Developmental sensory experience balances cortical excitation and inhibition.

Early in life, neural circuits are highly susceptible to outside influences. The organization of the primary auditory cortex (A1) in particular is governed by acoustic experience during the critical period, an epoch near the beginning of postnatal development throughout which cortical synapses and networks are especially plastic. This neonatal sensitivity to the pattern of sensory inputs is believed to be essential for constructing stable and adequately adapted representations of the auditory world and for the acquisition of language skills by children. One important principle of synaptic organization in mature brains is the balance between excitation and inhibition, which controls receptive field structure and spatiotemporal flow of neural activity, but it is unknown how and when this excitatory-inhibitory balance is initially established and calibrated. Here we use whole-cell recording to determine the processes underlying the development of synaptic receptive fields in rat A1. We find that, immediately after the onset of hearing, sensory-evoked excitatory and inhibitory responses are equally strong, although inhibition is less stimulus-selective and mismatched with excitation. However, during the third week of postnatal development, excitation and inhibition become highly correlated. Patterned sensory stimulation drives coordinated synaptic changes across receptive fields, rapidly improves excitatory-inhibitory coupling and prevents further exposure-induced modifications. Thus, the pace of cortical synaptic receptive field development is set by progressive, experience-dependent refinement of intracortical inhibition

DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose \underline{D}ecentralized \underline{SGD} with \underline{C}ommunication-optimal \underline{E}xact \underline{C}onsensus \underline{A}lgorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an $\tilde{O}(n^3)$ transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD's convergence analysis. Numerical experiments show the efficiency of DSGD-CECA

SDSS-IV MaNGA: The Roles of AGNs and Dynamical Processes in Star Formation Quenching in Nearby Disk Galaxies

We study how star formation (SF) is quenched in low-redshift disk galaxies with integral-field spectroscopy. We select 131 face-on spiral galaxies with stellar mass greater than $\rm 3\times10^{10}M_\odot$, and with spatially resolved spectrum from MaNGA DR13. We subdivide the sample into four groups based on the offset of their global specific star formation rate (SFR) from the star-forming main sequence and stack the radial profiles of stellar mass and SFR. By comparing the stacked profiles of quiescent and star-forming disk galaxies, we find that the decrease of the global SFR is caused by the suppression of SF at all radii, but with a more significant drop from the center to the outer regions following an inside-out pattern. As the global specific SFR decreases, the central stellar mass, the fraction of disk galaxies hosting stellar bars, and active galactic nuclei (AGNs; including both LINERs and Seyferts) all increase, indicating dynamical processes and AGN feedback are possible contributors to the inside-out quenching of SF in the local universe. However, if we include only Seyferts, or AGNs with ${\rm EW(H\alpha)>3\AA}$, the increasing trend of AGN fraction with decreasing global sSFR disappears. Therefore, if AGN feedback is contributing to quenching, we suspect that it operates in the low-luminosity AGN mode, as indicated by the increasing large bulge mass of the more passive disk galaxies.Comment: 12 pages, 7 figures, published in ApJ, typos corrected, references update

Efficacy and safety of anlotinib-based treatment in metastatic breast cancer patients

ObjectiveTo evaluate the efficacy and safety of anlotinib-based treatment in metastatic breast cancer (MBC) patients with failure of standard treatment.MethodsWe collected the medical data of 56 female patients with the diagnosis of MBC and had failed the standard treatment before. These patients received at least two cycles of anlotinib-based treatment as the second-line or beyond treatment between October 2019 and April 2022 in Jiangsu Cancer Hospital. The primary endpoint of our study was progression-free survival (PFS), and it was estimated with Kaplan-Meier. The second end points were disease control rate (DCR), objective response rate (ORR), and side effects.ResultsThe median PFS time of a total of 56 patients was 5.7 months (95% CI, 3.17–8.22months). The ORR and DCR was 28.6% and 71.4%, respectively. In second-line, third-line, and beyond treatment, the median PFS was 11.7 months, 8.7 months, and 4.7 months, respectively. In different subtype of breast cancer, the median PFS was 5.6 months, 5.7months, and 6.4 months in human epidermal growth factor receptor 2 positive (HER2+), hormone receptor positive and HER2 negative (HR+/HER2-), and triple negative breast cancer (TNBC) patients, respectively. Most adverse effects were clinically manageable, and the most common events were platelet count decrease (35.7%), hand-foot syndrome (19.6%), diarrhea (19.6%), and fatigue (17.9%). The most common grade 3 and 4 adverse events were platelet count decrease (25.0%), diarrhea (7.1%), and oral mucositis (5.4%).ConclusionAnlotinib-based treatment showed good efficacy and manageable toxicity in multi-line treatment of MBC patients who failed the standard treatment

Communication-Efficient Topologies for Decentralized Learning with O(1)O(1) Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the agents' information is ``mixed'' by communication. We found that popular communication topologies either have large maximum degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to measure the mixing efficiency. In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve an $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, theoretically and empirically. Our code is implemented through BlueFog and available at \url{https://github.com/kexinjinnn/EquiTopo}Comment: NeurIPS 202

OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models

The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields. To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named \emph{OccuQuest}, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories. We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries. By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations. Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM

Serum Helicobacter pylori NapA antibody as a potential biomarker for gastric cancer

Helicobacter pylori (H. pylori) infection is strongly associated with gastric cancer. However, only a minority of infected individuals ever develop gastric cancer. This risk stratification may be in part due to differences among strains. The relationship between neutrophil-activating protein (NapA) and gastric cancer is unclear. The purpose of this study is to evaluate the significance of NapA as a biomarker in gastric cancer. We used enzyme linked immunosorbent assay (ELISA) to determine the status of H. pylori infection. Indirect ELISA method was used for detection of NapA antibody titer in the serum of H. pyloriinfected individuals. Unconditional logistic regressions were adopted to analyze the variables and determine the association of NapA and gastric cancer. The results of study indicated serum H. pylori NapA antibody level were associated with a reduced risk for development of gastric cancer. It may be used in conjugation with other indicators for gastric cancer detection

Radiocarbon Dating of Charcoal and Bone Collagen Associated with Early Pottery at Yuchanyan Cave, Hunan Province, China

Yuchanyan Cave in Daoxian County, Hunan Province (People's Republic of China), yielded fragmentary remains of 2 or more ceramic vessels, in addition to large amounts of ash, a rich animal bone assemblage, cobble and flake artifacts, bone tools, and shell tools. The artifacts indicate that the cave was a Late Paleolithic foragers' camp. Here we report on the radiocarbon ages of the sediments based on analyses of charcoal and bone collagen. The best-preserved charcoal and bone samples were identified by prescreening in the field and laboratory. The dates range from around 21,000 to 13,800 cal BP. We show that the age of the ancient pottery ranges between 18,300 and 15,430 cal BP. Charcoal and bone collagen samples located above and below one of the fragments produced dates of around 18,000. These ceramic potsherds therefore provide some of the earliest evidence for pottery making in China.Anthropolog