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Background: Acquired immune deficiency syndrome and fracture are all serious hazards to human health that create a widespread alarm. Biomarkers that are closely linked to HIVRNA and fracture are unknown. Methods: 48 cases with HIV and fracture and 112 normal cases were recruited. Blood neutrophil count (NEU), white blood cell count (WBC), PAK1 and HIVRNA were measured. Pearson's chisquared test was used to evaluate the association between HIVRNA with fracture and NEU, WBC, PAK1. BP neural network model was constructed to analyze the predictive power of the combined effects of NEU, WBC, PAK1 for HIV RNA with fracture. Results: There exist strong correlations between PAK1, NEU, WBC and HIVRNA with fracture. The neural network model was successfully constructed. The overall determination coefficients of the training sample, validation sample, and test sample were 0.7235, 0.4795, 0.6188, 0.6792, respectively, indicating that the fitting effect between training sample and overall was good. Statistical determination coefficient of the goodness of fit R2 ≈ 0.82, it can be considered that degree of fit between the estimate and corresponding actual data is good. Conclusion: HIVRNA with fracture could be predicted using a neural network model based on NEU, WBC, PAK1. The neural network model is an innovative algorithm for forecasting HIVRNA levels with fracture.

Formation properties near a borehole are frequently altered by mechanical damage from drilling, leading to variations of formation elastic velocities in the radial direction. Inversions of the radial shear profiles are necessary for the real-time evaluation of the drilling conditions. In this study, we investigate acoustic wave propagation in a logging-while-drilling (LWD) model excited by a dipole transducer in cylindrically layered media. It is revealed that the dispersion characteristics of the dipole waves of the LWD are sensitive to the radial formation velocity variation. Accordingly, we develop a joint inversion method that evaluates radial shear-velocity profiles using the dipole modes of both the first and second orders. The propagation of these two modes is strongly influenced by formation alterations, but the inversion results obtained using a single dipole mode show ambiguity caused by the nonuniqueness problem. Through the proposed joint inversion, the additional second-order dipole wave can effectively help address the nonuniqueness issue in dipole acoustic logging, although its dispersion features differ in fast and slow formations. It is validated that the joint inversion has considerably improved convergence and accuracy in evaluating both the thickness and shear velocity of the altered zone, compared with the estimation by a single acoustic mode. Furthermore, we present numerical results based on synthetic logging data to demonstrate the feasibility of this inversion in a continuously varying profile, regardless of whether the formation surrounding the borehole is fast or slow. Even when strong drilling noises of low frequencies are added to the receiver signal arrays, the means of joint inversion can remain stable, and the shear-velocity profiles can be evaluated with good precision.

We proposed a facile porous organic cage (POC) composite membrane fabricated with the RCC3 cage crosslinked by terephthaloyl chloride for CO2/N2 and CH4/N2 separation. The amine-rich subnanochannel provided by RCC3 promoted the rapid penetration of CO2.

International audience; AbstractGold nanoparticles (AuNPs) represent an attractive inorganic matrix for laser desorption/ionization mass spectrometry (LDI-MS) detection of low-molecular-weight analytes; however, their direct use is hindered by severe aggregation. To limit AuNPs aggregation, hexagonal boron nitride nanosheets (h-BNNs) were employed as supports to improve their desorption/ionization efficiency. Thus, Au@BN was synthesized and systematically characterized. It showed low background noise and high sensitivity for LDI-MS of fipronil and its metabolites. Au@BN-assisted LDI-MS was validated using complex samples including blueberry juice, green tea beverage, and fish muscle, achieving low detection limits (0.05–0.20 µg·L−1 for liquid media, 0.82–1.25 ng·g−1 for fish muscle), wide linear ranges (0.2–100 µg·L−1 for liquid media, 3.00–1000 ng·g−1 for fish muscle), high reproducibility (7.55%–13.7%), and satisfactory recoveries (82.62%–109.1%). Furthermore, spatial distributions of analytes in strawberries and zebrafish were successfully imaged. This strategy allows for the quantitative analysis of other small molecules in complex substrates.

Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks. These models, powered by advanced deep learning techniques, have revolutionized the field of natural language processing (NLP) and have achieved remarkable results in various language-related tasks. LLMs have excelled in tasks such as machine translation, sentiment analysis, question answering, text generation, text classification, language modeling, and more. They have proven to be highly effective in capturing complex linguistic patterns, understanding context, and generating coherent and contextually relevant text. The attention scheme plays a crucial role in the architecture of large language models (LLMs). It is a fundamental component that enables the model to capture and utilize contextual information during language processing tasks effectively. Making the attention scheme computation faster is one of the central questions to speed up the LLMs computation. It is well-known that quantum machine has certain computational advantages compared to the classical machine. However, it is currently unknown whether quantum computing can aid in LLM. In this work, we focus on utilizing Grover's Search algorithm to compute a sparse attention computation matrix efficiently. We achieve a polynomial quantum speed-up over the classical method. Moreover, the attention matrix outputted by our quantum algorithm exhibits an extra low-rank structure that will be useful in obtaining a faster training algorithm for LLMs. Additionally, we present a detailed analysis of the algorithm's error analysis and time complexity within the context of computing the attention matrix.

Quadratic programming is a fundamental problem in the field of convex optimization. Many practical tasks can be formulated as quadratic programming, for example, the support vector machine (SVM). Linear SVM is one of the most popular tools over the last three decades in machine learning before deep learning method dominating. In general, a quadratic program has input size $\Theta(n^2)$ (where $n$ is the number of variables), thus takes $\Omega(n^2)$ time to solve. Nevertheless, quadratic programs coming from SVMs has input size $O(n)$, allowing the possibility of designing nearly-linear time algorithms. Two important classes of SVMs are programs admitting low-rank kernel factorizations and low-treewidth programs. Low-treewidth convex optimization has gained increasing interest in the past few years (e.g.~linear programming [Dong, Lee and Ye 2021] and semidefinite programming [Gu and Song 2022]). Therefore, an important open question is whether there exist nearly-linear time algorithms for quadratic programs with these nice structures. In this work, we provide the first nearly-linear time algorithm for solving quadratic programming with low-rank factorization or low-treewidth, and a small number of linear constraints. Our results imply nearly-linear time algorithms for low-treewidth or low-rank SVMs., Comment: arXiv admin note: text overlap with arXiv:2211.06033

Deep learning has been widely used in many fields, but the model training process usually consumes massive computational resources and time. Therefore, designing an efficient neural network training method with a provable convergence guarantee is a fundamental and important research question. In this paper, we present a static half-space report data structure that consists of a fully connected two-layer neural network for shifted ReLU activation to enable activated neuron identification in sublinear time via geometric search. We also prove that our algorithm can converge in $O(M^2/\epsilon^2)$ time with network size quadratic in the coefficient norm upper bound $M$ and error term $\epsilon$.

Vapor deposition technology can improve the surface wear resistance of titanium alloys, and prepare lightweight and corrosion-resistant self-lubricating spherical plain bearings made of titanium alloys. However, titanium alloys with hard films can be worn by soft self-lubricating fabrics. This paper focuses on the wear problem of TiN coating on the surfaces of self-lubricating spherical plain bearings based on titanium alloys. Ring-to-plate wear tests were carried out to study the tribological properties of TiN coating on TC4 titanium alloy against self-lubricating fabric under different working conditions (load: 500–2000 N and speed: 100–500 r/min), along with the investigation of the wear mechanism of TiN coating, and the evaluation of applicable working conditions of GE15 type self-lubricating spherical plain bearings through swing tests. The results have revealed that TiN coatings can maintain a certain friction distance without wear. Increasing friction speed and load can make TiN coatings more prone to wear. A thick transfer film can protect the TiN coating from wear. The main wear mechanism is attributed to fatigue wear induced by the repeated formation and peeling of transfer films. The GE15 bearing has achieved a self-lubricating fabric wear of approximately 0.04 mm when the swinging for 500 m (25,000 times) is under a specific condition of 27 kN and 0.2 Hz without damaging the inner ring of the bearing. The bearing is suitable for swing conditions with applied loads below 27 kN. This study provides a fundamental understanding of designing self-lubricating spherical plain bearings made of titanium alloys.

Hyperbolic polynomials is a class of real-roots polynomials that has wide range of applications in theoretical computer science. Each hyperbolic polynomial also induces a hyperbolic cone that is of particular interest in optimization due to its generality, as by choosing the polynomial properly, one can easily recover the classic optimization problems such as linear programming and semidefinite programming. In this work, we develop efficient algorithms for hyperbolic programming, the problem in each one wants to minimize a linear objective, under a system of linear constraints and the solution must be in the hyperbolic cone induced by the hyperbolic polynomial. Our algorithm is an instance of interior point method (IPM) that, instead of following the central path, it follows the central Swath, which is a generalization of central path. To implement the IPM efficiently, we utilize a relaxation of the hyperbolic program to a quadratic program, coupled with the first four moments of the hyperbolic eigenvalues that are crucial to update the optimization direction. We further show that, given an evaluation oracle of the polynomial, our algorithm only requires $O(n^2d^{2.5})$ oracle calls, where $n$ is the number of variables and $d$ is the degree of the polynomial, with extra $O((n+m)^3 d^{0.5})$ arithmetic operations, where $m$ is the number of constraints.

Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of the prompts. We also empirically observe that conventional prompt tuning methods cannot encode and learn sufficient task-relevant information from prompt tokens. In this work, we develop an information-theoretic framework that formulates soft prompt tuning as maximizing mutual information between prompts and other model parameters (or encoded representations). This novel view helps us to develop a more efficient, accurate and robust soft prompt tuning method InfoPrompt. With this framework, we develop two novel mutual information based loss functions, to (i) discover proper prompt initialization for the downstream tasks and learn sufficient task-relevant information from prompt tokens and (ii) encourage the output representation from the pretrained language model to be more aware of the task-relevant information captured in the learnt prompt. Extensive experiments validate that InfoPrompt can significantly accelerate the convergence of the prompt tuning and outperform traditional prompt tuning methods. Finally, we provide a formal theoretical result for showing to show that gradient descent type algorithm can be used to train our mutual information loss.

Computing the convolution $A \star B$ of two vectors of dimension $n$ is one of the most important computational primitives in many fields. For the non-negative convolution scenario, the classical solution is to leverage the Fast Fourier Transform whose time complexity is $O(n \log n)$. However, the vectors $A$ and $B$ could be very sparse and we can exploit such property to accelerate the computation to obtain the result. In this paper, we show that when $\|A \star B\|_{\geq c_1} = k$ and $\|A \star B\|_{\leq c_2} = n-k$ holds, we can approximately recover the all index in $\mathrm{supp}_{\geq c_1}(A \star B)$ with point-wise error of $o(1)$ in $O(k \log (n) \log(k)\log(k/\delta))$ time. We further show that we can iteratively correct the error and recover all index in $\mathrm{supp}_{\geq c_1}(A \star B)$ correctly in $O(k \log(n) \log^2(k) (\log(1/\delta) + \log\log(k)))$ time.

We report a new strategy to improve the reactivity and durability of a membrane electrode assembly (MEA)-type electrolyzer for CO2 electrolysis to CO by modifying the silver catalyst layer with urea. Our experimental and theoretical results show that mixing urea with the silver catalyst can promote electrochemical CO2 reduction (CO2R), relieve limitations of alkali cation transport from the anolyte, and mitigate salt precipitation in the gas diffusion electrode in long-term stability tests. In a 10 mM KHCO3 anolyte, the urea-modified Ag catalyst achieved CO selectivity 1.3 times better with energy efficiency 2.8-fold better than an untreated Ag catalyst, and operated stably at 100 mA cm-2 with a faradaic efficiency for CO above 85% for 200 h. Our work provides an alternative approach to fabricating catalyst interfaces in MEAs by modifying the catalyst structure and the local reaction environment for critical electrochemical applications such as CO2 electrolysis and fuel cells.

Currently, the relationship between temporary stoma reversal and the severity of postoperative complications (POCs) after the index surgery based on the Clavien-Dindo classification has not yet been explored.From July 2010 to June 2016, 380 patients undergoing sphincter-preserving surgery for rectal cancer with a temporary stoma in our hospital were included. Temporary stoma nonclosure rates, disease-free survival rates, and overall survival rates were estimated utilizing the Kaplan-Meier method.Of all the 380 patients, primary stomas were created in 335 patients and secondary stomas in 45 patients. After the index surgery, 36.6% (139/380) of patients developed at least one postoperative complication. In the first analysis, which included all the patients, 24.7% of temporary stomas remained unclosed. In the second analysis for 335 patients with a primary stoma, 23.3% were left with unclosed stomas. After the COX regression analysis, both major POCs and minor POCs were found to be independent risk factors for the permanent stoma, and there was an increasing tendency toward the risk of permanent stoma with the increase in POC severity.POCs are independent predictors of permanent stoma after rectal cancer surgery. Even minor POCs may affect the outcome, while there is a clear direct relationship between POC severity and permanent stoma rates.

Babesia microti is one of the most common causative agents of babesiosis. A sensitive and rapid detection is necessary for screening potentially infected individuals. In this study, B. microti cytochrome c oxidase subunit I (cox1) was selected as the target gene, multiple primers were designed, and optimized by a recombinase-aided amplification (RAA) assay. The optimal primers and probe were labeled with fluorescein. The sensitivity of fluorescent RAA (fRAA) was evaluated using gradient diluents of the cox1 recombinant plasmid and genomic DNA extracted from whole blood of B. microti infected mice. The specificity of fRAA was assessed by other transfusion transmitted parasites. The analytical sensitivity of the fRAA assay was 10 copies of recombinant plasmid per reaction and 10 fg/µl B. microti genomic DNA. No cross-reaction with any other blood-transmitted parasites was observed. Our results demonstrated that the fRAA assay would be rapid, sensitive, and specific for the detection of B. microti.

Many deep learning tasks have to deal with graphs (e.g., protein structures, social networks, source code abstract syntax trees). Due to the importance of these tasks, people turned to Graph Neural Networks (GNNs) as the de facto method for learning on graphs. GNNs have become widely applied due to their convincing performance. Unfortunately, one major barrier to using GNNs is that GNNs require substantial time and resources to train. Recently, a new method for learning on graph data is Graph Neural Tangent Kernel (GNTK) [Du, Hou, Salakhutdinov, Poczos, Wang and Xu 19]. GNTK is an application of Neural Tangent Kernel (NTK) [Jacot, Gabriel and Hongler 18] (a kernel method) on graph data, and solving NTK regression is equivalent to using gradient descent to train an infinite-wide neural network. The key benefit of using GNTK is that, similar to any kernel method, GNTK's parameters can be solved directly in a single step. This can avoid time-consuming gradient descent. Meanwhile, sketching has become increasingly used in speeding up various optimization problems, including solving kernel regression. Given a kernel matrix of $n$ graphs, using sketching in solving kernel regression can reduce the running time to $o(n^3)$. But unfortunately such methods usually require extensive knowledge about the kernel matrix beforehand, while in the case of GNTK we find that the construction of the kernel matrix is already $O(n^2N^4)$, assuming each graph has $N$ nodes. The kernel matrix construction time can be a major performance bottleneck when the size of graphs $N$ increases. A natural question to ask is thus whether we can speed up the kernel matrix construction to improve GNTK regression's end-to-end running time. This paper provides the first algorithm to construct the kernel matrix in $o(n^2N^3)$ running time., Comment: AAAI 2022

Deep learning has achieved impressive success in a variety of fields because of its good generalization. However, it has been a challenging problem to quickly train a neural network with a large number of layers. The existing works utilize the locality-sensitive hashing technique or some data structures on space partitioning to alleviate the training cost in each iteration. In this work, we try accelerating the computations in each iteration from the perspective of input data points. Specifically, for a two-layer fully connected neural network, when the training data have some special properties, e.g., Kronecker structure, each iteration can be completed in sublinear time in the data dimension.

Large language models (LLMs) have had a profound impact on numerous aspects of daily life including natural language processing, content generation, research methodologies and so on. However, one crucial issue concerning the inference results of large language models is security and privacy. In many scenarios, the results generated by LLMs could possibly leak many confidential or copyright information. A recent beautiful and breakthrough work [Vyas, Kakade and Barak 2023] focus on such privacy issue of the LLMs from theoretical perspective. It is well-known that computing the attention matrix is one of the major task during the LLMs computation. Thus, how to give a provable privately guarantees of computing the attention matrix is an important research direction. Previous work [Alman and Song 2023, Brand, Song and Zhou 2023] have proposed provable tight result for fast computation of attention without considering privacy concerns. One natural mathematical formulation to quantity the privacy in theoretical computer science graduate school textbook is differential privacy. Inspired by [Vyas, Kakade and Barak 2023], in this work, we provide a provable result for showing how to differentially private approximate the attention matrix. From technique perspective, our result replies on a pioneering work in the area of differential privacy by [Alabi, Kothari, Tankala, Venkat and Zhang 2022].

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods.

Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related tasks. The attention mechanism in the Transformer architecture is a critical component of LLMs, as it allows the model to selectively focus on specific input parts. The softmax unit, which is a key part of the attention mechanism, normalizes the attention scores. Hence, the performance of LLMs in various NLP tasks depends significantly on the crucial role played by the attention mechanism with the softmax unit. In-context learning, as one of the celebrated abilities of recent LLMs, is an important concept in querying LLMs such as ChatGPT. Without further parameter updates, Transformers can learn to predict based on few in-context examples. However, the reason why Transformers becomes in-context learners is not well understood. Recently, several works [ASA+22,GTLV22,ONR+22] have studied the in-context learning from a mathematical perspective based on a linear regression formulation $\min_x\| Ax - b \|_2$, which show Transformers' capability of learning linear functions in context. In this work, we study the in-context learning based on a softmax regression formulation $\min_{x} \| \langle \exp(Ax), {\bf 1}_n \rangle^{-1} \exp(Ax) - b \|_2$ of Transformer's attention mechanism. We show the upper bounds of the data transformations induced by a single self-attention layer and by gradient-descent on a $\ell_2$ regression loss for softmax prediction function, which imply that when training self-attention-only Transformers for fundamental regression tasks, the models learned by gradient-descent and Transformers show great similarity.

International audience

Large language models (LLMs) have made transformed changes for human society. One of the key computation in LLMs is the softmax unit. This operation is important in LLMs because it allows the model to generate a distribution over possible next words or phrases, given a sequence of input words. This distribution is then used to select the most likely next word or phrase, based on the probabilities assigned by the model. The softmax unit plays a crucial role in training LLMs, as it allows the model to learn from the data by adjusting the weights and biases of the neural network. In the area of convex optimization such as using central path method to solve linear programming. The softmax function has been used a crucial tool for controlling the progress and stability of potential function [Cohen, Lee and Song STOC 2019, Brand SODA 2020]. In this work, inspired the softmax unit, we define a softmax regression problem. Formally speaking, given a matrix $A \in \mathbb{R}^{n \times d}$ and a vector $b \in \mathbb{R}^n$, the goal is to use greedy type algorithm to solve \begin{align*} \min_{x} \| \langle \exp(Ax), {\bf 1}_n \rangle^{-1} \exp(Ax) - b \|_2^2. \end{align*} In certain sense, our provable convergence result provides theoretical support for why we can use greedy algorithm to train softmax function in practice.

Bacterial flagella are involved in infection through their roles in host cell adhesion, cell invasion, auto-agglutination, colonization, the formation of biofilms, and the regulation and secretion of nonflagellar bacterial proteins that are involved in the virulence process. In this study, we constructed a fusion protein vaccine (FliCD) containing the Clostridioides difficile flagellar proteins FliC and FliD.

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been presented to be a prognostic indicator in several cancers. We were supposed to evaluate the prognostic role of such inflammatory markers in hepatoblastoma (HB).MethodsTotal of 101 children, diagnosed with hepatoblastoma between January 2010 and January 2018, were enrolled before treatment in the study. The clinicopathological parameters, and outcomes were collected through laboratory analyses and patient follow-up. The association between NLR, PLR, and clinicopathological characters were analyzed with Wilcoxon test, Chi-Squared test, Kaplan-Meier, Log-rank and Cox regression analyses.ResultsNLR and PLR were significantly elevated in HB patients (P P P P P = 0.034) and lactate dehydrogenase (P = 0.026). The 3-year overall survival (OS) and event-free survival (EFS) were poor in the high-NLR group (OS: 44.3% vs. 90.3%, P P = 0.0001). The 3-year OS and EFS were poor in the high-PLR group (OS: 49.1% vs. 68.8%, P = 0.016, EFS: 39.6% vs. 64.6%, P = 0.0117). The multivariate analysis suggested that NLR (HR: 11.359, 95% CI: 1.218–105.947; P = 0.033) and risk stratification (HR: 44.905, 95% CI: 2.458–820.36; P = 0.01), were independent predictors of OS.ConclusionOur research showed that elevated NLR and PLR were the poor prognostic factors in HB patients before treatment. The NLR was an independent prognostic factor for OS.

Given a convex function $f$ on $\mathbb{R}^n$ with an integer minimizer, we show how to find an exact minimizer of $f$ using $O(n^2 \log n)$ calls to a separation oracle and $O(n^4 \log n)$ time. The previous best polynomial time algorithm for this problem given in [Jiang, SODA 2021, JACM 2022] achieves $\widetilde{O}(n^2)$ oracle complexity. However, the overall runtime of Jiang's algorithm is at least $\widetilde{\Omega}(n^8)$, due to expensive sub-routines such as the Lenstra-Lenstra-Lov\'asz (LLL) algorithm [Lenstra, Lenstra, Lov\'asz, Math. Ann. 1982] and random walk based cutting plane method [Bertsimas, Vempala, JACM 2004]. Our significant speedup is obtained by a nontrivial combination of a faster version of the LLL algorithm due to [Neumaier, Stehl\'e, ISSAC 2016] that gives similar guarantees, the volumetric center cutting plane method (CPM) by [Vaidya, FOCS 1989] and its fast implementation given in [Jiang, Lee, Song, Wong, STOC 2020]. For the special case of submodular function minimization (SFM), our result implies a strongly polynomial time algorithm for this problem using $O(n^3 \log n)$ calls to an evaluation oracle and $O(n^4 \log n)$ additional arithmetic operations. Both the oracle complexity and the number of arithmetic operations of our more general algorithm are better than the previous best-known runtime algorithms for this specific problem given in [Lee, Sidford, Wong, FOCS 2015] and [Dadush, V\'egh, Zambelli, SODA 2018, MOR 2021].

Adult neurogenesis declines with age due to the less functional neural stem cells (NSCs) and niches, but the underlying molecular bases for this impaired condition remain unclear. Here we analyzed >55,000 single-cell transcriptomes from two discrete neurogenic niches across the mouse lifespan, and identified new features and populations in NSCs, new markers, and neurogenic regional-specific alternations during aging. Intercellular communication analysis revealed defects in brain-derived neurotrophic factor (BDNF)-TrkB signaling cascade in old NSCs. Carboxypeptidase E (CPE) was found to be highly enriched in NSCs, and played a crucial role in mature/proBDNF balance and adult neurogenesis. Diminishment of CPE with aging resulted in impaired generation of BDNF, thus limiting the neurogenesis in old neurogenic niches. Restoring CPE expression markedly rescued the adult neurogenesis by increasing the production of mature BDNF, offering an attractive therapeutic strategy for the treatment of certain disorders in regions associated with constitutive neurogenesis.