Miracle-dz/UniFAR-FTUs huggingface dataset:
Arabidopsis T‐DNA mutants affected in TRDMT1/DNMT2 show differential protein synthesis and compromised stress tolerance

TRDMT1/DNMT2 belongs to the conserved family of nucleic acid methyltransferases. Unlike the animal systems, studies on TRDMT1/DNMT2 in land plants have been limited. We show that TRDMT1/DNMT2 is strongly conserved in the green lineage. Studies in mosses have previously shown that TRDMT1/DNMT2 plays a crucial role in modulating molecular networks involved in stress perception and signalling and in transcription/stability of specific tRNAs under stress. To gain deeper insight into its biological roles in a flowering plant, we examined more closely the previously reported Arabidopsis SALK_136635C line deficient in TRDMT1/DNMT2 function [Goll MG et al. (2006) Science 311, 395–398]. RNAs derived from Arabidopsis Dnmt2‐deficient plants lacked m5C38 in tRNAAsp. In this study, by transient expression assays we show that Arabidopsis TRDMT1/DNMT2 is distributed in the nucleus, cytoplasm and RNA‐processing bodies, suggesting a role for TRDMT1/DNMT2 in RNA metabolic processes possibly by shuttling between cellular compartments. Bright‐field and high‐resolution SEM and qPCR analysis reveal roles of TRDMT1/DNMT2 in proper growth and developmental progression. Quantitative proteome analysis by LC–MS/MS coupled with qPCR shows AtTRDMT1/AtDNMT2 function to be crucial for protein synthesis and cellular homeostasis via housekeeping roles and proteins with poly‐Asp stretches and RNA pol II activity on selected genes are affected in attrdmt1/atdnmt2. This shift in metabolic pathways primes the mutant plants to become increasingly sensitive to oxidative and osmotic stress. Taken together, our study sheds light on the mechanistic role of TRDMT1/DNMT2 in a flowering plant.
Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study

Background The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set. Objective This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information. Methods The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept. Results The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families. Conclusions The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization.
DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or trajectory-based control, leading to an inability to achieve fine-grained control in videos. Secondly, trajectory control research is still in its early stages, with most experiments being conducted on simple datasets like Human3.6M. This constraint limits the models' capability to process open-domain images and effectively handle complex curved trajectories. In this paper, we propose DragNUWA, an open-domain diffusion-based video generation model. To tackle the issue of insufficient control granularity in existing works, we simultaneously introduce text, image, and trajectory information to provide fine-grained control over video content from semantic, spatial, and temporal perspectives. To resolve the problem of limited open-domain trajectory control in current research, We propose trajectory modeling with three aspects: a Trajectory Sampler (TS) to enable open-domain control of arbitrary trajectories, a Multiscale Fusion (MF) to control trajectories in different granularities, and an Adaptive Training (AT) strategy to generate consistent videos following trajectories. Our experiments validate the effectiveness of DragNUWA, demonstrating its superior performance in fine-grained control in video generation. The homepage link is \url{https://www.microsoft. com/en-us/research/project/dragnuwa/}
A Graph Analysis of the Impact of COVID-19 on Emergency Housing Shelter Access Patterns

This paper investigates how COVID-19 disrupted emergency housing shelter access patterns in Calgary, Canada and what aspects of these changes persist to the present day. This analysis will utilize aggregated shelter access data for over 40,000 individuals from seven major urban shelters dating from 2018 to the present. A graph theoretic approach will be used to examine the journeys of individuals between shelters before, during and after the COVID-19 lockdown period. This approach treats shelters as nodes in a graph and a person's transition between shelter as an arrow or edge between nodes. This perspective is used to create both timeline and network diagrams that visualize shelter use and the flow of people between shelters. Statistical results are also presented that illustrate the differences between the cohorts of people who only used shelter pre/post-lockdown, people who stayed in shelter during lockdown and people who used shelter for the first time during lockdown. The results demonstrate not only how a complex system of care responded to the pandemic but also the characteristics of the people most likely to continue to rely on that system during an emergency.
Affordable Care Act Medicaid Expansion was Associated With Reductions in the Proportion of Hospitalizations That are Potentially Preventable Among Hispanic and White Adults

Objective: Using data on 5 years of postexpansion experience, we examined whether the coverage gains from Affordable Care Act Medicaid expansion among Black, Hispanic, and White individuals led to improvements in objective indicators of outpatient care adequacy and quality. Research Design: For the population of adults aged 45–64 with no insurance or Medicaid coverage, we obtained data on census population and hospitalizations for ambulatory care sensitive conditions (ACSCs) during 2010–2018 in 14 expansion and 7 nonexpansion states. Our primary outcome was the percentage share of hospitalizations due to ACSC out of all hospitalizations (“ACSC share”) among uninsured and Medicaid-covered patients. Secondary outcomes were the population rate of ACSC and all hospitalizations. We used multivariate regression models with an event-study difference-in-differences specification to estimate the change in the outcome measures associated with expansion in each of the 5 postexpansion years among Hispanic, Black, and White adults. Principal Findings: At baseline, ACSC share in the expansion states was 19.0%, 14.5%, and 14.3% among Black, Hispanic, and White adults. Over the 5 years after expansion, Medicaid expansion was associated with an annual reduction in ACSC share of 5.3% (95% CI, −7.4% to −3.1%) among Hispanic and 8.0% (95% CI, −11.3% to −4.5%) among White adults. Among Black adults, estimates were mixed and indicated either no change or a reduction in ACSC share. Conclusions: After Medicaid expansion, low-income Hispanic and White adults experienced a decrease in the proportion of potentially preventable hospitalizations out of all hospitalizations.
Whooping Crane Collisions with Power Lines: an Issue Paper

Collision with power lines is the greatest source of mortality for fledged whooping cranes (Grus americana) in the Aransas-Wood Buffalo population (AWBP) that migrate between the Northwest Territories, Canada to the Texas coast. This paper compiles 45 documented whooping crane mortalities from power line strikes in North America and provides known information on crane / power line interactions. A map of the AWBP whooping crane migration corridor was derived with 100 and 200-mile wide corridors delineated showing the location of known mortalities. Recommendations are provided to try to reduce this major threat facing whooping cranes. ProcEEDINGS of thE North AmErIcAN crANE WorkShoP 10:25–36
Time Travel in LLMs: Tracing Data Contamination in Large Language Models

Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on other tasks. We propose a straightforward yet effective method for identifying data contamination within LLMs. At its core, our approach starts by identifying potential contamination at the instance level; using this information, our approach then assesses wider contamination at the partition level. To estimate contamination of individual instances, we employ"guided instruction:"a prompt consisting of the dataset name, partition type, and the random-length initial segment of a reference instance, asking the LLM to complete it. An instance is flagged as contaminated if the LLM's output either exactly or nearly matches the latter segment of the reference. To understand if an entire partition is contaminated, we propose two ideas. The first idea marks a dataset partition as contaminated if the average overlap score with the reference instances (as measured by ROUGE-L or BLEURT) is statistically significantly better with the completions from guided instruction compared to a"general instruction"that does not include the dataset and partition name. The second idea marks a dataset partition as contaminated if a classifier based on GPT-4 with few-shot in-context learning prompt marks multiple generated completions as exact/near-exact matches of the corresponding reference instances. Our best method achieves an accuracy between 92% and 100% in detecting if an LLM is contaminated with seven datasets, containing train and test/validation partitions, when contrasted with manual evaluation by human experts. Further, our findings indicate that GPT-4 is contaminated with AG News, WNLI, and XSum datasets.
Helicity-conserving relaxation in unstable and merging magnetic flux ropes

Twisted magnetic flux ropes are reservoirs of free magnetic energy. In a highly-conducting plasma such as the solar corona, energy release through multiple magnetic reconnections can be modelled as a helicity-conserving relaxation to a minimum energy state. One possible trigger for this relaxation is the ideal kink instability in a twisted flux rope. We show that this provides a good description for confined solar flares, and develop from idealised cylindrical models to realistic models of coronal loops. Using 3D magnetohydrodynamic simulations combined with test-particle simulations of non-thermal electrons and ions, we predict multiple observational signatures of such flares. We then show how interactions and mergers of flux ropes can release free magnetic energy, using relaxation theory to complement simulations of merging-compression formation in spherical tokamaks and heating avalanches in the solar corona.
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer

Video-language pre-trained models have shown remarkable success in guiding video question-answering (VideoQA) tasks. However, due to the length of video sequences, training large-scale video-based models incurs considerably higher costs than training image-based ones. This motivates us to leverage the knowledge from image-based pretraining, despite the obvious gaps between image and video domains. To bridge these gaps, in this paper, we propose Tem-adapter, which enables the learning of temporal dynamics and complex semantics by a visual Temporal Aligner and a textual Semantic Aligner. Unlike conventional pretrained knowledge adaptation methods that only concentrate on the downstream task objective, the Temporal Aligner introduces an extra language-guided autoregressive task aimed at facilitating the learning of temporal dependencies, with the objective of predicting future states based on historical clues and language guidance that describes event progression. Besides, to reduce the semantic gap and adapt the textual representation for better event description, we introduce a Semantic Aligner that first designs a template to fuse question and answer pairs as event descriptions and then learns a Transformer decoder with the whole video sequence as guidance for refinement. We evaluate Tem-adapter and different pre-train transferring methods on two VideoQA benchmarks, and the significant performance improvement demonstrates the effectiveness of our method. 1
Syndromes Associated with Sexually Transmitted Infections in Lilongwe, Malawi: Burden and Trends, 2006 to 2015

Among sexually transmitted infection clinic patients in Lilongwe, genital discharge, lower abdominal pain, and ulcer syndromes were common. Trends in most sexually transmitted infection syndromes remained stable between 2006 and 2015. Supplemental digital content is available in the text. Abstract Monitoring the burden of and trends in sexually transmitted infection syndromes is useful in informing syndromic management guidelines. Among sexually transmitted infection clinic patients in Lilongwe, Malawi, between 2006 and 2015, genital discharge, lower abdominal pain, and genital ulcer syndromes were common. Prevalence of most syndromes remained stable during the 10-year period.
Galaxy Rotation Curve Fitting Using Machine Learning Tools

Galaxy rotation curve (RC) fitting is an important technique which allows the placement of constraints on different kinds of dark matter (DM) halo models. In the case of non-phenomenological DM profiles with no analytic expressions, the art of finding RC best-fits including the full baryonic + DM free parameters can be difficult and time-consuming. In the present work, we use a gradient descent method used in the backpropagation process of training a neural network, to fit the so-called Grand Rotation Curve of the Milky Way (MW) ranging from ∼1 pc all the way to ∼105 pc. We model the mass distribution of our Galaxy including a bulge (inner + main), a disk, and a fermionic dark matter (DM) halo known as the Ruffini-Argüelles-Rueda (RAR) model. This is a semi-analytical model built from first-principle physics such as (quantum) statistical mechanics and thermodynamics, whose more general density profile has a dense core–diluted halo morphology with no analytic expression. As shown recently and further verified here, the dark and compact fermion-core can work as an alternative to the central black hole in SgrA* when including data at milliparsec scales from the S-cluster stars. Thus, we show the ability of this state-of-the-art machine learning tool in providing the best-fit parameters to the overall MW RC in the 10−2–105 pc range, in a few hours of CPU time.
Environmental effects rather than relatedness determine gut microbiome similarity in a social mammal

In social species, group members commonly show substantial similarity in gut microbiome composition. Such similarities have been hypothesized to arise either by shared environmental effects or by host relatedness. However, disentangling these factors is difficult, because group members are often related, and social groups typically share similar environmental conditions. In this study, we conducted a cross‐foster experiment under controlled laboratory conditions in group‐living Damaraland mole‐rats (Fukomys damarensis) and used 16S amplicon sequencing to disentangle the effects of the environment and relatedness on gut microbiome similarity and diversity. Our results show that a shared environment is the main factor explaining gut microbiome similarity, overshadowing any effect of host relatedness. Together with studies in wild animal populations, our results suggest that among conspecifics environmental factors are more powerful drivers of gut microbiome composition similarity than host genetics.
The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.
Cognotyping by What–Where–When Retrieval Reveals the Potential Role of Temporal Memory and Its Neural Correlates in Understanding Individual Differences across Aging and Alzheimer Disease

Abstract Despite distinct neural representation of what, where, and when information, studies of individual differences in episodic memory have neglected to test the three components separately. Here, we used a componential episodic memory task to measure cognitive profiles across a wide age range and in Alzheimer disease (AD) and to examine the role of theta oscillations in explaining performance. In Experiment 1, we tested a group of 47 young adults (age 21–30 years, 21 women) while recording their scalp EEG. A separate behavioral experiment (Experiment 2) was performed in 42 older adults (age 66–85 years, 29 women) and in a group of 16 AD patients (age 80–90 years, 12 women). In Experiment 1, K-means clustering based on behavioral data resulted in three “cognotypes” whose memory profiles showed corresponding differences in their EEG markers: What and where memory depended on frontal theta power and when memory depended on theta modulation by temporal distance between retrieved items. In Experiment 2, healthy older adults showed three cognotypes similar to those found in younger adults; moreover, AD patients had an overlapping profile with one specific cognotype, characterized by marked difficulties in when memory. These findings highlight the utility of componential episodic memory tests and cognotyping in understanding individual strengths and vulnerabilities in age-related neurocognitive decline.
PENGARUH PENYULUHAN CTPS TERHADAP PENINGKATAN PENGETAHUAN SISWA SDN 169 BONTO PARANG DESA BARANA

Salah satu indikator dari Perilaku Hidup Bersih dan Sehat adalah CTPS . Cuci Tangan Pakai Sabun (CTPS) merupakan cara cuci tangan dengan sabun untuk mencegah berbagai penyakit. CTPS dilakukan untuk memutus mata rantai penularan penyakit. Masyarakat menganggap CTPS tidak penting, mereka cuci tangan pakai sabun ketika tangan berbau, berminyak dan kotor. Tujuan dari penelitian ini untuk mengetahui pengaruh penyuluhan CTPS terhadap pengetahuan siswa SDN 169 Bonto Parang Desa Barana Kecamatan Bangkala Barat Kabupaten Jeneponto. Jenis penelitian yang digunakan adalah quasi eksperimental dengan menggunakan rancangan one group pretest-posttest design. Subjek penelitian ini adalah siswa SD kelas IV dan V di SDN 169 Bonto Parang. Pengambilan sampel menggunakan teknik total sampling dengan jumlah responden sebanyak 30 siswa. Berdasarkan hasil penelitian diperoleh melalui hasil pretest dan post-test menunjukkan nilai taraf signifikan sebesar p=0,000 < 0,05, maka disimpulkan bahwa ada perbedaan antara pengetahuan responden penyuluhan PHBS tentang CTPS sebelum penyuluhan dengan pengetahuan responden tentang punyuluhan PHBS tentang CTPS setelah penyuluhan. Siswa diharapkan dapat mempunyai kesadaran yang tinggi dalam melakukan kebiasaaan CTPS untuk mencegah terjadinya penyakit-penyakit pada anak.
Pre-diagnosis blood DNA methylation profiling of twin pairs discordant for breast cancer points to the importance of environmental risk

Background: Breast cancer risk assessment typically relies on consideration of mammography, family history, reproductive history, in addition to assessment of major mutations. However, quantifying the impact of environmental factors, such as lifestyle, can be challenging. DNA methylation (DNAm) presents a promising opportunity for additional information, as it captures effects from both genetics and environment. Previous studies have identified associations and predicted the risk of breast cancer using DNAm in blood, however, these studies did not distinguish between genetic and environmental influences. Results: Pre-diagnosis blood samples were obtained from 32 monozygotic (MZ) and 76 dizygotic (DZ) female twin pairs discordant for breast cancer. DNAm was profiled using Illumina 450k and EPIC platform. Samples were collected at the mean age of 56 years (standard deviation (SD) =9.90), while the mean age at diagnosis was 66.76 years (SD=6.64). The mean age for censoring controls was 75.20 years (SD=9.45). To identify individual DNAm sites (Cytosine-phosphate-Guanine, CpG) and differentially methylated regions (DMRs) associated with breast cancer risk, survival analysis using paired Cox proportional hazard modeling was performed. Due to the paired modeling, shared genetic and environmental effects, age at entry, and age at sampling were controlled for. We identified 212 CpGs (p<6.4*10^-8) and 15 DMRs associated with breast cancer risk across all pairs, with three DMRs overlapping with the individual CpGs. All but one of the 212 CpGs had lower DNAm in cases, suggesting a prevailing trend of hypomethylation of blood DNA prior to breast cancer diagnosis. Altogether 197/212 significant CpGs were also differentially methylated within the 32 MZ twin pairs discordant for breast cancer, suggesting that DNAm at these CpGs is likely independent of genetic effects. Prior research suggests that estrogen regulates at least five of the top CpGs identified. Hence, methylation at these sites may reflect individual differences in estrogen exposure. Conclusion: In conclusion, most of the identified CpGs associated with future breast cancer diagnosis appear to be independent of genetic effects. This suggests that DNAm could potentially serve as a valuable biomarker for environmental risk factors for breast cancer and may offer potential benefits as a complementary tool to current risk assessment procedures.