松嫩平原西部碱尘气溶胶的元素特征分析

碱尘暴期间松嫩平原西部碱尘气溶胶的主要元素质量浓度排序为Na、Al、Fe、Ca、Mg、K、Ti、Ba、Zn，与当地盐碱化土壤的元素组成吻合，而与沙尘、城市气溶胶的元素组成有差别。风力对碱尘气溶胶元素组成有明显的影响，当风速在8．0～17．1m／s时，元素组成的质量浓度排序为Na、Ca、Al、Fe、Mg、K、Ba、Ti、Zn。Na元素质量浓度的激增是碱尘暴元素组分的一大特征，明显富集元素为Na和B；相对于当地背景大气气溶胶的富集情况，Zn、Cu、Ba、B的富集因子值在碱尘暴期间明显降低；在各种天气情况下，B、Cu均具有较高的富集因子。碱尘中Ti元素的富集因子值均小于1，而Ca、Fe、K、Mg、Mn、Sr、V的富集因子值为1～4。两个采样点的Ca、Na、Al、Fe、Mg、Ti、Zn的富集因子近似，表明该区碱尘的主要元素具有特定的一致性；而B、Ba、Cu、V的富集因子相差较大，可以作为区分不同碱尘源的标志性元素。

吉林西部碱尘的通量与物质特征分析

选择吉林西部两个典型地区,抽样采集碱尘样品,根据实测结果对日常形成的碱尘进行了通量测定;依据两个采样点的自然地理状况和实测采样记录,提出了吉林西部碱尘具有两种源区类型的认识,即以大布苏湖为代表的萎缩湖泡型和以姜家甸草场为代表的草场退化型。同时,通过对比分析不同时间和采样点的碱尘通量及源区特征,提出了吉林西部碱尘通量与风力条件、日照条件、pH值、物理性粘粒比例正相关,与植被覆盖度、地表物质粒级负相关。对碱尘元素分析结果表明,Ca、Na元素含量明显偏高,Fe、Al元素含量变化则较大;碱尘组成元素含量由多到少的顺序为:Ca、Na、Fe、Al、K、Mg、Ti,与普通尘暴元素组成有着明显的差别。

Laparoscopic splenectomy for splenic hamartoma:Case management and clinical consequences

Splenic hamartoma is a rare benign tumor,and although minimally invasive surgery may be suitable for this condition,there have only been 2 previous reports of laparoscopic surgery.Here we report the third case of splenic hamartoma managed by laparoscopic splenectomy.A 37-year-old male was incidentally diagnosed by abdominal ultrasonography with a hypoechoic mass measuring 2.5 cm × 2.4 cm in the spleen.Color Doppler sonography showed multiple flow signals within the mass and contrast-enhanced computed tomography revealed strong enhancement of the lesion.On T1-and T2-weighted magnetic resonance images,the splenic mass was demonstrated as isointense and hyperintense respectively.Although a malignant tumor could not be ruled out,a hand-assisted laparoscopic splenectomy was performed because the splenic mass was limited in size and had not invaded adjacent organs.The pathological diagnosis was splenic hamartoma.The postoperative course was uneventful and the patient was discharged by the seventh postoperative day.Although splenic hamartomas have some specific imaging features,more reports and analyses of these cases are required to increase the reliability of the diagnosis and management.Hand-assisted laparoscopic splenectomy may play a pivotal role in the postoperative diagnosis and management of this condition.

Effect of Daikenchuto (TJ-100) on abdominal bloating in hepatectomized patients

AIM: To evaluate the clinical usefulness of Daikenchuto (DKT) in hepatecomized patients. METHODS: Twenty patients were enrolled with informed consent. Two patients were excluded because of cancelled operations. The remaining 18 patients were randomly chosen for treatment with DKT alone or combination therapy of DKT and lactulose (n = 9, each group). Data were prospectively collected. Primary end points were Visual Analogue Scale (VAS) score for abdominal bloating, total Gastrointestinal Symptoms Rating Scale (GSRS) score for abdominal symptoms, and GSRS score for abdominal bloating. RESULTS: The VAS score for abdominal bloating and total GSRS score for abdominal symptoms recovered to levels that were not significantly different to preoperative levels by 10 d postoperation. Combination therapy of DKT and lactulose was associated with a significantly poorer outcome in terms of VAS and GSRS scores for abdominal bloating, total GSRS score, and total daily calorie intake, when compared with DKT alone therapy. CONCLUSION: DKT is a potentially effective drug for postoperative management of hepatectomized patients, not only to ameliorate abdominal bloating, but also to promote nutritional support by increasing postoperative dietary intake.

美容针灸的风险管理

In order to guarantee the clinical safety of cosmetic acupuncture,all kinds of potential risks are clarified and summarized.Three organizations for cosmetic acupuncture training have conducted the investigation on the risk of cosmetic acupuncture respectively.The investigation contents have been collected to be a guideline.It focuses on 12 treatment items that may cause the risks in cosmetic acupuncture and 3 treatment items that is coincident with cosmetic medicine.The potential risks are summarized in clinical practice under the risk management of cosmetic acupuncture.

Co-occurrence prediction in a large location-based social network

Location-based social network （LBSN） is at the forefront of emerging trends in social network services （SNS） since the users in LBSN are allowed to ＂check-in＂ the places （locations） when they visit them. The accurate geographi- cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co- occurrences and social ties, and the results show that the co- occurrences are strongly correlative with the social ties. Sec- ond, we present a study of predicting two users whether or not they will meet （co-occur） at a place in a given future time, by exploring their check-in habits. In particular, we first intro- duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user＇s check-in habits. Based on such bag rep- resentations, we define a similarity metric called habits sim- ilarity to measure the similarity between two users＇ check-in habits. Then we propose a machine !earning formula for pre- dicting co-occurrence based on the social ties and habits sim- ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.

Top-k Outlier Detection from Uncertain Data

Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence,this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore,a populated-cells list(PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm.An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.