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Impacts of florfenicol on the microbiota landscape and resistome as revealed by metagenomic analysis.
BACKGROUND:Drug-resistant fish pathogens can cause significant economic loss to fish farmers. Since 2012, florfenicol has become an approved drug for treating both septicemia and columnaris diseases in freshwater fish. Due to the limited drug options available for aquaculture, the impact of the therapeutical florfenicol treatment on the microbiota landscape as well as the resistome present in the aquaculture farm environment needs to be evaluated. RESULTS:Time-series metagenomic analyses were conducted to the aquatic microbiota present in the tank-based catfish production systems, in which catfish received standard therapeutic 10-day florfenicol treatment following the federal veterinary regulations. Results showed that the florfenicol treatment shifted the structure of the microbiota and reduced the biodiversity of it by acting as a strong stressor. Planctomycetes, Chloroflexi, and 13 other phyla were susceptible to the florfenicol treatment and their abundance was inhibited by the treatment. In contrast, the abundance of several bacteria belonging to the Proteobacteria, Bacteroidetes, Actinobacteria, and Verrucomicrobia phyla increased. These bacteria with increased abundance either harbor florfenicol-resistant genes (FRGs) or had beneficial mutations. The florfenicol treatment promoted the proliferation of florfenicol-resistant genes. The copy number of phenicol-specific resistance genes as well as multiple classes of antibiotic-resistant genes (ARGs) exhibited strong correlations across different genetic exchange communities (p < 0.05), indicating the horizontal transfer of florfenicol-resistant genes among these bacterial species or genera. Florfenicol treatment also induced mutation-driven resistance. Significant changes in single-nucleotide polymorphism (SNP) allele frequencies were observed in membrane transporters, genes involved in recombination, and in genes with primary functions of a resistance phenotype. CONCLUSIONS:The therapeutical level of florfenicol treatment significantly altered the microbiome and resistome present in catfish tanks. Both intra-population and inter-population horizontal ARG transfer was observed, with the intra-population transfer being more common. The oxazolidinone/phenicol-resistant gene optrA was the most prevalent transferred ARG. In addition to horizontal gene transfer, bacteria could also acquire florfenicol resistance by regulating the innate efflux systems via mutations. The observations made by this study are of great importance for guiding the strategic use of florfenicol, thus preventing the formation, persistence, and spreading of florfenicol-resistant bacteria and resistance genes in aquaculture
Federated Generalization via Information-Theoretic Distribution Diversification
Federated Learning (FL) has surged in prominence due to its capability of
collaborative model training without direct data sharing. However, the vast
disparity in local data distributions among clients, often termed the
non-Independent Identically Distributed (non-IID) challenge, poses a
significant hurdle to FL's generalization efficacy. The scenario becomes even
more complex when not all clients participate in the training process, a common
occurrence due to unstable network connections or limited computational
capacities. This can greatly complicate the assessment of the trained models'
generalization abilities. While a plethora of recent studies has centered on
the generalization gap pertaining to unseen data from participating clients
with diverse distributions, the divergence between the training distributions
of participating clients and the testing distributions of non-participating
ones has been largely overlooked. In response, our paper unveils an
information-theoretic generalization framework for FL. Specifically, it
quantifies generalization errors by evaluating the information entropy of local
distributions and discerning discrepancies across these distributions. Inspired
by our deduced generalization bounds, we introduce a weighted aggregation
approach and a duo of client selection strategies. These innovations aim to
bolster FL's generalization prowess by encompassing a more varied set of client
data distributions. Our extensive empirical evaluations reaffirm the potency of
our proposed methods, aligning seamlessly with our theoretical construct
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Pre-training is known to generate universal representations for downstream
tasks in large-scale deep learning such as large language models. Existing
literature, e.g., \cite{kim2020adversarial}, empirically observe that the
downstream tasks can inherit the adversarial robustness of the pre-trained
model. We provide theoretical justifications for this robustness inheritance
phenomenon. Our theoretical results reveal that feature purification plays an
important role in connecting the adversarial robustness of the pre-trained
model and the downstream tasks in two-layer neural networks. Specifically, we
show that (i) with adversarial training, each hidden node tends to pick only
one (or a few) feature; (ii) without adversarial training, the hidden nodes can
be vulnerable to attacks. This observation is valid for both supervised
pre-training and contrastive learning. With purified nodes, it turns out that
clean training is enough to achieve adversarial robustness in downstream tasks.Comment: To appear in AISTATS202
Advances in Atomic Time Scale imaging with a Fine Intrinsic Spatial Resolution
Atomic time scale imaging, opening a new era for studying dynamics in
microcosmos, is presently attracting immense research interesting on the global
level due to its powerful ability. On the atom level, physics, chemistry, and
biology are identical for researching atom motion and atomic state change. The
light possesses twoness, the information carrier and the research resource. The
most fundamental principle of this imaging is that light records the event
modulated light field by itself, so called all optical imaging. This paper can
answer what is the essential standard to develop and evaluate atomic time scale
imaging, what is the optimal imaging system, and what are the typical
techniques to implement this imaging, up to now. At present, the best record in
the experiment, made by multistage optical parametric amplification (MOPA), is
realizing 50 fs resolved optical imaging with a spatial resolution of ~83 lp/mm
at an effective framing rate of 10^13 fps for recording an ultrafast optical
lattice with its rotating speed up to 10^13 rad/s
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance
Federated Learning (FL) is a distributed learning paradigm to train a global
model across multiple devices without collecting local data. In FL, a server
typically selects a subset of clients for each training round to optimize
resource usage. Central to this process is the technique of unbiased client
sampling, which ensures a representative selection of clients. Current methods
primarily utilize a random sampling procedure which, despite its effectiveness,
achieves suboptimal efficiency owing to the loose upper bound caused by the
sampling variance. In this work, by adopting an independent sampling procedure,
we propose a federated optimization framework focused on adaptive unbiased
client sampling, improving the convergence rate via an online variance
reduction strategy. In particular, we present the first adaptive client
sampler, K-Vib, employing an independent sampling procedure. K-Vib achieves a
linear speed-up on the regret bound
within a set communication budget . Empirical studies indicate that K-Vib
doubles the speed compared to baseline algorithms, demonstrating significant
potential in federated optimization.Comment: Under revie
Estimating realized heritability for growth in Zhikong scallop (Chlamys farreri) using genome-wide complex trait analysis
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
We investigate various prompting strategies for enhancing personalized
recommendation performance with large language models (LLMs) through input
augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct
prompting strategies: (1) basic prompting, (2) recommendation-driven prompting,
(3) engagement-guided prompting, and (4) recommendation-driven +
engagement-guided prompting. Our empirical experiments show that incorporating
the augmented input text generated by LLM leads to improved recommendation
performance. Recommendation-driven and engagement-guided prompting strategies
are found to elicit LLM's understanding of global and local item
characteristics. This finding highlights the importance of leveraging diverse
prompts and input augmentation techniques to enhance the recommendation
capabilities with LLMs
Chemisorption Induced Formation of Biphenylene Dimer on Surfaces
We report an example that demonstrates the clear interdependence between
surface-supported reactions and molecular adsorption configurations. Two
biphenyl-based molecules with two and four bromine substituents, i.e.
2,2-dibromo-biphenyl (DBBP) and 2,2,6,6-tetrabromo-1,1-biphenyl (TBBP), show
completely different reaction pathways on a Ag(111) surface, leading to the
selective formation of dibenzo[e,l]pyrene and biphenylene dimer, respectively.
By combining low-temperature scanning tunneling microscopy, synchrotron
radiation photoemission spectroscopy, and density functional theory
calculations, we unravel the underlying reaction mechanism. After
debromination, a bi-radical biphenyl can be stabilized by surface Ag adatoms,
while a four-radical biphenyl undergoes spontaneous intramolecular annulation
due to its extreme instability on Ag(111). Such different chemisorption-induced
precursor states between DBBP and TBBP consequently lead to different reaction
pathways after further annealing. In addition, using bond-resolving scanning
tunneling microscopy and scanning tunneling spectroscopy, we determine the bond
length alternation of biphenylene dimer product with atomic precision, which
contains four-, six-, and eight-membered rings. The four-membered ring units
turn out to be radialene structures
Chemisorption-induced formation of biphenylene dimer on Ag(111)
We report an example that demonstrates the clear interdependence between surface-supported reactions and molecular-adsorption configurations. Two biphenyl-based molecules with two and four bromine substituents, i.e., 2,2′-dibromobiphenyl (DBBP) and 2,2′,6,6′-tetrabromo-1,1′-biphenyl (TBBP), show completely different reaction pathways on a Ag(111) surface, leading to the selective formation of dibenzo[e,l]pyrene and biphenylene dimer, respectively. By combining low-temperature scanning tunneling microscopy, synchrotron radiation photoemission spectroscopy, and density functional theory calculations, we unravel the underlying reaction mechanism. After debromination, a biradical biphenyl can be stabilized by surface Ag adatoms, while a four-radical biphenyl undergoes spontaneous intramolecular annulation due to its extreme instability on Ag(111). Such different chemisorption-induced precursor states between DBBP and TBBP consequently lead to different reaction pathways after further annealing. In addition, using bond-resolving scanning tunneling microscopy and scanning tunneling spectroscopy, we determine with atomic precision the bond-length alternation of the biphenylene dimer product, which contains 4-, 6-, and 8-membered rings. The 4-membered ring units turn out to be radialene structures.This work was financially supported by the National Natural Science Foundation of China (21773222, 51772285, 21872131, U1732272, and U1932214), the National Key R&D Program of China (2017YFA0403402, 2017YFA0403403, and 2019YFA0405601), and Users with Excellence Program of Hefei Science Center CAS (2020HSC-UE004). The work at Washington State University was primarily funded through the National Science Foundation CAREER program under Contract no. CBET-1653561. This work was also partially funded by the Joint Center for Deployment and Research in Earth Abundant Materials (JCDREAM) in Washington State. Most of the computational resources were provided by the Kamiak HPC under the Center for Institutional Research Computing at Washington State University. This research also used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract no. DE-AC02-05CH11231. The work at Donostia International Physics Center was primarily funded through the Juan de la Cierva Grant (no. FJC2019-041202-I) from Spanish Ministry of Economy and Competitiveness, the European Union’s Horizon 2020 Research and Innovation program (Marie Skłodowska-Curie Actions Individual Fellowship (no. 101022150), and the MCIN/AEI/ 10.13039/501100011033 (Grant no. PID2019-107338RB-C63).Peer reviewe
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