Miracle-dz/UniFAR-FTUs huggingface dataset:
Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Concatenate, Fine-tuning, and Re-training (CFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our concatenation strategy mitigates the mismatch between natural and 3D medical images. The concatenated images are then used for fine-tuning SAM, providing robust initialization pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our CFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. Extensive experiments validate that our CFR achieves significant improvements in both moderate annotation and scarce annotation across four datasets. In particular, CFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset.
Nanobodies targeting norovirus capsid reveal functional epitopes and potential mechanisms of neutralization

Norovirus is the leading cause of gastroenteritis worldwide. Despite recent developments in norovirus propagation in cell culture, these viruses are still challenging to grow routinely. Moreover, little is known on how norovirus infects the host cells, except that histo-blood group antigens (HBGAs) are important binding factors for infection and cell entry. Antibodies that bind at the HBGA pocket and block attachment to HBGAs are believed to neutralize the virus. However, additional neutralization epitopes elsewhere on the capsid likely exist and impeding the intrinsic structural dynamics of the capsid could be equally important. In the current study, we investigated a panel of Nanobodies in order to probe functional epitopes that could trigger capsid rearrangement and/ or interfere with HBGA binding interactions. The precise binding sites of six Nanobodies (Nano-4, Nano-14, Nano-26, Nano-27, Nano-32, and Nano-42) were identified using X-ray crystallography. We showed that these Nanobodies bound on the top, side, and bottom of the norovirus protruding domain. The impact of Nanobody binding on norovirus capsid morphology was analyzed using electron microscopy and dynamic light scattering. We discovered that distinct Nanobody epitopes were associated with varied changes in particle structural integrity and assembly. Interestingly, certain Nanobody-induced capsid morphological changes lead to the capsid protein degradation and viral RNA exposure. Moreover, Nanobodies employed multiple inhibition mechanisms to prevent norovirus attachment to HBGAs, which included steric obstruction (Nano-14), allosteric interference (Nano-32), and violation of normal capsid morphology (Nano-26 and Nano-85). Finally, we showed that two Nanobodies (Nano-26 and Nano-85) not only compromised capsid integrity and inhibited VLPs attachment to HBGAs, but also recognized a broad panel of norovirus genotypes with high affinities. Consequently, Nano-26 and Nano-85 have a great potential to function as novel therapeutic agents against human noroviruses.
Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation

Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test time. In TTA, both performance and, memory and time consumption serve as crucial considerations. A recent diffusion-based TTA approach for restoring corrupted images involves image-level updates. However, using pixel space diffusion significantly increases resource requirements compared to conventional model updating TTA approaches, revealing limitations as a TTA method. To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme. This scheme enhances the robustness of the diffusion model against distribution shifts by creating (clean, corrupted) image pairs and fine-tuning the model to edit corrupted images into clean ones. Moreover, we introduce a distilled variant to accelerate the model for corruption editing using only 4 network function evaluations (NFEs). We extensively validated our method across various architectures and datasets including image and video domains. Our model achieves the best performance with a 100 times faster runtime than that of a diffusion-based baseline. Furthermore, it outpaces the speed of the model updating TTA method based on data augmentation threefold, rendering an image-level updating approach more practical.
High prevalence of glucose abnormalities in patients with hepatitis C virus infection: a multivariate analysis considering the liver injury.

OBJECTIVE The aim of this study was to compare the prevalence of both impaired fasting glucose (IFG) and diabetes between hepatitis C virus (HCV)-infected patients and patients with other liver diseases but anti-HCV-, taking into account the degree of liver damage. RESEARCH DESIGN AND METHODS A total of 642 consecutive patients attending the outpatient liver unit of a university hospital (498 anti-HCV+ and 144 anti-HCV-) were prospectively recruited. Patients were classified as having chronic hepatitis (n = 472) or cirrhosis (n = 170) by means of the result of either a liver biopsy or by typical clinical features. A logistic regression model was used to determine independent associations of covariates (age, sex, BMI, HCV antibody status, and triglycerides) with the presence of glucose abnormalities. RESULTS A threefold increase in the prevalence of glucose abnormalities was observed in HCV+ patients with chronic hepatitis in comparison with HCV- subjects (32 vs. 12%; P = 0.0003). In contrast, among patients with cirrhosis, although both diabetes and IFG were more prevalent in anti-HCV+ patients (40%) than in anti-HCV- patients (36%), the differences were not statistically significant. Finally, the logistic regression analysis showed that HCV infection was independently related to glucose abnormalities in those patients with chronic hepatitis (odds ratio 4.26 [95% CI 2.03-8.93]). In contrast, HCV was not an independent predictor of glucose abnormalities in cirrhotic patients. CONCLUSIONS The high prevalence of IFG and diabetes found in HCV-infected patients observed in our study suggests that screening for glucose abnormalities should be indicated in these patients. In addition, we provide evidence that the genuine connection between HCV infection and diabetes is initiated at early stages of hepatic disease.
EPS mutual authentication and crypt-analyzing SPAKA

LTE's success as a high throughput, low latency, and umbrella technology for wireless heterogeneous networks is highly affected by the researchers' capability of solving its current security vulnerabilities. Mutual authentication was adopted by mobile networks to overcome vulnerabilities exploited by “IMSI catcher” and other active attacks. 3GPP's mutual authentication implementation in EPS AKA succeeded in enhancing the network's security, but failed to cover weaknesses inherited from its predecessor (UMTS). One of those vulnerabilities is the passive capturing of IMSIs during user identification in the Authentication and Key Agreement protocol. Many researchers tried over the past years, to propose an alternative for EPS AKA, able to ensure high levels of security and offer acceptable QoS performance. In this paper, we will crypt-analyze (SPAKA and PBKP) which was claimed to solve EPS AKA's privacy and mutual authentication weaknesses, then we will compare its QoS performance to EC-AKA and EPS AKA. Our proposed protocol “EC-AKA” is a real candidate to replace the current authentication and Key Agreement protocol, because of its excellent performance in all the studied parameters.
There is no recursive axiomatization for feasible functionals of type 2

The author shows a class of type-two feasible functionals, C/sub 2/, that satisfies Cook's conditions, (1990) and cannot be expressed as the lambda closure of type-one poly-time functions and any recursively enumerable set of type-two feasible functionals. Further, no class of total type-two functionals containing this class is representable as the lambda closure of a recursively enumerable set of type-two total computable functionals and type-one poly-time functions. The definition of C/sub 2/ provides a clear computational procedure for functionals of C/sub 2/. Using functionals of class C/sub 2/ a more general notion of polynomial-time reducibility between two arbitrary type-one functions can be introduced. <<ETX>>
Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects

Our paper addresses the challenge of inferring causal effects in social network data, characterized by complex interdependencies among individuals resulting in challenges such as non-independence of units, interference (where a unit's outcome is affected by neighbors' treatments), and introduction of additional confounding factors from neighboring units. We propose a novel methodology combining graph neural networks and double machine learning, enabling accurate and efficient estimation of direct and peer effects using a single observational social network. Our approach utilizes graph isomorphism networks in conjunction with double machine learning to effectively adjust for network confounders and consistently estimate the desired causal effects. We demonstrate that our estimator is both asymptotically normal and semiparametrically efficient. A comprehensive evaluation against four state-of-the-art baseline methods using three semi-synthetic social network datasets reveals our method's on-par or superior efficacy in precise causal effect estimation. Further, we illustrate the practical application of our method through a case study that investigates the impact of Self-Help Group participation on financial risk tolerance. The results indicate a significant positive direct effect, underscoring the potential of our approach in social network analysis. Additionally, we explore the effects of network sparsity on estimation performance.
The cytoskeleton and neurite initiation

Neurons begin their life as simple spheres, but can ultimately assume an elaborate morphology with numerous, highly arborized dendrites, and long axons. This is achieved via an astounding developmental progression which is dependent upon regulated assembly and dynamics of the cellular cytoskeleton. As neurites emerge out of the soma, neurons break their spherical symmetry and begin to acquire the morphological features that define their structure and function. Neurons regulate their cytoskeleton to achieve changes in cell shape, velocity, and direction as they migrate, extend neurites, and polarize. Of particular importance, the organization and dynamics of actin and microtubules directs the migration and morphogenesis of neurons. This review focuses on the regulation of intrinsic properties of the actin and microtubule cytoskeletons and how specific cytoskeletal structures and dynamics are associated with the earliest phase of neuronal morphogenesis—neuritogenesis.
Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning

In social network service platforms, crime suspects are likely to use cybercrime coded words for communication by adding criminal meanings to existing words or replacing them with similar words. For instance, the word 'ice' is often used to mean methamphetamine in drug crimes. To analyze the nature of cybercrime and the behavior of criminals, quickly detecting such words and further understanding their meaning are critical. In the automated cybercrime coded word detection problem, it is difficult to collect a sufficient amount of training data for supervised learning and to directly apply language models that utilize context information to better understand natural language. To overcome these limitations, we propose a new two-step approach, in which a mean latent vector is constructed for each cybercrime through one of five different AutoEncoder models in the first step, and cybercrime coded words are detected based on multi-level latent representations in the second step. Moreover, to deeply understand cybercrime coded words detected through the two-step approach, we propose three novel methods: (1) Detection of new words recently coined, (2) Detection of words frequently appeared in both drug and sex crimes, and (3) Automatic generation of word taxonomy. According to our experimental results, among various AutoEncoder models, the stacked AutoEncoder model shows the best performance. Additionally, the F1-score of the two-step approach is 0.991, which is higher than 0.987 and 0.903 of the existing dark-GloVe and dark-BERT models. By analyzing the experimental results of the three proposed methods, we can gain a deeper understanding of drug and sex crimes.
Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!

While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural"shortcuts", such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.
Reproduction in the male ferret: gonadal activity during the annual cycle; recrudescence and maturation.

Ten mature male ferrets and one castrate male ferret were housed outdoors and monthly blood samples and testis measurements were taken for one year. Plasma samples were assayed for testosterone. The size of the testes and the testosterone concentrations in the plasma showed a high correlation. The testis size was reduced (mean length 12 ± 1 mm) and the plasma testosterone concentrations were low (mean 3.3 ± 0.6 ng/ml) from August to December. Both parameters rose gradually until peak values of testis size (mean length 20 ± 0.2 mm) and plasma testosterone concentrations (mean 47.5 ± 2 ng/ml) were reached in April, May and June. In addition blood samples and testes measurements were taken every two weeks from November to April from a group of 12 mature and 9 immature male ferrets which were housed indoors under a simulated natural photoperiod regimen. Animals were sacrificed periodically for histological data. The purpose of this experiment was to compare the processes of recrudescence of the mature and maturation of the immature ferret. These two processes were found to be very similar with respect to the parameters measured and both groups of animals were reproductively competent when the females came into estrus in the spring.
A Review on Quantum Approximate Optimization Algorithm and its Applications

The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances
PinR mediates the generation of reversible population diversity in Streptococcus 1 zooepidemicus 2 3

28 Opportunistic pathogens must adapt to and survive in a wide range of complex ecosystems. 29 Streptococcus zooepidemicus is an opportunistic pathogen of horses and many other animals, 30 including man. The assembly of different surface architecture phenotypes from one genotype is likely 31 to be crucial to the successful exploitation of such an opportunistic lifestyle. Construction of a series of 32 mutants revealed that a serine recombinase, PinR, inverts 114 bp of the promoter of SZO_08560, 33 which is bordered by GTAGACTTTA and TAAAGTCTAC inverted repeats. Inversion acts as a switch, 34 controlling the transcription of this sortase-processed protein, which may enhance the attachment of 35 S. zooepidemicus to equine trachea. The genome of a recently sequenced strain of S. zooepidemicus, 36 strain 2329 (Sz2329), was found to contain a disruptive internal inversion of 7 kb of the FimIV pilus 37 locus, which is bordered by TAGAAA and TTTCTA inverted repeats. This strain lacks pinR and we 38 hypothesized that this inversion may have become irreversible following the loss of this recombinase. 39 Active inversion of FimIV was detected in three strains of S. zooepidemicus: 1770 (Sz1770), B260863 40 (SzB260863) and H050840501 (SzH050840501), all of which encoded pinR. A deletion mutant of 41 Sz1770 that lacked pinR was no longer capable of inverting its internal region of FimIV. Our data 42 highlight redundancy in the PinR sequence recognition motif around a short TAGA consensus and 43 suggest that PinR can reversibly influence the wider surface architecture of S. zooepidemicus, 44 providing this organism with a bet-hedging solution to survival in fluctuating environments. 45 46 47 48 49 50 51 52 53 54
Attraction of mosquitoes to volatiles associated with blood

ABSTRACT Responses of the mosquitoes Aedes aegypti, Culex quinquefasciatus, and Culex nigripalpus to volatiles and compounds associated with bovine and avian blood that were presented in collagen membranes were evaluated in olfactometer and landing assays. The presence of attractants produced by blood was supported by more attraction of all species to blood than water controls in the olfactometer. Females of Ae. aegypti and Cx. quinquefasciatus were more attracted to bovine blood than to avian blood, but there was no difference in Cx. nigripalpus responses. In landing assays, significantly more females of all species landed on casings with blood than on water controls. There was no difference in landing of Ae. aegypti on bovine or avian blood. However, significantly more females of Cx. quinquefasciatus and Cx. nigripalpus landed on avian blood compared to bovine blood. Blood presented in collagen casings was an effective method for evaluating in-flight attraction and landing in all three species. In the olfactometer, several individual compounds elicited attraction in all species, but none were as attractive as blood for all species. In landing assays, several organic acids and sulfides elicited landing, with Ae. aegypti responding to the greatest number of compounds. These assay methods are effective for evaluation of volatile compounds from blood, and although responses were obtained to several compounds, none were as effective as blood in the olfactometer and landing assays.
Gravitational Wave Searches for Post-Merger Remnants of GW170817 and GW190425

We present the results of two searches for gravitational waves from the post-merger remnants of the binary neutron star coalescence events GW170817 and GW190425. The searches are fully coherent over 1800~s of data from the 2nd (for GW170817) and 3rd (for GW190425) observing runs of the LIGO and Virgo observatories. The searches compute the matched filter $\mathcal{F}$-statistic, and use a piecewise model of the rapidly changing frequency evolution appropriate for young neutron stars. No detection is claimed. The peak root-sum-squared strain upper limit at 50\% detection probability ($h_{\text{rss}}^{50\%})$ of both searches occurs at 1700~Hz and is estimated at $1.64 \times 10^{-22}~\text{Hz}^{-1/2}$ for GW170817, and $1.0 \times 10^{-22}~\text{Hz}^{-1/2}$ for GW190425. This is the first gravitational wave search for a neutron star remnant of GW190425.
Artifact Feature Purification for Cross-domain Detection of AI-generated Images

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. Experiments show that for cross-generator detection, the average accuracy of APN is 5.6% ~ 16.4% higher than the previous 10 methods on GenImage dataset and 1.7% ~ 50.1% on DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method extracts flexible forgery patterns and condenses the forgery information diluted in irrelevant features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available on GitHub.