Miracle-dz/UniFAR-FTUs huggingface dataset:
Community stability increases the predictability of microeukaryote community coalescence outcomes

Mixing of entire microbial communities represents a frequent, yet understudied phenomenon. Here, we mimicked estuarine condition in a microcosm experiment by mixing a freshwater river community with a brackish sea community and assessed the effects of both environmental and community coalescences induced by varying mixing processes on microeukaryotic communities. Signs of shifted community composition of coalesced communities towards the sea parent community suggest asymmetrical community coalescence outcome, which, in addition, was generally less impacted by environmental coalescence. Diatoms were negatively impacted by coalescence, while fungi, ciliates, and cercozoans were promoted to varying extents, depending on the mixing ratios of the source (i.e., river or sea) communities. Community stability, inferred from community cohesion, suggests that the more stable parent community (i.e., community with greater negative cohesion attributed to competitive interactions) dominates the final, coalesced community, but the fate of its community members is influenced by mixing ratios and frequencies (i.e., one-time versus repeated coalescence). Generally, community coalescence increased alpha diversity and promoted competition from the introduction (or emergence) of additional (or rare) species. These competitive interactions in turn had community stabilizing effect as evidenced by the increased proportion of negative cohesion. Our study suggests that the predictability of coalescence outcomes was greater when the more stable parent community (i.e., sea microbes) dominated the final community and this predictability was further enhanced when communities collided repeatedly. Open research statement Sequencing data is deposited to NCBI SRA database under the accession number PRJNA922225. Data (OTU table, consensus taxonomy and metadata) are available in Open Science Framework (OSF) (http://osf.io/sme36).
Repurposing approved protein kinase inhibitors as potent anti-leishmanials targeting Leishmania MAP kinases

Leishmaniasis is a vast array of chronic diseases caused by the protozoan parasite of the genus Leishmania transmitted by the sandfly belonging to the genus Phlebotomus (Old World) and Lutzomyia (New World). Among the various factors that contribute to the virulence and pathogenesis of the disease, protein kinases play crucial role in modulating the parasites’ biology in the host system. There are no specific drugs identified to treat the disease specifically. Moreover, resistance against approved anti-leishmanials has made it important to look for a few pre-clinical candidates against visceral leishmaniasis. MAP kinase is one such candidate of protein kinase family that regulates cell proliferation in eukaryotes. In this study, we have identified 23 MAPKs from Leishmania genome and have screened 12 FDA approved protein kinase inhibitors against those MAPKs in search of a potential lead for new drug exploration. Among the inhibitors, sorafenib and imatinib have been identified as potential drug candidates based on multiple criteria including binding affinity, ADME scores, absorption and their ability to cross blood-brain barrier. Furthermore, these drugs showed excellent anti-proliferative effects in Leishmania promastigotes, resulted in change of cell morphology, flagellar length, promoted cell cycle arrest through ROS generation and reduced intra-macrophage parasitic burden. Collectively, these results imply involvement of MAP kinases in infectivity and survival of the parasite and can pave the avenue for repurposing sorafenib and imatinib as anti-leishmanial agents.
Population genomics of the rice landrace Acuce reveals exceptional dynamics of immune receptors

Landraces are important resources for breeding programs. However, the diversity harbored at the genomic level by landraces is yet to be analyzed. Here we sequenced more than 200 individual plants from the long-lasting and durably resistant rice landrace Acuce (Oryza sativa subsp. indica). We found that Acuce exhibits diversity levels as high as 38% of the entire indica sub-species. We pinpoint signatures of selection in favor of polymorphism associated with resistance, but not other agronomic trait genes. Furthermore, we provide evidences that emerging properties upon mixing genomic diversity also increase Acuce’s performances.
Mitochondrial dysfunction and autophagy responses to skeletal muscle stress

Autophagy plays an important role in mitochondrial maintenance, yet many details of skeletal muscle autophagic activity are unresolved in the context of muscle stress and/or damage. Skeletal muscles from mice were stressed either by fatiguing contractions, eccentric contraction-induced injury (ECCI), or freeze injury (FI) to establish a timeline of mitochondrial function and autophagy induction after different forms of muscle stress. Only FI was sufficient to elicit a reduction in mitochondrial function (−88%, p=0.006), yet both ECCI and FI resulted in greater autophagy-related protein content (28-fold, p≤0.008) suggesting a tunable autophagic response. Muscles from another cohort of mice were used to determine specific forms of autophagy, i.e., flux and mitochondrial-specific, in response to muscle damage. Mitochondrial-specific autophagy was evident by accumulation of autophagy-related proteins in mitochondrial-enriched muscle fractions following FI (37-fold, p=0.017); however, autophagy flux, assessed by LC3II accumulation with the lysosomal inhibitor chloroquine, was insignificant suggesting a physiological bottleneck in the clearance of dysfunctional organelles following FI. Ulk1 muscle-specific knockout (Ulk1 MKO) mice were used to determine if autophagy is necessary for the recovery of mitochondrial function after muscle damage. Ulk1 MKO mice were weaker (−12%, p=0.012) and demonstrated altered satellite cell dynamics (e.g., proliferation) during muscle regeneration after FI compared to littermate control mice, but determination of autophagy necessity for the recovery of mitochondrial function was inconclusive. This study concludes that autophagy is a tunable cellular response to muscle damaging stress and may influence muscle fiber regeneration through interaction with satellite cells. Key Points Summary Muscle contractility dysfunction is well characterized after many different types of muscle stress however, the timing and magnitude of mitochondrial dysfunction and autophagy induction after different types of muscle stress is largely unknown. In this study we found that only traumatic freeze injury causes mitochondria dysfunction compared to fatigue contractions and eccentric contraction-induced injury, and that the autophagic response to muscle stress scales to the magnitude of muscle damage, i.e., freeze vs. eccentric contraction-induced injury. We determined that total autophagy-related protein content has a greater response to muscle fiber damage compared to autophagy flux likely reflecting a bottleneck of autophagosomes awaiting degradation following muscle injury. Using a skeletal gmuscle-specific autophagy knockout mouse (Ulk1), we found that muscle contractility and satellite cell activity might be influenced by cellular events within the adult muscle fiber following muscle damage.
Exposure to cooler temperatures during pupal development increases Aedes aegypti vector competence and the R0 for Zika virus

Temperature profoundly affects various aspects of ectotherm biology. Notably, in mosquito species that spread viral diseases, temperature influences not only vector biology, but also the dynamics of pathogen-vector interactions. However, research attempting to address the role of the thermal environment in disease transmission often employs constant temperatures, which do not reflect the natural diurnal fluctuations these organisms experience. Additionally, most studies focus on adult mosquitoes in the period following virus infection. Much less attention has been paid to evidence indicating that temperatures experienced during earlier developmental stages may also affect the ability of disease vectors to be infected with and transmit viruses. Here, we show that Aedes aegypti exposed to temperatures below 25°C, specifically during the pupal stage of development, exhibit heightened susceptibility to Zika virus (ZIKV), which increases transmission efficiency. Modeling suggests that exposing mosquitoes to cooler fluctuating diurnal temperature ranges only during the relatively short pupal stage increases the R0 or reproductive number of ZIKV. Data loggers placed near Harris County Mosquito Control trap sites consistently recorded temperatures below 25°C, indicating natural exposure to such conditions. These results highlight the significance of thermal heterogeneity in the microhabitats where container-breeding mosquitoes undergo development. Such heterogeneity may play a more important role in the transmission of mosquito-borne diseases than previously recognized. Author Summary A paucity of information regarding how natural heterogeneity in thermal environments influences the spread of viral pathogens by mosquito species hinders our ability to decipher current and future patterns of disease transmission under changing climatic conditions. Here, we show that Ae. aegypti pupae exposed to cooler fluctuating diurnal temperature ranges, mimicking realistic field conditions, increases vector competence for ZIKV. Modeling cooler temperatures during immature life stages predicts slower development and increased mortality, counterbalancing increases in disease transmission. However, exposing mosquitoes to lower temperatures only during the relatively short pupal stage was predicted to increase the R0 value of ZIKV. These results suggest that environmental temperatures specifically experienced during the pupal stage may have an important role in disease transmission.
Memory adds no cost to lattice sieving for computers in 3 or more spatial dimensions

The security of lattice-based crytography (LWE, NTRU, and FHE) depends on the hardness of the shortest-vector problem (SVP). Sieving algorithms give the lowest asymptotic runtime to solve SVP, but depend on exponential memory. Memory access costs much more in reality than in the RAM model, so we consider a computational model where processors, memory, and meters of wire are in constant proportions to each other. While this adds substantial costs to route data during lattice sieving, we modify existing algorithms to amortize these costs and ﬁnd that, asymptotically, a classical computer can achieve the previous RAM model cost of 2 0 . 2925 d + o ( d ) to sieve a d -dimensional lattice for a computer existing in 3 or more spatial dimensions, and can reach 2 0 . 3113 d + o ( d ) in 2 spatial dimensions, where “spatial dimensions” are the dimensions of the physical geometry in which the computer exists. Under some assumptions about the constant terms of memory access, we estimate increases in bit security between 3 to 15 bits for different Kyber parameter sets and 4 to 15 bits for Dilithium.
Automatic 3D/2D Deformable Registration in Minimally Invasive Liver Resection using a Mesh Recovery Network

We propose the patient-specific Liver Mesh Recovery (LMR) framework, to automatically achieve Augmented Reality (AR) guidance by registering a preoperative 3D model in Minimally Invasive Liver Resection (MILR). Existing methods solve registration in MILR by pose estimation followed with numerical optimisation and suffer from a prohibitive intra-operative runtime. The proposed LMR is inspired by the recent Human Mesh Recovery (HMR) framework and forms the first learning-based method to solve registration in MILR. In contrast to existing methods, the computation load in LMR occurs preoperatively, at training time. We construct a patient-specific deformation model and generate patient-specific training data reproducing the typical defects of the automatically detected registration primitives. Experimental results show that LMR’s registration accuracy is on par with optimisation-based methods, whilst running in real-time intraoperatively.
The role of glacial‐interglacial climate change in shaping the genetic structure of eastern subterranean termites in the southern Appalachian Mountains, USA

Abstract The eastern subterranean termite, Reticulitermes flavipes, currently inhabits previously glaciated regions of the northeastern U.S., as well as the unglaciated southern Appalachian Mountains and surrounding areas. We hypothesized that Pleistocene climatic fluctuations have influenced the distribution of R. flavipes, and thus the evolutionary history of the species. We estimated contemporary and historical geographic distributions of R. flavipes by constructing Species Distribution Models (SDM). We also inferred the evolutionary and demographic history of the species using mitochondrial (cytochrome oxidase I and II) and nuclear (endo‐beta‐1,4‐glucanase) DNA sequence data. To do this, genetic populations were delineated using Bayesian spatial‐genetic clustering, competing hypotheses about population divergence were assessed using approximate Bayesian computation (ABC), and changes in population size were estimated using Bayesian skyline plots. SDMs identified areas in the north with suitable habitat during the transition from the Last Interglacial to the Last Glacial Maximum, as well as an expanding distribution from the mid‐Holocene to the present. Genetic analyses identified three geographically cohesive populations, corresponding with northern, central, and southern portions of the study region. Based on ABC analyses, divergence between the Northern and Southern populations was the oldest, estimated to have occurred 64.80 thousand years ago (kya), which corresponds with the timing of available habitat in the north. The Central and Northern populations diverged in the mid‐Holocene, 8.63 kya, after which the Central population continued to expand. Accordingly, phylogeographic patterns of R. flavipes in the southern Appalachians appear to have been strongly influenced by glacial‐interglacial climate change. OPEN RESEARCH BADGES This article has been awarded Open Materials, Open Data Badges. All materials and data are publicly accessible via the Open Science Framework at https://doi.org/10.5061/dryad.5hr7f31.
Memory Checking Requires Logarithmic Overhead

We study the complexity of memory checkers with computational security and prove the first general tight lower bound. Memory checkers, first introduced over 30 years ago by Blum, Evans, Gemmel, Kannan, and Naor (FOCS ’91, Algorithmica ’94), allow a user to store and maintain a large memory on a remote and unreliable server by using small trusted local storage. The user can issue instructions to the server and after every instruction, obtain either the correct value or a failure (but not an incorrect answer) with high probability. The main complexity measure of interest is the size of the local storage and the number of queries the memory checker makes upon every logical instruction. The most efficient known construction has query complexity O (log n/ log log n ) and local space proportional to a computational security parameter, assuming one-way functions, where n is the logical memory size. Dwork, Naor, Rothblum, and Vaikuntanathan (TCC ’09) showed that for a restricted class of “deterministic and non-adaptive” memory checkers, this construction is optimal, up to constant factors. However, going beyond the small class of deterministic and non-adaptive constructions has remained a major open problem. In this work, we fully resolve the complexity of memory checkers by showing that any construction with local space p and query complexity q must satisfy p
Artificial General Intelligence (AGI) for Education

Artiﬁcial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. AGI aims to replicate human intelligence through computer systems, which is one of the critical technologies having the potential to revolutionize the ﬁeld of education. Compared to conventional AI models, typically designed for a limited range of tasks, demand signiﬁcant amounts of domain-speciﬁc data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a signiﬁcant
Application-Aware Approximate Homomorphic Encryption: Configuring FHE for Practical Use

Fully Homomorphic Encryption (FHE) is a powerful tool for performing privacy-preserving analytics over encrypted data. A promising method for FHE over real and complex numbers is approximate homomorphic encryption, instantiated with the Cheon-Kim-Kim-Song (CKKS) scheme. The CKKS scheme enables efficient evaluation for many privacy-preserving machine learning applications. Despite its high efficiency, there is currently a lot of confusion on how to securely instantiate CKKS for a given application, especially after secret-key recovery attacks were proposed by Li and Micciancio (EURO-CRYPT’21) for the IND-CPA D setting, i.e., where decryption results are shared with other parties. On the one hand, the generic definition of IND-CPA D is application-agnostic and often requires impractically large parameters. On the other hand, practical CKKS implementations target specific applications and use tighter parameters. A good illustration are the recent secret-key recovery attacks against a CKKS implementation in the OpenFHE library by Guo et al. (USENIX Security’24). We show that these attacks misuse the library by employing different (incompatible) circuits during parameter estimation and run-time computation, yet they do not violate the generic (application-agnostic) IND-CPA D definition.
Threshold Raccoon: Practical Threshold Signatures from Standard Lattice Assumptions

Threshold signatures improve both availability and security of digital signatures by splitting the signing key into N shares handed out to different parties. Later on, any subset of at least T parties can cooperate to produce a signature on a given message. While threshold signatures have been extensively studied in the pre-quantum setting, they remain sparse from quantum-resilient assumptions. In this work, we show that the Raccoon signature scheme [dEK + 23] can be easily thresholdized. More precisely, we present Threshold Raccoon, a threshold signature that is very close to Rac-coon. Our scheme has signature size 13 KiB and communication cost 40 KiB per user, supporting a threshold size as large as 1024 signers. All operations used during signing are due to symmetric primitives and simple lattice operations; in particular our scheme does not need heavy tools such as threshold fully homomorphic encryption or homomorphic trapdoor commitments as in prior constructions. Our key technical idea is to use one-time additive masks to mitigate the leakage of the partial signing keys through partial signatures.
A Simpler and More Efficient Reduction of DLog to CDH for Abelian Group Actions

Abelian group actions appear in several areas of cryptography, especially isogeny-based post-quantum cryptography. A natural problem is to relate the analogues of the computational Diffie-Hellman (CDH) and discrete logarithm (DLog) problems for abelian group actions. Galbraith, Panny, Smith and Vercauteren (Mathematical Cryptol-ogy ’21) gave a quantum reduction of DLog to CDH, assuming a CDH oracle with perfect correctness. Montgomery and Zhandry (Asiacrypt ’22, best paper award) showed how to convert an unreliable CDH oracle into one that is correct with overwhelming probability. However, while a theoretical breakthrough, their reduction is quite inefficient: if the CDH oracle is correct with probability ϵ then their algorithm to amplify the success requires on the order of 1 /ϵ 21 calls to the CDH oracle. We revisit this line of work and give a much simpler and tighter algo-rithm. Our method only takes on the order of 1 /ϵ 4 CDH oracle calls and is conceptually simpler than the Montgomery-Zhandry reduction. Our algorithm is also fully black-box, whereas the Montgomery-Zhandry algo-rithm is slightly non-black-box. Our main tool is a thresholding technique that replaces the comparison of distributions in Montgomery-Zhandry with testing equality of thresholded sets.
MUSE: une planiﬁcation d’itin ´eraires inspir ´ee de S ´eparateurs Multimodaux

Le domaine des algorithmes de calcul de plus courts chemins connait un essor important avec le d´eveloppement du cloud. Quelques solutions, dites multimodales, sont conc¸ues pour combiner divers modes de transports, mais au prix d’une augmentation signiﬁcative de la complexit´e. Nous proposons ici MUSE, un algorithme bas´e sur les s´eparateurs de graphes, mais adapt´e au cas multimodal. Dans une phase de pr´etraitement, nous d´ecoupons tout d’abord le graphe en partitions ind´ependantes (ou cellules), chacune d´ecoup´ee en modes de transport aﬁn de pouvoir plus tard r´epondre `a n’importe quelle requˆete,. Ensuite, nous pr´ecalculons toutes les plus courtes routes, sur ce petit nombre de cellules, en tenant compte des labels (modes) de chaque arˆete. Nous pouvons ainsi r´epondre `a une requˆete tr`es rapidement dans la phase online : l’utilisateur sp´eciﬁe les s´equences de mode qu’il autorise, et exploite les plus courtes routes pr´e-calcul´ees.
Statistical models to understand factors associated with under-five child mortality in Tanzania.

The risk or probability of dying between birth and five years of age expressed per 1000 live births is known as Under-five mortality. The well-being of a child reflects household, community and national involvement on family health. This will have an immense future contribution towards the development of a country. Globally, a substantial progress in improving child survival since 1990 has been made. The decline globally in under-five mortality from approximately 12.7 million in 1990 to approximately 6.3 million in 2013 had been observed. Notably, all regions except Sub-Saharan Africa, Central Asia, Southern Asia and Oceania had reduced the rate by 52% or more in 2013. This study aims to identify factors that are associated with the under-five mortality in Tanzania. In order to robustly identify these factors, the study utilized different statistical models that accommodate a response which is dichotomous. Models studied include Logistic Regression (LR), Survey Logistic Regression (SLR), Generalized Linear Mixed Model (GLMM) and Generalized Additive Model (GAM). The result revealed that HIV status of the mother is associated with the under-five mortality. Furthermore, the results revealed that childbirth order number, breastfeeding and a total number of children alive affects the survival status of the child. The study shows that there is a need to intensify child health interventions to reduce the under-five mortality rate even more and to be in line with the millennium development goal 4(MDG4).