Evidence and implications for early intervention in bipolar disorder

Aims: To review the evidence that supports early intervention in the treatment of bipolar disorder.

Background: Bipolar disorder is a pleomorphic condition, with varying manifestations that are determined by a number of complex factors including the ‘‘stage’’ of illness. It is consequently a notoriously difficult illness to diagnose and as a corollary is associated with lengthy delays in recognition and the initiation of suitable treatment.

Methods: A literature search was conducted using MEDLINE augmented by a manual search.

Results: Emerging neuroimaging data suggests that, in contrast to schizophrenia, where at the time of a first-episode of illness there is already discernible volume loss, in bipolar disorder, gross brain structure is relatively preserved, and it is only with recurrences that there is a sequential, but marked loss of brain volume. Recent evidence suggests that both pharmacotherapy and psychotherapy are more effective if instituted early in the course of bipolar disorder, and that with multiple episodes and disease progression there is a noticeable decline in treatment response.

Conclusions: Such data supports the notion of clinical staging, and the tailored implementation of treatments according to the stage of illness. The progressive nature of bipolar disorder further supports the concept that the first episode is a period that requires energetic broad-based treatment, with the hope that this could alter the temporal trajectory of the illness. It also raises hope that prompt treatment may be neuroprotective and that this perhaps attenuates or even prevents the neurostructural and neurocognitive changes seen to emerge with chronicity. This highlights the need for early identification at a population level and the necessity of implementing treatments and services at a stage of the illness where prognosis is optimal.

What drives technology innovation among the emerging Chinese enterprises?

In the light of rapid development of technology/knowledge intensive firms arising from the emerging economy of China in recent time, this paper is aimed at developing an analytical framework, based on the institutional theory and resource-based view, to evaluate the drivers and antecedents of technology innovation among Chinese emerging multinational enterprises (MNEs). Use of case study approach, the study examined two large Chinese enterprises in Wuhan and found that a linear sequential pattern of technology innovation did not apply. In contrast, two enterprises investigated tended to combine several types of innovation (strategic cost, organisational and operational innovation) to manage their internal capabilities and other organisational activities and routines to change, learn, adapt and create technology innovation. Our finding in this study also suggests that the key factor for Chinese firms to be innovative is more internally driven by several human resource management strategies that helped build technological capabilities effectively. Main implications of this study are that organisational human resource managers, technology and system designers should work together to design and develop enterprise management systems conducive to enhance both technology and human creativity for emerging Chinese multinational enterprises.

Towards an extended international HRM model for emerging multinational enterprises

This paper critiques several exiting strategic international human resource management (SIHRM) frameworks and discusses their limitations to apply directly to emerging multinational enterprises (EMNEs), especially those generated from China and other BRICS nations. To complement the existing SIHRM frameworks, key variables relevant to emerging economies are identified and the extended model with particular reference to EMNEs is developed with several research propositions. It is believed that the extended model would better capture the recent development of MNEs in transition, and alert emerging international managers to address several human resource management challenges in the global context

What drives stock markets over short horizons? Evidence from emerging markets

The goal of this paper is to examine the importance of permanent and transitory shocks in explaining variations in stock prices for Singapore, Taiwan, and South Korea using a trend-cycle decomposition technique. This study is novel in that in measuring the impact of shocks we not only impose common trend restrictions but also common cycle restrictions. We later undertake a post-sample forecasting exercise to confirm the efficiency gains from imposing common cycle restrictions. We find that over short horizons, transitory shocks are the dominant source of variations in stock prices for South Korea, while permanent shocks explain the bulk of the variations in stock price of Singapore and Taiwan.

Emerging hypothesis verification using function-based geometric models and active vision strategies

This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is that the camera can foveate and track particular features. It is argued that simple 2D analytic descriptions of the movement of edges can infer 3D shape while the camera is moved. This uses an advantage of foveation i.e. the problem becomes object centred. The problem of correspondence for numerous edge points is overcome by the use of a tree based representation for the competing hypotheses. Numerous hypothesis are maintained simultaneously and it does not rely on a single kinematic model which assumes constant velocity or acceleration. The numerous advantages of this strategy are described.

Mining condensed sets of frequent episodes with more accurate frequencies from complex sequences

Many previous approaches to frequent episode discovery only accept simple sequences. Although a recent approach has been able to nd frequent episodes from complex sequences, the discovered sets are neither condensed nor accurate. This paper investigates the discovery of condensed sets of frequent episodes from complex sequences. We adopt a novel anti-monotonic frequency measure based on non-redundant occurrences, and dene a condensed set, nDaCF (the set of non-derivable approximately closed frequent episodes) within a given maximal error bound of support. We then introduce a series of effective pruning strategies, and develop a method, nDaCF-Miner, for discovering nDaCF sets. Experimental results show that, when the error bound is somewhat high, the discovered nDaCF sets are two orders of magnitude smaller than complete sets, and nDaCF-miner is more efficient than previous mining approaches. In addition, the nDaCF sets are more accurate than the sets found by previous approaches.

Book review : Entrepreneurship in emerging regions around the world : theory, evidence and implications

P.H. Phan, S. Venkataraman and S.R. Velamuri (2008) Entrepreneurship in emerging regions around the world: Theory, evidence and implications, Edward Elgar, Cheltenham, United Kingdom