Causal Function and Bias Correlation of the Orbitofrontal Cortex in Economic Choices

Economic choices entail two mental processes, value calculation and value comparison (Niehans, 1990). Studies in the last twenty years have shown that neurons in the orbitofrontal cortex (OFC) could support both processes. Namely, in the studies in which monkeys chose between two juice options with various amounts, three functional cell groups had been found in the OFC: offer value cells encode the value of individual juices, chosen juice cells encode the choice in a binary way and chosen value cells encode the value of the chosen juice (Padoa-Schioppa and Assad, 2006). These results suggest a decision circuit within OFC with offer value cells encoding the input and chosen juice cells encoding the output (Padoa-Schioppa, 2011). However, this proposal remains tentative. If OFC is crucial to the economic choices, neural activities in the OFC should 1) causally relate to the decisions and 2) explain the behavioral variabilities. Therefore, in my dissertation studies, I aim to examine these two aspects. In the first study, we use electrical stimulation to establish the causal link between neuronal activity in the OFC and the economic choices. We find that low current micro-stimulation increases the encoded values and facilitates the choices by inducing a range-dependent bias. On the other hand, high current micro-stimulation disrupts both the valuation and comparison stages, and affects the order bias under sequential offer and reduces the choice accuracy. In the second study, we focus on the neural correlates with behavioral biases under sequential offers. We train the monkeys to perform a task in which trials from simultaneous offers and sequential offers are randomly interleaved. We first confirm that the same neural circuit mechanism is adopted under simultaneous offers and sequential offers. This result provides the basis to examine the neural correlates using a unified decision model we proposed based on simultaneous offers. We then compare the behavioral patterns of simultaneous offers and sequential offers. We find that sequential offers show lower choice accuracy, bias in favor of the preferred juice (preference bias) and bias in favor of the second offer (order bias). Neural correlates of each of the biases reveal that low choice accuracy partly reflects the weaker value signals in sequential offers, order bias is correlated with comparison signals and preference bias emerges late in the comparison stage. Taken together, my dissertation studies fill in some important gaps between the neuronal activity of the OFC and the economic choices

Neuronal origins of reduced accuracy and biases in economic choices under sequential offers

Economic choices are characterized by a variety of biases. Understanding their origins is a long-term goal for neuroeconomics, but progress on this front has been limited. Here, we examined choice biases observed when two goods are offered sequentially. In the experiments, rhesus monkeys chose between different juices offered simultaneously or in sequence. Choices under sequential offers were less accurate (higher variability). They were also biased in favor of the second offer (order bias) and in favor of the preferred juice (preference bias). Analysis of neuronal activity recorded in the orbitofrontal cortex revealed that these phenomena emerged at different computational stages. Lower choice accuracy reflected weaker offer value signals (valuation stage), the order bias emerged during value comparison (decision stage), and the preference bias emerged late in the trial (post-comparison). By neuronal measures, each phenomenon reduced the value obtained on average in each trial and was thus costly to the monkey

Orbitofrontal cortex contributes to the comparison of values underlying economic choices

Economic choices between goods entail the computation and comparison of subjective values. Previous studies examined neuronal activity in the orbitofrontal cortex (OFC) of monkeys choosing between different types of juices. Three groups of neurons were identified: offer value cells encoding the value of individual offers, chosen juice cells encoding the identity of the chosen juice, and chosen value cells encoding the value of the chosen offer. The encoded variables capture both the input (offer value) and the output (chosen juice, chosen value) of the decision process, suggesting that values are compared within OFC. Recent work demonstrates that choices are causally linked to the activity of offer value cells. Conversely, the hypothesis that OFC contributes to value comparison has not been confirmed. Here we show that weak electrical stimulation of OFC specifically disrupts value comparison without altering offer values. This result implies that neuronal populations in OFC participate in value comparison

Context-TAP: Tracking Any Point Demands Spatial Context Features

We tackle the problem of Tracking Any Point (TAP) in videos, which specifically aims at estimating persistent long-term trajectories of query points in videos. Previous methods attempted to estimate these trajectories independently to incorporate longer image sequences, therefore, ignoring the potential benefits of incorporating spatial context features. We argue that independent video point tracking also demands spatial context features. To this end, we propose a novel framework Context-TAP, which effectively improves point trajectory accuracy by aggregating spatial context features in videos. Context-TAP contains two main modules: 1) a SOurse Feature Enhancement (SOFE) module, and 2) a TArget Feature Aggregation (TAFA) module. Context-TAP significantly improves PIPs all-sided, reducing 11.4% Average Trajectory Error of Occluded Points (ATE-Occ) on CroHD and increasing 11.8% Average Percentage of Correct Keypoint (A-PCK) on TAP-Vid-Kinectics. Demos are available at this $\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}$.Comment: Project Page: this $\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}

GRB 120729A: External Shock Origin for Both the Prompt Gamma-Ray Emission and Afterglow

Gamma-ray burst (GRB) 120729A was detected by Swift/BAT and Fermi/GBM, and then rapidly observed by Swift/XRT, Swift/UVOT, and ground-based telescopes. It had a single long and smooth \gamma-ray emission pulse, which extends continuously to the X-rays. We report Lick/KAIT observations of the source, and make temporal and spectral joint fits of the multiwavelength light curves of GRB 120729A. It exhibits achromatic light-curve behavior, consistent with the predictions of the external shock model. The light curves are decomposed into four typical phases: onset bump (Phase I), normal decay (Phase II), shallow decay (Phase III), and post-jet break (Phase IV). The spectral energy distribution (SED) evolves from prompt \gamma-ray emission to the afterglow with photon index from Γγ=1.36 to Γ≈1.75. There is no obvious evolution of the SED during the afterglow. ...(Please see article full tet for complete abstract.

A novel compound heterozygous mutation of COL6A3 in Chinese patients with isolated cervical dystonia

BackgroundThe etiology and pathogenesis of idiopathic dystonia remain obscure. Recent studies revealed that compound heterozygous mutations in collagen type VI alpha-3 gene COL6A3 may cause recessive isolated dystonia (DYT)-27. However, whether COL6A3 mutations are associated with Chinese patients with isolated dystonia is not yet reported.MethodsIn this study, 45 Chinese patients with isolated cervical dystonia were recruited, and their blood DNA samples were subjected to whole-exome sequencing. The potential causal variants of COL6A3 were identified based on the criteria of the American College of Medical Genetics and Genomics and by prediction software.ResultsAmong 45 isolated cervical dystonia patients, 18 patients (10 female patients and eight male patients) were found to have seven potential causal variants in the COL6A3 gene. Among these variants, a compound heterozygous mutation was found in one patient. One allele had a c.1264G>A mutation in exon 4 that resulted in an amino acid substitution of methionine for valine at codon 422 (p.Val422Met) and the other a c.8965+9G>A mutation involving a splicing change in exon 40. In addition, other five missense variants, including c.958G>A (p.Ala320Thr), c.1478T>C (p.Val493Ala), c.1597C>T (p.Arg533Cys), c.1762G>A (p.Asp588Asn), and c.4912G>A (p.Ala1638Thr), were identified as well.ConclusionWe identified a novel deleterious compound heterozygous mutation as well as five missense variants in the COL6A3 gene of Chinese patients with cervical dystonia. These findings may expand the spectrum of the COL6A3 genotype in isolated dystonia

VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner. The information of the frame triplet is iteratively fused onto the center frame. To extend TROF for handling more frames, we further propose a MOtion Propagation (MOP) module that bridges multiple TROFs and propagates motion features between adjacent TROFs. With the iterative flow estimation refinement, the information fused in individual TROFs can be propagated into the whole sequence via MOP. By effectively exploiting video information, VideoFlow presents extraordinary performance, ranking 1st on all public benchmarks. On the Sintel benchmark, VideoFlow achieves 1.649 and 0.991 average end-point-error (AEPE) on the final and clean passes, a 15.1% and 7.6% error reduction from the best-published results (1.943 and 1.073 from FlowFormer++). On the KITTI-2015 benchmark, VideoFlow achieves an F1-all error of 3.65%, a 19.2% error reduction from the best-published result (4.52% from FlowFormer++). Code is released at \url{https://github.com/XiaoyuShi97/VideoFlow}

Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the \textit{Code Usage Frequency} of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit \uline{c}ode-based \uline{s}elf-\uline{v}erification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset \textbf{(53.9\% $\to$ 84.3\%)}.Comment: Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificatio

Cancer-associated fibroblasts in hematologic malignancies: elucidating roles and spotlighting therapeutic targets

Hematologic malignancies comprise a diverse range of blood, bone marrow, and organ-related disorders that present significant challenges due to drug resistance, relapse, and treatment failure. Cancer-associated fibroblasts (CAFs) represent a critical component of the tumor microenvironment (TME) and have recently emerged as potential therapeutic targets. In this comprehensive review, we summarize the latest findings on the roles of CAFs in various hematologic malignancies, including acute leukemia, multiple myeloma, chronic lymphocytic leukemia, myeloproliferative neoplasms, and lymphoma. We also explore their involvement in tumor progression, drug resistance, and the various signaling pathways implicated in their activation and function. While the underlying mechanisms and the existence of multiple CAF subtypes pose challenges, targeting CAFs and their associated pathways offers a promising avenue for the development of innovative treatments to improve patient outcomes in hematologic malignancies

Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson’s (STAB1, KCNA10), Alzheimer’s (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson’s. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders