highlight the potential for sandprawns to increase resilience against eutrophication by limiting phytoplankton blooms, preventing extreme eutrophy and counteracting nanophytoplankton dominance. Incorporating endobenthic crustaceans into resilience-based management practices can assist in arresting future water quality declines in coastal ecosystems.
Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules
Authors: Selvam Minmini , Chandrasekharan Anupama , Sadanandan Abjasree , Anand Vikas Kumar , Murali Arunan , Krishnamurthi Ganapathy
Published: November 2023

In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.
Construction and application of flow pressure drop model of perforated well considering pressure loss of perforation hole
Authors: Jiang Hongfeng , Wu Muwang , Zheng Yongjian , Zhao Qibin , Gao Yongde
Published: November 2023

Perforating well is one of the main production wells in reservoir development. Perforating effect directly affects well production, so the optimization of perforating parameters has attracted wide attention. Because pressure difference serves as the driving force for fluid flowing from formation to wellbore, it is important to understand the composition of production pressure difference in perforating well, which can guide the optimization of perforating parameters and the evaluation of perforating effect. In order to clarify the composition of production pressure difference during the production process of perforated wells, a pressure drop model pressure drop model is established based on fluid mechanics theory, which includes a pressure drop model of formation and a pressure drop model of perforation hole. The pressure drop model of formation is firstly constructed based on the Darcy's law and the equivalent resistance method, and the pressure drop model of perforation hole is built by the fluid tube-flow theory. Secondly, the numerical calculation method is adopted to realize the coupling solution of models, and the accuracy of this model is verified by comparison of the Karakas-Tariq model. Finally, the effects of formation physical properties and perforating parameters on flow pressure drop are discussed. The results show that there is a difference of more than 2 orders of magnitude between the pressure drop generated in perforation hole and flow pressure difference, and pressure drop of perforation hole can be neglected in practical applications. Comparing with medium–high permeability reservoirs, optimizing perforation parameters in low permeability reservoirs has a more significant impact on flow pressure drop. Among perforating parameters, perforation length and perforation density have great influence on flow pressure difference, while perforation diameter and phase angle have relatively little influence. These results have certain guiding significance for optimizing perforating parameters in different permeability reservoirs.
A non-invasive method for concurrent detection of multiple early-stage cancers in women
Authors: Gupta Ankur , Siddiqui Zaved , Sagar Ganga , Rao Kanury V. S. , Saquib Najmuddin
Published: November 2023

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.
Altered Gut Microbiota and Short-chain Fatty Acids in Chinese Children with Constipated Autism Spectrum Disorder
Authors: He Jianquan , Gong Xiuhua , Hu Bing , Lin Lin , Lin Xiujuan , Gong Wenxiu , Zhang Bangzhou , Cao Man , Xu Yanzhi , Xia Rongmu , Zheng Guohua , Wu Shuijin , Zhang Yuying
Published: November 2023

Gastrointestinal symptoms are more prevalent in children with autism spectrum disorder (ASD) than in typically developing (TD) children. Constipation is a significant gastrointestinal comorbidity of ASD, but the associations among constipated autism spectrum disorder (C-ASD), microbiota and short-chain fatty acids (SCFAs) are still debated. We enrolled 80 children, divided into the C-ASD group (n = 40) and the TD group (n = 40). In this study, an integrated 16S rRNA gene sequencing and gas chromatography–mass spectrometry-based metabolomics approach was applied to explore the association of the gut microbiota and SCFAs in C-ASD children in China. The community diversity estimated by the Observe, Chao1, and ACE indices was significantly lower in the C-ASD group than in the TD group. We observed that Ruminococcaceae_UCG_002, Erysipelotrichaceae_UCG_003, Phascolarctobacterium, Megamonas, Ruminiclostridium_5, Parabacteroides, Prevotella_2, Fusobacterium, and Prevotella_9 were enriched in the C-ASD group, and Anaerostipes, Lactobacillus, Ruminococcus_gnavus_group, Lachnospiraceae_NK4A136_group, Ralstonia, Eubacterium_eligens_group, and Ruminococcus_1 were enriched in the TD group. The propionate levels, which were higher in the C-ASD group, were negatively correlated with the abundance of Lactobacillus taxa, but were positively correlated with the severity of ASD symptoms. The random forest model, based on the 16 representative discriminant genera, achieved a high accuracy (AUC = 0.924). In conclusion, we found that C-ASD is related to altered gut microbiota and SCFAs, especially decreased abundance of Lactobacillus and excessive propionate in faeces, which provide new clues to understand C-ASD and biomarkers for the diagnosis and potential strategies for treatment of the disorder. This study was registered in the Chinese Clinical Trial Registry (www.chictr.org.cn; trial registration number ChiCTR2100052106; date of registration: October 17, 2021).
Genome mining conformance to metabolite profile of Bacillus strains to control potato pathogens
Authors: Lagzian Arezoo , Riseh Roohallah Saberi , Sarikhan Sajjad , Ghorbani Abozar , Khodaygan Pejman , Borriss Rainer , Guzzi Pietro Hiram , Veltri Pierangelo
Published: November 2023

Biocontrol agents are safe and effective methods for controlling plant disease pathogens, such as Fusarium solani, which causes dry wilt, and Pectobacterium spp., responsible for potato soft rot disease. Discovering agents that can effectively control both fungal and bacterial pathogens in potatoes has always presented a challenge. Biological controls were investigated using 500 bacterial strains isolated from rhizospheric microbial communities, along with two promising biocontrol strains: Pseudomonas (T17-4 and VUPf5). Bacillus velezensis (Q12 and US1) and Pseudomonas chlororaphis VUPf5 exhibited the highest inhibition of fungal growth and pathogenicity in both laboratory (48%, 48%, 38%) and greenhouse (100%, 85%, 90%) settings. Q12 demonstrated better control against bacterial pathogens in vivo (approximately 50%). Whole-genome sequencing of Q12 and US1 revealed a genome size of approximately 4.1 Mb. Q12 had 4413 gene IDs and 4300 coding sequences, while US1 had 4369 gene IDs and 4255 coding sequences. Q12 exhibited a higher number of genes classified under functional subcategories related to stress response, cell wall, capsule, levansucrase synthesis, and polysaccharide metabolism. Both Q12 and US1 contained eleven secondary metabolite gene clusters as identified by the antiSMASH and RAST servers. Notably, Q12 possessed the antibacterial locillomycin and iturin A gene clusters, which were absent in US1. This genetic information suggests that Q12 may have a more pronounced control over bacterial pathogens compared to US1. Metabolic profiling of the superior strains, as determined by LC/MS/MS, validated our genetic findings. The investigated strains produced compounds such as iturin A, bacillomycin D, surfactin, fengycin, phenazine derivatives, etc. These compounds reduced spore production and caused deformation of the hyphae in F. solani. In contrast, B. velezensis UR1, which lacked the production of surfactin, fengycin, and iturin, did not affect these structures and failed to inhibit the growth of any pathogens. Our findings suggest that locillomycin and iturin A may contribute to the enhanced control of bacterial pectolytic rot by Q12.
Relay learning: a physically secure framework for clinical multi-site deep learning
Authors: Bo Zi-Hao , Guo Yuchen , Lyu Jinhao , Liang Hengrui , He Jianxing , Deng Shijie , Xu Feng , Lou Xin , Dai Qionghai
Published: November 2023

Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and reuse of data across sites. Existing approaches to multi-site clinical learning heavily depend on the security of the network firewall and system implementation. To address this issue, we propose Relay Learning, a secure deep-learning framework that physically isolates clinical data from external intruders while still leveraging the benefits of multi-site big data. We demonstrate the efficacy of Relay Learning in three medical tasks of different diseases and anatomical structures, including structure segmentation of retina fundus, mediastinum tumors diagnosis, and brain midline localization. We evaluate Relay Learning by comparing its performance to alternative solutions through multi-site validation and external validation. Incorporating a total of 41,038 medical images from 21 medical hosts, including 7 external hosts, with non-uniform distributions, we observe significant performance improvements with Relay Learning across all three tasks. Specifically, it achieves an average performance increase of 44.4%, 24.2%, and 36.7% for retinal fundus segmentation, mediastinum tumor diagnosis, and brain midline localization, respectively. Remarkably, Relay Learning even outperforms central learning on external test sets. In the meanwhile, Relay Learning keeps data sovereignty locally without cross-site network connections. We anticipate that Relay Learning will revolutionize clinical multi-site collaboration and reshape the landscape of healthcare in the future.
Non-isocyanate polyurethanes synthesized from terpenes using thiourea organocatalysis and thiol-ene-chemistry
Authors: Scheelje Frieda Clara M. , Meier Michael A. R.
Published: November 2023

The depletion of fossil resources as well as environmental concerns contribute to an increasing focus on finding more sustainable approaches for the synthesis of polymeric materials. In this work, a synthesis route towards non-isocyanate polyurethanes (NIPUs) using renewable starting materials is presented. Based on the terpenes limonene and carvone as renewable resources, five-membered cyclic carbonates are synthesized and ring-opened with allylamine, using thiourea compounds as benign and efficient organocatalysts. Thus, five renewable AA monomers are obtained, bearing one or two urethane units. Taking advantage of the terminal double bonds of these AA monomers, step-growth thiol-ene polymerization is performed using different dithiols, to yield NIPUs with molecular weights of above 10 kDa under mild conditions. Variation of the dithiol and amine leads to polymers with different properties, with Mn of up to 31 kDa and Tg’s ranging from 1 to 29 °C.
Unraveling the ultrafast dynamics of thermal-energy chemical reactions
Authors: Robinson Matthew S. , Küpper Jochen
Published: November 2023

We discuss how one can initiate, image, and disentangle the ultrafast elementary steps of thermal-energy chemical dynamics, building on advances in controlling molecules, producing ultrashort mid-infrared pulses, and frontier imaging techniques.
Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant
Authors: Hamada Mazen S. , Zaqoot Hossam Adel , Sethar Waqar Ahmed
Published: November 2023

This paper presents the use of a machine learning approach to predict the performance of a Gaza wastewater treatment plant.
A new risk assessment approach for design of hybrid microgrids considering stability issues
Authors: Azizi Ali , Blaabjerg Frede , Peyghami Saeed
Published: November 2023

Stability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk.