Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional simulation/printing-assisted surgery for symptomatic metastatic epidural spinal cord compression of posterior column: efficacy assessment based on 2-year follow-up

Background Surgical intervention is necessary for resolving the symptoms of the spinal cord and nerve compression caused by symptomatic metastatic epidural spinal cord compression. However, surgeons are constantly seeking ways to improve surgical efficiency and safety. This study aims to evaluate the efficacy of 3D simulation/printing-assisted surgery for symptomatic metastatic epidural spinal cord compression of the posterior column. Methods We retrospectively analyzed the clinical data of patients who underwent surgical treatment for symptomatic metastatic epidural spinal cord compression of the posterior column in our hospital from January 2015 to January 2020. The simulated group underwent a 3D digital simulation of the lesion area using imaging data before surgery. Twelve patients in the simulated group also received 3D printing, while the direct surgery group did not receive any 3D simulation or printing. All patients were followed up for at least 2 years. We collected clinical data, including operation time, intraoperative blood loss, pedicle screw adjustment rate, intraoperative fluoroscopy times, the incidence of dural injury and cerebrospinal fluid leakage, VAS score, postoperative neurological function improvement, and tumor recurrence. Statistical analysis was performed using SPSS23.0, and P < 0.05 was considered statistically significant. Results A total of 46 patients were included in this study, with 20 in the simulated group and 26 in the non-simulated group. The simulated group had better operation time, intraoperative blood loss, screw adjustment rate, fluoroscopy times, and incidence of dural injury/cerebrospinal fluid leakage compared to the non-simulated group. The VAS scores of the two groups improved significantly after the operation and at the last follow-up compared to before the operation. However, there was no statistically significant difference between the two groups. There was also no statistically significant difference in neurological function improvement between the two groups. In the simulated group, 25% of patients relapsed, while in the non-simulated group, 34.61% of patients relapsed. However, there was no statistical difference between the two groups. Conclusion Preoperative 3D simulation/printing-assisted surgery is a practical and feasible approach for treating symptomatic metastatic epidural spinal cord compression of the posterior column.
Synergistic effect of endurance training and nettle leaf extract on the IDO1-KYN-AHR pathway homeostasis and inhibiting of liver toxicity in rats with STZ-induced diabetes

Introduction Diabetes adversely affects a number of hepatic molecular pathways, including the kynurenine (KYN) pathway. KYN is produced by indoleamine 2,3-dioxygenase (IDO) and activates the aryl hydrocarbon receptor (AHR). This study evaluated the effect of endurance training (EndTr) and nettle leaf extract (NLE) on the IDO1-KYN-AHR pathway in the livers of rats with streptozotocin-induced diabetes. Methods We divided 48 rats into six groups: controls (Ct), treated with EndTr (EndTr), diabetes-induced (D), D treated with NLE (D + NLE), D treated with EndTr (D + EnTr), and D treated with EndTr and NLE (D + EndTr + NLE). EndTr, D + EnTr, and D + EndTr + NLE groups were subjected to training with running on treadmill for 8 weeks, 5 days per week, 25 min in first session to 59 min at last session with intensity of 55% to 65% VO2max. Using real-time PCR gene (Ahr, Cyp1a1, and Ido1) expressions and ELISA, malondialdehyde (MDA) and protein (IDO1, AHR, and CYP1A1) levels were determined in the liver samples. Results A significant three-way interaction of exercise, nettle, and diabetes was observed on the all variables (P< 0.001). In particular, significant increases in blood glucose level (BGL), in gene and protein expression, and in MDA and KYN levels were observed in the liver samples of the D group versus the Ct group (P< 0.05). BGL and liver MDA levels were significantly lower in the D + EndTr and D + NLE groups than that in the D group. However, the D + EndTr + NLE group showed a more significant decrease in these factors (P< 0.05). In addition, liver KYN levels were significantly lower in the EndTr group compared with that in the Ct group as well as in the D + EndTr + NLE and D + EndTr groups compared with that in the D groups (P< 0.05). Whereas both the EndTr and D + NLE groups showed lower Ahr expression and AHR level compared with the Ct and D groups, respectively (P< 0.05), the D + EndTr + NLE group showed a higher significant reduction in the AHR level than the D group (P< 0.05). The Cyp1a1 expression and IDO1 level significantly decreased only in the D + EndTr + NLE group compared to that in the D group (P< 0.05). Conclusion Overall, this study showed that the combination of EndTr and NLE may synergistically restore the imbalanced IDO1-KYN-AHR pathway in diabetic liver.
Conv-trans dual network for landslide detection of multi-channel optical remote sensing images

Landslide detection is crucial for disaster management and prevention. With the advent of multi-channel optical remote sensing technology, detecting landslides have become more accessible and more accurate. Although the use of the convolutional neural network (CNN) has significantly increased the accuracy of landslide detection on multi-channel optical remote sensing images, most previous methods using CNN lack the ability to obtain global context information due to the structural limitations of the convolution operation. Motivated by the powerful global modeling capability of the Swin transformer, we propose a new Conv-Trans Dual Network (CTDNet) based on Swin-Unet. First, we propose a dual-stream module (CTDBlock) that combines the advantages of ConvNeXt and Swin transformer, which can establish pixel-level connections and global dependencies from the CNN hierarchy to enhance the ability of the model to extract spatial information. Second, we apply an additional gating module (AGM) to effectively fuse the low-level information extracted by the shallow network and the high-level information extracted by the deep network and minimize the loss of detailed information when propagating. In addition, We conducted extensive subjective and objective comparison and ablation experiments on the Landslide4Sense dataset. Experimental results demonstrate that our proposed CTDNet outperforms other models currently applied in our experiments.
Stochasticity induced by Electrostatic Waves in Magnetized Plasma

In a plasma immersed in a uniform magnetic field and an electrostatic wave propagating perpendicular to the magnetic field, regions of the charged particle phase space become stochastic as the amplitude of the wave increases above a certain threshold. This thesis studies the development of such stochastic regions when the wave frequency is equal to or near a cyclotron harmonic. Two mechanisms underlie the development of stochasticity, namely: Chirikov overlapping of phase space islands and web stochasticity along the separatrices. Both analytical and numerical results of each mechanism are presented. Thesis Supervisor: Abraham Bers Title: Professor of Electrical Engineering Acknowledgments I would like to thank my thesis advisor Professor Abraham Bers for his continual guidance and support throughout this thesis project. He is a mentor and an educator who greatly enriched my experience in MIT. I would also like to thank Dr. Abhay Ram who gave me a lot of help in my research, Steve Schultz and Luigi Vacca who supported me during the group seminars as well as Laura von Bosau our group secretary who helped me in my paperwork. In addition, I am also very grateful to my parents who have supported me during my years at MIT and Uncle Richard Chan, who has given me much financial assistance. There are many others who have encouraged me throughout my years at MIT, including my academic advisor Professor Charles Leiserson, members of Hong Kong Student Society, Hong Kong Student Bible Study Group , Chinese Bible Fellowship and Chinese Bible Church of Greater Boston. Finally I have to acknowledge that it is God who makes all things bright and beautiful. After all, it is He who created the first and most important thermonuclear fusion reactor known to mankind.
Comparative effects of temperature and salinity on growth of four harmful Chattonella spp. (Raphidophyceae) from tropical Asian waters

In Asia, four harmful raphidophyte species, Chattonella malayana, C. marina, C. subsalsa, and C. tenuiplastida, coexist in the tropical waters but only C. marina was detected in temperate waters. This occurrence pattern pointed to a potentially distinct ecophysiological niche occupancy and possible species dispersion. The growth physiology of these species isolated from tropical Southeast Asia was investigated using unialgal cultures in ten temperatures (13.0–35.5°C) and five salinities (15–35) to better understand the factors driving their distribution. The highest maximum specific growth rates were observed in C. subsalsa (0.65 ± 0.01 d-1), followed by C. malayana (0.47 ± 0.03 d-1), C. marina (0.45 ± 0.02 d-1), and C. tenuiplastida (0.39 ± 0.01 d-1). Their optimal temperatures were 28.0, 30.5, 25.5, and 30.5°C, respectively, of which C. marina preferred colder water. C. subsalsa exhibited a wider growth temperature range (20.5–35.5°C), followed by C. marina (20.5–30.5°C), C. tenuiplastida (23.0–33.0°C), and C. malayana (25.5–33.0°C). Optimal salinities were similar between C. subsalsa and C. malayana (30), and between C. marina and C. tenuiplastida (25), but C. subsalsa and C. marina exhibited a similar growth salinity range of 15–35, while C. malayana and C. tenuiplastida was 20–35. High values of Fv/Fm were observed in C. subsalsa and C. marina (> 0.5) in all tested conditions, but Fv/Fm of C. malayana and C. tenuiplastida were significantly lower at 20.5°C. All four species achieved a maximum cell density of > 104 cells mL-1 in their optimal conditions. Optimal temperatures in C. subsalsa and C. marina were identical to previous reports. The high adaptability of C. subsalsa in various temperatures and salinities suggests its high competitiveness and bloom potential. The high adaptability of C. marina in colder waters compared to other species likely contributes to its wide distribution in the temperate Asian waters. The narrow temperature window of C. malayana and C. tenuiplastida suggests their endemicity and limited distribution in the tropical waters. This study provides evidence about the occurrences and bloom potential of Chattonella spp. in Asia, but the endemicity versus dispersion issue remains unresolved.
Monitoring of ion implantation in microelectronics production environment using multi-channel reflectometry

Optical metrology techniques such as ellipsometry and reflectometry are very powerful for routine process monitoring and control in the modern semiconductor manufacturing industry. However, both methods rely on optical modeling therefore, the optical properties of all materials in the stack need to be characterized a priori or determined during characterization. Some processes such as ion implantation and subsequent annealing produce slight variations in material properties within wafer, wafer-to-wafer, and lot-to-lot; such variation can degrade the dimensional measurement accuracy for both unpatterned optical measurements as well as patterned (2D and 3D) scatterometry measurements. These variations can be accounted for if the optical model of the structure under investigation allows one to extract not just dimensional but also material information already residing within the optical spectra. This paper focuses on modeling of ion implanted and annealed poly Si stacks typically used in high-k technology. Monitoring of ion implantation is often a blind spot in mass production due to capability issues and other limitations of common methods. Typically, the ion implantation dose can be controlled by research-grade ellipsometers with extended infrared range. We demonstrate that multi-channel spectroscopic reflectometry can also be used for ion implant monitoring in the mass-production environment. Our findings are applicable across all technology nodes.
Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs

Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.
Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets

Background Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or ‘omics’ experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.
A Conceptual Framework for Evaluating National Organ Donation and Transplantation Programs

Conceptual frameworks are valuable resources that can be used to guide the planning, evaluation, and development of healthcare services. However, there are currently no comprehensive frameworks focused on organ donation and transplantation that identify the critical factors underlying a successful national program. To address this knowledge gap, we developed a conceptual framework that takes into account all major domains of influence, including political and societal aspects as well as clinical implementation. The framework was initially constructed based on a targeted review of the relevant medical literature. Feedback provided by a panel of international experts was incorporated into the framework via an iterative process. The final framework features 16 essential domains that are critical for initiating and maintaining a successful program and improving the health of patients with organ failure. Of particular note, these domains are subject to three overarching health system principles: responsiveness, efficiency, and equity. This framework represents a first attempt to develop a whole-system view of the various factors that contribute to the success of a national program. These findings provide a useful tool that can be adapted to any jurisdiction and used to plan, evaluate, and improve organ donation and transplantation programs.
‘Choice’ in women’s abortion decision-making narratives: Introducing a supportability approach

Liberal abortion legislation emphasises pregnant persons’ autonomous choices in abortion decision-making. Within psychological theories, decision-making is understood as largely individual, rational and cognitive, with various factors affecting women’s 1 abortion decision-making. In this study, purposively recruited from three sites in South Africa and three sites in Zimbabwe, 25 and 18 women, respectively, participated in narrative interviews which were analysed using thematic analysis and a supportability framework. Participants’ narratives constructed continuation of the pregnancy as a ‘non-option’, abortion emerging as the only solution. Economic resources, gender norms and partnerships, and the undesirability of the pregnancy meant the pregnancy was unsupportable at micro- and macro-levels, and sometimes despite parenting being desired by the women. A supportability framework offers opportunities to understand reproductive decision-making as imbricated in the circumstances of the pregnancy which render it (un)supportable, therefore opening up or closing down particular decisions. This framework enables a necessary shift towards systemic understandings of decision-making, and a possible reduction in abortion-related stigma. The debate on abortion largely revolves around reproductive autonomy: the right of a pregnant person to make decisions concerning their body. Liberal abortion
Conceptual Shifts within Problem Spaces as a Function of Years of Knowledge Building Experience

This thesis explores teaching practice as a function of years of experience with Knowledge Building pedagogy, emphasizing teachers’ continual improvement of practice while they foster continual improvement of students’ ideas. Knowledge Building practice places students’ ideas at the centre of the classroom enterprise, with the principal challenge being enabling students to take effective responsibility for improvement of ideas. Building on a variety of models of teacher thinking and development, a problem space model is developed specifically geared to the development of Knowledge Building practices. This model is used to guide the investigation and provide a theoreticallyand empirically-based description of shifts teachers undergo as they gain skill in Knowledge Building pedagogy. The model also serves to convey how Knowledge Building teachers differ from other skillful teachers. The principal shift is from a centrist to relational (or systemic) perspective. This perspectival shift is examined in five problem spaces: Curriculum/Standards, Social Interaction, Student Capability, Classroom Structures and Constraints, and Technology. Underlying the centrist perspective is a belief in established procedures and goals typically understood to characterize effective teaching. Underlying the relational perspective is a belief in the capacity of students to develop and improve their own ideas, and a belief that in doing so students will not only mature as knowledge-
Bayesian Modelling by Method of Normal Regression: A Case of Modelling Gluten Content in terms of Protein Content in a Variety of Wheat

This article is about the Bayesian modelling of the parameters of a simple linear regression with normal errors. It studies the use of non-informative normal priors to the regression parameters. It has an application on modelling gluten content in terms of protein content of a variety of wheat. The exact estimations of credible sets of the regression parameters obtained from real and simulated data by using MCMC. The posterior estimates of the gluten content in terms of protein content are better in this regression model with normal non-informative prior. Journal of Institute of Science and Technology, 2014, 19(1): 121-128
Supersymmetric small-field inflation with tribrid superpotentials

In this thesis, we study supersymmetric models of tribrid inflation and new inflation, both of which can be realized with a tribrid superpotential. In the first part, we focus on tribrid inflation, deriving relations between cosmological observables and model parameters as well as providing guidelines for embedding tribrid inflation in realistic particle physics models. As an example application, we show how tribrid inflation can be realized in an explicit model of the leptonic flavour structure based on a discrete family symmetry, and how the production of topological defects after inflation can be avoided in such a model. Finally, we consider the possibility of generating the non-renormalizable operators of the tribrid superpotential at a sub-Planckian mass scale, which extends the applicability of tribrid inflation to some models with intermediate energy scales. In the second part, we study new inflation with tribrid superpotentials. We first calculate the effects of the imaginary inflaton component on the primordial perturbations, which are generic for supersymmetric realizations of new inflation and not specific to the tribrid superpotential. Afterwards, we show how the tribrid superpotential coupling provides not only a mechanism for dynamically generating the initial conditions for new inflation, but also an effective inflaton decay channel for reheating after inflation. We also study the different preheating mechanisms in this setup and discuss under which conditions preheating can affect the final reheating phase. In the last part, we consider whether small-field models of slow-roll inflation would remain viable if large primordial tensor perturbations were observed. In particular, we explain why such an observation would imply a scale dependent running of the spectral index, and we derive a general slow-roll bound to prove that an observation of tensor modes close to the current upper bound has the potential to rule out the entire framework of small-field slow-roll inflation employed throughout this thesis.